Adaptive algorithm of magnetic heading detection

NASA Astrophysics Data System (ADS)

Liu, Gong-Xu; Shi, Ling-Feng

2017-11-01

Magnetic data obtained from a magnetic sensor usually fluctuate in a certain range, which makes it difficult to estimate the magnetic heading accurately. In fact, magnetic heading information is usually submerged in noise because of all kinds of electromagnetic interference and the diversity of the pedestrian’s motion states. In order to solve this problem, a new adaptive algorithm based on the (typically) right-angled corridors of a building or residential buildings is put forward to process heading information. First, a 3D indoor localization platform is set up based on MPU9250. Then, several groups of data are measured by changing the experimental environment and pedestrian’s motion pace. The raw data from the attached inertial measurement unit are calibrated and arranged into a time-stamped array and written to a data file. Later, the data file is imported into MATLAB for processing and analysis using the proposed adaptive algorithm. Finally, the algorithm is verified by comparison with the existing algorithm. The experimental results show that the algorithm has strong robustness and good fault tolerance, which can detect the heading information accurately and in real-time.

Implementation of Rivest Shamir Adleman Algorithm (RSA) and Vigenere Cipher In Web Based Information System

NASA Astrophysics Data System (ADS)

Aryanti, Aryanti; Mekongga, Ikhthison

2018-02-01

Data security and confidentiality is one of the most important aspects of information systems at the moment. One attempt to secure data such as by using cryptography. In this study developed a data security system by implementing the cryptography algorithm Rivest, Shamir Adleman (RSA) and Vigenere Cipher. The research was done by combining Rivest, Shamir Adleman (RSA) and Vigenere Cipher cryptographic algorithms to document file either word, excel, and pdf. This application includes the process of encryption and decryption of data, which is created by using PHP software and my SQL. Data encryption is done on the transmit side through RSA cryptographic calculations using the public key, then proceed with Vigenere Cipher algorithm which also uses public key. As for the stage of the decryption side received by using the Vigenere Cipher algorithm still use public key and then the RSA cryptographic algorithm using a private key. Test results show that the system can encrypt files, decrypt files and transmit files. Tests performed on the process of encryption and decryption of files with different file sizes, file size affects the process of encryption and decryption. The larger the file size the longer the process of encryption and decryption.

An algorithmic approach to crustal deformation analysis

NASA Technical Reports Server (NTRS)

Iz, Huseyin Baki

1987-01-01

In recent years the analysis of crustal deformation measurements has become important as a result of current improvements in geodetic methods and an increasing amount of theoretical and observational data provided by several earth sciences. A first-generation data analysis algorithm which combines a priori information with current geodetic measurements was proposed. Relevant methods which can be used in the algorithm were discussed. Prior information is the unifying feature of this algorithm. Some of the problems which may arise through the use of a priori information in the analysis were indicated and preventive measures were demonstrated. The first step in the algorithm is the optimal design of deformation networks. The second step in the algorithm identifies the descriptive model of the deformation field. The final step in the algorithm is the improved estimation of deformation parameters. Although deformation parameters are estimated in the process of model discrimination, they can further be improved by the use of a priori information about them. According to the proposed algorithm this information must first be tested against the estimates calculated using the sample data only. Null-hypothesis testing procedures were developed for this purpose. Six different estimators which employ a priori information were examined. Emphasis was put on the case when the prior information is wrong and analytical expressions for possible improvements under incompatible prior information were derived.

NASA's Experience with UV Remote Using SBUV and TOMS Instruments

NASA Technical Reports Server (NTRS)

Bhartia, P. K.

1999-01-01

This paper will discuss key features of the NASA algorithm that has been used to produce several highly popular geophysical products from the Solar Backscatter Ultraviolet (SBUV) and Total Ozone Mapping Spectrometer (TOMS) series of instruments. Since these instruments have a limited number of wavelengths, many innovative algorithmic approaches have been developed over the years to derive maximum information from these sensors. We will use Global Ozone Monitoring Experiment (GOME) data to test the assumptions made in these algorithms and show what additional information is contained in the GOME hyperspectral data. At NASA we are using this information to improve the SBUV and TOMS algorithms, as well as to develop more efficient algorithms to process GOME data.

A new image enhancement algorithm with applications to forestry stand mapping

NASA Technical Reports Server (NTRS)

Kan, E. P. F. (Principal Investigator); Lo, J. K.

1975-01-01

The author has identified the following significant results. Results show that the new algorithm produced cleaner classification maps in which holes of small predesignated sizes were eliminated and significant boundary information was preserved. These cleaner post-processed maps better resemble true life timber stand maps and are thus more usable products than the pre-post-processing ones: Compared to an accepted neighbor-checking post-processing technique, the new algorithm is more appropriate for timber stand mapping.

Iterative Neighbour-Information Gathering for Ranking Nodes in Complex Networks

NASA Astrophysics Data System (ADS)

Xu, Shuang; Wang, Pei; Lü, Jinhu

2017-01-01

Designing node influence ranking algorithms can provide insights into network dynamics, functions and structures. Increasingly evidences reveal that node’s spreading ability largely depends on its neighbours. We introduce an iterative neighbourinformation gathering (Ing) process with three parameters, including a transformation matrix, a priori information and an iteration time. The Ing process iteratively combines priori information from neighbours via the transformation matrix, and iteratively assigns an Ing score to each node to evaluate its influence. The algorithm appropriates for any types of networks, and includes some traditional centralities as special cases, such as degree, semi-local, LeaderRank. The Ing process converges in strongly connected networks with speed relying on the first two largest eigenvalues of the transformation matrix. Interestingly, the eigenvector centrality corresponds to a limit case of the algorithm. By comparing with eight renowned centralities, simulations of susceptible-infected-removed (SIR) model on real-world networks reveal that the Ing can offer more exact rankings, even without a priori information. We also observe that an optimal iteration time is always in existence to realize best characterizing of node influence. The proposed algorithms bridge the gaps among some existing measures, and may have potential applications in infectious disease control, designing of optimal information spreading strategies.

Block Architecture Problem with Depth First Search Solution and Its Application

NASA Astrophysics Data System (ADS)

Rahim, Robbi; Abdullah, Dahlan; Simarmata, Janner; Pranolo, Andri; Saleh Ahmar, Ansari; Hidayat, Rahmat; Napitupulu, Darmawan; Nurdiyanto, Heri; Febriadi, Bayu; Zamzami, Z.

2018-01-01

Searching is a common process performed by many computer users, Raita algorithm is one algorithm that can be used to match and find information in accordance with the patterns entered. Raita algorithm applied to the file search application using java programming language and the results obtained from the testing process of the file search quickly and with accurate results and support many data types.

ALGORITHM OF CARDIO COMPLEX DETECTION AND SORTING FOR PROCESSING THE DATA OF CONTINUOUS CARDIO SIGNAL MONITORING.

PubMed

Krasichkov, A S; Grigoriev, E B; Nifontov, E M; Shapovalov, V V

The paper presents an algorithm of cardio complex classification as part of processing the data of continuous cardiac monitoring. R-wave detection concurrently with cardio complex sorting is discussed. The core of this approach is the use of prior information about. cardio complex forms, segmental structure, and degree of kindness. Results of the sorting algorithm testing are provided.

A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification.

PubMed

Zhengming Li; Zhihui Lai; Yong Xu; Jian Yang; Zhang, David

2017-02-01

Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.

The Goddard Profiling Algorithm (GPROF): Description and Current Applications

NASA Technical Reports Server (NTRS)

Olson, William S.; Yang, Song; Stout, John E.; Grecu, Mircea

2004-01-01

Atmospheric scientists use different methods for interpreting satellite data. In the early days of satellite meteorology, the analysis of cloud pictures from satellites was primarily subjective. As computer technology improved, satellite pictures could be processed digitally, and mathematical algorithms were developed and applied to the digital images in different wavelength bands to extract information about the atmosphere in an objective way. The kind of mathematical algorithm one applies to satellite data may depend on the complexity of the physical processes that lead to the observed image, and how much information is contained in the satellite images both spatially and at different wavelengths. Imagery from satellite-borne passive microwave radiometers has limited horizontal resolution, and the observed microwave radiances are the result of complex physical processes that are not easily modeled. For this reason, a type of algorithm called a Bayesian estimation method is utilized to interpret passive microwave imagery in an objective, yet computationally efficient manner.

A robust fuzzy local Information c-means clustering algorithm with noise detection

NASA Astrophysics Data System (ADS)

Shang, Jiayu; Li, Shiren; Huang, Junwei

2018-04-01

Fuzzy c-means clustering (FCM), especially with spatial constraints (FCM_S), is an effective algorithm suitable for image segmentation. Its reliability contributes not only to the presentation of fuzziness for belongingness of every pixel but also to exploitation of spatial contextual information. But these algorithms still remain some problems when processing the image with noise, they are sensitive to the parameters which have to be tuned according to prior knowledge of the noise. In this paper, we propose a new FCM algorithm, combining the gray constraints and spatial constraints, called spatial and gray-level denoised fuzzy c-means (SGDFCM) algorithm. This new algorithm conquers the parameter disadvantages mentioned above by considering the possibility of noise of each pixel, which aims to improve the robustness and obtain more detail information. Furthermore, the possibility of noise can be calculated in advance, which means the algorithm is effective and efficient.

Image gathering and processing - Information and fidelity

NASA Technical Reports Server (NTRS)

Huck, F. O.; Fales, C. L.; Halyo, N.; Samms, R. W.; Stacy, K.

1985-01-01

In this paper we formulate and use information and fidelity criteria to assess image gathering and processing, combining optical design with image-forming and edge-detection algorithms. The optical design of the image-gathering system revolves around the relationship among sampling passband, spatial response, and signal-to-noise ratio (SNR). Our formulations of information, fidelity, and optimal (Wiener) restoration account for the insufficient sampling (i.e., aliasing) common in image gathering as well as for the blurring and noise that conventional formulations account for. Performance analyses and simulations for ordinary optical-design constraints and random scences indicate that (1) different image-forming algorithms prefer different optical designs; (2) informationally optimized designs maximize the robustness of optimal image restorations and lead to the highest-spatial-frequency channel (relative to the sampling passband) for which edge detection is reliable (if the SNR is sufficiently high); and (3) combining the informationally optimized design with a 3 by 3 lateral-inhibitory image-plane-processing algorithm leads to a spatial-response shape that approximates the optimal edge-detection response of (Marr's model of) human vision and thus reduces the data preprocessing and transmission required for machine vision.

A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images

NASA Astrophysics Data System (ADS)

Liu, Likun

2018-01-01

In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.

The development of algorithms for the deployment of new version of GEM-detector-based acquisition system

NASA Astrophysics Data System (ADS)

Krawczyk, Rafał D.; Czarski, Tomasz; Kolasiński, Piotr; Linczuk, Paweł; Poźniak, Krzysztof T.; Chernyshova, Maryna; Kasprowicz, Grzegorz; Wojeński, Andrzej; Zabolotny, Wojciech; Zienkiewicz, Paweł

2016-09-01

This article is an overview of what has been implemented in the process of development and testing the GEM detector based acquisition system in terms of post-processing algorithms. Information is given on mex functions for extended statistics collection, unified hex topology and optimized S-DAQ algorithm for splitting overlapped signals. Additional discussion on bottlenecks and major factors concerning optimization is presented.

Single-intensity-recording optical encryption technique based on phase retrieval algorithm and QR code

NASA Astrophysics Data System (ADS)

Wang, Zhi-peng; Zhang, Shuai; Liu, Hong-zhao; Qin, Yi

2014-12-01

Based on phase retrieval algorithm and QR code, a new optical encryption technology that only needs to record one intensity distribution is proposed. In this encryption process, firstly, the QR code is generated from the information to be encrypted; and then the generated QR code is placed in the input plane of 4-f system to have a double random phase encryption. For only one intensity distribution in the output plane is recorded as the ciphertext, the encryption process is greatly simplified. In the decryption process, the corresponding QR code is retrieved using phase retrieval algorithm. A priori information about QR code is used as support constraint in the input plane, which helps solve the stagnation problem. The original information can be recovered without distortion by scanning the QR code. The encryption process can be implemented either optically or digitally, and the decryption process uses digital method. In addition, the security of the proposed optical encryption technology is analyzed. Theoretical analysis and computer simulations show that this optical encryption system is invulnerable to various attacks, and suitable for harsh transmission conditions.

Modelling Evolutionary Algorithms with Stochastic Differential Equations.

PubMed

Heredia, Jorge Pérez

2017-11-20

There has been renewed interest in modelling the behaviour of evolutionary algorithms (EAs) by more traditional mathematical objects, such as ordinary differential equations or Markov chains. The advantage is that the analysis becomes greatly facilitated due to the existence of well established methods. However, this typically comes at the cost of disregarding information about the process. Here, we introduce the use of stochastic differential equations (SDEs) for the study of EAs. SDEs can produce simple analytical results for the dynamics of stochastic processes, unlike Markov chains which can produce rigorous but unwieldy expressions about the dynamics. On the other hand, unlike ordinary differential equations (ODEs), they do not discard information about the stochasticity of the process. We show that these are especially suitable for the analysis of fixed budget scenarios and present analogues of the additive and multiplicative drift theorems from runtime analysis. In addition, we derive a new more general multiplicative drift theorem that also covers non-elitist EAs. This theorem simultaneously allows for positive and negative results, providing information on the algorithm's progress even when the problem cannot be optimised efficiently. Finally, we provide results for some well-known heuristics namely Random Walk (RW), Random Local Search (RLS), the (1+1) EA, the Metropolis Algorithm (MA), and the Strong Selection Weak Mutation (SSWM) algorithm.

Advanced biologically plausible algorithms for low-level image processing

NASA Astrophysics Data System (ADS)

Gusakova, Valentina I.; Podladchikova, Lubov N.; Shaposhnikov, Dmitry G.; Markin, Sergey N.; Golovan, Alexander V.; Lee, Seong-Whan

1999-08-01

At present, in computer vision, the approach based on modeling the biological vision mechanisms is extensively developed. However, up to now, real world image processing has no effective solution in frameworks of both biologically inspired and conventional approaches. Evidently, new algorithms and system architectures based on advanced biological motivation should be developed for solution of computational problems related to this visual task. Basic problems that should be solved for creation of effective artificial visual system to process real world imags are a search for new algorithms of low-level image processing that, in a great extent, determine system performance. In the present paper, the result of psychophysical experiments and several advanced biologically motivated algorithms for low-level processing are presented. These algorithms are based on local space-variant filter, context encoding visual information presented in the center of input window, and automatic detection of perceptually important image fragments. The core of latter algorithm are using local feature conjunctions such as noncolinear oriented segment and composite feature map formation. Developed algorithms were integrated into foveal active vision model, the MARR. It is supposed that proposed algorithms may significantly improve model performance while real world image processing during memorizing, search, and recognition.

Development of an algorithm for improving quality and information processing capacity of MathSpeak synthetic speech renderings.

PubMed

Isaacson, M D; Srinivasan, S; Lloyd, L L

2010-01-01

MathSpeak is a set of rules for non speaking of mathematical expressions. These rules have been incorporated into a computerised module that translates printed mathematics into the non-ambiguous MathSpeak form for synthetic speech rendering. Differences between individual utterances produced with the translator module are difficult to discern because of insufficient pausing between utterances; hence, the purpose of this study was to develop an algorithm for improving the synthetic speech rendering of MathSpeak. To improve synthetic speech renderings, an algorithm for inserting pauses was developed based upon recordings of middle and high school math teachers speaking mathematic expressions. Efficacy testing of this algorithm was conducted with college students without disabilities and high school/college students with visual impairments. Parameters measured included reception accuracy, short-term memory retention, MathSpeak processing capacity and various rankings concerning the quality of synthetic speech renderings. All parameters measured showed statistically significant improvements when the algorithm was used. The algorithm improves the quality and information processing capacity of synthetic speech renderings of MathSpeak. This increases the capacity of individuals with print disabilities to perform mathematical activities and to successfully fulfill science, technology, engineering and mathematics academic and career objectives.

Mapping the Information Trace in Local Field Potentials by a Computational Method of Two-Dimensional Time-Shifting Synchronization Likelihood Based on Graphic Processing Unit Acceleration.

PubMed

Zhao, Zi-Fang; Li, Xue-Zhu; Wan, You

2017-12-01

The local field potential (LFP) is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood (SL) is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit (GPU)-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes, delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals (like EEG and fMRI) using similar recording techniques.

Genetic algorithm to solve the problems of lectures and practicums scheduling

NASA Astrophysics Data System (ADS)

Syahputra, M. F.; Apriani, R.; Sawaluddin; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

2018-02-01

Generally, the scheduling process is done manually. However, this method has a low accuracy level, along with possibilities that a scheduled process collides with another scheduled process. When doing theory class and practicum timetable scheduling process, there are numerous problems, such as lecturer teaching schedule collision, schedule collision with another schedule, practicum lesson schedules that collides with theory class, and the number of classrooms available. In this research, genetic algorithm is implemented to perform theory class and practicum timetable scheduling process. The algorithm will be used to process the data containing lists of lecturers, courses, and class rooms, obtained from information technology department at University of Sumatera Utara. The result of scheduling process using genetic algorithm is the most optimal timetable that conforms to available time slots, class rooms, courses, and lecturer schedules.

IMPROVING THE ENVIRONMENTAL PERFORMANCE OF CHEMICAL PROCESSES THROUGH THE USE OF INFORMATION TECHNOLOGY

EPA Science Inventory

Efforts are currently underway at the USEPA to develop information technology applications to improve the environmental performance of the chemical process industry. These efforts include the use of genetic algorithms to optimize different process options for minimal environmenta...

Description of the AILS Alerting Algorithm

NASA Technical Reports Server (NTRS)

Samanant, Paul; Jackson, Mike

2000-01-01

This document provides a complete description of the Airborne Information for Lateral Spacing (AILS) alerting algorithms. The purpose of AILS is to provide separation assurance between aircraft during simultaneous approaches to closely spaced parallel runways. AILS will allow independent approaches to be flown in such situations where dependent approaches were previously required (typically under Instrument Meteorological Conditions (IMC)). This is achieved by providing multiple levels of alerting for pairs of aircraft that are in parallel approach situations. This document#s scope is comprehensive and covers everything from general overviews, definitions, and concepts down to algorithmic elements and equations. The entire algorithm is presented in complete and detailed pseudo-code format. This can be used by software programmers to program AILS into a software language. Additional supporting information is provided in the form of coordinate frame definitions, data requirements, calling requirements as well as all necessary pre-processing and post-processing requirements. This is important and required information for the implementation of AILS into an analysis, a simulation, or a real-time system.

Information-based management mode based on value network analysis for livestock enterprises

NASA Astrophysics Data System (ADS)

Liu, Haoqi; Lee, Changhoon; Han, Mingming; Su, Zhongbin; Padigala, Varshinee Anu; Shen, Weizheng

2018-01-01

With the development of computer and IT technologies, enterprise management has gradually become information-based management. Moreover, due to poor technical competence and non-uniform management, most breeding enterprises show a lack of organisation in data collection and management. In addition, low levels of efficiency result in increasing production costs. This paper adopts 'struts2' in order to construct an information-based management system for standardised and normalised management within the process of production in beef cattle breeding enterprises. We present a radio-frequency identification system by studying multiple-tag anti-collision via a dynamic grouping ALOHA algorithm. This algorithm is based on the existing ALOHA algorithm and uses an improved packet dynamic of this algorithm, which is characterised by a high-throughput rate. This new algorithm can reach a throughput 42% higher than that of the general ALOHA algorithm. With a change in the number of tags, the system throughput is relatively stable.

An adaptive grid algorithm for 3-D GIS landform optimization based on improved ant algorithm

NASA Astrophysics Data System (ADS)

Wu, Chenhan; Meng, Lingkui; Deng, Shijun

2005-07-01

The key technique of 3-D GIS is to realize quick and high-quality 3-D visualization, in which 3-D roaming system based on landform plays an important role. However how to increase efficiency of 3-D roaming engine and process a large amount of landform data is a key problem in 3-D landform roaming system and improper process of the problem would result in tremendous consumption of system resources. Therefore it has become the key of 3-D roaming system design that how to realize high-speed process of distributed data for landform DEM (Digital Elevation Model) and high-speed distributed modulation of various 3-D landform data resources. In the paper we improved the basic ant algorithm and designed the modulation strategy of 3-D GIS landform resources based on the improved ant algorithm. By initially hypothetic road weights σi , the change of the information factors in the original algorithm would transform from ˜τj to ∆τj+σi and the weights was decided by 3-D computative capacity of various nodes in network environment. So during the course of initial phase of task assignment, increasing the resource information factors of high task-accomplishing rate and decreasing ones of low accomplishing rate would make load accomplishing rate approach the same value as quickly as possible, then in the later process of task assignment, the load balanced ability of the system was further improved. Experimental results show by improving ant algorithm, our system not only decreases many disadvantage of the traditional ant algorithm, but also like ants looking for food effectively distributes the complicated landform algorithm to many computers to process cooperatively and gains a satisfying search result.

Hierarchical Dirichlet process model for gene expression clustering

PubMed Central

2013-01-01

Clustering is an important data processing tool for interpreting microarray data and genomic network inference. In this article, we propose a clustering algorithm based on the hierarchical Dirichlet processes (HDP). The HDP clustering introduces a hierarchical structure in the statistical model which captures the hierarchical features prevalent in biological data such as the gene express data. We develop a Gibbs sampling algorithm based on the Chinese restaurant metaphor for the HDP clustering. We apply the proposed HDP algorithm to both regulatory network segmentation and gene expression clustering. The HDP algorithm is shown to outperform several popular clustering algorithms by revealing the underlying hierarchical structure of the data. For the yeast cell cycle data, we compare the HDP result to the standard result and show that the HDP algorithm provides more information and reduces the unnecessary clustering fragments. PMID:23587447

Framework for Integrating Science Data Processing Algorithms Into Process Control Systems

NASA Technical Reports Server (NTRS)

Mattmann, Chris A.; Crichton, Daniel J.; Chang, Albert Y.; Foster, Brian M.; Freeborn, Dana J.; Woollard, David M.; Ramirez, Paul M.

2011-01-01

A software framework called PCS Task Wrapper is responsible for standardizing the setup, process initiation, execution, and file management tasks surrounding the execution of science data algorithms, which are referred to by NASA as Product Generation Executives (PGEs). PGEs codify a scientific algorithm, some step in the overall scientific process involved in a mission science workflow. The PCS Task Wrapper provides a stable operating environment to the underlying PGE during its execution lifecycle. If the PGE requires a file, or metadata regarding the file, the PCS Task Wrapper is responsible for delivering that information to the PGE in a manner that meets its requirements. If the PGE requires knowledge of upstream or downstream PGEs in a sequence of executions, that information is also made available. Finally, if information regarding disk space, or node information such as CPU availability, etc., is required, the PCS Task Wrapper provides this information to the underlying PGE. After this information is collected, the PGE is executed, and its output Product file and Metadata generation is managed via the PCS Task Wrapper framework. The innovation is responsible for marshalling output Products and Metadata back to a PCS File Management component for use in downstream data processing and pedigree. In support of this, the PCS Task Wrapper leverages the PCS Crawler Framework to ingest (during pipeline processing) the output Product files and Metadata produced by the PGE. The architectural components of the PCS Task Wrapper framework include PGE Task Instance, PGE Config File Builder, Config File Property Adder, Science PGE Config File Writer, and PCS Met file Writer. This innovative framework is really the unifying bridge between the execution of a step in the overall processing pipeline, and the available PCS component services as well as the information that they collectively manage.

76 FR 45804 - Agency Information Collection Request; 60-Day Public Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-01

... an algorithm that enables reliable prediction of a certain event. A responder could submit the correct algorithm, but without the methodology, the evaluation process could not be adequately performed...

Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment

NASA Astrophysics Data System (ADS)

Yao, Xi-Wei; Wang, Hengyan; Liao, Zeyang; Chen, Ming-Cheng; Pan, Jian; Li, Jun; Zhang, Kechao; Lin, Xingcheng; Wang, Zhehui; Luo, Zhihuang; Zheng, Wenqiang; Li, Jianzhong; Zhao, Meisheng; Peng, Xinhua; Suter, Dieter

2017-07-01

Processing of digital images is continuously gaining in volume and relevance, with concomitant demands on data storage, transmission, and processing power. Encoding the image information in quantum-mechanical systems instead of classical ones and replacing classical with quantum information processing may alleviate some of these challenges. By encoding and processing the image information in quantum-mechanical systems, we here demonstrate the framework of quantum image processing, where a pure quantum state encodes the image information: we encode the pixel values in the probability amplitudes and the pixel positions in the computational basis states. Our quantum image representation reduces the required number of qubits compared to existing implementations, and we present image processing algorithms that provide exponential speed-up over their classical counterparts. For the commonly used task of detecting the edge of an image, we propose and implement a quantum algorithm that completes the task with only one single-qubit operation, independent of the size of the image. This demonstrates the potential of quantum image processing for highly efficient image and video processing in the big data era.

Formation of the predicted training parameters in the form of a discrete information stream

NASA Astrophysics Data System (ADS)

Smolentseva, T. E.; Sumin, V. I.; Zolnikov, V. K.; Lavlinsky, V. V.

2018-03-01

In work process of training in the form of a discrete information stream is considered. On each of stages of the considered process portions of the training information and quality of their assimilation are analysed. Individual characteristics and reaction trained for every portion of information on appropriate sections are defined. The control algorithm of training with the predicted number of control checks of the trainee who allows to define what operating influence is considered it is necessary to create for the trainee. On the basis of this algorithm the vector of probabilities of ignorance of elements of the training information is received. As a result of the conducted researches the algorithm on formation of the predicted training parameters is developed. In work the task of comparison of duration of training received experimentally with predicted on the basis of it is solved the conclusion is drawn on efficiency of formation of the predicted training parameters. The program complex on the basis of the values of individual parameters received as a result of experiments on each trainee who allows to calculate individual characteristics is developed, to form rating and to monitor process of change of parameters of training.

An Informative Interpretation of Decision Theory: The Information Theoretic Basis for Signal-to-Noise Ratio and Log Likelihood Ratio

DOE PAGES

Polcari, J.

2013-08-16

The signal processing concept of signal-to-noise ratio (SNR), in its role as a performance measure, is recast within the more general context of information theory, leading to a series of useful insights. Establishing generalized SNR (GSNR) as a rigorous information theoretic measure inherent in any set of observations significantly strengthens its quantitative performance pedigree while simultaneously providing a specific definition under general conditions. This directly leads to consideration of the log likelihood ratio (LLR): first, as the simplest possible information-preserving transformation (i.e., signal processing algorithm) and subsequently, as an absolute, comparable measure of information for any specific observation exemplar. Furthermore,more » the information accounting methodology that results permits practical use of both GSNR and LLR as diagnostic scalar performance measurements, directly comparable across alternative system/algorithm designs, applicable at any tap point within any processing string, in a form that is also comparable with the inherent performance bounds due to information conservation.« less

Dichotomy in the definition of prescriptive information suggests both prescribed data and prescribed algorithms: biosemiotics applications in genomic systems.

PubMed

D'Onofrio, David J; Abel, David L; Johnson, Donald E

2012-03-14

The fields of molecular biology and computer science have cooperated over recent years to create a synergy between the cybernetic and biosemiotic relationship found in cellular genomics to that of information and language found in computational systems. Biological information frequently manifests its "meaning" through instruction or actual production of formal bio-function. Such information is called prescriptive information (PI). PI programs organize and execute a prescribed set of choices. Closer examination of this term in cellular systems has led to a dichotomy in its definition suggesting both prescribed data and prescribed algorithms are constituents of PI. This paper looks at this dichotomy as expressed in both the genetic code and in the central dogma of protein synthesis. An example of a genetic algorithm is modeled after the ribosome, and an examination of the protein synthesis process is used to differentiate PI data from PI algorithms.

Combining spatial and spectral information to improve crop/weed discrimination algorithms

NASA Astrophysics Data System (ADS)

Yan, L.; Jones, G.; Villette, S.; Paoli, J. N.; Gée, C.

2012-01-01

Reduction of herbicide spraying is an important key to environmentally and economically improve weed management. To achieve this, remote sensors such as imaging systems are commonly used to detect weed plants. We developed spatial algorithms that detect the crop rows to discriminate crop from weeds. These algorithms have been thoroughly tested and provide robust and accurate results without learning process but their detection is limited to inter-row areas. Crop/Weed discrimination using spectral information is able to detect intra-row weeds but generally needs a prior learning process. We propose a method based on spatial and spectral information to enhance the discrimination and overcome the limitations of both algorithms. The classification from the spatial algorithm is used to build the training set for the spectral discrimination method. With this approach we are able to improve the range of weed detection in the entire field (inter and intra-row). To test the efficiency of these algorithms, a relevant database of virtual images issued from SimAField model has been used and combined to LOPEX93 spectral database. The developed method based is evaluated and compared with the initial method in this paper and shows an important enhancement from 86% of weed detection to more than 95%.

Methodologies, Models and Algorithms for Patients Rehabilitation.

PubMed

Fardoun, H M; Mashat, A S

2016-01-01

This editorial is part of the Focus Theme of Methods of Information in Medicine on "Methodologies, Models and Algorithms for Patients Rehabilitation". The objective of this focus theme is to present current solutions by means of technologies and human factors related to the use of Information and Communication Technologies (ICT) for improving patient rehabilitation. The focus theme examines distinctive measurements of strengthening methodologies, models and algorithms for disabled people in terms of rehabilitation and health care, and to explore the extent to which ICT is a useful tool in this process. The focus theme records a set of solutions for ICT systems developed to improve the rehabilitation process of disabled people and to help them in carrying out their daily life. The development and subsequent setting up of computers for the patients' rehabilitation process is of continuous interest and growth.

An image-processing strategy to extract important information suitable for a low-size stimulus pattern in a retinal prosthesis.

PubMed

Chen, Yili; Fu, Jixiang; Chu, Dawei; Li, Rongmao; Xie, Yaoqin

2017-11-27

A retinal prosthesis is designed to help the blind to obtain some sight. It consists of an external part and an internal part. The external part is made up of a camera, an image processor and an RF transmitter. The internal part is made up of an RF receiver, implant chip and microelectrode. Currently, the number of microelectrodes is in the hundreds, and we do not know the mechanism for using an electrode to stimulate the optic nerve. A simple hypothesis is that the pixels in an image correspond to the electrode. The images captured by the camera should be processed by suitable strategies to correspond to stimulation from the electrode. Thus, it is a question of how to obtain the important information from the image captured in the picture. Here, we use the region of interest (ROI), a useful algorithm for extracting the ROI, to retain the important information, and to remove the redundant information. This paper explains the details of the principles and functions of the ROI. Because we are investigating a real-time system, we need a fast processing ROI as a useful algorithm to extract the ROI. Thus, we simplified the ROI algorithm and used it in an outside image-processing digital signal processing (DSP) system of the retinal prosthesis. The results show that our image-processing strategies are suitable for a real-time retinal prosthesis and can eliminate redundant information and provide useful information for expression in a low-size image.

Elements of an algorithm for optimizing a parameter-structural neural network

NASA Astrophysics Data System (ADS)

Mrówczyńska, Maria

2016-06-01

The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.

A quantum causal discovery algorithm

NASA Astrophysics Data System (ADS)

Giarmatzi, Christina; Costa, Fabio

2018-03-01

Finding a causal model for a set of classical variables is now a well-established task—but what about the quantum equivalent? Even the notion of a quantum causal model is controversial. Here, we present a causal discovery algorithm for quantum systems. The input to the algorithm is a process matrix describing correlations between quantum events. Its output consists of different levels of information about the underlying causal model. Our algorithm determines whether the process is causally ordered by grouping the events into causally ordered non-signaling sets. It detects if all relevant common causes are included in the process, which we label Markovian, or alternatively if some causal relations are mediated through some external memory. For a Markovian process, it outputs a causal model, namely the causal relations and the corresponding mechanisms, represented as quantum states and channels. Our algorithm opens the route to more general quantum causal discovery methods.

The power of neural nets

NASA Technical Reports Server (NTRS)

Ryan, J. P.; Shah, B. H.

1987-01-01

Implementation of the Hopfield net which is used in the image processing type of applications where only partial information about the image may be available is discussed. The image classification type of algorithm of Hopfield and other learning algorithms, such as the Boltzmann machine and the back-propagation training algorithm, have many vital applications in space.

Structural health monitoring feature design by genetic programming

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Todd, Michael D.

2014-09-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.

Competitive Parallel Processing For Compression Of Data

NASA Technical Reports Server (NTRS)

Diner, Daniel B.; Fender, Antony R. H.

1990-01-01

Momentarily-best compression algorithm selected. Proposed competitive-parallel-processing system compresses data for transmission in channel of limited band-width. Likely application for compression lies in high-resolution, stereoscopic color-television broadcasting. Data from information-rich source like color-television camera compressed by several processors, each operating with different algorithm. Referee processor selects momentarily-best compressed output.

[Research and realization of signal processing algorithms based on FPGA in digital ophthalmic ultrasonography imaging].

PubMed

Fang, Simin; Zhou, Sheng; Wang, Xiaochun; Ye, Qingsheng; Tian, Ling; Ji, Jianjun; Wang, Yanqun

2015-01-01

To design and improve signal processing algorithms of ophthalmic ultrasonography based on FPGA. Achieved three signal processing modules: full parallel distributed dynamic filter, digital quadrature demodulation, logarithmic compression, using Verilog HDL hardware language in Quartus II. Compared to the original system, the hardware cost is reduced, the whole image shows clearer and more information of the deep eyeball contained in the image, the depth of detection increases from 5 cm to 6 cm. The new algorithms meet the design requirements and achieve the system's optimization that they can effectively improve the image quality of existing equipment.

[State Recognition of Solid Fermentation Process Based on Near Infrared Spectroscopy with Adaboost and Spectral Regression Discriminant Analysis].

PubMed

Yu, Shuang; Liu, Guo-hai; Xia, Rong-sheng; Jiang, Hui

2016-01-01

In order to achieve the rapid monitoring of process state of solid state fermentation (SSF), this study attempted to qualitative identification of process state of SSF of feed protein by use of Fourier transform near infrared (FT-NIR) spectroscopy analysis technique. Even more specifically, the FT-NIR spectroscopy combined with Adaboost-SRDA-NN integrated learning algorithm as an ideal analysis tool was used to accurately and rapidly monitor chemical and physical changes in SSF of feed protein without the need for chemical analysis. Firstly, the raw spectra of all the 140 fermentation samples obtained were collected by use of Fourier transform near infrared spectrometer (Antaris II), and the raw spectra obtained were preprocessed by use of standard normal variate transformation (SNV) spectral preprocessing algorithm. Thereafter, the characteristic information of the preprocessed spectra was extracted by use of spectral regression discriminant analysis (SRDA). Finally, nearest neighbors (NN) algorithm as a basic classifier was selected and building state recognition model to identify different fermentation samples in the validation set. Experimental results showed as follows: the SRDA-NN model revealed its superior performance by compared with other two different NN models, which were developed by use of the feature information form principal component analysis (PCA) and linear discriminant analysis (LDA), and the correct recognition rate of SRDA-NN model achieved 94.28% in the validation set. In this work, in order to further improve the recognition accuracy of the final model, Adaboost-SRDA-NN ensemble learning algorithm was proposed by integrated the Adaboost and SRDA-NN methods, and the presented algorithm was used to construct the online monitoring model of process state of SSF of feed protein. Experimental results showed as follows: the prediction performance of SRDA-NN model has been further enhanced by use of Adaboost lifting algorithm, and the correct recognition rate of the Adaboost-SRDA-NN model achieved 100% in the validation set. The overall results demonstrate that SRDA algorithm can effectively achieve the spectral feature information extraction to the spectral dimension reduction in model calibration process of qualitative analysis of NIR spectroscopy. In addition, the Adaboost lifting algorithm can improve the classification accuracy of the final model. The results obtained in this work can provide research foundation for developing online monitoring instruments for the monitoring of SSF process.

General A Scheme to Share Information via Employing Discrete Algorithm to Quantum States

NASA Astrophysics Data System (ADS)

Kang, Guo-Dong; Fang, Mao-Fa

2011-02-01

We propose a protocol for information sharing between two legitimate parties (Bob and Alice) via public-key cryptography. In particular, we specialize the protocol by employing discrete algorithm under mod that maps integers to quantum states via photon rotations. Based on this algorithm, we find that the protocol is secure under various classes of attacks. Specially, owe to the algorithm, the security of the classical privacy contained in the quantum public-key and the corresponding ciphertext is guaranteed. And the protocol is robust against the impersonation attack and the active wiretapping attack by designing particular checking processing, thus the protocol is valid.

Research on Palmprint Identification Method Based on Quantum Algorithms

PubMed Central

Zhang, Zhanzhan

2014-01-01

Quantum image recognition is a technology by using quantum algorithm to process the image information. It can obtain better effect than classical algorithm. In this paper, four different quantum algorithms are used in the three stages of palmprint recognition. First, quantum adaptive median filtering algorithm is presented in palmprint filtering processing. Quantum filtering algorithm can get a better filtering result than classical algorithm through the comparison. Next, quantum Fourier transform (QFT) is used to extract pattern features by only one operation due to quantum parallelism. The proposed algorithm exhibits an exponential speed-up compared with discrete Fourier transform in the feature extraction. Finally, quantum set operations and Grover algorithm are used in palmprint matching. According to the experimental results, quantum algorithm only needs to apply square of N operations to find out the target palmprint, but the traditional method needs N times of calculation. At the same time, the matching accuracy of quantum algorithm is almost 100%. PMID:25105165

A novel speech processing algorithm based on harmonicity cues in cochlear implant

NASA Astrophysics Data System (ADS)

Wang, Jian; Chen, Yousheng; Zhang, Zongping; Chen, Yan; Zhang, Weifeng

2017-08-01

This paper proposed a novel speech processing algorithm in cochlear implant, which used harmonicity cues to enhance tonal information in Mandarin Chinese speech recognition. The input speech was filtered by a 4-channel band-pass filter bank. The frequency ranges for the four bands were: 300-621, 621-1285, 1285-2657, and 2657-5499 Hz. In each pass band, temporal envelope and periodicity cues (TEPCs) below 400 Hz were extracted by full wave rectification and low-pass filtering. The TEPCs were modulated by a sinusoidal carrier, the frequency of which was fundamental frequency (F0) and its harmonics most close to the center frequency of each band. Signals from each band were combined together to obtain an output speech. Mandarin tone, word, and sentence recognition in quiet listening conditions were tested for the extensively used continuous interleaved sampling (CIS) strategy and the novel F0-harmonic algorithm. Results found that the F0-harmonic algorithm performed consistently better than CIS strategy in Mandarin tone, word, and sentence recognition. In addition, sentence recognition rate was higher than word recognition rate, as a result of contextual information in the sentence. Moreover, tone 3 and 4 performed better than tone 1 and tone 2, due to the easily identified features of the former. In conclusion, the F0-harmonic algorithm could enhance tonal information in cochlear implant speech processing due to the use of harmonicity cues, thereby improving Mandarin tone, word, and sentence recognition. Further study will focus on the test of the F0-harmonic algorithm in noisy listening conditions.

GPU-based real-time trinocular stereo vision

NASA Astrophysics Data System (ADS)

Yao, Yuanbin; Linton, R. J.; Padir, Taskin

2013-01-01

Most stereovision applications are binocular which uses information from a 2-camera array to perform stereo matching and compute the depth image. Trinocular stereovision with a 3-camera array has been proved to provide higher accuracy in stereo matching which could benefit applications like distance finding, object recognition, and detection. This paper presents a real-time stereovision algorithm implemented on a GPGPU (General-purpose graphics processing unit) using a trinocular stereovision camera array. Algorithm employs a winner-take-all method applied to perform fusion of disparities in different directions following various image processing techniques to obtain the depth information. The goal of the algorithm is to achieve real-time processing speed with the help of a GPGPU involving the use of Open Source Computer Vision Library (OpenCV) in C++ and NVidia CUDA GPGPU Solution. The results are compared in accuracy and speed to verify the improvement.

77 FR 13294 - Announcing Approval of Federal Information Processing Standard (FIPS) Publication 180-4, Secure...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-06

... hash algorithms in many computer network applications. On February 11, 2011, NIST published a notice in... Information Security Management Act (FISMA) of 2002 (Pub. L. 107-347), the Secretary of Commerce is authorized to approve Federal Information Processing Standards (FIPS). NIST activities to develop computer...

Parallel asynchronous systems and image processing algorithms

NASA Technical Reports Server (NTRS)

Coon, D. D.; Perera, A. G. U.

1989-01-01

A new hardware approach to implementation of image processing algorithms is described. The approach is based on silicon devices which would permit an independent analog processing channel to be dedicated to evey pixel. A laminar architecture consisting of a stack of planar arrays of the device would form a two-dimensional array processor with a 2-D array of inputs located directly behind a focal plane detector array. A 2-D image data stream would propagate in neuronlike asynchronous pulse coded form through the laminar processor. Such systems would integrate image acquisition and image processing. Acquisition and processing would be performed concurrently as in natural vision systems. The research is aimed at implementation of algorithms, such as the intensity dependent summation algorithm and pyramid processing structures, which are motivated by the operation of natural vision systems. Implementation of natural vision algorithms would benefit from the use of neuronlike information coding and the laminar, 2-D parallel, vision system type architecture. Besides providing a neural network framework for implementation of natural vision algorithms, a 2-D parallel approach could eliminate the serial bottleneck of conventional processing systems. Conversion to serial format would occur only after raw intensity data has been substantially processed. An interesting challenge arises from the fact that the mathematical formulation of natural vision algorithms does not specify the means of implementation, so that hardware implementation poses intriguing questions involving vision science.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2018-03-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2017-12-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

A weighted information criterion for multiple minor components and its adaptive extraction algorithms.

PubMed

Gao, Yingbin; Kong, Xiangyu; Zhang, Huihui; Hou, Li'an

2017-05-01

Minor component (MC) plays an important role in signal processing and data analysis, so it is a valuable work to develop MC extraction algorithms. Based on the concepts of weighted subspace and optimum theory, a weighted information criterion is proposed for searching the optimum solution of a linear neural network. This information criterion exhibits a unique global minimum attained if and only if the state matrix is composed of the desired MCs of an autocorrelation matrix of an input signal. By using gradient ascent method and recursive least square (RLS) method, two algorithms are developed for multiple MCs extraction. The global convergences of the proposed algorithms are also analyzed by the Lyapunov method. The proposed algorithms can extract the multiple MCs in parallel and has advantage in dealing with high dimension matrices. Since the weighted matrix does not require an accurate value, it facilitates the system design of the proposed algorithms for practical applications. The speed and computation advantages of the proposed algorithms are verified through simulations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Understanding how replication processes can maintain systems away from equilibrium using Algorithmic Information Theory.

PubMed

Devine, Sean D

2016-02-01

Replication can be envisaged as a computational process that is able to generate and maintain order far-from-equilibrium. Replication processes, can self-regulate, as the drive to replicate can counter degradation processes that impact on a system. The capability of replicated structures to access high quality energy and eject disorder allows Landauer's principle, in conjunction with Algorithmic Information Theory, to quantify the entropy requirements to maintain a system far-from-equilibrium. Using Landauer's principle, where destabilising processes, operating under the second law of thermodynamics, change the information content or the algorithmic entropy of a system by ΔH bits, replication processes can access order, eject disorder, and counter the change without outside interventions. Both diversity in replicated structures, and the coupling of different replicated systems, increase the ability of the system (or systems) to self-regulate in a changing environment as adaptation processes select those structures that use resources more efficiently. At the level of the structure, as selection processes minimise the information loss, the irreversibility is minimised. While each structure that emerges can be said to be more entropically efficient, as such replicating structures proliferate, the dissipation of the system as a whole is higher than would be the case for inert or simpler structures. While a detailed application to most real systems would be difficult, the approach may well be useful in understanding incremental changes to real systems and provide broad descriptions of system behaviour. Copyright © 2016 The Author. Published by Elsevier Ireland Ltd.. All rights reserved.

Results of research on development of an intellectual information system of bankruptcy risk assessment of the enterprise

NASA Astrophysics Data System (ADS)

Telipenko, E.; Chernysheva, T.; Zakharova, A.; Dumchev, A.

2015-10-01

The article represents research results about the knowledge base development for the intellectual information system for the bankruptcy risk assessment of the enterprise. It is described the process analysis of the knowledge base development; the main process stages, some problems and their solutions are given. The article introduces the connectionist model for the bankruptcy risk assessment based on the analysis of industrial enterprise financial accounting. The basis for this connectionist model is a three-layer perceptron with the back propagation of error algorithm. The knowledge base for the intellectual information system consists of processed information and the processing operation method represented as the connectionist model. The article represents the structure of the intellectual information system, the knowledge base, and the information processing algorithm for neural network training. The paper shows mean values of 10 indexes for industrial enterprises; with the help of them it is possible to carry out a financial analysis of industrial enterprises and identify correctly the current situation for well-timed managerial decisions. Results are given about neural network testing on the data of both bankrupt and financially strong enterprises, which were not included into training and test sets.

Integrating digital topology in image-processing libraries.

PubMed

Lamy, Julien

2007-01-01

This paper describes a method to integrate digital topology informations in image-processing libraries. This additional information allows a library user to write algorithms respecting topological constraints, for example, a seed fill or a skeletonization algorithm. As digital topology is absent from most image-processing libraries, such constraints cannot be fulfilled. We describe and give code samples for all the structures necessary for this integration, and show a use case in the form of a homotopic thinning filter inside ITK. The obtained filter can be up to a hundred times as fast as ITK's thinning filter and works for any image dimension. This paper mainly deals of integration within ITK, but can be adapted with only minor modifications to other image-processing libraries.

FPGA based hardware optimized implementation of signal processing system for LFM pulsed radar

NASA Astrophysics Data System (ADS)

Azim, Noor ul; Jun, Wang

2016-11-01

Signal processing is one of the main parts of any radar system. Different signal processing algorithms are used to extract information about different parameters like range, speed, direction etc, of a target in the field of radar communication. This paper presents LFM (Linear Frequency Modulation) pulsed radar signal processing algorithms which are used to improve target detection, range resolution and to estimate the speed of a target. Firstly, these algorithms are simulated in MATLAB to verify the concept and theory. After the conceptual verification in MATLAB, the simulation is converted into implementation on hardware using Xilinx FPGA. Chosen FPGA is Xilinx Virtex-6 (XC6LVX75T). For hardware implementation pipeline optimization is adopted and also other factors are considered for resources optimization in the process of implementation. Focusing algorithms in this work for improving target detection, range resolution and speed estimation are hardware optimized fast convolution processing based pulse compression and pulse Doppler processing.

Information theoretic methods for image processing algorithm optimization

NASA Astrophysics Data System (ADS)

Prokushkin, Sergey F.; Galil, Erez

2015-01-01

Modern image processing pipelines (e.g., those used in digital cameras) are full of advanced, highly adaptive filters that often have a large number of tunable parameters (sometimes > 100). This makes the calibration procedure for these filters very complex, and the optimal results barely achievable in the manual calibration; thus an automated approach is a must. We will discuss an information theory based metric for evaluation of algorithm adaptive characteristics ("adaptivity criterion") using noise reduction algorithms as an example. The method allows finding an "orthogonal decomposition" of the filter parameter space into the "filter adaptivity" and "filter strength" directions. This metric can be used as a cost function in automatic filter optimization. Since it is a measure of a physical "information restoration" rather than perceived image quality, it helps to reduce the set of the filter parameters to a smaller subset that is easier for a human operator to tune and achieve a better subjective image quality. With appropriate adjustments, the criterion can be used for assessment of the whole imaging system (sensor plus post-processing).

The visual information system

Treesearch

Merlyn J. Paulson

1979-01-01

This paper outlines a project level process (V.I.S.) which utilizes very accurate and flexible computer algorithms in combination with contemporary site analysis and design techniques for visual evaluation, design and management. The process provides logical direction and connecting bridges through problem identification, information collection and verification, visual...

How Analysts Cognitively “Connect the Dots”

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

Bradel, Lauren; Self, Jessica S.; Endert, Alexander

2013-06-04

As analysts attempt to make sense of a collection of documents, such as intelligence analysis reports, they may wish to “connect the dots” between pieces of information that may initially seem unrelated. This process of synthesizing information between information requires users to make connections between pairs of documents, creating a conceptual story. We conducted a user study to analyze the process by which users connect pairs of documents and how they spatially arrange information. Users created conceptual stories that connected the dots using organizational strategies that ranged in complexity. We propose taxonomies for cognitive connections and physical structures used whenmore » trying to “connect the dots” between two documents. We compared the user-created stories with a data-mining algorithm that constructs chains of documents using co-occurrence metrics. Using the insight gained into the storytelling process, we offer design considerations for the existing data mining algorithm and corresponding tools to combine the power of data mining and the complex cognitive processing of analysts.« less

Quantum simulator review

NASA Astrophysics Data System (ADS)

Bednar, Earl; Drager, Steven L.

2007-04-01

Quantum information processing's objective is to utilize revolutionary computing capability based on harnessing the paradigm shift offered by quantum computing to solve classically hard and computationally challenging problems. Some of our computationally challenging problems of interest include: the capability for rapid image processing, rapid optimization of logistics, protecting information, secure distributed simulation, and massively parallel computation. Currently, one important problem with quantum information processing is that the implementation of quantum computers is difficult to realize due to poor scalability and great presence of errors. Therefore, we have supported the development of Quantum eXpress and QuIDD Pro, two quantum computer simulators running on classical computers for the development and testing of new quantum algorithms and processes. This paper examines the different methods used by these two quantum computing simulators. It reviews both simulators, highlighting each simulators background, interface, and special features. It also demonstrates the implementation of current quantum algorithms on each simulator. It concludes with summary comments on both simulators.

Recursive least-squares learning algorithms for neural networks

NASA Astrophysics Data System (ADS)

Lewis, Paul S.; Hwang, Jenq N.

1990-11-01

This paper presents the development of a pair of recursive least squares (ItLS) algorithms for online training of multilayer perceptrons which are a class of feedforward artificial neural networks. These algorithms incorporate second order information about the training error surface in order to achieve faster learning rates than are possible using first order gradient descent algorithms such as the generalized delta rule. A least squares formulation is derived from a linearization of the training error function. Individual training pattern errors are linearized about the network parameters that were in effect when the pattern was presented. This permits the recursive solution of the least squares approximation either via conventional RLS recursions or by recursive QR decomposition-based techniques. The computational complexity of the update is 0(N2) where N is the number of network parameters. This is due to the estimation of the N x N inverse Hessian matrix. Less computationally intensive approximations of the ilLS algorithms can be easily derived by using only block diagonal elements of this matrix thereby partitioning the learning into independent sets. A simulation example is presented in which a neural network is trained to approximate a two dimensional Gaussian bump. In this example RLS training required an order of magnitude fewer iterations on average (527) than did training with the generalized delta rule (6 1 BACKGROUND Artificial neural networks (ANNs) offer an interesting and potentially useful paradigm for signal processing and pattern recognition. The majority of ANN applications employ the feed-forward multilayer perceptron (MLP) network architecture in which network parameters are " trained" by a supervised learning algorithm employing the generalized delta rule (GDIt) [1 2]. The GDR algorithm approximates a fixed step steepest descent algorithm using derivatives computed by error backpropagatiori. The GDII algorithm is sometimes referred to as the backpropagation algorithm. However in this paper we will use the term backpropagation to refer only to the process of computing error derivatives. While multilayer perceptrons provide a very powerful nonlinear modeling capability GDR training can be very slow and inefficient. In linear adaptive filtering the analog of the GDR algorithm is the leastmean- squares (LMS) algorithm. Steepest descent-based algorithms such as GDR or LMS are first order because they use only first derivative or gradient information about the training error to be minimized. To speed up the training process second order algorithms may be employed that take advantage of second derivative or Hessian matrix information. Second order information can be incorporated into MLP training in different ways. In many applications especially in the area of pattern recognition the training set is finite. In these cases block learning can be applied using standard nonlinear optimization techniques [3 4 5].

[Digital signal processing of a novel neuron discharge model stimulation strategy for cochlear implants].

PubMed

Yang, Yiwei; Xu, Yuejin; Miu, Jichang; Zhou, Linghong; Xiao, Zhongju

2012-10-01

To apply the classic leakage integrate-and-fire models, based on the mechanism of the generation of physiological auditory stimulation, in the information processing coding of cochlear implants to improve the auditory result. The results of algorithm simulation in digital signal processor (DSP) were imported into Matlab for a comparative analysis. Compared with CIS coding, the algorithm of membrane potential integrate-and-fire (MPIF) allowed more natural pulse discharge in a pseudo-random manner to better fit the physiological structures. The MPIF algorithm can effectively solve the problem of the dynamic structure of the delivered auditory information sequence issued in the auditory center and allowed integration of the stimulating pulses and time coding to ensure the coherence and relevance of the stimulating pulse time.

a Metadata Based Approach for Analyzing Uav Datasets for Photogrammetric Applications

NASA Astrophysics Data System (ADS)

Dhanda, A.; Remondino, F.; Santana Quintero, M.

2018-05-01

This paper proposes a methodology for pre-processing and analysing Unmanned Aerial Vehicle (UAV) datasets before photogrammetric processing. In cases where images are gathered without a detailed flight plan and at regular acquisition intervals the datasets can be quite large and be time consuming to process. This paper proposes a method to calculate the image overlap and filter out images to reduce large block sizes and speed up photogrammetric processing. The python-based algorithm that implements this methodology leverages the metadata in each image to determine the end and side overlap of grid-based UAV flights. Utilizing user input, the algorithm filters out images that are unneeded for photogrammetric processing. The result is an algorithm that can speed up photogrammetric processing and provide valuable information to the user about the flight path.

High-performance technology for indexing of high volumes of Earth remote sensing data

NASA Astrophysics Data System (ADS)

Strotov, Valery V.; Taganov, Alexander I.; Kolesenkov, Aleksandr N.; Kostrov, Boris V.

2017-10-01

The present paper has suggested a technology for search, indexing, cataloging and distribution of aerospace images on the basis of geo-information approach, cluster and spectral analysis. It has considered information and algorithmic support of the system. Functional circuit of the system and structure of the geographical data base have been developed on the basis of the geographical online portal technology. Taking into account heterogeneity of information obtained from various sources it is reasonable to apply a geoinformation platform that allows analyzing space location of objects and territories and executing complex processing of information. Geoinformation platform is based on cartographic fundamentals with the uniform coordinate system, the geographical data base, a set of algorithms and program modules for execution of various tasks. The technology for adding by particular users and companies of images taken by means of professional and amateur devices and also processed by various software tools to the array system has been suggested. Complex usage of visual and instrumental approaches allows significantly expanding an application area of Earth remote sensing data. Development and implementation of new algorithms based on the complex usage of new methods for processing of structured and unstructured data of high volumes will increase periodicity and rate of data updating. The paper has shown that application of original algorithms for search, indexing and cataloging of aerospace images will provide an easy access to information spread by hundreds of suppliers and allow increasing an access rate to aerospace images up to 5 times in comparison with current analogues.

Bounded-Degree Approximations of Stochastic Networks

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

Quinn, Christopher J.; Pinar, Ali; Kiyavash, Negar

2017-06-01

We propose algorithms to approximate directed information graphs. Directed information graphs are probabilistic graphical models that depict causal dependencies between stochastic processes in a network. The proposed algorithms identify optimal and near-optimal approximations in terms of Kullback-Leibler divergence. The user-chosen sparsity trades off the quality of the approximation against visual conciseness and computational tractability. One class of approximations contains graphs with speci ed in-degrees. Another class additionally requires that the graph is connected. For both classes, we propose algorithms to identify the optimal approximations and also near-optimal approximations, using a novel relaxation of submodularity. We also propose algorithms to identifymore » the r-best approximations among these classes, enabling robust decision making.« less

Information spread of emergency events: path searching on social networks.

PubMed

Dai, Weihui; Hu, Hongzhi; Wu, Tunan; Dai, Yonghui

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.

Multichannel Doppler Processing for an Experimental Low-Angle Tracking System

DTIC Science & Technology

1990-05-01

estimation techniques at sea. Because of clutter and noise, it is necessary to use a number of different processing algorithms to extract the required...a number of different processing algorithms to extract the required information. Consequently, the ELAT radar system is composed of multiple...corresponding to RF frequencies, f, and f2. For mode 3, the ambiguities occur at vbi = 15.186 knots and vb2 = 16.96 knots. The sea clutter, with a spectrum

[Improvement of magnetic resonance phase unwrapping method based on Goldstein Branch-cut algorithm].

PubMed

Guo, Lin; Kang, Lili; Wang, Dandan

2013-02-01

The phase information of magnetic resonance (MR) phase image can be used in many MR imaging techniques, but phase wrapping of the images often results in inaccurate phase information and phase unwrapping is essential for MR imaging techniques. In this paper we analyze the causes of errors in phase unwrapping with the commonly used Goldstein Brunch-cut algorithm and propose an improved algorithm. During the unwrapping process, masking, filtering, dipole- remover preprocessor, and the Prim algorithm of the minimum spanning tree were introduced to optimize the residues essential for the Goldstein Brunch-cut algorithm. Experimental results showed that the residues, branch-cuts and continuous unwrapped phase surface were efficiently reduced and the quality of MR phase images was obviously improved with the proposed method.

Online POMDP Algorithms for Very Large Observation Spaces

DTIC Science & Technology

2017-06-06

stochastic optimization: From sets to paths." In Advances in Neural Information Processing Systems, pp. 1585- 1593 . 2015. • Luo, Yuanfu, Haoyu Bai...and Wee Sun Lee. "Adaptive stochastic optimization: From sets to paths." In Advances in Neural Information Processing Systems, pp. 1585- 1593 . 2015

Fast parallel algorithm for slicing STL based on pipeline

NASA Astrophysics Data System (ADS)

Ma, Xulong; Lin, Feng; Yao, Bo

2016-05-01

In Additive Manufacturing field, the current researches of data processing mainly focus on a slicing process of large STL files or complicated CAD models. To improve the efficiency and reduce the slicing time, a parallel algorithm has great advantages. However, traditional algorithms can't make full use of multi-core CPU hardware resources. In the paper, a fast parallel algorithm is presented to speed up data processing. A pipeline mode is adopted to design the parallel algorithm. And the complexity of the pipeline algorithm is analyzed theoretically. To evaluate the performance of the new algorithm, effects of threads number and layers number are investigated by a serial of experiments. The experimental results show that the threads number and layers number are two remarkable factors to the speedup ratio. The tendency of speedup versus threads number reveals a positive relationship which greatly agrees with the Amdahl's law, and the tendency of speedup versus layers number also keeps a positive relationship agreeing with Gustafson's law. The new algorithm uses topological information to compute contours with a parallel method of speedup. Another parallel algorithm based on data parallel is used in experiments to show that pipeline parallel mode is more efficient. A case study at last shows a suspending performance of the new parallel algorithm. Compared with the serial slicing algorithm, the new pipeline parallel algorithm can make full use of the multi-core CPU hardware, accelerate the slicing process, and compared with the data parallel slicing algorithm, the new slicing algorithm in this paper adopts a pipeline parallel model, and a much higher speedup ratio and efficiency is achieved.

An Ontology for Identifying Cyber Intrusion Induced Faults in Process Control Systems

NASA Astrophysics Data System (ADS)

Hieb, Jeffrey; Graham, James; Guan, Jian

This paper presents an ontological framework that permits formal representations of process control systems, including elements of the process being controlled and the control system itself. A fault diagnosis algorithm based on the ontological model is also presented. The algorithm can identify traditional process elements as well as control system elements (e.g., IP network and SCADA protocol) as fault sources. When these elements are identified as a likely fault source, the possibility exists that the process fault is induced by a cyber intrusion. A laboratory-scale distillation column is used to illustrate the model and the algorithm. Coupled with a well-defined statistical process model, this fault diagnosis approach provides cyber security enhanced fault diagnosis information to plant operators and can help identify that a cyber attack is underway before a major process failure is experienced.

Application of independent component analysis for speech-music separation using an efficient score function estimation

NASA Astrophysics Data System (ADS)

Pishravian, Arash; Aghabozorgi Sahaf, Masoud Reza

2012-12-01

In this paper speech-music separation using Blind Source Separation is discussed. The separating algorithm is based on the mutual information minimization where the natural gradient algorithm is used for minimization. In order to do that, score function estimation from observation signals (combination of speech and music) samples is needed. The accuracy and the speed of the mentioned estimation will affect on the quality of the separated signals and the processing time of the algorithm. The score function estimation in the presented algorithm is based on Gaussian mixture based kernel density estimation method. The experimental results of the presented algorithm on the speech-music separation and comparing to the separating algorithm which is based on the Minimum Mean Square Error estimator, indicate that it can cause better performance and less processing time

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

PubMed Central

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks.

PubMed

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem.

Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

NASA Astrophysics Data System (ADS)

Ahmad, Siti Rohaidah; Yusop, Nurhafizah Moziyana Mohd; Bakar, Azuraliza Abu; Yaakub, Mohd Ridzwan

2017-10-01

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the ACO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.

Discovering the influential users oriented to viral marketing based on online social networks

NASA Astrophysics Data System (ADS)

Zhu, Zhiguo

2013-08-01

The target of viral marketing on the platform of popular online social networks is to rapidly propagate marketing information at lower cost and increase sales, in which a key problem is how to precisely discover the most influential users in the process of information diffusion. A novel method is proposed in this paper for helping companies to identify such users as seeds to maximize information diffusion in the viral marketing. Firstly, the user trust network oriented to viral marketing and users’ combined interest degree in the network including isolated users are extensively defined. Next, we construct a model considering the time factor to simulate the process of information diffusion in viral marketing and propose a dynamic algorithm description. Finally, experiments are conducted with a real dataset extracted from the famous SNS website Epinions. The experimental results indicate that the proposed algorithm has better scalability and is less time-consuming. Compared with the classical model, the proposed algorithm achieved a better performance than does the classical method on the two aspects of network coverage rate and time-consumption in our four sub-datasets.

76 FR 11433 - Federal Transition To Secure Hash Algorithm (SHA)-256

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-02

... generating digital signatures. Current information systems, Web servers, applications and workstation operating systems were designed to process, and use SHA-1 generated signatures. National Institute of... cryptographic keys, and more robust algorithms by December 2013. Government systems may begin to encounter...

A Hybrid Search Algorithm for Swarm Robots Searching in an Unknown Environment

PubMed Central

Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao

2014-01-01

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency. PMID:25386855

A hybrid search algorithm for swarm robots searching in an unknown environment.

PubMed

Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao

2014-01-01

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency.

A novel algorithm for fully automated mapping of geospatial ontologies

NASA Astrophysics Data System (ADS)

Chaabane, Sana; Jaziri, Wassim

2018-01-01

Geospatial information is collected from different sources thus making spatial ontologies, built for the same geographic domain, heterogeneous; therefore, different and heterogeneous conceptualizations may coexist. Ontology integrating helps creating a common repository of the geospatial ontology and allows removing the heterogeneities between the existing ontologies. Ontology mapping is a process used in ontologies integrating and consists in finding correspondences between the source ontologies. This paper deals with the "mapping" process of geospatial ontologies which consist in applying an automated algorithm in finding the correspondences between concepts referring to the definitions of matching relationships. The proposed algorithm called "geographic ontologies mapping algorithm" defines three types of mapping: semantic, topological and spatial.

Study on Electro-polymerization Nano-micro Wiring System Imitating Axonal Growth of Artificial Neurons towards Machine Learning

NASA Astrophysics Data System (ADS)

Dang, Nguyen Tuan; Akai-Kasada, Megumi; Asai, Tetsuya; Saito, Akira; Kuwahara, Yuji; Hokkaido University Collaboration

2015-03-01

Machine learning using the artificial neuron network research is supposed to be the best way to understand how the human brain trains itself to process information. In this study, we have successfully developed the programs using supervised machine learning algorithm. However, these supervised learning processes for the neuron network required the very strong computing configuration. Derivation from the necessity of increasing in computing ability and in reduction of power consumption, accelerator circuits become critical. To develop such accelerator circuits using supervised machine learning algorithm, conducting polymer micro/nanowires growing process was realized and applied as a synaptic weigh controller. In this work, high conductivity Polypyrrole (PPy) and Poly (3, 4 - ethylenedioxythiophene) PEDOT wires were potentiostatically grown crosslinking the designated electrodes, which were prefabricated by lithography, when appropriate square wave AC voltage and appropriate frequency were applied. Micro/nanowire growing process emulated the neurotransmitter release process of synapses inside a biological neuron and wire's resistance variation during the growing process was preferred to as the variation of synaptic weigh in machine learning algorithm. In a cooperation with Graduate School of Information Science and Technology, Hokkaido University.

Efficiently hiding sensitive itemsets with transaction deletion based on genetic algorithms.

PubMed

Lin, Chun-Wei; Zhang, Binbin; Yang, Kuo-Tung; Hong, Tzung-Pei

2014-01-01

Data mining is used to mine meaningful and useful information or knowledge from a very large database. Some secure or private information can be discovered by data mining techniques, thus resulting in an inherent risk of threats to privacy. Privacy-preserving data mining (PPDM) has thus arisen in recent years to sanitize the original database for hiding sensitive information, which can be concerned as an NP-hard problem in sanitization process. In this paper, a compact prelarge GA-based (cpGA2DT) algorithm to delete transactions for hiding sensitive itemsets is thus proposed. It solves the limitations of the evolutionary process by adopting both the compact GA-based (cGA) mechanism and the prelarge concept. A flexible fitness function with three adjustable weights is thus designed to find the appropriate transactions to be deleted in order to hide sensitive itemsets with minimal side effects of hiding failure, missing cost, and artificial cost. Experiments are conducted to show the performance of the proposed cpGA2DT algorithm compared to the simple GA-based (sGA2DT) algorithm and the greedy approach in terms of execution time and three side effects.

Skeletonization with hollow detection on gray image by gray weighted distance transform

NASA Astrophysics Data System (ADS)

Bhattacharya, Prabir; Qian, Kai; Cao, Siqi; Qian, Yi

1998-10-01

A skeletonization algorithm that could be used to process non-uniformly distributed gray-scale images with hollows was presented. This algorithm is based on the Gray Weighted Distance Transformation. The process includes a preliminary phase of investigation in the hollows in the gray-scale image, whether these hollows are considered as topological constraints for the skeleton structure depending on their statistically significant depth. We then extract the resulting skeleton that has certain meaningful information for understanding the object in the image. This improved algorithm can overcome the possible misinterpretation of some complicated images in the extracted skeleton, especially in images with asymmetric hollows and asymmetric features. This algorithm can be executed on a parallel machine as all the operations are executed in local. Some examples are discussed to illustrate the algorithm.

Hyperspectral image processing methods

USDA-ARS?s Scientific Manuscript database

Hyperspectral image processing refers to the use of computer algorithms to extract, store and manipulate both spatial and spectral information contained in hyperspectral images across the visible and near-infrared portion of the electromagnetic spectrum. A typical hyperspectral image processing work...

Road extraction from aerial images using a region competition algorithm.

PubMed

Amo, Miriam; Martínez, Fernando; Torre, Margarita

2006-05-01

In this paper, we present a user-guided method based on the region competition algorithm to extract roads, and therefore we also provide some clues concerning the placement of the points required by the algorithm. The initial points are analyzed in order to find out whether it is necessary to add more initial points, and this process will be based on image information. Not only is the algorithm able to obtain the road centerline, but it also recovers the road sides. An initial simple model is deformed by using region growing techniques to obtain a rough road approximation. This model will be refined by region competition. The result of this approach is that it delivers the simplest output vector information, fully recovering the road details as they are on the image, without performing any kind of symbolization. Therefore, we tried to refine a general road model by using a reliable method to detect transitions between regions. This method is proposed in order to obtain information for feeding large-scale Geographic Information System.

Multi-Agent Patrolling under Uncertainty and Threats.

PubMed

Chen, Shaofei; Wu, Feng; Shen, Lincheng; Chen, Jing; Ramchurn, Sarvapali D

2015-01-01

We investigate a multi-agent patrolling problem where information is distributed alongside threats in environments with uncertainties. Specifically, the information and threat at each location are independently modelled as multi-state Markov chains, whose states are not observed until the location is visited by an agent. While agents will obtain information at a location, they may also suffer damage from the threat at that location. Therefore, the goal of the agents is to gather as much information as possible while mitigating the damage incurred. To address this challenge, we formulate the single-agent patrolling problem as a Partially Observable Markov Decision Process (POMDP) and propose a computationally efficient algorithm to solve this model. Building upon this, to compute patrols for multiple agents, the single-agent algorithm is extended for each agent with the aim of maximising its marginal contribution to the team. We empirically evaluate our algorithm on problems of multi-agent patrolling and show that it outperforms a baseline algorithm up to 44% for 10 agents and by 21% for 15 agents in large domains.

I/O efficient algorithms and applications in geographic information systems

NASA Astrophysics Data System (ADS)

Danner, Andrew

Modern remote sensing methods such a laser altimetry (lidar) and Interferometric Synthetic Aperture Radar (IfSAR) produce georeferenced elevation data at unprecedented rates. Many Geographic Information System (GIS) algorithms designed for terrain modelling applications cannot process these massive data sets. The primary problem is that these data sets are too large to fit in the main internal memory of modern computers and must therefore reside on larger, but considerably slower disks. In these applications, the transfer of data between disk and main memory, or I/O, becomes the primary bottleneck. Working in a theoretical model that more accurately represents this two level memory hierarchy, we can develop algorithms that are I/O-efficient and reduce the amount of disk I/O needed to solve a problem. In this thesis we aim to modernize GIS algorithms and develop a number of I/O-efficient algorithms for processing geographic data derived from massive elevation data sets. For each application, we convert a geographic question to an algorithmic question, develop an I/O-efficient algorithm that is theoretically efficient, implement our approach and verify its performance using real-world data. The applications we consider include constructing a gridded digital elevation model (DEM) from an irregularly spaced point cloud, removing topological noise from a DEM, modeling surface water flow over a terrain, extracting river networks and watershed hierarchies from the terrain, and locating polygons containing query points in a planar subdivision. We initially developed solutions to each of these applications individually. However, we also show how to combine individual solutions to form a scalable geo-processing pipeline that seamlessly solves a sequence of sub-problems with little or no manual intervention. We present experimental results that demonstrate orders of magnitude improvement over previously known algorithms.

Information Spread of Emergency Events: Path Searching on Social Networks

PubMed Central

Hu, Hongzhi; Wu, Tunan

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning. PMID:24600323

Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information.

PubMed

Fan, Yue; Wang, Xiao; Peng, Qinke

2017-01-01

Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result.

PDF text classification to leverage information extraction from publication reports.

PubMed

Bui, Duy Duc An; Del Fiol, Guilherme; Jonnalagadda, Siddhartha

2016-06-01

Data extraction from original study reports is a time-consuming, error-prone process in systematic review development. Information extraction (IE) systems have the potential to assist humans in the extraction task, however majority of IE systems were not designed to work on Portable Document Format (PDF) document, an important and common extraction source for systematic review. In a PDF document, narrative content is often mixed with publication metadata or semi-structured text, which add challenges to the underlining natural language processing algorithm. Our goal is to categorize PDF texts for strategic use by IE systems. We used an open-source tool to extract raw texts from a PDF document and developed a text classification algorithm that follows a multi-pass sieve framework to automatically classify PDF text snippets (for brevity, texts) into TITLE, ABSTRACT, BODYTEXT, SEMISTRUCTURE, and METADATA categories. To validate the algorithm, we developed a gold standard of PDF reports that were included in the development of previous systematic reviews by the Cochrane Collaboration. In a two-step procedure, we evaluated (1) classification performance, and compared it with machine learning classifier, and (2) the effects of the algorithm on an IE system that extracts clinical outcome mentions. The multi-pass sieve algorithm achieved an accuracy of 92.6%, which was 9.7% (p<0.001) higher than the best performing machine learning classifier that used a logistic regression algorithm. F-measure improvements were observed in the classification of TITLE (+15.6%), ABSTRACT (+54.2%), BODYTEXT (+3.7%), SEMISTRUCTURE (+34%), and MEDADATA (+14.2%). In addition, use of the algorithm to filter semi-structured texts and publication metadata improved performance of the outcome extraction system (F-measure +4.1%, p=0.002). It also reduced of number of sentences to be processed by 44.9% (p<0.001), which corresponds to a processing time reduction of 50% (p=0.005). The rule-based multi-pass sieve framework can be used effectively in categorizing texts extracted from PDF documents. Text classification is an important prerequisite step to leverage information extraction from PDF documents. Copyright © 2016 Elsevier Inc. All rights reserved.

A linear recurrent kernel online learning algorithm with sparse updates.

PubMed

Fan, Haijin; Song, Qing

2014-02-01

In this paper, we propose a recurrent kernel algorithm with selectively sparse updates for online learning. The algorithm introduces a linear recurrent term in the estimation of the current output. This makes the past information reusable for updating of the algorithm in the form of a recurrent gradient term. To ensure that the reuse of this recurrent gradient indeed accelerates the convergence speed, a novel hybrid recurrent training is proposed to switch on or off learning the recurrent information according to the magnitude of the current training error. Furthermore, the algorithm includes a data-dependent adaptive learning rate which can provide guaranteed system weight convergence at each training iteration. The learning rate is set as zero when the training violates the derived convergence conditions, which makes the algorithm updating process sparse. Theoretical analyses of the weight convergence are presented and experimental results show the good performance of the proposed algorithm in terms of convergence speed and estimation accuracy. Copyright © 2013 Elsevier Ltd. All rights reserved.

On the definition of the concepts thinking, consciousness, and conscience.

PubMed Central

Monin, A S

1992-01-01

A complex system (CS) is defined as a set of elements, with connections between them, singled out of the environment, capable of getting information from the environment, capable of making decisions (i.e., of choosing between alternatives), and having purposefulness (i.e., an urge towards preferable states or other goals). Thinking is a process that takes place (or which can take place) in some of the CS and consists of (i) receiving information from the environment (and from itself), (ii) memorizing the information, (iii) the subconscious, and (iv) consciousness. Life is a process that takes place in some CS and consists of functions i and ii, as well as (v) reproduction with passing of hereditary information to progeny, and (vi) oriented energy and matter exchange with the environment sufficient for the maintenance of all life processes. Memory is a complex of processes of placing information in memory banks, keeping it there, and producing it according to prescriptions available in the system or to inquiries arising in it. Consciousness is a process of realization by the thinking CS of some set of algorithms consisting of the comparison of its knowledge, intentions, decisions, and actions with reality--i.e., with accumulated and continuously received internal and external information. Conscience is a realization of an algorithm of good and evil pattern recognition. PMID:1631060

Internet (WWW) based system of ultrasonic image processing tools for remote image analysis.

PubMed

Zeng, Hong; Fei, Ding-Yu; Fu, Cai-Ting; Kraft, Kenneth A

2003-07-01

Ultrasonic Doppler color imaging can provide anatomic information and simultaneously render flow information within blood vessels for diagnostic purpose. Many researchers are currently developing ultrasound image processing algorithms in order to provide physicians with accurate clinical parameters from the images. Because researchers use a variety of computer languages and work on different computer platforms to implement their algorithms, it is difficult for other researchers and physicians to access those programs. A system has been developed using World Wide Web (WWW) technologies and HTTP communication protocols to publish our ultrasonic Angle Independent Doppler Color Image (AIDCI) processing algorithm and several general measurement tools on the Internet, where authorized researchers and physicians can easily access the program using web browsers to carry out remote analysis of their local ultrasonic images or images provided from the database. In order to overcome potential incompatibility between programs and users' computer platforms, ActiveX technology was used in this project. The technique developed may also be used for other research fields.

Autonomous learning based on cost assumptions: theoretical studies and experiments in robot control.

PubMed

Ribeiro, C H; Hemerly, E M

2000-02-01

Autonomous learning techniques are based on experience acquisition. In most realistic applications, experience is time-consuming: it implies sensor reading, actuator control and algorithmic update, constrained by the learning system dynamics. The information crudeness upon which classical learning algorithms operate make such problems too difficult and unrealistic. Nonetheless, additional information for facilitating the learning process ideally should be embedded in such a way that the structural, well-studied characteristics of these fundamental algorithms are maintained. We investigate in this article a more general formulation of the Q-learning method that allows for a spreading of information derived from single updates towards a neighbourhood of the instantly visited state and converges to optimality. We show how this new formulation can be used as a mechanism to safely embed prior knowledge about the structure of the state space, and demonstrate it in a modified implementation of a reinforcement learning algorithm in a real robot navigation task.

Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes. Part 3; The Map and Related Decoding Algirithms

NASA Technical Reports Server (NTRS)

Lin, Shu; Fossorier, Marc

1998-01-01

In a coded communication system with equiprobable signaling, MLD minimizes the word error probability and delivers the most likely codeword associated with the corresponding received sequence. This decoding has two drawbacks. First, minimization of the word error probability is not equivalent to minimization of the bit error probability. Therefore, MLD becomes suboptimum with respect to the bit error probability. Second, MLD delivers a hard-decision estimate of the received sequence, so that information is lost between the input and output of the ML decoder. This information is important in coded schemes where the decoded sequence is further processed, such as concatenated coding schemes, multi-stage and iterative decoding schemes. In this chapter, we first present a decoding algorithm which both minimizes bit error probability, and provides the corresponding soft information at the output of the decoder. This algorithm is referred to as the MAP (maximum aposteriori probability) decoding algorithm.

Classification algorithm of lung lobe for lung disease cases based on multislice CT images

NASA Astrophysics Data System (ADS)

Matsuhiro, M.; Kawata, Y.; Niki, N.; Nakano, Y.; Mishima, M.; Ohmatsu, H.; Tsuchida, T.; Eguchi, K.; Kaneko, M.; Moriyama, N.

2011-03-01

With the development of multi-slice CT technology, to obtain an accurate 3D image of lung field in a short time is possible. To support that, a lot of image processing methods need to be developed. In clinical setting for diagnosis of lung cancer, it is important to study and analyse lung structure. Therefore, classification of lung lobe provides useful information for lung cancer analysis. In this report, we describe algorithm which classify lungs into lung lobes for lung disease cases from multi-slice CT images. The classification algorithm of lung lobes is efficiently carried out using information of lung blood vessel, bronchus, and interlobar fissure. Applying the classification algorithms to multi-slice CT images of 20 normal cases and 5 lung disease cases, we demonstrate the usefulness of the proposed algorithms.

Synchronization Of Parallel Discrete Event Simulations

NASA Technical Reports Server (NTRS)

Steinman, Jeffrey S.

1992-01-01

Adaptive, parallel, discrete-event-simulation-synchronization algorithm, Breathing Time Buckets, developed in Synchronous Parallel Environment for Emulation and Discrete Event Simulation (SPEEDES) operating system. Algorithm allows parallel simulations to process events optimistically in fluctuating time cycles that naturally adapt while simulation in progress. Combines best of optimistic and conservative synchronization strategies while avoiding major disadvantages. Algorithm processes events optimistically in time cycles adapting while simulation in progress. Well suited for modeling communication networks, for large-scale war games, for simulated flights of aircraft, for simulations of computer equipment, for mathematical modeling, for interactive engineering simulations, and for depictions of flows of information.

Online Community Detection for Large Complex Networks

PubMed Central

Pan, Gang; Zhang, Wangsheng; Wu, Zhaohui; Li, Shijian

2014-01-01

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance. PMID:25061683

Quantum computation in the analysis of hyperspectral data

NASA Astrophysics Data System (ADS)

Gomez, Richard B.; Ghoshal, Debabrata; Jayanna, Anil

2004-08-01

Recent research on the topic of quantum computation provides us with some quantum algorithms with higher efficiency and speedup compared to their classical counterparts. In this paper, it is our intent to provide the results of our investigation of several applications of such quantum algorithms - especially the Grover's Search algorithm - in the analysis of Hyperspectral Data. We found many parallels with Grover's method in existing data processing work that make use of classical spectral matching algorithms. Our efforts also included the study of several methods dealing with hyperspectral image analysis work where classical computation methods involving large data sets could be replaced with quantum computation methods. The crux of the problem in computation involving a hyperspectral image data cube is to convert the large amount of data in high dimensional space to real information. Currently, using the classical model, different time consuming methods and steps are necessary to analyze these data including: Animation, Minimum Noise Fraction Transform, Pixel Purity Index algorithm, N-dimensional scatter plot, Identification of Endmember spectra - are such steps. If a quantum model of computation involving hyperspectral image data can be developed and formalized - it is highly likely that information retrieval from hyperspectral image data cubes would be a much easier process and the final information content would be much more meaningful and timely. In this case, dimensionality would not be a curse, but a blessing.

Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL

NASA Astrophysics Data System (ADS)

Lazcano, R.; Madroñal, D.; Fabelo, H.; Ortega, S.; Salvador, R.; Callicó, G. M.; Juárez, E.; Sanz, C.

2017-10-01

Hyperspectral Imaging (HI) assembles high resolution spectral information from hundreds of narrow bands across the electromagnetic spectrum, thus generating 3D data cubes in which each pixel gathers the spectral information of the reflectance of every spatial pixel. As a result, each image is composed of large volumes of data, which turns its processing into a challenge, as performance requirements have been continuously tightened. For instance, new HI applications demand real-time responses. Hence, parallel processing becomes a necessity to achieve this requirement, so the intrinsic parallelism of the algorithms must be exploited. In this paper, a spatial-spectral classification approach has been implemented using a dataflow language known as RVCCAL. This language represents a system as a set of functional units, and its main advantage is that it simplifies the parallelization process by mapping the different blocks over different processing units. The spatial-spectral classification approach aims at refining the classification results previously obtained by using a K-Nearest Neighbors (KNN) filtering process, in which both the pixel spectral value and the spatial coordinates are considered. To do so, KNN needs two inputs: a one-band representation of the hyperspectral image and the classification results provided by a pixel-wise classifier. Thus, spatial-spectral classification algorithm is divided into three different stages: a Principal Component Analysis (PCA) algorithm for computing the one-band representation of the image, a Support Vector Machine (SVM) classifier, and the KNN-based filtering algorithm. The parallelization of these algorithms shows promising results in terms of computational time, as the mapping of them over different cores presents a speedup of 2.69x when using 3 cores. Consequently, experimental results demonstrate that real-time processing of hyperspectral images is achievable.

Engineering workstation: Sensor modeling

NASA Technical Reports Server (NTRS)

Pavel, M; Sweet, B.

1993-01-01

The purpose of the engineering workstation is to provide an environment for rapid prototyping and evaluation of fusion and image processing algorithms. Ideally, the algorithms are designed to optimize the extraction of information that is useful to a pilot for all phases of flight operations. Successful design of effective fusion algorithms depends on the ability to characterize both the information available from the sensors and the information useful to a pilot. The workstation is comprised of subsystems for simulation of sensor-generated images, image processing, image enhancement, and fusion algorithms. As such, the workstation can be used to implement and evaluate both short-term solutions and long-term solutions. The short-term solutions are being developed to enhance a pilot's situational awareness by providing information in addition to his direct vision. The long term solutions are aimed at the development of complete synthetic vision systems. One of the important functions of the engineering workstation is to simulate the images that would be generated by the sensors. The simulation system is designed to use the graphics modeling and rendering capabilities of various workstations manufactured by Silicon Graphics Inc. The workstation simulates various aspects of the sensor-generated images arising from phenomenology of the sensors. In addition, the workstation can be used to simulate a variety of impairments due to mechanical limitations of the sensor placement and due to the motion of the airplane. Although the simulation is currently not performed in real-time, sequences of individual frames can be processed, stored, and recorded in a video format. In that way, it is possible to examine the appearance of different dynamic sensor-generated and fused images.

Study on Privacy Protection Algorithm Based on K-Anonymity

NASA Astrophysics Data System (ADS)

FeiFei, Zhao; LiFeng, Dong; Kun, Wang; Yang, Li

Basing on the study of K-Anonymity algorithm in privacy protection issue, this paper proposed a "Degree Priority" method of visiting Lattice nodes on the generalization tree to improve the performance of K-Anonymity algorithm. This paper also proposed a "Two Times K-anonymity" methods to reduce the information loss in the process of K-Anonymity. Finally, we used experimental results to demonstrate the effectiveness of these methods.

Automatic Classification Using Supervised Learning in a Medical Document Filtering Application.

ERIC Educational Resources Information Center

Mostafa, J.; Lam, W.

2000-01-01

Presents a multilevel model of the information filtering process that permits document classification. Evaluates a document classification approach based on a supervised learning algorithm, measures the accuracy of the algorithm in a neural network that was trained to classify medical documents on cell biology, and discusses filtering…

Commodity cluster and hardware-based massively parallel implementations of hyperspectral imaging algorithms

NASA Astrophysics Data System (ADS)

Plaza, Antonio; Chang, Chein-I.; Plaza, Javier; Valencia, David

2006-05-01

The incorporation of hyperspectral sensors aboard airborne/satellite platforms is currently producing a nearly continual stream of multidimensional image data, and this high data volume has soon introduced new processing challenges. The price paid for the wealth spatial and spectral information available from hyperspectral sensors is the enormous amounts of data that they generate. Several applications exist, however, where having the desired information calculated quickly enough for practical use is highly desirable. High computing performance of algorithm analysis is particularly important in homeland defense and security applications, in which swift decisions often involve detection of (sub-pixel) military targets (including hostile weaponry, camouflage, concealment, and decoys) or chemical/biological agents. In order to speed-up computational performance of hyperspectral imaging algorithms, this paper develops several fast parallel data processing techniques. Techniques include four classes of algorithms: (1) unsupervised classification, (2) spectral unmixing, and (3) automatic target recognition, and (4) onboard data compression. A massively parallel Beowulf cluster (Thunderhead) at NASA's Goddard Space Flight Center in Maryland is used to measure parallel performance of the proposed algorithms. In order to explore the viability of developing onboard, real-time hyperspectral data compression algorithms, a Xilinx Virtex-II field programmable gate array (FPGA) is also used in experiments. Our quantitative and comparative assessment of parallel techniques and strategies may help image analysts in selection of parallel hyperspectral algorithms for specific applications.

The performance of Yonsei CArbon Retrieval (YCAR) algorithm with improved aerosol information using GOSAT measurements over East Asia

NASA Astrophysics Data System (ADS)

Jung, Y.; Kim, J.; Kim, W.; Boesch, H.; Yoshida, Y.; Cho, C.; Lee, H.; Goo, T. Y.

2016-12-01

The Greenhouse Gases Observing SATellite (GOSAT) is the first satellite dedicated to measure atmospheric CO2 concentrations from space that can able to improve our knowledge about carbon cycle. Several studies have performed to develop the CO2 retrieval algorithms using GOSAT measurements, but limitations in spatial coverage and uncertainties due to aerosols and thin cirrus clouds are still remained as a problem for monitoring CO2 concentration globally. In this study, we develop the Yonsei CArbon Retrieval (YCAR) algorithm based on optimal estimation method to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) with optimized a priori CO2 profiles and aerosol models over East Asia. In previous studies, the aerosol optical properties (AOP) and the aerosol top height used to cause significant errors in retrieved XCO2 up to 2.5 ppm. Since this bias comes from a rough assumption of aerosol information in the forward model used in CO2 retrieval process, the YCAR algorithm improves the process to take into account AOPs as well as aerosol vertical distribution; total AOD and the fine mode fraction (FMF) are obtained from the ground-based measurements closely located, and other parameters are obtained from a priori information. Comparing to ground-based XCO2 measurements, the YCAR XCO2 product has a bias of 0.59±0.48 ppm and 2.16±0.87 ppm at Saga and Tsukuba sites, respectively, showing lower biases and higher correlations rather than the GOSAT standard products. These results reveal that considering better aerosol information can improve the accuracy of CO2 retrieval algorithm and provide more useful XCO2 information with reduced uncertainties.

A highly scalable particle tracking algorithm using partitioned global address space (PGAS) programming for extreme-scale turbulence simulations

NASA Astrophysics Data System (ADS)

Buaria, D.; Yeung, P. K.

2017-12-01

A new parallel algorithm utilizing a partitioned global address space (PGAS) programming model to achieve high scalability is reported for particle tracking in direct numerical simulations of turbulent fluid flow. The work is motivated by the desire to obtain Lagrangian information necessary for the study of turbulent dispersion at the largest problem sizes feasible on current and next-generation multi-petaflop supercomputers. A large population of fluid particles is distributed among parallel processes dynamically, based on instantaneous particle positions such that all of the interpolation information needed for each particle is available either locally on its host process or neighboring processes holding adjacent sub-domains of the velocity field. With cubic splines as the preferred interpolation method, the new algorithm is designed to minimize the need for communication, by transferring between adjacent processes only those spline coefficients determined to be necessary for specific particles. This transfer is implemented very efficiently as a one-sided communication, using Co-Array Fortran (CAF) features which facilitate small data movements between different local partitions of a large global array. The cost of monitoring transfer of particle properties between adjacent processes for particles migrating across sub-domain boundaries is found to be small. Detailed benchmarks are obtained on the Cray petascale supercomputer Blue Waters at the University of Illinois, Urbana-Champaign. For operations on the particles in a 81923 simulation (0.55 trillion grid points) on 262,144 Cray XE6 cores, the new algorithm is found to be orders of magnitude faster relative to a prior algorithm in which each particle is tracked by the same parallel process at all times. This large speedup reduces the additional cost of tracking of order 300 million particles to just over 50% of the cost of computing the Eulerian velocity field at this scale. Improving support of PGAS models on major compilers suggests that this algorithm will be of wider applicability on most upcoming supercomputers.

An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data

USGS Publications Warehouse

Tan, B.; Morisette, J.T.; Wolfe, R.E.; Gao, F.; Ederer, G.A.; Nightingale, J.; Pedelty, J.A.

2011-01-01

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates. ?? 2010 IEEE.

An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics from MODIS Data

NASA Technical Reports Server (NTRS)

Tan, Bin; Morisette, Jeffrey T.; Wolfe, Robert E.; Gao, Feng; Ederer, Gregory A.; Nightingale, Joanne; Pedelty, Jeffrey A.

2012-01-01

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates.

Automatic detection of zebra crossings from mobile LiDAR data

NASA Astrophysics Data System (ADS)

Riveiro, B.; González-Jorge, H.; Martínez-Sánchez, J.; Díaz-Vilariño, L.; Arias, P.

2015-07-01

An algorithm for the automatic detection of zebra crossings from mobile LiDAR data is developed and tested to be applied for road management purposes. The algorithm consists of several subsequent processes starting with road segmentation by performing a curvature analysis for each laser cycle. Then, intensity images are created from the point cloud using rasterization techniques, in order to detect zebra crossing using the Standard Hough Transform and logical constrains. To optimize the results, image processing algorithms are applied to the intensity images from the point cloud. These algorithms include binarization to separate the painting area from the rest of the pavement, median filtering to avoid noisy points, and mathematical morphology to fill the gaps between the pixels in the border of white marks. Once the road marking is detected, its position is calculated. This information is valuable for inventorying purposes of road managers that use Geographic Information Systems. The performance of the algorithm has been evaluated over several mobile LiDAR strips accounting for a total of 30 zebra crossings. That test showed a completeness of 83%. Non-detected marks mainly come from painting deterioration of the zebra crossing or by occlusions in the point cloud produced by other vehicles on the road.

Segmentation of pomegranate MR images using spatial fuzzy c-means (SFCM) algorithm

NASA Astrophysics Data System (ADS)

Moradi, Ghobad; Shamsi, Mousa; Sedaaghi, M. H.; Alsharif, M. R.

2011-10-01

Segmentation is one of the fundamental issues of image processing and machine vision. It plays a prominent role in a variety of image processing applications. In this paper, one of the most important applications of image processing in MRI segmentation of pomegranate is explored. Pomegranate is a fruit with pharmacological properties such as being anti-viral and anti-cancer. Having a high quality product in hand would be critical factor in its marketing. The internal quality of the product is comprehensively important in the sorting process. The determination of qualitative features cannot be manually made. Therefore, the segmentation of the internal structures of the fruit needs to be performed as accurately as possible in presence of noise. Fuzzy c-means (FCM) algorithm is noise-sensitive and pixels with noise are classified inversely. As a solution, in this paper, the spatial FCM algorithm in pomegranate MR images' segmentation is proposed. The algorithm is performed with setting the spatial neighborhood information in FCM and modification of fuzzy membership function for each class. The segmentation algorithm results on the original and the corrupted Pomegranate MR images by Gaussian, Salt Pepper and Speckle noises show that the SFCM algorithm operates much more significantly than FCM algorithm. Also, after diverse steps of qualitative and quantitative analysis, we have concluded that the SFCM algorithm with 5×5 window size is better than the other windows.

Skeletonization of gray-scale images by gray weighted distance transform

NASA Astrophysics Data System (ADS)

Qian, Kai; Cao, Siqi; Bhattacharya, Prabir

1997-07-01

In pattern recognition, thinning algorithms are often a useful tool to represent a digital pattern by means of a skeletonized image, consisting of a set of one-pixel-width lines that highlight the significant features interest in applying thinning directly to gray-scale images, motivated by the desire of processing images characterized by meaningful information distributed over different levels of gray intensity. In this paper, a new algorithm is presented which can skeletonize both black-white and gray pictures. This algorithm is based on the gray distance transformation and can be used to process any non-well uniformly distributed gray-scale picture and can preserve the topology of original picture. This process includes a preliminary phase of investigation in the 'hollows' in the gray-scale image; these hollows are considered not as topological constrains for the skeleton structure depending on their statistically significant depth. This algorithm can also be executed on a parallel machine as all the operations are executed in local. Some examples are discussed to illustrate the algorithm.

Interfacing External Quantum Devices to a Universal Quantum Computer

PubMed Central

Lagana, Antonio A.; Lohe, Max A.; von Smekal, Lorenz

2011-01-01

We present a scheme to use external quantum devices using the universal quantum computer previously constructed. We thereby show how the universal quantum computer can utilize networked quantum information resources to carry out local computations. Such information may come from specialized quantum devices or even from remote universal quantum computers. We show how to accomplish this by devising universal quantum computer programs that implement well known oracle based quantum algorithms, namely the Deutsch, Deutsch-Jozsa, and the Grover algorithms using external black-box quantum oracle devices. In the process, we demonstrate a method to map existing quantum algorithms onto the universal quantum computer. PMID:22216276

Interfacing external quantum devices to a universal quantum computer.

PubMed

Lagana, Antonio A; Lohe, Max A; von Smekal, Lorenz

2011-01-01

We present a scheme to use external quantum devices using the universal quantum computer previously constructed. We thereby show how the universal quantum computer can utilize networked quantum information resources to carry out local computations. Such information may come from specialized quantum devices or even from remote universal quantum computers. We show how to accomplish this by devising universal quantum computer programs that implement well known oracle based quantum algorithms, namely the Deutsch, Deutsch-Jozsa, and the Grover algorithms using external black-box quantum oracle devices. In the process, we demonstrate a method to map existing quantum algorithms onto the universal quantum computer. © 2011 Lagana et al.

Optimal Full Information Synthesis for Flexible Structures Implemented on Cray Supercomputers

NASA Technical Reports Server (NTRS)

Lind, Rick; Balas, Gary J.

1995-01-01

This paper considers an algorithm for synthesis of optimal controllers for full information feedback. The synthesis procedure reduces to a single linear matrix inequality which may be solved via established convex optimization algorithms. The computational cost of the optimization is investigated. It is demonstrated the problem dimension and corresponding matrices can become large for practical engineering problems. This algorithm represents a process that is impractical for standard workstations for large order systems. A flexible structure is presented as a design example. Control synthesis requires several days on a workstation but may be solved in a reasonable amount of time using a Cray supercomputer.

Proposing a new iterative learning control algorithm based on a non-linear least square formulation - Minimising draw-in errors

NASA Astrophysics Data System (ADS)

Endelt, B.

2017-09-01

Forming operation are subject to external disturbances and changing operating conditions e.g. new material batch, increasing tool temperature due to plastic work, material properties and lubrication is sensitive to tool temperature. It is generally accepted that forming operations are not stable over time and it is not uncommon to adjust the process parameters during the first half hour production, indicating that process instability is gradually developing over time. Thus, in-process feedback control scheme might not-be necessary to stabilize the process and an alternative approach is to apply an iterative learning algorithm, which can learn from previously produced parts i.e. a self learning system which gradually reduces error based on historical process information. What is proposed in the paper is a simple algorithm which can be applied to a wide range of sheet-metal forming processes. The input to the algorithm is the final flange edge geometry and the basic idea is to reduce the least-square error between the current flange geometry and a reference geometry using a non-linear least square algorithm. The ILC scheme is applied to a square deep-drawing and the Numisheet’08 S-rail benchmark problem, the numerical tests shows that the proposed control scheme is able control and stabilise both processes.

Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance structures. We then use this new algorithm in a genetic algorithm based Expectation-Maximization process that can accurately calculate parameters describing complex clusters in a mixture model routine. Using the accuracy of this GEM algorithm, we assign information scores to cluster calculations in order to best identify the number of mixture components in a multivariate data set. We will showcase how these algorithms can be used to process multivariate data from astronomical observations.

Convective and Stratiform Precipitation Processes and their Relationship to Latent Heating

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Lang, Steve; Zeng, Xiping; Shige, Shoichi; Takayabu, Yukari

2009-01-01

The global hydrological cycle is central to the Earth's climate system, with rainfall and the physics of its formation acting as the key links in the cycle. Two-thirds of global rainfall occurs in the Tropics. Associated with this rainfall is a vast amount of heat, which is known as latent heat. It arises mainly due to the phase change of water vapor condensing into liquid droplets; three-fourths of the total heat energy available to the Earth's atmosphere comes from tropical rainfall. In addition, fresh water provided by tropical rainfall and its variability exerts a large impact upon the structure and motions of the upper ocean layer. An improved convective -stratiform heating (CSH) algorithm has been developed to obtain the 3D structure of cloud heating over the Tropics based on two sources of information: 1) rainfall information, namely its amount and the fraction due to light rain intensity, observed directly from the Precipitation Radar (PR) on board the TRMM satellite and 2) synthetic cloud physics information obtained from cloud-resolving model (CRM) simulations of cloud systems. The cloud simulations provide details on cloud processes, specifically latent heating, eddy heat flux convergence and radiative heating/cooling, that. are not directly observable by satellite. The new CSH algorithm-derived heating has a noticeably different heating structure over both ocean and land regions compared to the previous CSH algorithm. One of the major differences between new and old algorithms is that the level of maximum cloud heating occurs 1 to 1.5 km lower in the atmosphere in the new algorithm. This can effect the structure of the implied air currents associated with the general circulation of the atmosphere in the Tropics. The new CSH algorithm will be used provide retrieved heating data to other heating algorithms to supplement their performance.

Distribution majorization of corner points by reinforcement learning for moving object detection

NASA Astrophysics Data System (ADS)

Wu, Hao; Yu, Hao; Zhou, Dongxiang; Cheng, Yongqiang

2018-04-01

Corner points play an important role in moving object detection, especially in the case of free-moving camera. Corner points provide more accurate information than other pixels and reduce the computation which is unnecessary. Previous works only use intensity information to locate the corner points, however, the information that former and the last frames provided also can be used. We utilize the information to focus on more valuable area and ignore the invaluable area. The proposed algorithm is based on reinforcement learning, which regards the detection of corner points as a Markov process. In the Markov model, the video to be detected is regarded as environment, the selections of blocks for one corner point are regarded as actions and the performance of detection is regarded as state. Corner points are assigned to be the blocks which are seperated from original whole image. Experimentally, we select a conventional method which uses marching and Random Sample Consensus algorithm to obtain objects as the main framework and utilize our algorithm to improve the result. The comparison between the conventional method and the same one with our algorithm show that our algorithm reduce 70% of the false detection.

Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data

PubMed Central

Munoz-Organero, Mario; Lotfi, Ahmad

2016-01-01

Human activity recognition algorithms based on information obtained from wearable sensors are successfully applied in detecting many basic activities. Identified activities with time-stationary features are characterised inside a predefined temporal window by using different machine learning algorithms on extracted features from the measured data. Better accuracy, precision and recall levels could be achieved by combining the information from different sensors. However, detecting short and sporadic human movements, gestures and actions is still a challenging task. In this paper, a novel algorithm to detect human basic movements from wearable measured data is proposed and evaluated. The proposed algorithm is designed to minimise computational requirements while achieving acceptable accuracy levels based on characterising some particular points in the temporal series obtained from a single sensor. The underlying idea is that this algorithm would be implemented in the sensor device in order to pre-process the sensed data stream before sending the information to a central point combining the information from different sensors to improve accuracy levels. Intra- and inter-person validation is used for two particular cases: single step detection and fall detection and classification using a single tri-axial accelerometer. Relevant results for the above cases and pertinent conclusions are also presented. PMID:27618063

Hand and goods judgment algorithm based on depth information

NASA Astrophysics Data System (ADS)

Li, Mingzhu; Zhang, Jinsong; Yan, Dan; Wang, Qin; Zhang, Ruiqi; Han, Jing

2016-03-01

A tablet computer with a depth camera and a color camera is loaded on a traditional shopping cart. The inside information of the shopping cart is obtained by two cameras. In the shopping cart monitoring field, it is very important for us to determine whether the customer with goods in or out of the shopping cart. This paper establishes a basic framework for judging empty hand, it includes the hand extraction process based on the depth information, process of skin color model building based on WPCA (Weighted Principal Component Analysis), an algorithm for judging handheld products based on motion and skin color information, statistical process. Through this framework, the first step can ensure the integrity of the hand information, and effectively avoids the influence of sleeve and other debris, the second step can accurately extract skin color and eliminate the similar color interference, light has little effect on its results, it has the advantages of fast computation speed and high efficiency, and the third step has the advantage of greatly reducing the noise interference and improving the accuracy.

Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms

PubMed Central

Elhadad, N.; Claassen, J.; Perotte, R.; Goldstein, A.; Hripcsak, G.

2018-01-01

We study the question of how to represent or summarize raw laboratory data taken from an electronic health record (EHR) using parametric model selection to reduce or cope with biases induced through clinical care. It has been previously demonstrated that the health care process (Hripcsak and Albers, 2012, 2013), as defined by measurement context (Hripcsak and Albers, 2013; Albers et al., 2012) and measurement patterns (Albers and Hripcsak, 2010, 2012), can influence how EHR data are distributed statistically (Kohane and Weber, 2013; Pivovarov et al., 2014). We construct an algorithm, PopKLD, which is based on information criterion model selection (Burnham and Anderson, 2002; Claeskens and Hjort, 2008), is intended to reduce and cope with health care process biases and to produce an intuitively understandable continuous summary. The PopKLD algorithm can be automated and is designed to be applicable in high-throughput settings; for example, the output of the PopKLD algorithm can be used as input for phenotyping algorithms. Moreover, we develop the PopKLD-CAT algorithm that transforms the continuous PopKLD summary into a categorical summary useful for applications that require categorical data such as topic modeling. We evaluate our methodology in two ways. First, we apply the method to laboratory data collected in two different health care contexts, primary versus intensive care. We show that the PopKLD preserves known physiologic features in the data that are lost when summarizing the data using more common laboratory data summaries such as mean and standard deviation. Second, for three disease-laboratory measurement pairs, we perform a phenotyping task: we use the PopKLD and PopKLD-CAT algorithms to define high and low values of the laboratory variable that are used for defining a disease state. We then compare the relationship between the PopKLD-CAT summary disease predictions and the same predictions using empirically estimated mean and standard deviation to a gold standard generated by clinical review of patient records. We find that the PopKLD laboratory data summary is substantially better at predicting disease state. The PopKLD or PopKLD-CAT algorithms are not meant to be used as phenotyping algorithms, but we use the phenotyping task to show what information can be gained when using a more informative laboratory data summary. In the process of evaluation our method we show that the different clinical contexts and laboratory measurements necessitate different statistical summaries. Similarly, leveraging the principle of maximum entropy we argue that while some laboratory data only have sufficient information to estimate a mean and standard deviation, other laboratory data captured in an EHR contain substantially more information than can be captured in higher-parameter models. PMID:29369797

Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms.

PubMed

Albers, D J; Elhadad, N; Claassen, J; Perotte, R; Goldstein, A; Hripcsak, G

2018-02-01

We study the question of how to represent or summarize raw laboratory data taken from an electronic health record (EHR) using parametric model selection to reduce or cope with biases induced through clinical care. It has been previously demonstrated that the health care process (Hripcsak and Albers, 2012, 2013), as defined by measurement context (Hripcsak and Albers, 2013; Albers et al., 2012) and measurement patterns (Albers and Hripcsak, 2010, 2012), can influence how EHR data are distributed statistically (Kohane and Weber, 2013; Pivovarov et al., 2014). We construct an algorithm, PopKLD, which is based on information criterion model selection (Burnham and Anderson, 2002; Claeskens and Hjort, 2008), is intended to reduce and cope with health care process biases and to produce an intuitively understandable continuous summary. The PopKLD algorithm can be automated and is designed to be applicable in high-throughput settings; for example, the output of the PopKLD algorithm can be used as input for phenotyping algorithms. Moreover, we develop the PopKLD-CAT algorithm that transforms the continuous PopKLD summary into a categorical summary useful for applications that require categorical data such as topic modeling. We evaluate our methodology in two ways. First, we apply the method to laboratory data collected in two different health care contexts, primary versus intensive care. We show that the PopKLD preserves known physiologic features in the data that are lost when summarizing the data using more common laboratory data summaries such as mean and standard deviation. Second, for three disease-laboratory measurement pairs, we perform a phenotyping task: we use the PopKLD and PopKLD-CAT algorithms to define high and low values of the laboratory variable that are used for defining a disease state. We then compare the relationship between the PopKLD-CAT summary disease predictions and the same predictions using empirically estimated mean and standard deviation to a gold standard generated by clinical review of patient records. We find that the PopKLD laboratory data summary is substantially better at predicting disease state. The PopKLD or PopKLD-CAT algorithms are not meant to be used as phenotyping algorithms, but we use the phenotyping task to show what information can be gained when using a more informative laboratory data summary. In the process of evaluation our method we show that the different clinical contexts and laboratory measurements necessitate different statistical summaries. Similarly, leveraging the principle of maximum entropy we argue that while some laboratory data only have sufficient information to estimate a mean and standard deviation, other laboratory data captured in an EHR contain substantially more information than can be captured in higher-parameter models. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

In Search of Ideal Information Pricing.

ERIC Educational Resources Information Center

Hawkins, Donald T.

1989-01-01

Reviews some of the models used for pricing online information services and discusses some of the implications of these pricing algorithms. Topics discussed include online versus print pricing; charges for the retrieval process; charges for the retrieved information; telecommunications charges; and the pricing policies of Chemical Abstracts…

IJA: an efficient algorithm for query processing in sensor networks.

PubMed

Lee, Hyun Chang; Lee, Young Jae; Lim, Ji Hyang; Kim, Dong Hwa

2011-01-01

One of main features in sensor networks is the function that processes real time state information after gathering needed data from many domains. The component technologies consisting of each node called a sensor node that are including physical sensors, processors, actuators and power have advanced significantly over the last decade. Thanks to the advanced technology, over time sensor networks have been adopted in an all-round industry sensing physical phenomenon. However, sensor nodes in sensor networks are considerably constrained because with their energy and memory resources they have a very limited ability to process any information compared to conventional computer systems. Thus query processing over the nodes should be constrained because of their limitations. Due to the problems, the join operations in sensor networks are typically processed in a distributed manner over a set of nodes and have been studied. By way of example while simple queries, such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor networks environments. At the same time, the simulation result shows that the proposed IJA algorithm significantly reduces the number of bytes to be moved to join nodes compared to the popular synopsis join algorithm.

IJA: An Efficient Algorithm for Query Processing in Sensor Networks

PubMed Central

Lee, Hyun Chang; Lee, Young Jae; Lim, Ji Hyang; Kim, Dong Hwa

2011-01-01

One of main features in sensor networks is the function that processes real time state information after gathering needed data from many domains. The component technologies consisting of each node called a sensor node that are including physical sensors, processors, actuators and power have advanced significantly over the last decade. Thanks to the advanced technology, over time sensor networks have been adopted in an all-round industry sensing physical phenomenon. However, sensor nodes in sensor networks are considerably constrained because with their energy and memory resources they have a very limited ability to process any information compared to conventional computer systems. Thus query processing over the nodes should be constrained because of their limitations. Due to the problems, the join operations in sensor networks are typically processed in a distributed manner over a set of nodes and have been studied. By way of example while simple queries, such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor networks environments. At the same time, the simulation result shows that the proposed IJA algorithm significantly reduces the number of bytes to be moved to join nodes compared to the popular synopsis join algorithm. PMID:22319375

An improved stochastic fractal search algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Sun, Chuan; Wang, Bin; Wang, Xiaojun

2018-05-03

Protein structure prediction (PSP) is a significant area for biological information research, disease treatment, and drug development and so on. In this paper, three-dimensional structures of proteins are predicted based on the known amino acid sequences, and the structure prediction problem is transformed into a typical NP problem by an AB off-lattice model. This work applies a novel improved Stochastic Fractal Search algorithm (ISFS) to solve the problem. The Stochastic Fractal Search algorithm (SFS) is an effective evolutionary algorithm that performs well in exploring the search space but falls into local minimums sometimes. In order to avoid the weakness, Lvy flight and internal feedback information are introduced in ISFS. In the experimental process, simulations are conducted by ISFS algorithm on Fibonacci sequences and real peptide sequences. Experimental results prove that the ISFS performs more efficiently and robust in terms of finding the global minimum and avoiding getting stuck in local minimums.

Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization.

PubMed

Sun, Yanfeng; Gao, Junbin; Hong, Xia; Mishra, Bamdev; Yin, Baocai

2016-03-01

Tensor clustering is an important tool that exploits intrinsically rich structures in real-world multiarray or Tensor datasets. Often in dealing with those datasets, standard practice is to use subspace clustering that is based on vectorizing multiarray data. However, vectorization of tensorial data does not exploit complete structure information. In this paper, we propose a subspace clustering algorithm without adopting any vectorization process. Our approach is based on a novel heterogeneous Tucker decomposition model taking into account cluster membership information. We propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model. All but the last mode have closed-form updates. Updating the last mode reduces to optimizing over the multinomial manifold for which we investigate second order Riemannian geometry and propose a trust-region algorithm. Numerical experiments show that our proposed algorithm compete effectively with state-of-the-art clustering algorithms that are based on tensor factorization.

Base Stock Policy in a Join-Type Production Line with Advanced Demand Information

NASA Astrophysics Data System (ADS)

Hiraiwa, Mikihiko; Tsubouchi, Satoshi; Nakade, Koichi

Production control such as the base stock policy, the kanban policy and the constant work-in-process policy in a serial production line has been studied by many researchers. Production lines, however, usually have fork-type, join-type or network-type figures. In addition, in most previous studies on production control, a finished product is required at the same time as arrival of demand at the system. Demand information is, however, informed before due date in practice. In this paper a join-type (assembly) production line under base stock control with advanced demand information in discrete time is analyzed. The recursive equations for the work-in-process are derived. The heuristic algorithm for finding appropriate base stock levels of all machines at short time is proposed and the effect of advanced demand information is examined by simulation with the proposed algorithm. It is shown that the inventory cost can decreases with little backlogs by using the appropriate amount of demand information and setting appropriate base stock levels.

Empirical comparison of heuristic load distribution in point-to-point multicomputer networks

NASA Technical Reports Server (NTRS)

Grunwald, Dirk C.; Nazief, Bobby A. A.; Reed, Daniel A.

1990-01-01

The study compared several load placement algorithms using instrumented programs and synthetic program models. Salient characteristics of these program traces (total computation time, total number of messages sent, and average message time) span two orders of magnitude. Load distribution algorithms determine the initial placement for processes, a precursor to the more general problem of load redistribution. It is found that desirable workload distribution strategies will place new processes globally, rather than locally, to spread processes rapidly, but that local information should be used to refine global placement.

Processing Digital Imagery to Enhance Perceptions of Realism

NASA Technical Reports Server (NTRS)

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

2003-01-01

Multi-scale retinex with color restoration (MSRCR) is a method of processing digital image data based on Edwin Land s retinex (retina + cortex) theory of human color vision. An outgrowth of basic scientific research and its application to NASA s remote-sensing mission, MSRCR is embodied in a general-purpose algorithm that greatly improves the perception of visual realism and the quantity and quality of perceived information in a digitized image. In addition, the MSRCR algorithm includes provisions for automatic corrections to accelerate and facilitate what could otherwise be a tedious image-editing process. The MSRCR algorithm has been, and is expected to continue to be, the basis for development of commercial image-enhancement software designed to extend and refine its capabilities for diverse applications.

Information filtering via biased heat conduction.

PubMed

Liu, Jian-Guo; Zhou, Tao; Guo, Qiang

2011-09-01

The process of heat conduction has recently found application in personalized recommendation [Zhou et al., Proc. Natl. Acad. Sci. USA 107, 4511 (2010)], which is of high diversity but low accuracy. By decreasing the temperatures of small-degree objects, we present an improved algorithm, called biased heat conduction, which could simultaneously enhance the accuracy and diversity. Extensive experimental analyses demonstrate that the accuracy on MovieLens, Netflix, and Delicious datasets could be improved by 43.5%, 55.4% and 19.2%, respectively, compared with the standard heat conduction algorithm and also the diversity is increased or approximately unchanged. Further statistical analyses suggest that the present algorithm could simultaneously identify users' mainstream and special tastes, resulting in better performance than the standard heat conduction algorithm. This work provides a creditable way for highly efficient information filtering.

A research of road centerline extraction algorithm from high resolution remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Yushan; Xu, Tingfa

2017-09-01

Satellite remote sensing technology has become one of the most effective methods for land surface monitoring in recent years, due to its advantages such as short period, large scale and rich information. Meanwhile, road extraction is an important field in the applications of high resolution remote sensing images. An intelligent and automatic road extraction algorithm with high precision has great significance for transportation, road network updating and urban planning. The fuzzy c-means (FCM) clustering segmentation algorithms have been used in road extraction, but the traditional algorithms did not consider spatial information. An improved fuzzy C-means clustering algorithm combined with spatial information (SFCM) is proposed in this paper, which is proved to be effective for noisy image segmentation. Firstly, the image is segmented using the SFCM. Secondly, the segmentation result is processed by mathematical morphology to remover the joint region. Thirdly, the road centerlines are extracted by morphology thinning and burr trimming. The average integrity of the centerline extraction algorithm is 97.98%, the average accuracy is 95.36% and the average quality is 93.59%. Experimental results show that the proposed method in this paper is effective for road centerline extraction.

Informational approach to the analysis of acoustic signals

NASA Astrophysics Data System (ADS)

Senkevich, Yuriy; Dyuk, Vyacheslav; Mishchenko, Mikhail; Solodchuk, Alexandra

2017-10-01

The example of linguistic processing of acoustic signals of a seismic event would be an information approach to the processing of non-stationary signals. The method for converting an acoustic signal into an information message is described by identifying repetitive self-similar patterns. The definitions of the event selection indicators in the symbolic recording of the acoustic signal are given. The results of processing an acoustic signal by a computer program realizing the processing of linguistic data are shown. Advantages and disadvantages of using software algorithms are indicated.

Overhead longwave infrared hyperspectral material identification using radiometric models

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

Zelinski, M. E.

Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimalmore » atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.« less

Marcus canonical integral for non-Gaussian processes and its computation: pathwise simulation and tau-leaping algorithm.

PubMed

Li, Tiejun; Min, Bin; Wang, Zhiming

2013-03-14

The stochastic integral ensuring the Newton-Leibnitz chain rule is essential in stochastic energetics. Marcus canonical integral has this property and can be understood as the Wong-Zakai type smoothing limit when the driving process is non-Gaussian. However, this important concept seems not well-known for physicists. In this paper, we discuss Marcus integral for non-Gaussian processes and its computation in the context of stochastic energetics. We give a comprehensive introduction to Marcus integral and compare three equivalent definitions in the literature. We introduce the exact pathwise simulation algorithm and give the error analysis. We show how to compute the thermodynamic quantities based on the pathwise simulation algorithm. We highlight the information hidden in the Marcus mapping, which plays the key role in determining thermodynamic quantities. We further propose the tau-leaping algorithm, which advance the process with deterministic time steps when tau-leaping condition is satisfied. The numerical experiments and its efficiency analysis show that it is very promising.

Close coupling of pre- and post-processing vision stations using inexact algorithms

NASA Astrophysics Data System (ADS)

Shih, Chi-Hsien V.; Sherkat, Nasser; Thomas, Peter D.

1996-02-01

Work has been reported using lasers to cut deformable materials. Although the use of laser reduces material deformation, distortion due to mechanical feed misalignment persists. Changes in the lace patten are also caused by the release of tension in the lace structure as it is cut. To tackle the problem of distortion due to material flexibility, the 2VMethod together with the Piecewise Error Compensation Algorithm incorporating the inexact algorithms, i.e., fuzzy logic, neural networks and neural fuzzy technique, are developed. A spring mounted pen is used to emulate the distortion of the lace pattern caused by tactile cutting and feed misalignment. Using pre- and post-processing vision systems, it is possible to monitor the scalloping process and generate on-line information for the artificial intelligence engines. This overcomes the problems of lace distortion due to the trimming process. Applying the algorithms developed, the system can produce excellent results, much better than a human operator.

Information surfing with the JHU/APL coherent imager

NASA Astrophysics Data System (ADS)

Ratto, Christopher R.; Shipley, Kara R.; Beagley, Nathaniel; Wolfe, Kevin C.

2015-05-01

The ability to perform remote forensics in situ is an important application of autonomous undersea vehicles (AUVs). Forensics objectives may include remediation of mines and/or unexploded ordnance, as well as monitoring of seafloor infrastructure. At JHU/APL, digital holography is being explored for the potential application to underwater imaging and integration with an AUV. In previous work, a feature-based approach was developed for processing the holographic imagery and performing object recognition. In this work, the results of the image processing method were incorporated into a Bayesian framework for autonomous path planning referred to as information surfing. The framework was derived assuming that the location of the object of interest is known a priori, but the type of object and its pose are unknown. The path-planning algorithm adaptively modifies the trajectory of the sensing platform based on historical performance of object and pose classification. The algorithm is called information surfing because the direction of motion is governed by the local information gradient. Simulation experiments were carried out using holographic imagery collected from submerged objects. The autonomous sensing algorithm was compared to a deterministic sensing CONOPS, and demonstrated improved accuracy and faster convergence in several cases.

An evaluation of the NQF Quality Data Model for representing Electronic Health Record driven phenotyping algorithms.

PubMed

Thompson, William K; Rasmussen, Luke V; Pacheco, Jennifer A; Peissig, Peggy L; Denny, Joshua C; Kho, Abel N; Miller, Aaron; Pathak, Jyotishman

2012-01-01

The development of Electronic Health Record (EHR)-based phenotype selection algorithms is a non-trivial and highly iterative process involving domain experts and informaticians. To make it easier to port algorithms across institutions, it is desirable to represent them using an unambiguous formal specification language. For this purpose we evaluated the recently developed National Quality Forum (NQF) information model designed for EHR-based quality measures: the Quality Data Model (QDM). We selected 9 phenotyping algorithms that had been previously developed as part of the eMERGE consortium and translated them into QDM format. Our study concluded that the QDM contains several core elements that make it a promising format for EHR-driven phenotyping algorithms for clinical research. However, we also found areas in which the QDM could be usefully extended, such as representing information extracted from clinical text, and the ability to handle algorithms that do not consist of Boolean combinations of criteria.

Efficient terrestrial laser scan segmentation exploiting data structure

NASA Astrophysics Data System (ADS)

Mahmoudabadi, Hamid; Olsen, Michael J.; Todorovic, Sinisa

2016-09-01

New technologies such as lidar enable the rapid collection of massive datasets to model a 3D scene as a point cloud. However, while hardware technology continues to advance, processing 3D point clouds into informative models remains complex and time consuming. A common approach to increase processing efficiently is to segment the point cloud into smaller sections. This paper proposes a novel approach for point cloud segmentation using computer vision algorithms to analyze panoramic representations of individual laser scans. These panoramas can be quickly created using an inherent neighborhood structure that is established during the scanning process, which scans at fixed angular increments in a cylindrical or spherical coordinate system. In the proposed approach, a selected image segmentation algorithm is applied on several input layers exploiting this angular structure including laser intensity, range, normal vectors, and color information. These segments are then mapped back to the 3D point cloud so that modeling can be completed more efficiently. This approach does not depend on pre-defined mathematical models and consequently setting parameters for them. Unlike common geometrical point cloud segmentation methods, the proposed method employs the colorimetric and intensity data as another source of information. The proposed algorithm is demonstrated on several datasets encompassing variety of scenes and objects. Results show a very high perceptual (visual) level of segmentation and thereby the feasibility of the proposed algorithm. The proposed method is also more efficient compared to Random Sample Consensus (RANSAC), which is a common approach for point cloud segmentation.

Hybrid ontology for semantic information retrieval model using keyword matching indexing system.

PubMed

Uthayan, K R; Mala, G S Anandha

2015-01-01

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.

Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System

PubMed Central

Uthayan, K. R.; Anandha Mala, G. S.

2015-01-01

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology. PMID:25922851

Land use mapping from CBERS-2 images with open source tools by applying different classification algorithms

NASA Astrophysics Data System (ADS)

Sanhouse-García, Antonio J.; Rangel-Peraza, Jesús Gabriel; Bustos-Terrones, Yaneth; García-Ferrer, Alfonso; Mesas-Carrascosa, Francisco J.

2016-02-01

Land cover classification is often based on different characteristics between their classes, but with great homogeneity within each one of them. This cover is obtained through field work or by mean of processing satellite images. Field work involves high costs; therefore, digital image processing techniques have become an important alternative to perform this task. However, in some developing countries and particularly in Casacoima municipality in Venezuela, there is a lack of geographic information systems due to the lack of updated information and high costs in software license acquisition. This research proposes a low cost methodology to develop thematic mapping of local land use and types of coverage in areas with scarce resources. Thematic mapping was developed from CBERS-2 images and spatial information available on the network using open source tools. The supervised classification method per pixel and per region was applied using different classification algorithms and comparing them among themselves. Classification method per pixel was based on Maxver algorithms (maximum likelihood) and Euclidean distance (minimum distance), while per region classification was based on the Bhattacharya algorithm. Satisfactory results were obtained from per region classification, where overall reliability of 83.93% and kappa index of 0.81% were observed. Maxver algorithm showed a reliability value of 73.36% and kappa index 0.69%, while Euclidean distance obtained values of 67.17% and 0.61% for reliability and kappa index, respectively. It was demonstrated that the proposed methodology was very useful in cartographic processing and updating, which in turn serve as a support to develop management plans and land management. Hence, open source tools showed to be an economically viable alternative not only for forestry organizations, but for the general public, allowing them to develop projects in economically depressed and/or environmentally threatened areas.

Global rotational motion and displacement estimation of digital image stabilization based on the oblique vectors matching algorithm

NASA Astrophysics Data System (ADS)

Yu, Fei; Hui, Mei; Zhao, Yue-jin

2009-08-01

The image block matching algorithm based on motion vectors of correlative pixels in oblique direction is presented for digital image stabilization. The digital image stabilization is a new generation of image stabilization technique which can obtains the information of relative motion among frames of dynamic image sequences by the method of digital image processing. In this method the matching parameters are calculated from the vectors projected in the oblique direction. The matching parameters based on the vectors contain the information of vectors in transverse and vertical direction in the image blocks at the same time. So the better matching information can be obtained after making correlative operation in the oblique direction. And an iterative weighted least square method is used to eliminate the error of block matching. The weights are related with the pixels' rotational angle. The center of rotation and the global emotion estimation of the shaking image can be obtained by the weighted least square from the estimation of each block chosen evenly from the image. Then, the shaking image can be stabilized with the center of rotation and the global emotion estimation. Also, the algorithm can run at real time by the method of simulated annealing in searching method of block matching. An image processing system based on DSP was used to exam this algorithm. The core processor in the DSP system is TMS320C6416 of TI, and the CCD camera with definition of 720×576 pixels was chosen as the input video signal. Experimental results show that the algorithm can be performed at the real time processing system and have an accurate matching precision.

Solving the multiple-set split equality common fixed-point problem of firmly quasi-nonexpansive operators.

PubMed

Zhao, Jing; Zong, Haili

2018-01-01

In this paper, we propose parallel and cyclic iterative algorithms for solving the multiple-set split equality common fixed-point problem of firmly quasi-nonexpansive operators. We also combine the process of cyclic and parallel iterative methods and propose two mixed iterative algorithms. Our several algorithms do not need any prior information about the operator norms. Under mild assumptions, we prove weak convergence of the proposed iterative sequences in Hilbert spaces. As applications, we obtain several iterative algorithms to solve the multiple-set split equality problem.

Infrared and visible images registration with adaptable local-global feature integration for rail inspection

NASA Astrophysics Data System (ADS)

Tang, Chaoqing; Tian, Gui Yun; Chen, Xiaotian; Wu, Jianbo; Li, Kongjing; Meng, Hongying

2017-12-01

Active thermography provides infrared images that contain sub-surface defect information, while visible images only reveal surface information. Mapping infrared information to visible images offers more comprehensive visualization for decision-making in rail inspection. However, the common information for registration is limited due to different modalities in both local and global level. For example, rail track which has low temperature contrast reveals rich details in visible images, but turns blurry in the infrared counterparts. This paper proposes a registration algorithm called Edge-Guided Speeded-Up-Robust-Features (EG-SURF) to address this issue. Rather than sequentially integrating local and global information in matching stage which suffered from buckets effect, this algorithm adaptively integrates local and global information into a descriptor to gather more common information before matching. This adaptability consists of two facets, an adaptable weighting factor between local and global information, and an adaptable main direction accuracy. The local information is extracted using SURF while the global information is represented by shape context from edges. Meanwhile, in shape context generation process, edges are weighted according to local scale and decomposed into bins using a vector decomposition manner to provide more accurate descriptor. The proposed algorithm is qualitatively and quantitatively validated using eddy current pulsed thermography scene in the experiments. In comparison with other algorithms, better performance has been achieved.

MIQSTURE: An Experimental Online Language for Army Tactical Intelligence Information Processing

DTIC Science & Technology

1978-07-01

algorithms. The most critical component of an active information processing model for Army tactical intelligence is the user interface, which must be based on...1976)** defined some preliminary notions of an active information model centered around a data base that can introspect about its contents and...34An Introspective Data Base for an Active Information Model." OSI Technical Note N76-017, 17 November 1976 1-4 L4 beyond optimistic expectations and

Selecting materialized views using random algorithm

NASA Astrophysics Data System (ADS)

Zhou, Lijuan; Hao, Zhongxiao; Liu, Chi

2007-04-01

The data warehouse is a repository of information collected from multiple possibly heterogeneous autonomous distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The selection of the materialized views is one of the most important decisions in designing a data warehouse. Materialized views are stored in the data warehouse for the purpose of efficiently implementing on-line analytical processing queries. The first issue for the user to consider is query response time. So in this paper, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query_cost view_ selection problem. First, cost graph and cost model of query_cost view_ selection problem are presented. Second, the methods for selecting materialized views by using random algorithms are presented. The genetic algorithm is applied to the materialized views selection problem. But with the development of genetic process, the legal solution produced become more and more difficult, so a lot of solutions are eliminated and producing time of the solutions is lengthened in genetic algorithm. Therefore, improved algorithm has been presented in this paper, which is the combination of simulated annealing algorithm and genetic algorithm for the purpose of solving the query cost view selection problem. Finally, in order to test the function and efficiency of our algorithms experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and works better in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.

A pruning algorithm for Meta-blocking based on cumulative weight

NASA Astrophysics Data System (ADS)

Zhang, Fulin; Gao, Zhipeng; Niu, Kun

2017-08-01

Entity Resolution is an important process in data cleaning and data integration. It usually employs a blocking method to avoid the quadratic complexity work when scales to large data sets. Meta-blocking can perform better in the context of highly heterogeneous information spaces. Yet, its precision and efficiency still have room to improve. In this paper, we present a new pruning algorithm for Meta-Blocking. It can achieve a higher precision than the existing WEP algorithm at a small cost of recall. In addition, can reduce the runtime of the blocking process. We evaluate our proposed method over five real-world data sets.

Ensemble-type numerical uncertainty information from single model integrations

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

Rauser, Florian, E-mail: florian.rauser@mpimet.mpg.de; Marotzke, Jochem; Korn, Peter

2015-07-01

We suggest an algorithm that quantifies the discretization error of time-dependent physical quantities of interest (goals) for numerical models of geophysical fluid dynamics. The goal discretization error is estimated using a sum of weighted local discretization errors. The key feature of our algorithm is that these local discretization errors are interpreted as realizations of a random process. The random process is determined by the model and the flow state. From a class of local error random processes we select a suitable specific random process by integrating the model over a short time interval at different resolutions. The weights of themore » influences of the local discretization errors on the goal are modeled as goal sensitivities, which are calculated via automatic differentiation. The integration of the weighted realizations of local error random processes yields a posterior ensemble of goal approximations from a single run of the numerical model. From the posterior ensemble we derive the uncertainty information of the goal discretization error. This algorithm bypasses the requirement of detailed knowledge about the models discretization to generate numerical error estimates. The algorithm is evaluated for the spherical shallow-water equations. For two standard test cases we successfully estimate the error of regional potential energy, track its evolution, and compare it to standard ensemble techniques. The posterior ensemble shares linear-error-growth properties with ensembles of multiple model integrations when comparably perturbed. The posterior ensemble numerical error estimates are of comparable size as those of a stochastic physics ensemble.« less

Distributed Load Shedding over Directed Communication Networks with Time Delays

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

Yang, Tao; Wu, Di

When generation is insufficient to support all loads under emergencies, effective and efficient load shedding needs to be deployed in order to maintain the supply-demand balance. This paper presents a distributed load shedding algorithm, which makes efficient decision based on the discovered global information. In the global information discovery process, each load only communicates with its neighboring load via directed communication links possibly with arbitrarily large but bounded time varying communication delays. We propose a novel distributed information discovery algorithm based on ratio consensus. Simulation results are used to validate the proposed method.

A Survey of Stemming Algorithms in Information Retrieval

ERIC Educational Resources Information Center

Moral, Cristian; de Antonio, Angélica; Imbert, Ricardo; Ramírez, Jaime

2014-01-01

Background: During the last fifty years, improved information retrieval techniques have become necessary because of the huge amount of information people have available, which continues to increase rapidly due to the use of new technologies and the Internet. Stemming is one of the processes that can improve information retrieval in terms of…

A Bayesian Uncertainty Framework for Conceptual Snowmelt and Hydrologic Models Applied to the Tenderfoot Creek Experimental Forest

NASA Astrophysics Data System (ADS)

Smith, T.; Marshall, L.

2007-12-01

In many mountainous regions, the single most important parameter in forecasting the controls on regional water resources is snowpack (Williams et al., 1999). In an effort to bridge the gap between theoretical understanding and functional modeling of snow-driven watersheds, a flexible hydrologic modeling framework is being developed. The aim is to create a suite of models that move from parsimonious structures, concentrated on aggregated watershed response, to those focused on representing finer scale processes and distributed response. This framework will operate as a tool to investigate the link between hydrologic model predictive performance, uncertainty, model complexity, and observable hydrologic processes. Bayesian methods, and particularly Markov chain Monte Carlo (MCMC) techniques, are extremely useful in uncertainty assessment and parameter estimation of hydrologic models. However, these methods have some difficulties in implementation. In a traditional Bayesian setting, it can be difficult to reconcile multiple data types, particularly those offering different spatial and temporal coverage, depending on the model type. These difficulties are also exacerbated by sensitivity of MCMC algorithms to model initialization and complex parameter interdependencies. As a way of circumnavigating some of the computational complications, adaptive MCMC algorithms have been developed to take advantage of the information gained from each successive iteration. Two adaptive algorithms are compared is this study, the Adaptive Metropolis (AM) algorithm, developed by Haario et al (2001), and the Delayed Rejection Adaptive Metropolis (DRAM) algorithm, developed by Haario et al (2006). While neither algorithm is truly Markovian, it has been proven that each satisfies the desired ergodicity and stationarity properties of Markov chains. Both algorithms were implemented as the uncertainty and parameter estimation framework for a conceptual rainfall-runoff model based on the Probability Distributed Model (PDM), developed by Moore (1985). We implement the modeling framework in Stringer Creek watershed in the Tenderfoot Creek Experimental Forest (TCEF), Montana. The snowmelt-driven watershed offers that additional challenge of modeling snow accumulation and melt and current efforts are aimed at developing a temperature- and radiation-index snowmelt model. Auxiliary data available from within TCEF's watersheds are used to support in the understanding of information value as it relates to predictive performance. Because the model is based on lumped parameters, auxiliary data are hard to incorporate directly. However, these additional data offer benefits through the ability to inform prior distributions of the lumped, model parameters. By incorporating data offering different information into the uncertainty assessment process, a cross-validation technique is engaged to better ensure that modeled results reflect real process complexity.

An overview of selected information storage and retrieval issues in computerized document processing

NASA Technical Reports Server (NTRS)

Dominick, Wayne D. (Editor); Ihebuzor, Valentine U.

1984-01-01

The rapid development of computerized information storage and retrieval techniques has introduced the possibility of extending the word processing concept to document processing. A major advantage of computerized document processing is the relief of the tedious task of manual editing and composition usually encountered by traditional publishers through the immense speed and storage capacity of computers. Furthermore, computerized document processing provides an author with centralized control, the lack of which is a handicap of the traditional publishing operation. A survey of some computerized document processing techniques is presented with emphasis on related information storage and retrieval issues. String matching algorithms are considered central to document information storage and retrieval and are also discussed.

Modeling ECM fiber formation: structure information extracted by analysis of 2D and 3D image sets

NASA Astrophysics Data System (ADS)

Wu, Jun; Voytik-Harbin, Sherry L.; Filmer, David L.; Hoffman, Christoph M.; Yuan, Bo; Chiang, Ching-Shoei; Sturgis, Jennis; Robinson, Joseph P.

2002-05-01

Recent evidence supports the notion that biological functions of extracellular matrix (ECM) are highly correlated to its structure. Understanding this fibrous structure is very crucial in tissue engineering to develop the next generation of biomaterials for restoration of tissues and organs. In this paper, we integrate confocal microscopy imaging and image-processing techniques to analyze the structural properties of ECM. We describe a 2D fiber middle-line tracing algorithm and apply it via Euclidean distance maps (EDM) to extract accurate fibrous structure information, such as fiber diameter, length, orientation, and density, from single slices. Based on a 2D tracing algorithm, we extend our analysis to 3D tracing via Euclidean distance maps to extract 3D fibrous structure information. We use computer simulation to construct the 3D fibrous structure which is subsequently used to test our tracing algorithms. After further image processing, these models are then applied to a variety of ECM constructions from which results of 2D and 3D traces are statistically analyzed.

File text security using Hybrid Cryptosystem with Playfair Cipher Algorithm and Knapsack Naccache-Stern Algorithm

NASA Astrophysics Data System (ADS)

Amalia; Budiman, M. A.; Sitepu, R.

2018-03-01

Cryptography is one of the best methods to keep the information safe from security attack by unauthorized people. At present, Many studies had been done by previous researchers to generate a more robust cryptographic algorithm to provide high security for data communication. To strengthen data security, one of the methods is hybrid cryptosystem method that combined symmetric and asymmetric algorithm. In this study, we observed a hybrid cryptosystem method contain Modification Playfair Cipher 16x16 algorithm as a symmetric algorithm and Knapsack Naccache-Stern as an asymmetric algorithm. We observe a running time of this hybrid algorithm with some of the various experiments. We tried different amount of characters to be tested which are 10, 100, 1000, 10000 and 100000 characters and we also examined the algorithm with various key’s length which are 10, 20, 30, 40 of key length. The result of our study shows that the processing time for encryption and decryption process each algorithm is linearly proportional, it means the longer messages character then, the more significant times needed to encrypt and decrypt the messages. The encryption running time of Knapsack Naccache-Stern algorithm takes a longer time than its decryption, while the encryption running time of modification Playfair Cipher 16x16 algorithm takes less time than its decryption.

Information processing for aerospace structural health monitoring

NASA Astrophysics Data System (ADS)

Lichtenwalner, Peter F.; White, Edward V.; Baumann, Erwin W.

1998-06-01

Structural health monitoring (SHM) technology provides a means to significantly reduce life cycle of aerospace vehicles by eliminating unnecessary inspections, minimizing inspection complexity, and providing accurate diagnostics and prognostics to support vehicle life extension. In order to accomplish this, a comprehensive SHM system will need to acquire data from a wide variety of diverse sensors including strain gages, accelerometers, acoustic emission sensors, crack growth gages, corrosion sensors, and piezoelectric transducers. Significant amounts of computer processing will then be required to convert this raw sensor data into meaningful information which indicates both the diagnostics of the current structural integrity as well as the prognostics necessary for planning and managing the future health of the structure in a cost effective manner. This paper provides a description of the key types of information processing technologies required in an effective SHM system. These include artificial intelligence techniques such as neural networks, expert systems, and fuzzy logic for nonlinear modeling, pattern recognition, and complex decision making; signal processing techniques such as Fourier and wavelet transforms for spectral analysis and feature extraction; statistical algorithms for optimal detection, estimation, prediction, and fusion; and a wide variety of other algorithms for data analysis and visualization. The intent of this paper is to provide an overview of the role of information processing for SHM, discuss various technologies which can contribute to accomplishing this role, and present some example applications of information processing for SHM implemented at the Boeing Company.

Automatic identification of comparative effectiveness research from Medline citations to support clinicians’ treatment information needs

PubMed Central

Zhang, Mingyuan; Fiol, Guilherme Del; Grout, Randall W.; Jonnalagadda, Siddhartha; Medlin, Richard; Mishra, Rashmi; Weir, Charlene; Liu, Hongfang; Mostafa, Javed; Fiszman, Marcelo

2014-01-01

Online knowledge resources such as Medline can address most clinicians’ patient care information needs. Yet, significant barriers, notably lack of time, limit the use of these sources at the point of care. The most common information needs raised by clinicians are treatment-related. Comparative effectiveness studies allow clinicians to consider multiple treatment alternatives for a particular problem. Still, solutions are needed to enable efficient and effective consumption of comparative effectiveness research at the point of care. Objective Design and assess an algorithm for automatically identifying comparative effectiveness studies and extracting the interventions investigated in these studies. Methods The algorithm combines semantic natural language processing, Medline citation metadata, and machine learning techniques. We assessed the algorithm in a case study of treatment alternatives for depression. Results Both precision and recall for identifying comparative studies was 0.83. A total of 86% of the interventions extracted perfectly or partially matched the gold standard. Conclusion Overall, the algorithm achieved reasonable performance. The method provides building blocks for the automatic summarization of comparative effectiveness research to inform point of care decision-making. PMID:23920677

Online Tracking Algorithms on GPUs for the P̅ANDA Experiment at FAIR

NASA Astrophysics Data System (ADS)

Bianchi, L.; Herten, A.; Ritman, J.; Stockmanns, T.; Adinetz, A.; Kraus, J.; Pleiter, D.

2015-12-01

P̅ANDA is a future hadron and nuclear physics experiment at the FAIR facility in construction in Darmstadt, Germany. In contrast to the majority of current experiments, PANDA's strategy for data acquisition is based on event reconstruction from free-streaming data, performed in real time entirely by software algorithms using global detector information. This paper reports the status of the development of algorithms for the reconstruction of charged particle tracks, optimized online data processing applications, using General-Purpose Graphic Processing Units (GPU). Two algorithms for trackfinding, the Triplet Finder and the Circle Hough, are described, and details of their GPU implementations are highlighted. Average track reconstruction times of less than 100 ns are obtained running the Triplet Finder on state-of- the-art GPU cards. In addition, a proof-of-concept system for the dispatch of data to tracking algorithms using Message Queues is presented.

Scalable software-defined optical networking with high-performance routing and wavelength assignment algorithms.

PubMed

Lee, Chankyun; Cao, Xiaoyuan; Yoshikane, Noboru; Tsuritani, Takehiro; Rhee, June-Koo Kevin

2015-10-19

The feasibility of software-defined optical networking (SDON) for a practical application critically depends on scalability of centralized control performance. The paper, highly scalable routing and wavelength assignment (RWA) algorithms are investigated on an OpenFlow-based SDON testbed for proof-of-concept demonstration. Efficient RWA algorithms are proposed to achieve high performance in achieving network capacity with reduced computation cost, which is a significant attribute in a scalable centralized-control SDON. The proposed heuristic RWA algorithms differ in the orders of request processes and in the procedures of routing table updates. Combined in a shortest-path-based routing algorithm, a hottest-request-first processing policy that considers demand intensity and end-to-end distance information offers both the highest throughput of networks and acceptable computation scalability. We further investigate trade-off relationship between network throughput and computation complexity in routing table update procedure by a simulation study.

Image encryption using fingerprint as key based on phase retrieval algorithm and public key cryptography

NASA Astrophysics Data System (ADS)

Zhao, Tieyu; Ran, Qiwen; Yuan, Lin; Chi, Yingying; Ma, Jing

2015-09-01

In this paper, a novel image encryption system with fingerprint used as a secret key is proposed based on the phase retrieval algorithm and RSA public key algorithm. In the system, the encryption keys include the fingerprint and the public key of RSA algorithm, while the decryption keys are the fingerprint and the private key of RSA algorithm. If the users share the fingerprint, then the system will meet the basic agreement of asymmetric cryptography. The system is also applicable for the information authentication. The fingerprint as secret key is used in both the encryption and decryption processes so that the receiver can identify the authenticity of the ciphertext by using the fingerprint in decryption process. Finally, the simulation results show the validity of the encryption scheme and the high robustness against attacks based on the phase retrieval technique.

Data-driven gradient algorithm for high-precision quantum control

NASA Astrophysics Data System (ADS)

Wu, Re-Bing; Chu, Bing; Owens, David H.; Rabitz, Herschel

2018-04-01

In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.

A real time sorting algorithm to time sort any deterministic time disordered data stream

NASA Astrophysics Data System (ADS)

Saini, J.; Mandal, S.; Chakrabarti, A.; Chattopadhyay, S.

2017-12-01

In new generation high intensity high energy physics experiments, millions of free streaming high rate data sources are to be readout. Free streaming data with associated time-stamp can only be controlled by thresholds as there is no trigger information available for the readout. Therefore, these readouts are prone to collect large amount of noise and unwanted data. For this reason, these experiments can have output data rate of several orders of magnitude higher than the useful signal data rate. It is therefore necessary to perform online processing of the data to extract useful information from the full data set. Without trigger information, pre-processing on the free streaming data can only be done with time based correlation among the data set. Multiple data sources have different path delays and bandwidth utilizations and therefore the unsorted merged data requires significant computational efforts for real time manifestation of sorting before analysis. Present work reports a new high speed scalable data stream sorting algorithm with its architectural design, verified through Field programmable Gate Array (FPGA) based hardware simulation. Realistic time based simulated data likely to be collected in an high energy physics experiment have been used to study the performance of the algorithm. The proposed algorithm uses parallel read-write blocks with added memory management and zero suppression features to make it efficient for high rate data-streams. This algorithm is best suited for online data streams with deterministic time disorder/unsorting on FPGA like hardware.

Distributed resource allocation under communication constraints

NASA Astrophysics Data System (ADS)

Dodin, Pierre; Nimier, Vincent

2001-03-01

This paper deals with a study of the multi-sensor management problem for multi-target tracking. The collaboration between many sensors observing the same target means that they are able to fuse their data during the information process. Then one must take into account this possibility to compute the optimal association sensors-target at each step of time. In order to solve this problem for real large scale system, one must both consider the information aspect and the control aspect of the problem. To unify these problems, one possibility is to use a decentralized filtering algorithm locally driven by an assignment algorithm. The decentralized filtering algorithm we use in our model is the filtering algorithm of Grime, which relaxes the usual full-connected hypothesis. By full-connected, one means that the information in a full-connected system is totally distributed everywhere at the same moment, which is unacceptable for a real large scale system. We modelize the distributed assignment decision with the help of a greedy algorithm. Each sensor performs a global optimization, in order to estimate other information sets. A consequence of the relaxation of the full- connected hypothesis is that the sensors' information set are not the same at each step of time, producing an information dis- symmetry in the system. The assignment algorithm uses a local knowledge of this dis-symmetry. By testing the reactions and the coherence of the local assignment decisions of our system, against maneuvering targets, we show that it is still possible to manage with decentralized assignment control even though the system is not full-connected.

Problems of Automation and Management Principles Information Flow in Manufacturing

NASA Astrophysics Data System (ADS)

Grigoryuk, E. N.; Bulkin, V. V.

2017-07-01

Automated control systems of technological processes are complex systems that are characterized by the presence of elements of the overall focus, the systemic nature of the implemented algorithms for the exchange and processing of information, as well as a large number of functional subsystems. The article gives examples of automatic control systems and automated control systems of technological processes held parallel between them by identifying strengths and weaknesses. Other proposed non-standard control system of technological process.

Learning Time-Varying Coverage Functions

PubMed Central

Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

2015-01-01

Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data. PMID:25960624

Learning Time-Varying Coverage Functions.

PubMed

Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

2014-12-08

Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data.

Design and implementation of a vision-based hovering and feature tracking algorithm for a quadrotor

NASA Astrophysics Data System (ADS)

Lee, Y. H.; Chahl, J. S.

2016-10-01

This paper demonstrates an approach to the vision-based control of the unmanned quadrotors for hover and object tracking. The algorithms used the Speed Up Robust Features (SURF) algorithm to detect objects. The pose of the object in the image was then calculated in order to pass the pose information to the flight controller. Finally, the flight controller steered the quadrotor to approach the object based on the calculated pose data. The above processes was run using standard onboard resources found in the 3DR Solo quadrotor in an embedded computing environment. The obtained results showed that the algorithm behaved well during its missions, tracking and hovering, although there were significant latencies due to low CPU performance of the onboard image processing system.

Deducing chemical structure from crystallographically determined atomic coordinates

PubMed Central

Bruno, Ian J.; Shields, Gregory P.; Taylor, Robin

2011-01-01

An improved algorithm has been developed for assigning chemical structures to incoming entries to the Cambridge Structural Database, using only the information available in the deposited CIF. Steps in the algorithm include detection of bonds, selection of polymer unit, resolution of disorder, and assignment of bond types and formal charges. The chief difficulty is posed by the large number of metallo-organic crystal structures that must be processed, given our aspiration that assigned chemical structures should accurately reflect properties such as the oxidation states of metals and redox-active ligands, metal coordination numbers and hapticities, and the aromaticity or otherwise of metal ligands. Other complications arise from disorder, especially when it is symmetry imposed or modelled with the SQUEEZE algorithm. Each assigned structure is accompanied by an estimate of reliability and, where necessary, diagnostic information indicating probable points of error. Although the algorithm was written to aid building of the Cambridge Structural Database, it has the potential to develop into a general-purpose tool for adding chemical information to newly determined crystal structures. PMID:21775812

Decentralized cooperative TOA/AOA target tracking for hierarchical wireless sensor networks.

PubMed

Chen, Ying-Chih; Wen, Chih-Yu

2012-11-08

This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation.

A decision support tool for vehicle infrastructure integration : understanding information effects and advancing data fusion algorithms for traffic management applications.

DOT National Transportation Integrated Search

2009-10-15

This research seeks to explore vehicle-to-vehicle information networks to understand the interplay : between the information communicated and traffic conditions on the network. A longer-term goal is to : develop a decision support tool for processing...

A Real-Time System for Lane Detection Based on FPGA and DSP

NASA Astrophysics Data System (ADS)

Xiao, Jing; Li, Shutao; Sun, Bin

2016-12-01

This paper presents a real-time lane detection system including edge detection and improved Hough Transform based lane detection algorithm and its hardware implementation with field programmable gate array (FPGA) and digital signal processor (DSP). Firstly, gradient amplitude and direction information are combined to extract lane edge information. Then, the information is used to determine the region of interest. Finally, the lanes are extracted by using improved Hough Transform. The image processing module of the system consists of FPGA and DSP. Particularly, the algorithms implemented in FPGA are working in pipeline and processing in parallel so that the system can run in real-time. In addition, DSP realizes lane line extraction and display function with an improved Hough Transform. The experimental results show that the proposed system is able to detect lanes under different road situations efficiently and effectively.

PDF file encryption on mobile phone using super-encryption of Variably Modified Permutation Composition (VMPC) and two square cipher algorithm

NASA Astrophysics Data System (ADS)

Rachmawati, D.; Budiman, M. A.; Atika, F.

2018-03-01

Data security is becoming one of the most significant challenges in the digital world. Retrieval of data by unauthorized parties will result in harm to the owner of the data. PDF data are also susceptible to data security disorder. These things affect the security of the information. To solve the security problem, it needs a method to maintain the protection of the data, such as cryptography. In cryptography, several algorithms can encode data, one of them is Two Square Cipher algorithm which is a symmetric algorithm. At this research, Two Square Cipher algorithm has already developed into a 16 x 16 key aims to enter the various plaintexts. However, for more enhancement security it will be combined with the VMPC algorithm which is a symmetric algorithm. The combination of the two algorithms is called with the super-encryption. At this point, the data already can be stored on a mobile phone allowing users to secure data flexibly and can be accessed anywhere. The application of PDF document security on this research built by Android-platform. At this study will also calculate the complexity of algorithms and process time. Based on the test results the complexity of the algorithm is θ (n) for Two Square Cipher and θ (n) for VMPC algorithm, so the complexity of the super-encryption is also θ (n). VMPC algorithm processing time results quicker than on Two Square Cipher. And the processing time is directly proportional to the length of the plaintext and passwords.

Information filtering via biased heat conduction

NASA Astrophysics Data System (ADS)

Liu, Jian-Guo; Zhou, Tao; Guo, Qiang

2011-09-01

The process of heat conduction has recently found application in personalized recommendation [Zhou , Proc. Natl. Acad. Sci. USA PNASA60027-842410.1073/pnas.1000488107107, 4511 (2010)], which is of high diversity but low accuracy. By decreasing the temperatures of small-degree objects, we present an improved algorithm, called biased heat conduction, which could simultaneously enhance the accuracy and diversity. Extensive experimental analyses demonstrate that the accuracy on MovieLens, Netflix, and Delicious datasets could be improved by 43.5%, 55.4% and 19.2%, respectively, compared with the standard heat conduction algorithm and also the diversity is increased or approximately unchanged. Further statistical analyses suggest that the present algorithm could simultaneously identify users' mainstream and special tastes, resulting in better performance than the standard heat conduction algorithm. This work provides a creditable way for highly efficient information filtering.

An efficient and scalable graph modeling approach for capturing information at different levels in next generation sequencing reads

PubMed Central

2013-01-01

Background Next generation sequencing technologies have greatly advanced many research areas of the biomedical sciences through their capability to generate massive amounts of genetic information at unprecedented rates. The advent of next generation sequencing has led to the development of numerous computational tools to analyze and assemble the millions to billions of short sequencing reads produced by these technologies. While these tools filled an important gap, current approaches for storing, processing, and analyzing short read datasets generally have remained simple and lack the complexity needed to efficiently model the produced reads and assemble them correctly. Results Previously, we presented an overlap graph coarsening scheme for modeling read overlap relationships on multiple levels. Most current read assembly and analysis approaches use a single graph or set of clusters to represent the relationships among a read dataset. Instead, we use a series of graphs to represent the reads and their overlap relationships across a spectrum of information granularity. At each information level our algorithm is capable of generating clusters of reads from the reduced graph, forming an integrated graph modeling and clustering approach for read analysis and assembly. Previously we applied our algorithm to simulated and real 454 datasets to assess its ability to efficiently model and cluster next generation sequencing data. In this paper we extend our algorithm to large simulated and real Illumina datasets to demonstrate that our algorithm is practical for both sequencing technologies. Conclusions Our overlap graph theoretic algorithm is able to model next generation sequencing reads at various levels of granularity through the process of graph coarsening. Additionally, our model allows for efficient representation of the read overlap relationships, is scalable for large datasets, and is practical for both Illumina and 454 sequencing technologies. PMID:24564333

The TOMS V9 Algorithm for OMPS Nadir Mapper Total Ozone: An Enhanced Design That Ensures Data Continuity

NASA Astrophysics Data System (ADS)

Haffner, D. P.; McPeters, R. D.; Bhartia, P. K.; Labow, G. J.

2015-12-01

The TOMS V9 total ozone algorithm will be applied to the OMPS Nadir Mapper instrument to supersede the exisiting V8.6 data product in operational processing and re-processing for public release. Becuase the quality of the V8.6 data is already quite high, enchancements in V9 are mainly with information provided by the retrieval and simplifcations to the algorithm. The design of the V9 algorithm has been influenced by improvements both in our knowledge of atmospheric effects, such as those of clouds made possible by studies with OMI, and also limitations in the V8 algorithms applied to both OMI and OMPS. But the namesake instruments of the TOMS algorithm are substantially more limited in their spectral and noise characterisitics, and a requirement of our algorithm is to also apply the algorithm to these discrete band spectrometers which date back to 1978. To achieve continuity for all these instruments, the TOMS V9 algorithm continues to use radiances in discrete bands, but now uses Rodgers optimal estimation to retrieve a coarse profile and provide uncertainties for each retrieval. The algorithm remains capable of achieving high accuracy results with a small number of discrete wavelengths, and in extreme cases, such as unusual profile shapes and high solar zenith angles, the quality of the retrievals is improved. Despite the intended design to use limited wavlenegths, the algorithm can also utilitze additional wavelengths from hyperspectral sensors like OMPS to augment the retreival's error detection and information content; for example SO2 detection and correction of Ring effect on atmospheric radiances. We discuss these and other aspects of the V9 algorithm as it will be applied to OMPS, and will mention potential improvements which aim to take advantage of a synergy with OMPS Limb Profiler and Nadir Mapper to further improve the quality of total ozone from the OMPS instrument.

pySPACE—a signal processing and classification environment in Python

PubMed Central

Krell, Mario M.; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Teiwes, Johannes; Metzen, Jan H.; Kirchner, Elsa A.; Kirchner, Frank

2013-01-01

In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries. PMID:24399965

pySPACE-a signal processing and classification environment in Python.

PubMed

Krell, Mario M; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Teiwes, Johannes; Metzen, Jan H; Kirchner, Elsa A; Kirchner, Frank

2013-01-01

In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries.

An efficient sampling algorithm for uncertain abnormal data detection in biomedical image processing and disease prediction.

PubMed

Liu, Fei; Zhang, Xi; Jia, Yan

2015-01-01

In this paper, we propose a computer information processing algorithm that can be used for biomedical image processing and disease prediction. A biomedical image is considered a data object in a multi-dimensional space. Each dimension is a feature that can be used for disease diagnosis. We introduce a new concept of the top (k1,k2) outlier. It can be used to detect abnormal data objects in the multi-dimensional space. This technique focuses on uncertain space, where each data object has several possible instances with distinct probabilities. We design an efficient sampling algorithm for the top (k1,k2) outlier in uncertain space. Some improvement techniques are used for acceleration. Experiments show our methods' high accuracy and high efficiency.

Hardware-software and algorithmic provision of multipoint systems for long-term monitoring of dynamic processes

NASA Astrophysics Data System (ADS)

Yakunin, A. G.; Hussein, H. M.

2017-08-01

An example of information-measuring systems for climate monitoring and operational control of energy resources consumption of the university campus that is functioning in the Altai State Technical University since 2009. The advantages of using such systems for studying various physical processes are discussed. General principles of construction of similar systems, their software, hardware and algorithmic support are considered. It is shown that their fundamental difference from traditional SCADA - systems is the use of databases for storing the results of the observation with a specialized data structure, and by preprocessing of the input signal for its compression. Another difference is the absence of clear criteria for detecting the anomalies in the time series of the observed process. The examples of algorithms that solve this problem are given.

The algorithm of fast image stitching based on multi-feature extraction

NASA Astrophysics Data System (ADS)

Yang, Chunde; Wu, Ge; Shi, Jing

2018-05-01

This paper proposed an improved image registration method combining Hu-based invariant moment contour information and feature points detection, aiming to solve the problems in traditional image stitching algorithm, such as time-consuming feature points extraction process, redundant invalid information overload and inefficiency. First, use the neighborhood of pixels to extract the contour information, employing the Hu invariant moment as similarity measure to extract SIFT feature points in those similar regions. Then replace the Euclidean distance with Hellinger kernel function to improve the initial matching efficiency and get less mismatching points, further, estimate affine transformation matrix between the images. Finally, local color mapping method is adopted to solve uneven exposure, using the improved multiresolution fusion algorithm to fuse the mosaic images and realize seamless stitching. Experimental results confirm high accuracy and efficiency of method proposed in this paper.

Improved parallel data partitioning by nested dissection with applications to information retrieval.

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

Wolf, Michael M.; Chevalier, Cedric; Boman, Erik Gunnar

The computational work in many information retrieval and analysis algorithms is based on sparse linear algebra. Sparse matrix-vector multiplication is a common kernel in many of these computations. Thus, an important related combinatorial problem in parallel computing is how to distribute the matrix and the vectors among processors so as to minimize the communication cost. We focus on minimizing the total communication volume while keeping the computation balanced across processes. In [1], the first two authors presented a new 2D partitioning method, the nested dissection partitioning algorithm. In this paper, we improve on that algorithm and show that it ismore » a good option for data partitioning in information retrieval. We also show partitioning time can be substantially reduced by using the SCOTCH software, and quality improves in some cases, too.« less

Quantum Clock Synchronization with a Single Qudit

NASA Astrophysics Data System (ADS)

Tavakoli, Armin; Cabello, Adán; Żukowski, Marek; Bourennane, Mohamed

2015-01-01

Clock synchronization for nonfaulty processes in multiprocess networks is indispensable for a variety of technologies. A reliable system must be able to resynchronize the nonfaulty processes upon some components failing causing the distribution of incorrect or conflicting information in the network. The task of synchronizing such networks is related to Byzantine agreement (BA), which can classically be solved using recursive algorithms if and only if less than one-third of the processes are faulty. Here we introduce a nonrecursive quantum algorithm, based on a quantum solution of the detectable BA, which achieves clock synchronization in the presence of arbitrary many faulty processes by using only a single quantum system.

A fast elitism Gaussian estimation of distribution algorithm and application for PID optimization.

PubMed

Xu, Qingyang; Zhang, Chengjin; Zhang, Li

2014-01-01

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.

A Fast Elitism Gaussian Estimation of Distribution Algorithm and Application for PID Optimization

PubMed Central

Xu, Qingyang; Zhang, Chengjin; Zhang, Li

2014-01-01

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA. PMID:24892059

Automating "Word of Mouth" to Recommend Classes to Students: An Application of Social Information Filtering Algorithms

ERIC Educational Resources Information Center

Booker, Queen Esther

2009-01-01

An approach used to tackle the problem of helping online students find the classes they want and need is a filtering technique called "social information filtering," a general approach to personalized information filtering. Social information filtering essentially automates the process of "word-of-mouth" recommendations: items are recommended to a…

Probabilistic streamflow forecasting for hydroelectricity production: A comparison of two non-parametric system identification algorithms

NASA Astrophysics Data System (ADS)

Pande, Saket; Sharma, Ashish

2014-05-01

This study is motivated by the need to robustly specify, identify, and forecast runoff generation processes for hydroelectricity production. It atleast requires the identification of significant predictors of runoff generation and the influence of each such significant predictor on runoff response. To this end, we compare two non-parametric algorithms of predictor subset selection. One is based on information theory that assesses predictor significance (and hence selection) based on Partial Information (PI) rationale of Sharma and Mehrotra (2014). The other algorithm is based on a frequentist approach that uses bounds on probability of error concept of Pande (2005), assesses all possible predictor subsets on-the-go and converges to a predictor subset in an computationally efficient manner. Both the algorithms approximate the underlying system by locally constant functions and select predictor subsets corresponding to these functions. The performance of the two algorithms is compared on a set of synthetic case studies as well as a real world case study of inflow forecasting. References: Sharma, A., and R. Mehrotra (2014), An information theoretic alternative to model a natural system using observational information alone, Water Resources Research, 49, doi:10.1002/2013WR013845. Pande, S. (2005), Generalized local learning in water resource management, PhD dissertation, Utah State University, UT-USA, 148p.

Exploring context and content links in social media: a latent space method.

PubMed

Qi, Guo-Jun; Aggarwal, Charu; Tian, Qi; Ji, Heng; Huang, Thomas S

2012-05-01

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.

DARIS (Deformation Analysis Using Recursive Interferometric Systems) A New Algorithm for Displacement Measurements Though SAR Interferometry

NASA Astrophysics Data System (ADS)

Redavid, Antonio; Bovenga, Fabio

2010-03-01

In the present work we describe a new and alternative repeat-pass interferometry algorithm designed and developed with the aim to: i) increase the robustness wrt to noise by increasing the number of differential interferograms and consequently the information redundancy; ii) guarantee high performances in the detection of non linear deformation without the need of specifying in input a particular cinematic model.The starting point is a previous paper [4] dedicated to the optimization of the InSAR coregistration by finding an ad hoc path between the images which minimizes the expected total decorrelation as in the SABS-like approaches [3]. The main difference wrt the PS-like algorithms [1],[2] is the use of couples of images which potentially can show high spatial coherence and, which are neglected by the standard PSI processing.The present work presents a detailed description of the algorithm processing steps as well as the results obtained by processing simulated InSAR data with the aim to evaluate the algorithm performances. Moreover, the algorithm has been also applied on a real test case in Poland, to study the subsidence affecting the Wieliczka Salt Mine. A cross validation wrt SPINUA PSI-like algorithm [5] has been carried out by comparing the resultant displacement fields.

An improved algorithm of image processing technique for film thickness measurement in a horizontal stratified gas-liquid two-phase flow

NASA Astrophysics Data System (ADS)

Kuntoro, Hadiyan Yusuf; Hudaya, Akhmad Zidni; Dinaryanto, Okto; Majid, Akmal Irfan; Deendarlianto

2016-06-01

Due to the importance of the two-phase flow researches for the industrial safety analysis, many researchers developed various methods and techniques to study the two-phase flow phenomena on the industrial cases, such as in the chemical, petroleum and nuclear industries cases. One of the developing methods and techniques is image processing technique. This technique is widely used in the two-phase flow researches due to the non-intrusive capability to process a lot of visualization data which are contain many complexities. Moreover, this technique allows to capture direct-visual information data of the flow which are difficult to be captured by other methods and techniques. The main objective of this paper is to present an improved algorithm of image processing technique from the preceding algorithm for the stratified flow cases. The present algorithm can measure the film thickness (hL) of stratified flow as well as the geometrical properties of the interfacial waves with lower processing time and random-access memory (RAM) usage than the preceding algorithm. Also, the measurement results are aimed to develop a high quality database of stratified flow which is scanty. In the present work, the measurement results had a satisfactory agreement with the previous works.

Parallel Processing Systems for Passive Ranging During Helicopter Flight

NASA Technical Reports Server (NTRS)

Sridhar, Bavavar; Suorsa, Raymond E.; Showman, Robert D. (Technical Monitor)

1994-01-01

The complexity of rotorcraft missions involving operations close to the ground result in high pilot workload. In order to allow a pilot time to perform mission-oriented tasks, sensor-aiding and automation of some of the guidance and control functions are highly desirable. Images from an electro-optical sensor provide a covert way of detecting objects in the flight path of a low-flying helicopter. Passive ranging consists of processing a sequence of images using techniques based on optical low computation and recursive estimation. The passive ranging algorithm has to extract obstacle information from imagery at rates varying from five to thirty or more frames per second depending on the helicopter speed. We have implemented and tested the passive ranging algorithm off-line using helicopter-collected images. However, the real-time data and computation requirements of the algorithm are beyond the capability of any off-the-shelf microprocessor or digital signal processor. This paper describes the computational requirements of the algorithm and uses parallel processing technology to meet these requirements. Various issues in the selection of a parallel processing architecture are discussed and four different computer architectures are evaluated regarding their suitability to process the algorithm in real-time. Based on this evaluation, we conclude that real-time passive ranging is a realistic goal and can be achieved with a short time.

An improved algorithm of image processing technique for film thickness measurement in a horizontal stratified gas-liquid two-phase flow

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

Kuntoro, Hadiyan Yusuf, E-mail: hadiyan.y.kuntoro@mail.ugm.ac.id; Majid, Akmal Irfan; Deendarlianto, E-mail: deendarlianto@ugm.ac.id

Due to the importance of the two-phase flow researches for the industrial safety analysis, many researchers developed various methods and techniques to study the two-phase flow phenomena on the industrial cases, such as in the chemical, petroleum and nuclear industries cases. One of the developing methods and techniques is image processing technique. This technique is widely used in the two-phase flow researches due to the non-intrusive capability to process a lot of visualization data which are contain many complexities. Moreover, this technique allows to capture direct-visual information data of the flow which are difficult to be captured by other methodsmore » and techniques. The main objective of this paper is to present an improved algorithm of image processing technique from the preceding algorithm for the stratified flow cases. The present algorithm can measure the film thickness (h{sub L}) of stratified flow as well as the geometrical properties of the interfacial waves with lower processing time and random-access memory (RAM) usage than the preceding algorithm. Also, the measurement results are aimed to develop a high quality database of stratified flow which is scanty. In the present work, the measurement results had a satisfactory agreement with the previous works.« less

An Improved Aerial Target Localization Method with a Single Vector Sensor

PubMed Central

Zhao, Anbang; Bi, Xuejie; Hui, Juan; Zeng, Caigao; Ma, Lin

2017-01-01

This paper focuses on the problems encountered in the actual data processing with the use of the existing aerial target localization methods, analyzes the causes of the problems, and proposes an improved algorithm. Through the processing of the sea experiment data, it is found that the existing algorithms have higher requirements for the accuracy of the angle estimation. The improved algorithm reduces the requirements of the angle estimation accuracy and obtains the robust estimation results. The closest distance matching estimation algorithm and the horizontal distance estimation compensation algorithm are proposed. The smoothing effect of the data after being post-processed by using the forward and backward two-direction double-filtering method has been improved, thus the initial stage data can be filtered, so that the filtering results retain more useful information. In this paper, the aerial target height measurement methods are studied, the estimation results of the aerial target are given, so as to realize the three-dimensional localization of the aerial target and increase the understanding of the underwater platform to the aerial target, so that the underwater platform has better mobility and concealment. PMID:29135956

Optical Guidance for a Robotic Submarine

NASA Astrophysics Data System (ADS)

Schulze, Karl R.; LaFlash, Chris

2002-11-01

There is a need for autonomous submarines that can quickly and safely complete jobs, such as the recovery of a downed aircraft's black box recorder. In order to complete this feat, it is necessary to use an optical processing algorithm that distinguishes a desired target and uses the feedback from the algorithm to retrieve the target. The algorithm itself uses many bit mask filters for particle information, and then uses a unique rectation method in order to resolve complete objects. The algorithm has been extensively tested on an AUV platform, and proven to succeed repeatedly in approximately five or more feet of water clarity.

A new image encryption algorithm based on the fractional-order hyperchaotic Lorenz system

NASA Astrophysics Data System (ADS)

Wang, Zhen; Huang, Xia; Li, Yu-Xia; Song, Xiao-Na

2013-01-01

We propose a new image encryption algorithm on the basis of the fractional-order hyperchaotic Lorenz system. While in the process of generating a key stream, the system parameters and the derivative order are embedded in the proposed algorithm to enhance the security. Such an algorithm is detailed in terms of security analyses, including correlation analysis, information entropy analysis, run statistic analysis, mean-variance gray value analysis, and key sensitivity analysis. The experimental results demonstrate that the proposed image encryption scheme has the advantages of large key space and high security for practical image encryption.

The research of network database security technology based on web service

NASA Astrophysics Data System (ADS)

Meng, Fanxing; Wen, Xiumei; Gao, Liting; Pang, Hui; Wang, Qinglin

2013-03-01

Database technology is one of the most widely applied computer technologies, its security is becoming more and more important. This paper introduced the database security, network database security level, studies the security technology of the network database, analyzes emphatically sub-key encryption algorithm, applies this algorithm into the campus-one-card system successfully. The realization process of the encryption algorithm is discussed, this method is widely used as reference in many fields, particularly in management information system security and e-commerce.

ITO-based evolutionary algorithm to solve traveling salesman problem

NASA Astrophysics Data System (ADS)

Dong, Wenyong; Sheng, Kang; Yang, Chuanhua; Yi, Yunfei

2014-03-01

In this paper, a ITO algorithm inspired by ITO stochastic process is proposed for Traveling Salesmen Problems (TSP), so far, many meta-heuristic methods have been successfully applied to TSP, however, as a member of them, ITO needs further demonstration for TSP. So starting from designing the key operators, which include the move operator, wave operator, etc, the method based on ITO for TSP is presented, and moreover, the ITO algorithm performance under different parameter sets and the maintenance of population diversity information are also studied.

Color enhancement and image defogging in HSI based on Retinex model

NASA Astrophysics Data System (ADS)

Gao, Han; Wei, Ping; Ke, Jun

2015-08-01

Retinex is a luminance perceptual algorithm based on color consistency. It has a good performance in color enhancement. But in some cases, the traditional Retinex algorithms, both Single-Scale Retinex(SSR) and Multi-Scale Retinex(MSR) in RGB color space, do not work well and will cause color deviation. To solve this problem, we present improved SSR and MSR algorithms. Compared to other Retinex algorithms, we implement Retinex algorithms in HSI(Hue, Saturation, Intensity) color space, and use a parameter αto improve quality of the image. Moreover, the algorithms presented in this paper has a good performance in image defogging. Contrasted with traditional Retinex algorithms, we use intensity channel to obtain reflection information of an image. The intensity channel is processed using a Gaussian center-surround image filter to get light information, which should be removed from intensity channel. After that, we subtract the light information from intensity channel to obtain the reflection image, which only includes the attribute of the objects in image. Using the reflection image and a parameter α, which is an arbitrary scale factor set manually, we improve the intensity channel, and complete the color enhancement. Our experiments show that this approach works well compared with existing methods for color enhancement. Besides a better performance in color deviation problem and image defogging, a visible improvement in the image quality for human contrast perception is also observed.

Algorithms and theory for the design and programming of industrial control systems materialized with PLC's

NASA Astrophysics Data System (ADS)

Montoya Villena, Rafael

According to its title, the general objective of the Thesis consists in developing a clear, simple and systematic methodology for programming type PLC devices. With this aim in mind, we will use the following elements: Codification of all variables types. This section is very important since it allows us working with little information. The necessary rules are given to codify all type of phrases produced in industrial processes. An algorithm that describes process evolution and that has been called process D.F. This is one of the most important contributions, since it will allow us, together with information codification, representing the process evolution in a graphic way and with any design theory used. Theory selection. Evidently, the use of some kind of design method is necessary to obtain logic equations. For this particular case, we will use binodal theory, an ideal theory for wired technologies, since it can obtain highly reduced schemas for relatively simple automatisms, which means a minimum number of components used. User program outline algorithm (D.F.P.). This is another necessary contribution and perhaps the most important one, since logic equations resulting from binodal theory are compatible with process evolution if wired technology is used, whether it is electric, electronic, pneumatic, etc. On the other hand, PLC devices performance characteristics force the program instructions order to validate or not the automatism, as we have proven in different articles and lectures at congresses both national and international. Therefore, we will codify any information concerning the automating process, graphically represent its temporal evolution and, applying binodal theory and D.F.P (previously adapted), succeed in making logic equations compatible with the process to be automated and the device in which they will be implemented (PLC in our case)

Improved Collaborative Filtering Algorithm via Information Transformation

NASA Astrophysics Data System (ADS)

Liu, Jian-Guo; Wang, Bing-Hong; Guo, Qiang

In this paper, we propose a spreading activation approach for collaborative filtering (SA-CF). By using the opinion spreading process, the similarity between any users can be obtained. The algorithm has remarkably higher accuracy than the standard collaborative filtering using the Pearson correlation. Furthermore, we introduce a free parameter β to regulate the contributions of objects to user-user correlations. The numerical results indicate that decreasing the influence of popular objects can further improve the algorithmic accuracy and personality. We argue that a better algorithm should simultaneously require less computation and generate higher accuracy. Accordingly, we further propose an algorithm involving only the top-N similar neighbors for each target user, which has both less computational complexity and higher algorithmic accuracy.

An Efficient Distributed Compressed Sensing Algorithm for Decentralized Sensor Network.

PubMed

Liu, Jing; Huang, Kaiyu; Zhang, Guoxian

2017-04-20

We consider the joint sparsity Model 1 (JSM-1) in a decentralized scenario, where a number of sensors are connected through a network and there is no fusion center. A novel algorithm, named distributed compact sensing matrix pursuit (DCSMP), is proposed to exploit the computational and communication capabilities of the sensor nodes. In contrast to the conventional distributed compressed sensing algorithms adopting a random sensing matrix, the proposed algorithm focuses on the deterministic sensing matrices built directly on the real acquisition systems. The proposed DCSMP algorithm can be divided into two independent parts, the common and innovation support set estimation processes. The goal of the common support set estimation process is to obtain an estimated common support set by fusing the candidate support set information from an individual node and its neighboring nodes. In the following innovation support set estimation process, the measurement vector is projected into a subspace that is perpendicular to the subspace spanned by the columns indexed by the estimated common support set, to remove the impact of the estimated common support set. We can then search the innovation support set using an orthogonal matching pursuit (OMP) algorithm based on the projected measurement vector and projected sensing matrix. In the proposed DCSMP algorithm, the process of estimating the common component/support set is decoupled with that of estimating the innovation component/support set. Thus, the inaccurately estimated common support set will have no impact on estimating the innovation support set. It is proven that under the condition the estimated common support set contains the true common support set, the proposed algorithm can find the true innovation set correctly. Moreover, since the innovation support set estimation process is independent of the common support set estimation process, there is no requirement for the cardinality of both sets; thus, the proposed DCSMP algorithm is capable of tackling the unknown sparsity problem successfully.

An analysis of neural receptive field plasticity by point process adaptive filtering

PubMed Central

Brown, Emery N.; Nguyen, David P.; Frank, Loren M.; Wilson, Matthew A.; Solo, Victor

2001-01-01

Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the Appendix. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields. PMID:11593043

The crack detection algorithm of pavement image based on edge information

NASA Astrophysics Data System (ADS)

Yang, Chunde; Geng, Mingyue

2018-05-01

As the images of pavement cracks are affected by a large amount of complicated noises, such as uneven illumination and water stains, the detected cracks are discontinuous and the main body information at the edge of the cracks is easily lost. In order to solve the problem, a crack detection algorithm in pavement image based on edge information is proposed. Firstly, the image is pre-processed by the nonlinear gray-scale transform function and reconstruction filter to enhance the linear characteristic of the crack. At the same time, an adaptive thresholding method is designed to coarsely extract the cracks edge according to the gray-scale gradient feature and obtain the crack gradient information map. Secondly, the candidate edge points are obtained according to the gradient information, and the edge is detected based on the single pixel percolation processing, which is improved by using the local difference between pixels in the fixed region. Finally, complete crack is obtained by filling the crack edge. Experimental results show that the proposed method can accurately detect pavement cracks and preserve edge information.

An on-line modified least-mean-square algorithm for training neurofuzzy controllers.

PubMed

Tan, Woei Wan

2007-04-01

The problem hindering the use of data-driven modelling methods for training controllers on-line is the lack of control over the amount by which the plant is excited. As the operating schedule determines the information available on-line, the knowledge of the process may degrade if the setpoint remains constant for an extended period. This paper proposes an identification algorithm that alleviates "learning interference" by incorporating fuzzy theory into the normalized least-mean-square update rule. The ability of the proposed methodology to achieve faster learning is examined by employing the algorithm to train a neurofuzzy feedforward controller for controlling a liquid level process. Since the proposed identification strategy has similarities with the normalized least-mean-square update rule and the recursive least-square estimator, the on-line learning rates of these algorithms are also compared.

Floyd-warshall algorithm to determine the shortest path based on android

NASA Astrophysics Data System (ADS)

Ramadiani; Bukhori, D.; Azainil; Dengen, N.

2018-04-01

The development of technology has made all areas of life easier now, one of which is the ease of obtaining geographic information. The use of geographic information may vary according to need, for example, the digital map learning, navigation systems, observations area, and much more. With the support of adequate infrastructure, almost no one will ever get lost to a destination even to foreign places or that have never been visited before. The reasons why many institutions and business entities use technology to improve services to consumers and to streamline the production process undertaken and so forth. Speaking of the efficient, there are many elements related to efficiency in navigation systems, and one of them is the efficiency in terms of distance. The shortest distance determination algorithm required in this research is used Floyd-Warshall Algorithm. Floyd-Warshall algorithm is the algorithm to find the fastest path and the shortest distance between 2 nodes, while the program is intended to find the path of more than 2 nodes.

Design and implementation of intelligent electronic warfare decision making algorithm

NASA Astrophysics Data System (ADS)

Peng, Hsin-Hsien; Chen, Chang-Kuo; Hsueh, Chi-Shun

2017-05-01

Electromagnetic signals and the requirements of timely response have been a rapid growth in modern electronic warfare. Although jammers are limited resources, it is possible to achieve the best electronic warfare efficiency by tactical decisions. This paper proposes the intelligent electronic warfare decision support system. In this work, we develop a novel hybrid algorithm, Digital Pheromone Particle Swarm Optimization, based on Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Shuffled Frog Leaping Algorithm (SFLA). We use PSO to solve the problem and combine the concept of pheromones in ACO to accumulate more useful information in spatial solving process and speed up finding the optimal solution. The proposed algorithm finds the optimal solution in reasonable computation time by using the method of matrix conversion in SFLA. The results indicated that jammer allocation was more effective. The system based on the hybrid algorithm provides electronic warfare commanders with critical information to assist commanders in effectively managing the complex electromagnetic battlefield.

The infection algorithm: an artificial epidemic approach for dense stereo correspondence.

PubMed

Olague, Gustavo; Fernández, Francisco; Pérez, Cynthia B; Lutton, Evelyne

2006-01-01

We present a new bio-inspired approach applied to a problem of stereo image matching. This approach is based on an artificial epidemic process, which we call the infection algorithm. The problem at hand is a basic one in computer vision for 3D scene reconstruction. It has many complex aspects and is known as an extremely difficult one. The aim is to match the contents of two images in order to obtain 3D information that allows the generation of simulated projections from a viewpoint that is different from the ones of the initial photographs. This process is known as view synthesis. The algorithm we propose exploits the image contents in order to produce only the necessary 3D depth information, while saving computational time. It is based on a set of distributed rules, which propagate like an artificial epidemic over the images. Experiments on a pair of real images are presented, and realistic reprojected images have been generated.

Asymmetric neighborhood functions accelerate ordering process of self-organizing maps

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

Ota, Kaiichiro; Aoki, Takaaki; Kurata, Koji

2011-02-15

A self-organizing map (SOM) algorithm can generate a topographic map from a high-dimensional stimulus space to a low-dimensional array of units. Because a topographic map preserves neighborhood relationships between the stimuli, the SOM can be applied to certain types of information processing such as data visualization. During the learning process, however, topological defects frequently emerge in the map. The presence of defects tends to drastically slow down the formation of a globally ordered topographic map. To remove such topological defects, it has been reported that an asymmetric neighborhood function is effective, but only in the simple case of mapping one-dimensionalmore » stimuli to a chain of units. In this paper, we demonstrate that even when high-dimensional stimuli are used, the asymmetric neighborhood function is effective for both artificial and real-world data. Our results suggest that applying the asymmetric neighborhood function to the SOM algorithm improves the reliability of the algorithm. In addition, it enables processing of complicated, high-dimensional data by using this algorithm.« less

Data Streams: An Overview and Scientific Applications

NASA Astrophysics Data System (ADS)

Aggarwal, Charu C.

In recent years, advances in hardware technology have facilitated the ability to collect data continuously. Simple transactions of everyday life such as using a credit card, a phone, or browsing the web lead to automated data storage. Similarly, advances in information technology have lead to large flows of data across IP networks. In many cases, these large volumes of data can be mined for interesting and relevant information in a wide variety of applications. When the volume of the underlying data is very large, it leads to a number of computational and mining challenges: With increasing volume of the data, it is no longer possible to process the data efficiently by using multiple passes. Rather, one can process a data item at most once. This leads to constraints on the implementation of the underlying algorithms. Therefore, stream mining algorithms typically need to be designed so that the algorithms work with one pass of the data. In most cases, there is an inherent temporal component to the stream mining process. This is because the data may evolve over time. This behavior of data streams is referred to as temporal locality. Therefore, a straightforward adaptation of one-pass mining algorithms may not be an effective solution to the task. Stream mining algorithms need to be carefully designed with a clear focus on the evolution of the underlying data. Another important characteristic of data streams is that they are often mined in a distributed fashion. Furthermore, the individual processors may have limited processing and memory. Examples of such cases include sensor networks, in which it may be desirable to perform in-network processing of data stream with limited processing and memory [1, 2]. This chapter will provide an overview of the key challenges in stream mining algorithms which arise from the unique setup in which these problems are encountered. This chapter is organized as follows. In the next section, we will discuss the generic challenges that stream mining poses to a variety of data management and data mining problems. The next section also deals with several issues which arise in the context of data stream management. In Sect. 3, we discuss several mining algorithms on the data stream model. Section 4 discusses various scientific applications of data streams. Section 5 discusses the research directions and conclusions.

Neural network for processing both spatial and temporal data with time based back-propagation

NASA Technical Reports Server (NTRS)

Villarreal, James A. (Inventor); Shelton, Robert O. (Inventor)

1993-01-01

Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.

Research on personalized recommendation algorithm based on spark

NASA Astrophysics Data System (ADS)

Li, Zeng; Liu, Yu

2018-04-01

With the increasing amount of data in the past years, the traditional recommendation algorithm has been unable to meet people's needs. Therefore, how to better recommend their products to users of interest, become the opportunities and challenges of the era of big data development. At present, each platform enterprise has its own recommendation algorithm, but how to make efficient and accurate push information is still an urgent problem for personalized recommendation system. In this paper, a hybrid algorithm based on user collaborative filtering and content-based recommendation algorithm is proposed on Spark to improve the efficiency and accuracy of recommendation by weighted processing. The experiment shows that the recommendation under this scheme is more efficient and accurate.

Decrease of Fisher information and the information geometry of evolution equations for quantum mechanical probability amplitudes.

PubMed

Cafaro, Carlo; Alsing, Paul M

2018-04-01

The relevance of the concept of Fisher information is increasing in both statistical physics and quantum computing. From a statistical mechanical standpoint, the application of Fisher information in the kinetic theory of gases is characterized by its decrease along the solutions of the Boltzmann equation for Maxwellian molecules in the two-dimensional case. From a quantum mechanical standpoint, the output state in Grover's quantum search algorithm follows a geodesic path obtained from the Fubini-Study metric on the manifold of Hilbert-space rays. Additionally, Grover's algorithm is specified by constant Fisher information. In this paper, we present an information geometric characterization of the oscillatory or monotonic behavior of statistically parametrized squared probability amplitudes originating from special functional forms of the Fisher information function: constant, exponential decay, and power-law decay. Furthermore, for each case, we compute both the computational speed and the availability loss of the corresponding physical processes by exploiting a convenient Riemannian geometrization of useful thermodynamical concepts. Finally, we briefly comment on the possibility of using the proposed methods of information geometry to help identify a suitable trade-off between speed and thermodynamic efficiency in quantum search algorithms.

Decrease of Fisher information and the information geometry of evolution equations for quantum mechanical probability amplitudes

NASA Astrophysics Data System (ADS)

Cafaro, Carlo; Alsing, Paul M.

2018-04-01

The relevance of the concept of Fisher information is increasing in both statistical physics and quantum computing. From a statistical mechanical standpoint, the application of Fisher information in the kinetic theory of gases is characterized by its decrease along the solutions of the Boltzmann equation for Maxwellian molecules in the two-dimensional case. From a quantum mechanical standpoint, the output state in Grover's quantum search algorithm follows a geodesic path obtained from the Fubini-Study metric on the manifold of Hilbert-space rays. Additionally, Grover's algorithm is specified by constant Fisher information. In this paper, we present an information geometric characterization of the oscillatory or monotonic behavior of statistically parametrized squared probability amplitudes originating from special functional forms of the Fisher information function: constant, exponential decay, and power-law decay. Furthermore, for each case, we compute both the computational speed and the availability loss of the corresponding physical processes by exploiting a convenient Riemannian geometrization of useful thermodynamical concepts. Finally, we briefly comment on the possibility of using the proposed methods of information geometry to help identify a suitable trade-off between speed and thermodynamic efficiency in quantum search algorithms.

MRI brain tumor segmentation based on improved fuzzy c-means method

NASA Astrophysics Data System (ADS)

Deng, Wankai; Xiao, Wei; Pan, Chao; Liu, Jianguo

2009-10-01

This paper focuses on the image segmentation, which is one of the key problems in medical image processing. A new medical image segmentation method is proposed based on fuzzy c- means algorithm and spatial information. Firstly, we classify the image into the region of interest and background using fuzzy c means algorithm. Then we use the information of the tissues' gradient and the intensity inhomogeneities of regions to improve the quality of segmentation. The sum of the mean variance in the region and the reciprocal of the mean gradient along the edge of the region are chosen as an objective function. The minimum of the sum is optimum result. The result shows that the clustering segmentation algorithm is effective.

Enhanced Traceability for Bulk Processing of Sentinel-Derived Information Products

NASA Astrophysics Data System (ADS)

Lankester, Thomas; Hubbard, Steven; Knowelden, Richard

2016-08-01

The advent of widely available, systematically acquired and advanced Earth observations from the Sentinel platforms is spurring development of a wide range of derived information products. Whilst welcome, this rapid rate of development inevitably leads to some processing instability as algorithms and production steps are required to evolve accordingly. To mitigate this instability, the provenance of EO-derived information products needs to be traceable and transparent.Airbus Defence and Space (Airbus DS) has developed the Airbus Processing Cloud (APC) as a virtualised processing farm for bulk production of EO-derived data and information products. The production control system of the APC transforms internal configuration control information into an INSPIRE metadata file containing a stepwise set of processing steps and data source elements that provide the complete and transparent provenance of each product generated.

An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks

PubMed Central

Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

2016-01-01

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001

Real-time Imaging Orientation Determination System to Verify Imaging Polarization Navigation Algorithm

PubMed Central

Lu, Hao; Zhao, Kaichun; Wang, Xiaochu; You, Zheng; Huang, Kaoli

2016-01-01

Bio-inspired imaging polarization navigation which can provide navigation information and is capable of sensing polarization information has advantages of high-precision and anti-interference over polarization navigation sensors that use photodiodes. Although all types of imaging polarimeters exist, they may not qualify for the research on the imaging polarization navigation algorithm. To verify the algorithm, a real-time imaging orientation determination system was designed and implemented. Essential calibration procedures for the type of system that contained camera parameter calibration and the inconsistency of complementary metal oxide semiconductor calibration were discussed, designed, and implemented. Calibration results were used to undistort and rectify the multi-camera system. An orientation determination experiment was conducted. The results indicated that the system could acquire and compute the polarized skylight images throughout the calibrations and resolve orientation by the algorithm to verify in real-time. An orientation determination algorithm based on image processing was tested on the system. The performance and properties of the algorithm were evaluated. The rate of the algorithm was over 1 Hz, the error was over 0.313°, and the population standard deviation was 0.148° without any data filter. PMID:26805851

A Motion Detection Algorithm Using Local Phase Information

PubMed Central

Lazar, Aurel A.; Ukani, Nikul H.; Zhou, Yiyin

2016-01-01

Previous research demonstrated that global phase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithm by using only local phase information. We also demonstrate that local phase alone can be effectively used to detect local motion. The local phase-based motion detector is akin to models employed to detect motion in biological vision, for example, the Reichardt detector. The local phase-based motion detection algorithm introduced here consists of two building blocks. The first building block measures/evaluates the temporal change of the local phase. The temporal derivative of the local phase is shown to exhibit the structure of a second order Volterra kernel with two normalized inputs. We provide an efficient, FFT-based algorithm for implementing the change of the local phase. The second processing building block implements the detector; it compares the maximum of the Radon transform of the local phase derivative with a chosen threshold. We demonstrate examples of applying the local phase-based motion detection algorithm on several video sequences. We also show how the locally detected motion can be used for segmenting moving objects in video scenes and compare our local phase-based algorithm to segmentation achieved with a widely used optic flow algorithm. PMID:26880882

Efficient hiding of confidential high-utility itemsets with minimal side effects

NASA Astrophysics Data System (ADS)

Lin, Jerry Chun-Wei; Hong, Tzung-Pei; Fournier-Viger, Philippe; Liu, Qiankun; Wong, Jia-Wei; Zhan, Justin

2017-11-01

Privacy preserving data mining (PPDM) is an emerging research problem that has become critical in the last decades. PPDM consists of hiding sensitive information to ensure that it cannot be discovered by data mining algorithms. Several PPDM algorithms have been developed. Most of them are designed for hiding sensitive frequent itemsets or association rules. Hiding sensitive information in a database can have several side effects such as hiding other non-sensitive information and introducing redundant information. Finding the set of itemsets or transactions to be sanitised that minimises side effects is an NP-hard problem. In this paper, a genetic algorithm (GA) using transaction deletion is designed to hide sensitive high-utility itemsets for PPUM. A flexible fitness function with three adjustable weights is used to evaluate the goodness of each chromosome for hiding sensitive high-utility itemsets. To speed up the evolution process, the pre-large concept is adopted in the designed algorithm. It reduces the number of database scans required for verifying the goodness of an evaluated chromosome. Substantial experiments are conducted to compare the performance of the designed GA approach (with/without the pre-large concept), with a GA-based approach relying on transaction insertion and a non-evolutionary algorithm, in terms of execution time, side effects, database integrity and utility integrity. Results demonstrate that the proposed algorithm hides sensitive high-utility itemsets with fewer side effects than previous studies, while preserving high database and utility integrity.

Digital health technology and trauma: development of an app to standardize care.

PubMed

Hsu, Jeremy M

2015-04-01

Standardized practice results in less variation, therefore reducing errors and improving outcome. Optimal trauma care is achieved through standardization, as is evidenced by the widespread adoption of the Advanced Trauma Life Support approach. The challenge for an individual institution is how does one educate and promulgate these standardized processes widely and efficiently? In today's world, digital health technology must be considered in the process. The aim of this study was to describe the process of developing an app, which includes standardized trauma algorithms. The objective of the app was to allow easy, real-time access to trauma algorithms, and therefore reduce omissions/errors. A set of trauma algorithms, relevant to the local setting, was derived from the best available evidence. After obtaining grant funding, a collaborative endeavour was undertaken with an external specialist app developing company. The process required 6 months to translate the existing trauma algorithms into an app. The app contains 32 separate trauma algorithms, formatted as a single-page flow diagram. It utilizes specific smartphone features such as 'pinch to zoom', jump-words and pop-ups to allow rapid access to the desired information. Improvements in trauma care outcomes result from reducing variation. By incorporating digital health technology, a trauma app has been developed, allowing easy and intuitive access to evidenced-based algorithms. © 2015 Royal Australasian College of Surgeons.

Development of Cloud and Precipitation Property Retrieval Algorithms and Measurement Simulators from ASR Data

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

Mace, Gerald G.

What has made the ASR program unique is the amount of information that is available. The suite of recently deployed instruments significantly expands the scope of the program (Mather and Voyles, 2013). The breadth of this information allows us to pose sophisticated process-level questions. Our ASR project, now entering its third year, has been about developing algorithms that use this information in ways that fully exploit the new capacity of the ARM data streams. Using optimal estimation (OE) and Markov Chain Monte Carlo (MCMC) inversion techniques, we have developed methodologies that allow us to use multiple radar frequency Doppler spectramore » along with lidar and passive constraints where data streams can be added or subtracted efficiently and algorithms can be reformulated for various combinations of hydrometeors by exchanging sets of empirical coefficients. These methodologies have been applied to boundary layer clouds, mixed phase snow cloud systems, and cirrus.« less

Adaptive structured dictionary learning for image fusion based on group-sparse-representation

NASA Astrophysics Data System (ADS)

Yang, Jiajie; Sun, Bin; Luo, Chengwei; Wu, Yuzhong; Xu, Limei

2018-04-01

Dictionary learning is the key process of sparse representation which is one of the most widely used image representation theories in image fusion. The existing dictionary learning method does not use the group structure information and the sparse coefficients well. In this paper, we propose a new adaptive structured dictionary learning algorithm and a l1-norm maximum fusion rule that innovatively utilizes grouped sparse coefficients to merge the images. In the dictionary learning algorithm, we do not need prior knowledge about any group structure of the dictionary. By using the characteristics of the dictionary in expressing the signal, our algorithm can automatically find the desired potential structure information that hidden in the dictionary. The fusion rule takes the physical meaning of the group structure dictionary, and makes activity-level judgement on the structure information when the images are being merged. Therefore, the fused image can retain more significant information. Comparisons have been made with several state-of-the-art dictionary learning methods and fusion rules. The experimental results demonstrate that, the dictionary learning algorithm and the fusion rule both outperform others in terms of several objective evaluation metrics.

Synthesis of an optoelectronic system for tracking (OEST) the information track of the optical record carrier based on the acceleration control principle

NASA Astrophysics Data System (ADS)

Zalogin, Stanislav M.; Zalogin, M. S.

1997-02-01

The problem for construction of control algorithm in OEST the information track of the optical record carrier the realization of which is based on the use of accelerations is considered. Such control algorithms render the designed system the properties of adaptability, feeble sensitivity to the system parameter change and the action of disturbing forces what gives known advantages to information carriers with such system under operation in hard climate conditions as well as at maladjustment, workpieces wear and change of friction in the system. In the paper are investigated dynamic characteristics of a closed OEST, it is shown, that the designed stable system with given quality indices is a high-precision one. The validated recommendations as to design of control algorithms parameters are confirmed by results of mathematical simulation of controlled processes. The proposed methods for OEST synthesis on the basis of the control acceleration principle can be recommended for the use at industrial production of optical information record carriers.

Spatial information technologies for remote sensing today and tomorrow; Proceedings of the Ninth Pecora Symposium, Sioux Falls, SD, October 2-4, 1984

NASA Technical Reports Server (NTRS)

1984-01-01

Topics discussed at the symposium include hardware, geographic information system (GIS) implementation, processing remotely sensed data, spatial data structures, and NASA programs in remote sensing information systems. Attention is also given GIS applications, advanced techniques, artificial intelligence, graphics, spatial navigation, and classification. Papers are included on the design of computer software for geographic image processing, concepts for a global resource information system, algorithm development for spatial operators, and an application of expert systems technology to remotely sensed image analysis.

Automated sleep stage detection with a classical and a neural learning algorithm--methodological aspects.

PubMed

Schwaibold, M; Schöchlin, J; Bolz, A

2002-01-01

For classification tasks in biosignal processing, several strategies and algorithms can be used. Knowledge-based systems allow prior knowledge about the decision process to be integrated, both by the developer and by self-learning capabilities. For the classification stages in a sleep stage detection framework, three inference strategies were compared regarding their specific strengths: a classical signal processing approach, artificial neural networks and neuro-fuzzy systems. Methodological aspects were assessed to attain optimum performance and maximum transparency for the user. Due to their effective and robust learning behavior, artificial neural networks could be recommended for pattern recognition, while neuro-fuzzy systems performed best for the processing of contextual information.

A Doppler centroid estimation algorithm for SAR systems optimized for the quasi-homogeneous source

NASA Technical Reports Server (NTRS)

Jin, Michael Y.

1989-01-01

Radar signal processing applications frequently require an estimate of the Doppler centroid of a received signal. The Doppler centroid estimate is required for synthetic aperture radar (SAR) processing. It is also required for some applications involving target motion estimation and antenna pointing direction estimation. In some cases, the Doppler centroid can be accurately estimated based on available information regarding the terrain topography, the relative motion between the sensor and the terrain, and the antenna pointing direction. Often, the accuracy of the Doppler centroid estimate can be improved by analyzing the characteristics of the received SAR signal. This kind of signal processing is also referred to as clutterlock processing. A Doppler centroid estimation (DCE) algorithm is described which contains a linear estimator optimized for the type of terrain surface that can be modeled by a quasi-homogeneous source (QHS). Information on the following topics is presented: (1) an introduction to the theory of Doppler centroid estimation; (2) analysis of the performance characteristics of previously reported DCE algorithms; (3) comparison of these analysis results with experimental results; (4) a description and performance analysis of a Doppler centroid estimator which is optimized for a QHS; and (5) comparison of the performance of the optimal QHS Doppler centroid estimator with that of previously reported methods.

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Navigable points estimation for mobile robots using binary image skeletonization

NASA Astrophysics Data System (ADS)

Martinez S., Fernando; Jacinto G., Edwar; Montiel A., Holman

2017-02-01

This paper describes the use of image skeletonization for the estimation of all the navigable points, inside a scene of mobile robots navigation. Those points are used for computing a valid navigation path, using standard methods. The main idea is to find the middle and the extreme points of the obstacles in the scene, taking into account the robot size, and create a map of navigable points, in order to reduce the amount of information for the planning algorithm. Those points are located by means of the skeletonization of a binary image of the obstacles and the scene background, along with some other digital image processing algorithms. The proposed algorithm automatically gives a variable number of navigable points per obstacle, depending on the complexity of its shape. As well as, the way how the algorithm can change some of their parameters in order to change the final number of the resultant key points is shown. The results shown here were obtained applying different kinds of digital image processing algorithms on static scenes.

Problems of collaborative work of the automated process control system (APCS) and the its information security and solutions.

NASA Astrophysics Data System (ADS)

Arakelyan, E. K.; Andryushin, A. V.; Mezin, S. V.; Kosoy, A. A.; Kalinina, Ya V.; Khokhlov, I. S.

2017-11-01

The principle of interaction of the specified systems of technological protections by the Automated process control system (APCS) and information safety in case of incorrect execution of the algorithm of technological protection is offered. - checking the correctness of the operation of technological protection in each specific situation using the functional relationship between the monitored parameters. The methodology for assessing the economic feasibility of developing and implementing an information security system.

Research on robot mobile obstacle avoidance control based on visual information

NASA Astrophysics Data System (ADS)

Jin, Jiang

2018-03-01

Robots to detect obstacles and control robots to avoid obstacles has been a key research topic of robot control. In this paper, a scheme of visual information acquisition is proposed. By judging visual information, the visual information is transformed into the information source of path processing. In accordance with the established route, in the process of encountering obstacles, the algorithm real-time adjustment trajectory to meet the purpose of intelligent control of mobile robots. Simulation results show that, through the integration of visual sensing information, the obstacle information is fully obtained, while the real-time and accuracy of the robot movement control is guaranteed.

Identifying protein complexes based on brainstorming strategy.

PubMed

Shen, Xianjun; Zhou, Jin; Yi, Li; Hu, Xiaohua; He, Tingting; Yang, Jincai

2016-11-01

Protein complexes comprising of interacting proteins in protein-protein interaction network (PPI network) play a central role in driving biological processes within cells. Recently, more and more swarm intelligence based algorithms to detect protein complexes have been emerging, which have become the research hotspot in proteomics field. In this paper, we propose a novel algorithm for identifying protein complexes based on brainstorming strategy (IPC-BSS), which is integrated into the main idea of swarm intelligence optimization and the improved K-means algorithm. Distance between the nodes in PPI network is defined by combining the network topology and gene ontology (GO) information. Inspired by human brainstorming process, IPC-BSS algorithm firstly selects the clustering center nodes, and then they are separately consolidated with the other nodes with short distance to form initial clusters. Finally, we put forward two ways of updating the initial clusters to search optimal results. Experimental results show that our IPC-BSS algorithm outperforms the other classic algorithms on yeast and human PPI networks, and it obtains many predicted protein complexes with biological significance. Copyright © 2016 Elsevier Inc. All rights reserved.

ICAP: An Interactive Cluster Analysis Procedure for analyzing remotely sensed data. [to classify the radiance data to produce a thematic map

NASA Technical Reports Server (NTRS)

Wharton, S. W.

1980-01-01

An Interactive Cluster Analysis Procedure (ICAP) was developed to derive classifier training statistics from remotely sensed data. The algorithm interfaces the rapid numerical processing capacity of a computer with the human ability to integrate qualitative information. Control of the clustering process alternates between the algorithm, which creates new centroids and forms clusters and the analyst, who evaluate and elect to modify the cluster structure. Clusters can be deleted or lumped pairwise, or new centroids can be added. A summary of the cluster statistics can be requested to facilitate cluster manipulation. The ICAP was implemented in APL (A Programming Language), an interactive computer language. The flexibility of the algorithm was evaluated using data from different LANDSAT scenes to simulate two situations: one in which the analyst is assumed to have no prior knowledge about the data and wishes to have the clusters formed more or less automatically; and the other in which the analyst is assumed to have some knowledge about the data structure and wishes to use that information to closely supervise the clustering process. For comparison, an existing clustering method was also applied to the two data sets.

Applying natural language processing techniques to develop a task-specific EMR interface for timely stroke thrombolysis: A feasibility study.

PubMed

Sung, Sheng-Feng; Chen, Kuanchin; Wu, Darren Philbert; Hung, Ling-Chien; Su, Yu-Hsiang; Hu, Ya-Han

2018-04-01

To reduce errors in determining eligibility for intravenous thrombolytic therapy (IVT) in stroke patients through use of an enhanced task-specific electronic medical record (EMR) interface powered by natural language processing (NLP) techniques. The information processing algorithm utilized MetaMap to extract medical concepts from IVT eligibility criteria and expanded the concepts using the Unified Medical Language System Metathesaurus. Concepts identified from clinical notes by MetaMap were compared to those from IVT eligibility criteria. The task-specific EMR interface displays IVT-relevant information by highlighting phrases that contain matched concepts. Clinical usability was assessed with clinicians staffing the acute stroke team by comparing user performance while using the task-specific and the current EMR interfaces. The algorithm identified IVT-relevant concepts with micro-averaged precisions, recalls, and F1 measures of 0.998, 0.812, and 0.895 at the phrase level and of 1, 0.972, and 0.986 at the document level. Users using the task-specific interface achieved a higher accuracy score than those using the current interface (91% versus 80%, p = 0.016) in assessing the IVT eligibility criteria. The completion time between the interfaces was statistically similar (2.46 min versus 1.70 min, p = 0.754). Although the information processing algorithm had room for improvement, the task-specific EMR interface significantly reduced errors in assessing IVT eligibility criteria. The study findings provide evidence to support an NLP enhanced EMR system to facilitate IVT decision-making by presenting meaningful and timely information to clinicians, thereby offering a new avenue for improvements in acute stroke care. Copyright © 2018 Elsevier B.V. All rights reserved.

Video image processing

NASA Technical Reports Server (NTRS)

Murray, N. D.

1985-01-01

Current technology projections indicate a lack of availability of special purpose computing for Space Station applications. Potential functions for video image special purpose processing are being investigated, such as smoothing, enhancement, restoration and filtering, data compression, feature extraction, object detection and identification, pixel interpolation/extrapolation, spectral estimation and factorization, and vision synthesis. Also, architectural approaches are being identified and a conceptual design generated. Computationally simple algorithms will be research and their image/vision effectiveness determined. Suitable algorithms will be implimented into an overall architectural approach that will provide image/vision processing at video rates that are flexible, selectable, and programmable. Information is given in the form of charts, diagrams and outlines.

Combined process automation for large-scale EEG analysis.

PubMed

Sfondouris, John L; Quebedeaux, Tabitha M; Holdgraf, Chris; Musto, Alberto E

2012-01-01

Epileptogenesis is a dynamic process producing increased seizure susceptibility. Electroencephalography (EEG) data provides information critical in understanding the evolution of epileptiform changes throughout epileptic foci. We designed an algorithm to facilitate efficient large-scale EEG analysis via linked automation of multiple data processing steps. Using EEG recordings obtained from electrical stimulation studies, the following steps of EEG analysis were automated: (1) alignment and isolation of pre- and post-stimulation intervals, (2) generation of user-defined band frequency waveforms, (3) spike-sorting, (4) quantification of spike and burst data and (5) power spectral density analysis. This algorithm allows for quicker, more efficient EEG analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

Error-proofing test system of industrial components based on image processing

NASA Astrophysics Data System (ADS)

Huang, Ying; Huang, Tao

2018-05-01

Due to the improvement of modern industrial level and accuracy, conventional manual test fails to satisfy the test standards of enterprises, so digital image processing technique should be utilized to gather and analyze the information on the surface of industrial components, so as to achieve the purpose of test. To test the installation parts of automotive engine, this paper employs camera to capture the images of the components. After these images are preprocessed including denoising, the image processing algorithm relying on flood fill algorithm is used to test the installation of the components. The results prove that this system has very high test accuracy.

A Foot-Mounted Inertial Measurement Unit (IMU) Positioning Algorithm Based on Magnetic Constraint

PubMed Central

Zou, Jiaheng

2018-01-01

With the development of related applications, indoor positioning techniques have been more and more widely developed. Based on Wi-Fi, Bluetooth low energy (BLE) and geomagnetism, indoor positioning techniques often rely on the physical location of fingerprint information. The focus and difficulty of establishing the fingerprint database are in obtaining a relatively accurate physical location with as little given information as possible. This paper presents a foot-mounted inertial measurement unit (IMU) positioning algorithm under the loop closure constraint based on magnetic information. It can provide relatively reliable position information without maps and geomagnetic information and provides a relatively accurate coordinate for the collection of a fingerprint database. In the experiment, the features extracted by the multi-level Fourier transform method proposed in this paper are validated and the validity of loop closure matching is tested with a RANSAC-based method. Moreover, the loop closure detection results show that the cumulative error of the trajectory processed by the graph optimization algorithm is significantly suppressed, presenting a good accuracy. The average error of the trajectory under loop closure constraint is controlled below 2.15 m. PMID:29494542

A Foot-Mounted Inertial Measurement Unit (IMU) Positioning Algorithm Based on Magnetic Constraint.

PubMed

Wang, Yan; Li, Xin; Zou, Jiaheng

2018-03-01

With the development of related applications, indoor positioning techniques have been more and more widely developed. Based on Wi-Fi, Bluetooth low energy (BLE) and geomagnetism, indoor positioning techniques often rely on the physical location of fingerprint information. The focus and difficulty of establishing the fingerprint database are in obtaining a relatively accurate physical location with as little given information as possible. This paper presents a foot-mounted inertial measurement unit (IMU) positioning algorithm under the loop closure constraint based on magnetic information. It can provide relatively reliable position information without maps and geomagnetic information and provides a relatively accurate coordinate for the collection of a fingerprint database. In the experiment, the features extracted by the multi-level Fourier transform method proposed in this paper are validated and the validity of loop closure matching is tested with a RANSAC-based method. Moreover, the loop closure detection results show that the cumulative error of the trajectory processed by the graph optimization algorithm is significantly suppressed, presenting a good accuracy. The average error of the trajectory under loop closure constraint is controlled below 2.15 m.

Could a neuroscientist understand a microprocessor?

DOE PAGES

Jonas, Eric; Kording, Konrad Paul; Diedrichsen, Jorn

2017-01-12

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods frommore » neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Furthermore, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.« less

Could a Neuroscientist Understand a Microprocessor?

PubMed Central

Kording, Konrad Paul

2017-01-01

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods. PMID:28081141

Could a Neuroscientist Understand a Microprocessor?

PubMed

Jonas, Eric; Kording, Konrad Paul

2017-01-01

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.

Could a neuroscientist understand a microprocessor?

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

Jonas, Eric; Kording, Konrad Paul; Diedrichsen, Jorn

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods frommore » neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Furthermore, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.« less

Two-dimensional thermal video analysis of offshore bird and bat flight

DOE PAGES

Matzner, Shari; Cullinan, Valerie I.; Duberstein, Corey A.

2015-09-11

Thermal infrared video can provide essential information about bird and bat presence and activity for risk assessment studies, but the analysis of recorded video can be time-consuming and may not extract all of the available information. Automated processing makes continuous monitoring over extended periods of time feasible, and maximizes the information provided by video. This is especially important for collecting data in remote locations that are difficult for human observers to access, such as proposed offshore wind turbine sites. We present guidelines for selecting an appropriate thermal camera based on environmental conditions and the physical characteristics of the target animals.more » We developed new video image processing algorithms that automate the extraction of bird and bat flight tracks from thermal video, and that characterize the extracted tracks to support animal identification and behavior inference. The algorithms use a video peak store process followed by background masking and perceptual grouping to extract flight tracks. The extracted tracks are automatically quantified in terms that could then be used to infer animal type and possibly behavior. The developed automated processing generates results that are reproducible and verifiable, and reduces the total amount of video data that must be retained and reviewed by human experts. Finally, we suggest models for interpreting thermal imaging information.« less

Two-dimensional thermal video analysis of offshore bird and bat flight

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

Matzner, Shari; Cullinan, Valerie I.; Duberstein, Corey A.

Thermal infrared video can provide essential information about bird and bat presence and activity for risk assessment studies, but the analysis of recorded video can be time-consuming and may not extract all of the available information. Automated processing makes continuous monitoring over extended periods of time feasible, and maximizes the information provided by video. This is especially important for collecting data in remote locations that are difficult for human observers to access, such as proposed offshore wind turbine sites. We present guidelines for selecting an appropriate thermal camera based on environmental conditions and the physical characteristics of the target animals.more » We developed new video image processing algorithms that automate the extraction of bird and bat flight tracks from thermal video, and that characterize the extracted tracks to support animal identification and behavior inference. The algorithms use a video peak store process followed by background masking and perceptual grouping to extract flight tracks. The extracted tracks are automatically quantified in terms that could then be used to infer animal type and possibly behavior. The developed automated processing generates results that are reproducible and verifiable, and reduces the total amount of video data that must be retained and reviewed by human experts. Finally, we suggest models for interpreting thermal imaging information.« less

Computer algorithms in the search for unrelated stem cell donors.

PubMed

Steiner, David

2012-01-01

Hematopoietic stem cell transplantation (HSCT) is a medical procedure in the field of hematology and oncology, most often performed for patients with certain cancers of the blood or bone marrow. A lot of patients have no suitable HLA-matched donor within their family, so physicians must activate a "donor search process" by interacting with national and international donor registries who will search their databases for adult unrelated donors or cord blood units (CBU). Information and communication technologies play a key role in the donor search process in donor registries both nationally and internationaly. One of the major challenges for donor registry computer systems is the development of a reliable search algorithm. This work discusses the top-down design of such algorithms and current practice. Based on our experience with systems used by several stem cell donor registries, we highlight typical pitfalls in the implementation of an algorithm and underlying data structure.

An Information Theoretic Framework and Self-organizing Agent- based Sensor Network Architecture for Power Plant Condition Monitoring

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

Loparo, Kenneth; Kolacinski, Richard; Threeanaew, Wanchat

A central goal of the work was to enable both the extraction of all relevant information from sensor data, and the application of information gained from appropriate processing and fusion at the system level to operational control and decision-making at various levels of the control hierarchy through: 1. Exploiting the deep connection between information theory and the thermodynamic formalism, 2. Deployment using distributed intelligent agents with testing and validation in a hardware-in-the loop simulation environment. Enterprise architectures are the organizing logic for key business processes and IT infrastructure and, while the generality of current definitions provides sufficient flexibility, the currentmore » architecture frameworks do not inherently provide the appropriate structure. Of particular concern is that existing architecture frameworks often do not make a distinction between ``data'' and ``information.'' This work defines an enterprise architecture for health and condition monitoring of power plant equipment and further provides the appropriate foundation for addressing shortcomings in current architecture definition frameworks through the discovery of the information connectivity between the elements of a power generation plant. That is, to identify the correlative structure between available observations streams using informational measures. The principle focus here is on the implementation and testing of an emergent, agent-based, algorithm based on the foraging behavior of ants for eliciting this structure and on measures for characterizing differences between communication topologies. The elicitation algorithms are applied to data streams produced by a detailed numerical simulation of Alstom’s 1000 MW ultra-super-critical boiler and steam plant. The elicitation algorithm and topology characterization can be based on different informational metrics for detecting connectivity, e.g. mutual information and linear correlation.« less

Illustration Watermarking for Digital Images: An Investigation of Hierarchical Signal Inheritances for Nested Object-based Embedding

DTIC Science & Technology

2007-02-23

approach for signal-level watermark inheritance. 15. SUBJECT TERMS EOARD, Steganography , Image Fusion, Data Mining, Image ...in watermarking algorithms , a program interface and protocol has been de - veloped, which allows control of the embedding and retrieval processes by the...watermarks in an image . Watermarking algorithm (DLL) Watermarking editor (Delphi) - User marks all objects: ci - class information oi - object instance

Enhancements and Algorithms for Avionic Information Processing System Design Methodology.

DTIC Science & Technology

1982-06-16

programming algorithm is enhanced by incorporating task precedence constraints and hardware failures. Stochastic network methods are used to analyze...allocations in the presence of random fluctuations. Graph theoretic methods are used to analyze hardware designs, and new designs are constructed with...There, spatial dynamic programming (SDP) was used to solve a static, deterministic software allocation problem. Under the current contract the SDP

Kalman/Map filtering-aided fast normalized cross correlation-based Wi-Fi fingerprinting location sensing.

PubMed

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-11-13

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.

Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing

PubMed Central

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-01-01

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results. PMID:24233027

A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices.

PubMed

Filho, Mercedes; Ma, Zhen; Tavares, João Manuel R S

2015-11-01

In recent years, the incidence of skin cancer cases has risen, worldwide, mainly due to the prolonged exposure to harmful ultraviolet radiation. Concurrently, the computer-assisted medical diagnosis of skin cancer has undergone major advances, through an improvement in the instrument and detection technology, and the development of algorithms to process the information. Moreover, because there has been an increased need to store medical data, for monitoring, comparative and assisted-learning purposes, algorithms for data processing and storage have also become more efficient in handling the increase of data. In addition, the potential use of common mobile devices to register high-resolution images of skin lesions has also fueled the need to create real-time processing algorithms that may provide a likelihood for the development of malignancy. This last possibility allows even non-specialists to monitor and follow-up suspected skin cancer cases. In this review, we present the major steps in the pre-processing, processing and post-processing of skin lesion images, with a particular emphasis on the quantification and classification of pigmented skin lesions. We further review and outline the future challenges for the creation of minimum-feature, automated and real-time algorithms for the detection of skin cancer from images acquired via common mobile devices.

A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem

PubMed Central

Liu, Dong-sheng; Fan, Shu-jiang

2014-01-01

In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389

RayPlus: a Web-Based Platform for Medical Image Processing.

PubMed

Yuan, Rong; Luo, Ming; Sun, Zhi; Shi, Shuyue; Xiao, Peng; Xie, Qingguo

2017-04-01

Medical image can provide valuable information for preclinical research, clinical diagnosis, and treatment. As the widespread use of digital medical imaging, many researchers are currently developing medical image processing algorithms and systems in order to accommodate a better result to clinical community, including accurate clinical parameters or processed images from the original images. In this paper, we propose a web-based platform to present and process medical images. By using Internet and novel database technologies, authorized users can easily access to medical images and facilitate their workflows of processing with server-side powerful computing performance without any installation. We implement a series of algorithms of image processing and visualization in the initial version of Rayplus. Integration of our system allows much flexibility and convenience for both research and clinical communities.

Neural network-based run-to-run controller using exposure and resist thickness adjustment

NASA Astrophysics Data System (ADS)

Geary, Shane; Barry, Ronan

2003-06-01

This paper describes the development of a run-to-run control algorithm using a feedforward neural network, trained using the backpropagation training method. The algorithm is used to predict the critical dimension of the next lot using previous lot information. It is compared to a common prediction algorithm - the exponentially weighted moving average (EWMA) and is shown to give superior prediction performance in simulations. The manufacturing implementation of the final neural network showed significantly improved process capability when compared to the case where no run-to-run control was utilised.

Automated Recognition of 3D Features in GPIR Images

NASA Technical Reports Server (NTRS)

Park, Han; Stough, Timothy; Fijany, Amir

2007-01-01

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Space Situational Awareness Data Processing Scalability Utilizing Google Cloud Services

NASA Astrophysics Data System (ADS)

Greenly, D.; Duncan, M.; Wysack, J.; Flores, F.

Space Situational Awareness (SSA) is a fundamental and critical component of current space operations. The term SSA encompasses the awareness, understanding and predictability of all objects in space. As the population of orbital space objects and debris increases, the number of collision avoidance maneuvers grows and prompts the need for accurate and timely process measures. The SSA mission continually evolves to near real-time assessment and analysis demanding the need for higher processing capabilities. By conventional methods, meeting these demands requires the integration of new hardware to keep pace with the growing complexity of maneuver planning algorithms. SpaceNav has implemented a highly scalable architecture that will track satellites and debris by utilizing powerful virtual machines on the Google Cloud Platform. SpaceNav algorithms for processing CDMs outpace conventional means. A robust processing environment for tracking data, collision avoidance maneuvers and various other aspects of SSA can be created and deleted on demand. Migrating SpaceNav tools and algorithms into the Google Cloud Platform will be discussed and the trials and tribulations involved. Information will be shared on how and why certain cloud products were used as well as integration techniques that were implemented. Key items to be presented are: 1.Scientific algorithms and SpaceNav tools integrated into a scalable architecture a) Maneuver Planning b) Parallel Processing c) Monte Carlo Simulations d) Optimization Algorithms e) SW Application Development/Integration into the Google Cloud Platform 2. Compute Engine Processing a) Application Engine Automated Processing b) Performance testing and Performance Scalability c) Cloud MySQL databases and Database Scalability d) Cloud Data Storage e) Redundancy and Availability

Automated Classification of Heritage Buildings for As-Built Bim Using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Bassier, M.; Vergauwen, M.; Van Genechten, B.

2017-08-01

Semantically rich three dimensional models such as Building Information Models (BIMs) are increasingly used in digital heritage. They provide the required information to varying stakeholders during the different stages of the historic buildings life cyle which is crucial in the conservation process. The creation of as-built BIM models is based on point cloud data. However, manually interpreting this data is labour intensive and often leads to misinterpretations. By automatically classifying the point cloud, the information can be proccesed more effeciently. A key aspect in this automated scan-to-BIM process is the classification of building objects. In this research we look to automatically recognise elements in existing buildings to create compact semantic information models. Our algorithm efficiently extracts the main structural components such as floors, ceilings, roofs, walls and beams despite the presence of significant clutter and occlusions. More specifically, Support Vector Machines (SVM) are proposed for the classification. The algorithm is evaluated using real data of a variety of existing buildings. The results prove that the used classifier recognizes the objects with both high precision and recall. As a result, entire data sets are reliably labelled at once. The approach enables experts to better document and process heritage assets.

Decentralized modal identification using sparse blind source separation

NASA Astrophysics Data System (ADS)

Sadhu, A.; Hazra, B.; Narasimhan, S.; Pandey, M. D.

2011-12-01

Popular ambient vibration-based system identification methods process information collected from a dense array of sensors centrally to yield the modal properties. In such methods, the need for a centralized processing unit capable of satisfying large memory and processing demands is unavoidable. With the advent of wireless smart sensor networks, it is now possible to process information locally at the sensor level, instead. The information at the individual sensor level can then be concatenated to obtain the global structure characteristics. A novel decentralized algorithm based on wavelet transforms to infer global structure mode information using measurements obtained using a small group of sensors at a time is proposed in this paper. The focus of the paper is on algorithmic development, while the actual hardware and software implementation is not pursued here. The problem of identification is cast within the framework of under-determined blind source separation invoking transformations of measurements to the time-frequency domain resulting in a sparse representation. The partial mode shape coefficients so identified are then combined to yield complete modal information. The transformations are undertaken using stationary wavelet packet transform (SWPT), yielding a sparse representation in the wavelet domain. Principal component analysis (PCA) is then performed on the resulting wavelet coefficients, yielding the partial mixing matrix coefficients from a few measurement channels at a time. This process is repeated using measurements obtained from multiple sensor groups, and the results so obtained from each group are concatenated to obtain the global modal characteristics of the structure.

Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients.

PubMed

Jin, Shuo; Li, Dengwang; Wang, Hongjun; Yin, Yong

2013-01-07

Accurate registration of 18F-FDG PET (positron emission tomography) and CT (computed tomography) images has important clinical significance in radiation oncology. PET and CT images are acquired from (18)F-FDG PET/CT scanner, but the two acquisition processes are separate and take a long time. As a result, there are position errors in global and deformable errors in local caused by respiratory movement or organ peristalsis. The purpose of this work was to implement and validate a deformable CT to PET image registration method in esophageal cancer to eventually facilitate accurate positioning the tumor target on CT, and improve the accuracy of radiation therapy. Global registration was firstly utilized to preprocess position errors between PET and CT images, achieving the purpose of aligning these two images on the whole. Demons algorithm, based on optical flow field, has the features of fast process speed and high accuracy, and the gradient of mutual information-based demons (GMI demons) algorithm adds an additional external force based on the gradient of mutual information (GMI) between two images, which is suitable for multimodality images registration. In this paper, GMI demons algorithm was used to achieve local deformable registration of PET and CT images, which can effectively reduce errors between internal organs. In addition, to speed up the registration process, maintain its robustness, and avoid the local extremum, multiresolution image pyramid structure was used before deformable registration. By quantitatively and qualitatively analyzing cases with esophageal cancer, the registration scheme proposed in this paper can improve registration accuracy and speed, which is helpful for precisely positioning tumor target and developing the radiation treatment planning in clinical radiation therapy application.

Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Choi, Sunghoon; Kim, Hee-Joung

2018-03-01

When processing medical images, image denoising is an important pre-processing step. Various image denoising algorithms have been developed in the past few decades. Recently, image denoising using the deep learning method has shown excellent performance compared to conventional image denoising algorithms. In this study, we introduce an image denoising technique based on a convolutional denoising autoencoder (CDAE) and evaluate clinical applications by comparing existing image denoising algorithms. We train the proposed CDAE model using 3000 chest radiograms training data. To evaluate the performance of the developed CDAE model, we compare it with conventional denoising algorithms including median filter, total variation (TV) minimization, and non-local mean (NLM) algorithms. Furthermore, to verify the clinical effectiveness of the developed denoising model with CDAE, we investigate the performance of the developed denoising algorithm on chest radiograms acquired from real patients. The results demonstrate that the proposed denoising algorithm developed using CDAE achieves a superior noise-reduction effect in chest radiograms compared to TV minimization and NLM algorithms, which are state-of-the-art algorithms for image noise reduction. For example, the peak signal-to-noise ratio and structure similarity index measure of CDAE were at least 10% higher compared to conventional denoising algorithms. In conclusion, the image denoising algorithm developed using CDAE effectively eliminated noise without loss of information on anatomical structures in chest radiograms. It is expected that the proposed denoising algorithm developed using CDAE will be effective for medical images with microscopic anatomical structures, such as terminal bronchioles.

Algorithm of reducing the false positives in IDS based on correlation Analysis

NASA Astrophysics Data System (ADS)

Liu, Jianyi; Li, Sida; Zhang, Ru

2018-03-01

This paper proposes an algorithm of reducing the false positives in IDS based on correlation Analysis. Firstly, the algorithm analyzes the distinguishing characteristics of false positives and real alarms, and preliminary screen the false positives; then use the method of attribute similarity clustering to the alarms and further reduces the amount of alarms; finally, according to the characteristics of multi-step attack, associated it by the causal relationship. The paper also proposed a reverse causation algorithm based on the attack association method proposed by the predecessors, turning alarm information into a complete attack path. Experiments show that the algorithm simplifies the number of alarms, improve the efficiency of alarm processing, and contribute to attack purposes identification and alarm accuracy improvement.

Approximate spatial reasoning

NASA Technical Reports Server (NTRS)

Dutta, Soumitra

1988-01-01

A model for approximate spatial reasoning using fuzzy logic to represent the uncertainty in the environment is presented. Algorithms are developed which can be used to reason about spatial information expressed in the form of approximate linguistic descriptions similar to the kind of spatial information processed by humans. Particular attention is given to static spatial reasoning.

Common Ground: An Interactive Visual Exploration and Discovery for Complex Health Data

DTIC Science & Technology

2015-04-01

working with Intermountain Healthcare on a new rich dataset extracted directly from medical notes using natural language processing ( NLP ) algorithms...probabilities based on a state- of-the-art NLP classifiers. At that stage the data did not include geographic information or temporal information but we

Signal processing methods for MFE plasma diagnostics

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

Candy, J.V.; Casper, T.; Kane, R.

1985-02-01

The application of various signal processing methods to extract energy storage information from plasma diamagnetism sensors occurring during physics experiments on the Tandom Mirror Experiment-Upgrade (TMX-U) is discussed. We show how these processing techniques can be used to decrease the uncertainty in the corresponding sensor measurements. The algorithms suggested are implemented using SIG, an interactive signal processing package developed at LLNL.

ID card number detection algorithm based on convolutional neural network

NASA Astrophysics Data System (ADS)

Zhu, Jian; Ma, Hanjie; Feng, Jie; Dai, Leiyan

2018-04-01

In this paper, a new detection algorithm based on Convolutional Neural Network is presented in order to realize the fast and convenient ID information extraction in multiple scenarios. The algorithm uses the mobile device equipped with Android operating system to locate and extract the ID number; Use the special color distribution of the ID card, select the appropriate channel component; Use the image threshold segmentation, noise processing and morphological processing to take the binary processing for image; At the same time, the image rotation and projection method are used for horizontal correction when image was tilting; Finally, the single character is extracted by the projection method, and recognized by using Convolutional Neural Network. Through test shows that, A single ID number image from the extraction to the identification time is about 80ms, the accuracy rate is about 99%, It can be applied to the actual production and living environment.

Research of building information extraction and evaluation based on high-resolution remote-sensing imagery

NASA Astrophysics Data System (ADS)

Cao, Qiong; Gu, Lingjia; Ren, Ruizhi; Wang, Lang

2016-09-01

Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection

Correction of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEO.

PubMed

Hernandez-Vicen, Juan; Martinez, Santiago; Garcia-Haro, Juan Miguel; Balaguer, Carlos

2018-03-25

New applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems related to image processing makes the application of visual information within robot control algorithms difficult. Camera-based systems have inherent errors that affect the quality and reliability of the information obtained. The need of correcting image distortion slows down image parameter computing, which decreases performance of control algorithms. In this paper, a new approach to correcting several sources of visual distortions on images in only one computing step is proposed. The goal of this system/algorithm is the computation of the tilt angle of an object transported by a robot, minimizing image inherent errors and increasing computing speed. After capturing the image, the computer system extracts the angle using a Fuzzy filter that corrects at the same time all possible distortions, obtaining the real angle in only one processing step. This filter has been developed by the means of Neuro-Fuzzy learning techniques, using datasets with information obtained from real experiments. In this way, the computing time has been decreased and the performance of the application has been improved. The resulting algorithm has been tried out experimentally in robot transportation tasks in the humanoid robot TEO (Task Environment Operator) from the University Carlos III of Madrid.

Correction of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEO

PubMed Central

2018-01-01

New applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems related to image processing makes the application of visual information within robot control algorithms difficult. Camera-based systems have inherent errors that affect the quality and reliability of the information obtained. The need of correcting image distortion slows down image parameter computing, which decreases performance of control algorithms. In this paper, a new approach to correcting several sources of visual distortions on images in only one computing step is proposed. The goal of this system/algorithm is the computation of the tilt angle of an object transported by a robot, minimizing image inherent errors and increasing computing speed. After capturing the image, the computer system extracts the angle using a Fuzzy filter that corrects at the same time all possible distortions, obtaining the real angle in only one processing step. This filter has been developed by the means of Neuro-Fuzzy learning techniques, using datasets with information obtained from real experiments. In this way, the computing time has been decreased and the performance of the application has been improved. The resulting algorithm has been tried out experimentally in robot transportation tasks in the humanoid robot TEO (Task Environment Operator) from the University Carlos III of Madrid. PMID:29587392

Using trend templates in a neonatal seizure algorithm improves detection of short seizures in a foetal ovine model.

PubMed

Zwanenburg, Alex; Andriessen, Peter; Jellema, Reint K; Niemarkt, Hendrik J; Wolfs, Tim G A M; Kramer, Boris W; Delhaas, Tammo

2015-03-01

Seizures below one minute in duration are difficult to assess correctly using seizure detection algorithms. We aimed to improve neonatal detection algorithm performance for short seizures through the use of trend templates for seizure onset and end. Bipolar EEG were recorded within a transiently asphyxiated ovine model at 0.7 gestational age, a common experimental model for studying brain development in humans of 30-34 weeks of gestation. Transient asphyxia led to electrographic seizures within 6-8 h. A total of 3159 seizures, 2386 shorter than one minute, were annotated in 1976 h-long EEG recordings from 17 foetal lambs. To capture EEG characteristics, five features, sensitive to seizures, were calculated and used to derive trend information. Feature values and trend information were used as input for support vector machine classification and subsequently post-processed. Performance metrics, calculated after post-processing, were compared between analyses with and without employing trend information. Detector performance was assessed after five-fold cross-validation conducted ten times with random splits. The use of trend templates for seizure onset and end in a neonatal seizure detection algorithm significantly improves the correct detection of short seizures using two-channel EEG recordings from 54.3% (52.6-56.1) to 59.5% (58.5-59.9) at FDR 2.0 (median (range); p < 0.001, Wilcoxon signed rank test). Using trend templates might therefore aid in detection of short seizures by EEG monitoring at the NICU.

Design and development of a medical big data processing system based on Hadoop.

PubMed

Yao, Qin; Tian, Yu; Li, Peng-Fei; Tian, Li-Li; Qian, Yang-Ming; Li, Jing-Song

2015-03-01

Secondary use of medical big data is increasingly popular in healthcare services and clinical research. Understanding the logic behind medical big data demonstrates tendencies in hospital information technology and shows great significance for hospital information systems that are designing and expanding services. Big data has four characteristics--Volume, Variety, Velocity and Value (the 4 Vs)--that make traditional systems incapable of processing these data using standalones. Apache Hadoop MapReduce is a promising software framework for developing applications that process vast amounts of data in parallel with large clusters of commodity hardware in a reliable, fault-tolerant manner. With the Hadoop framework and MapReduce application program interface (API), we can more easily develop our own MapReduce applications to run on a Hadoop framework that can scale up from a single node to thousands of machines. This paper investigates a practical case of a Hadoop-based medical big data processing system. We developed this system to intelligently process medical big data and uncover some features of hospital information system user behaviors. This paper studies user behaviors regarding various data produced by different hospital information systems for daily work. In this paper, we also built a five-node Hadoop cluster to execute distributed MapReduce algorithms. Our distributed algorithms show promise in facilitating efficient data processing with medical big data in healthcare services and clinical research compared with single nodes. Additionally, with medical big data analytics, we can design our hospital information systems to be much more intelligent and easier to use by making personalized recommendations.

Underwater sonar image detection: A combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm.

PubMed

Wang, Xingmei; Liu, Shu; Liu, Zhipeng

2017-01-01

This paper proposes a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm to detect underwater objects in sonar images. Specifically, for the first time, the problem of inappropriate filtering degree parameter which commonly occurs in non-local spatial information and seriously affects the denoising performance in sonar images, was solved with the method utilizing a novel filtering degree parameter. Then, a quantum-inspired shuffled frog leaping algorithm based on new search mechanism (QSFLA-NSM) is proposed to precisely and quickly detect sonar images. Each frog individual is directly encoded by real numbers, which can greatly simplify the evolution process of the quantum-inspired shuffled frog leaping algorithm (QSFLA). Meanwhile, a fitness function combining intra-class difference with inter-class difference is adopted to evaluate frog positions more accurately. On this basis, recurring to an analysis of the quantum-behaved particle swarm optimization (QPSO) and the shuffled frog leaping algorithm (SFLA), a new search mechanism is developed to improve the searching ability and detection accuracy. At the same time, the time complexity is further reduced. Finally, the results of comparative experiments using the original sonar images, the UCI data sets and the benchmark functions demonstrate the effectiveness and adaptability of the proposed method.

Underwater sonar image detection: A combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm

PubMed Central

Liu, Zhipeng

2017-01-01

This paper proposes a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm to detect underwater objects in sonar images. Specifically, for the first time, the problem of inappropriate filtering degree parameter which commonly occurs in non-local spatial information and seriously affects the denoising performance in sonar images, was solved with the method utilizing a novel filtering degree parameter. Then, a quantum-inspired shuffled frog leaping algorithm based on new search mechanism (QSFLA-NSM) is proposed to precisely and quickly detect sonar images. Each frog individual is directly encoded by real numbers, which can greatly simplify the evolution process of the quantum-inspired shuffled frog leaping algorithm (QSFLA). Meanwhile, a fitness function combining intra-class difference with inter-class difference is adopted to evaluate frog positions more accurately. On this basis, recurring to an analysis of the quantum-behaved particle swarm optimization (QPSO) and the shuffled frog leaping algorithm (SFLA), a new search mechanism is developed to improve the searching ability and detection accuracy. At the same time, the time complexity is further reduced. Finally, the results of comparative experiments using the original sonar images, the UCI data sets and the benchmark functions demonstrate the effectiveness and adaptability of the proposed method. PMID:28542266

An algorithm for automated layout of process description maps drawn in SBGN

PubMed Central

Genc, Begum; Dogrusoz, Ugur

2016-01-01

Motivation: Evolving technology has increased the focus on genomics. The combination of today’s advanced techniques with decades of molecular biology research has yielded huge amounts of pathway data. A standard, named the Systems Biology Graphical Notation (SBGN), was recently introduced to allow scientists to represent biological pathways in an unambiguous, easy-to-understand and efficient manner. Although there are a number of automated layout algorithms for various types of biological networks, currently none specialize on process description (PD) maps as defined by SBGN. Results: We propose a new automated layout algorithm for PD maps drawn in SBGN. Our algorithm is based on a force-directed automated layout algorithm called Compound Spring Embedder (CoSE). On top of the existing force scheme, additional heuristics employing new types of forces and movement rules are defined to address SBGN-specific rules. Our algorithm is the only automatic layout algorithm that properly addresses all SBGN rules for drawing PD maps, including placement of substrates and products of process nodes on opposite sides, compact tiling of members of molecular complexes and extensively making use of nested structures (compound nodes) to properly draw cellular locations and molecular complex structures. As demonstrated experimentally, the algorithm results in significant improvements over use of a generic layout algorithm such as CoSE in addressing SBGN rules on top of commonly accepted graph drawing criteria. Availability and implementation: An implementation of our algorithm in Java is available within ChiLay library (https://github.com/iVis-at-Bilkent/chilay). Contact: ugur@cs.bilkent.edu.tr or dogrusoz@cbio.mskcc.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26363029

Automated Recognition of Geologically Significant Shapes in MER PANCAM and MI Images

NASA Technical Reports Server (NTRS)

Morris, Robert; Shipman, Mark; Roush, Ted L.

2004-01-01

Autonomous recognition of scientifically important information provides the capability of: 1) Prioritizing data return; 2) Intelligent data compression; 3) Reactive behavior onboard robotic vehicles. Such capabilities are desirable as mission scenarios include longer durations with decreasing interaction from mission control. To address such issues, we have implemented several computer algorithms, intended to autonomously recognize morphological shapes of scientific interest within a software architecture envisioned for future rover missions. Mars Exploration Rovers (MER) instrument payloads include a Panoramic Camera (PANCAM) and Microscopic Imager (MI). These provide a unique opportunity to evaluate our algorithms when applied to data obtained from the surface of Mars. Early in the mission we applied our algorithms to images available at the mission web site (http://marsrovers.jpl.nasa.gov/gallery/images.html), even though these are not at full resolution. Some algorithms would normally use ancillary information, e.g. camera pointing and position of the sun, but these data were not readily available. The initial results of applying our algorithms to the PANCAM and MI images are encouraging. The horizon is recognized in all images containing it; such information could be used to eliminate unwanted areas from the image prior to data transmission to Earth. Additionally, several rocks were identified that represent targets for the mini-thermal emission spectrometer. Our algorithms also recognize the layers, identified by mission scientists. Such information could be used to prioritize data return or in a decision-making process regarding future rover activities. The spherules seen in MI images were also autonomously recognized. Our results indicate that reliable recognition of scientifically relevant morphologies in images is feasible.

Wildland fire management. Volume 2: Wildland fire control 1985-1995. [satellite information system for California fire problems

NASA Technical Reports Server (NTRS)

Saveker, D. R. (Editor)

1973-01-01

The preliminary design of a satellite plus computer earth resources information system is proposed for potential uses in fire prevention and control in the wildland fire community. Suggested are satellite characteristics, sensor characteristics, discrimination algorithms, data communication techniques, data processing requirements, display characteristics, and costs in achieving the integrated wildland fire information system.

The Application of Multiobjective Evolutionary Algorithms to an Educational Computational Model of Science Information Processing: A Computational Experiment in Science Education

ERIC Educational Resources Information Center

Lamb, Richard L.; Firestone, Jonah B.

2017-01-01

Conflicting explanations and unrelated information in science classrooms increase cognitive load and decrease efficiency in learning. This reduced efficiency ultimately limits one's ability to solve reasoning problems in the science. In reasoning, it is the ability of students to sift through and identify critical pieces of information that is of…

Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters: Part II

PubMed Central

Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg Y.; Fernández, Eduardo

2010-01-01

In this paper, the fast least-mean-squares (LMS) algorithm was used to both eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications, and improve the convergence rate of the filtering process based on the conventional LMS algorithm. The response of the accelerometer under test was corrupted by process and measurement noise, and the signal processing stage was carried out by using both conventional filtering, which was already shown in a previous paper, and optimal adaptive filtering. The adaptive filtering process relied on the LMS adaptive filtering family, which has shown to have very good convergence and robustness properties, and here a comparative analysis between the results of the application of the conventional LMS algorithm and the fast LMS algorithm to solve a real-life filtering problem was carried out. In short, in this paper the piezoresistive accelerometer was tested for a multi-frequency acceleration excitation. Due to the kind of test conducted in this paper, the use of conventional filtering was discarded and the choice of one adaptive filter over the other was based on the signal-to-noise ratio improvement and the convergence rate. PMID:22315579

Validation of a Natural Language Processing Algorithm for Detecting Infectious Disease Symptoms in Primary Care Electronic Medical Records in Singapore.

PubMed

Hardjojo, Antony; Gunachandran, Arunan; Pang, Long; Abdullah, Mohammed Ridzwan Bin; Wah, Win; Chong, Joash Wen Chen; Goh, Ee Hui; Teo, Sok Huang; Lim, Gilbert; Lee, Mong Li; Hsu, Wynne; Lee, Vernon; Chen, Mark I-Cheng; Wong, Franco; Phang, Jonathan Siung King

2018-06-11

Free-text clinical records provide a source of information that complements traditional disease surveillance. To electronically harness these records, they need to be transformed into codified fields by natural language processing algorithms. The aim of this study was to develop, train, and validate Clinical History Extractor for Syndromic Surveillance (CHESS), an natural language processing algorithm to extract clinical information from free-text primary care records. CHESS is a keyword-based natural language processing algorithm to extract 48 signs and symptoms suggesting respiratory infections, gastrointestinal infections, constitutional, as well as other signs and symptoms potentially associated with infectious diseases. The algorithm also captured the assertion status (affirmed, negated, or suspected) and symptom duration. Electronic medical records from the National Healthcare Group Polyclinics, a major public sector primary care provider in Singapore, were randomly extracted and manually reviewed by 2 human reviewers, with a third reviewer as the adjudicator. The algorithm was evaluated based on 1680 notes against the human-coded result as the reference standard, with half of the data used for training and the other half for validation. The symptoms most commonly present within the 1680 clinical records at the episode level were those typically present in respiratory infections such as cough (744/7703, 9.66%), sore throat (591/7703, 7.67%), rhinorrhea (552/7703, 7.17%), and fever (928/7703, 12.04%). At the episode level, CHESS had an overall performance of 96.7% precision and 97.6% recall on the training dataset and 96.0% precision and 93.1% recall on the validation dataset. Symptoms suggesting respiratory and gastrointestinal infections were all detected with more than 90% precision and recall. CHESS correctly assigned the assertion status in 97.3%, 97.9%, and 89.8% of affirmed, negated, and suspected signs and symptoms, respectively (97.6% overall accuracy). Symptom episode duration was correctly identified in 81.2% of records with known duration status. We have developed an natural language processing algorithm dubbed CHESS that achieves good performance in extracting signs and symptoms from primary care free-text clinical records. In addition to the presence of symptoms, our algorithm can also accurately distinguish affirmed, negated, and suspected assertion statuses and extract symptom durations. ©Antony Hardjojo, Arunan Gunachandran, Long Pang, Mohammed Ridzwan Bin Abdullah, Win Wah, Joash Wen Chen Chong, Ee Hui Goh, Sok Huang Teo, Gilbert Lim, Mong Li Lee, Wynne Hsu, Vernon Lee, Mark I-Cheng Chen, Franco Wong, Jonathan Siung King Phang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 11.06.2018.

Parametric boundary reconstruction algorithm for industrial CT metrology application.

PubMed

Yin, Zhye; Khare, Kedar; De Man, Bruno

2009-01-01

High-energy X-ray computed tomography (CT) systems have been recently used to produce high-resolution images in various nondestructive testing and evaluation (NDT/NDE) applications. The accuracy of the dimensional information extracted from CT images is rapidly approaching the accuracy achieved with a coordinate measuring machine (CMM), the conventional approach to acquire the metrology information directly. On the other hand, CT systems generate the sinogram which is transformed mathematically to the pixel-based images. The dimensional information of the scanned object is extracted later by performing edge detection on reconstructed CT images. The dimensional accuracy of this approach is limited by the grid size of the pixel-based representation of CT images since the edge detection is performed on the pixel grid. Moreover, reconstructed CT images usually display various artifacts due to the underlying physical process and resulting object boundaries from the edge detection fail to represent the true boundaries of the scanned object. In this paper, a novel algorithm to reconstruct the boundaries of an object with uniform material composition and uniform density is presented. There are three major benefits in the proposed approach. First, since the boundary parameters are reconstructed instead of image pixels, the complexity of the reconstruction algorithm is significantly reduced. The iterative approach, which can be computationally intensive, will be practical with the parametric boundary reconstruction. Second, the object of interest in metrology can be represented more directly and accurately by the boundary parameters instead of the image pixels. By eliminating the extra edge detection step, the overall dimensional accuracy and process time can be improved. Third, since the parametric reconstruction approach shares the boundary representation with other conventional metrology modalities such as CMM, boundary information from other modalities can be directly incorporated as prior knowledge to improve the convergence of an iterative approach. In this paper, the feasibility of parametric boundary reconstruction algorithm is demonstrated with both simple and complex simulated objects. Finally, the proposed algorithm is applied to the experimental industrial CT system data.

Arc-Welding Spectroscopic Monitoring based on Feature Selection and Neural Networks.

PubMed

Garcia-Allende, P Beatriz; Mirapeix, Jesus; Conde, Olga M; Cobo, Adolfo; Lopez-Higuera, Jose M

2008-10-21

A new spectral processing technique designed for application in the on-line detection and classification of arc-welding defects is presented in this paper. A noninvasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed in two consecutive stages. A compression algorithm is first applied to the data, allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in previous works, giving rise to an improvement in the performance of the monitoring system.

Basic firefly algorithm for document clustering

NASA Astrophysics Data System (ADS)

Mohammed, Athraa Jasim; Yusof, Yuhanis; Husni, Husniza

2015-12-01

The Document clustering plays significant role in Information Retrieval (IR) where it organizes documents prior to the retrieval process. To date, various clustering algorithms have been proposed and this includes the K-means and Particle Swarm Optimization. Even though these algorithms have been widely applied in many disciplines due to its simplicity, such an approach tends to be trapped in a local minimum during its search for an optimal solution. To address the shortcoming, this paper proposes a Basic Firefly (Basic FA) algorithm to cluster text documents. The algorithm employs the Average Distance to Document Centroid (ADDC) as the objective function of the search. Experiments utilizing the proposed algorithm were conducted on the 20Newsgroups benchmark dataset. Results demonstrate that the Basic FA generates a more robust and compact clusters than the ones produced by K-means and Particle Swarm Optimization (PSO).

Denni Algorithm An Enhanced Of SMS (Scan, Move and Sort) Algorithm

NASA Astrophysics Data System (ADS)

Aprilsyah Lubis, Denni; Salim Sitompul, Opim; Marwan; Tulus; Andri Budiman, M.

2017-12-01

Sorting has been a profound area for the algorithmic researchers, and many resources are invested to suggest a more working sorting algorithm. For this purpose many existing sorting algorithms were observed in terms of the efficiency of the algorithmic complexity. Efficient sorting is important to optimize the use of other algorithms that require sorted lists to work correctly. Sorting has been considered as a fundamental problem in the study of algorithms that due to many reasons namely, the necessary to sort information is inherent in many applications, algorithms often use sorting as a key subroutine, in algorithm design there are many essential techniques represented in the body of sorting algorithms, and many engineering issues come to the fore when implementing sorting algorithms., Many algorithms are very well known for sorting the unordered lists, and one of the well-known algorithms that make the process of sorting to be more economical and efficient is SMS (Scan, Move and Sort) algorithm, an enhancement of Quicksort invented Rami Mansi in 2010. This paper presents a new sorting algorithm called Denni-algorithm. The Denni algorithm is considered as an enhancement on the SMS algorithm in average, and worst cases. The Denni algorithm is compared with the SMS algorithm and the results were promising.

Using quantum theory to simplify input-output processes

NASA Astrophysics Data System (ADS)

Thompson, Jayne; Garner, Andrew J. P.; Vedral, Vlatko; Gu, Mile

2017-02-01

All natural things process and transform information. They receive environmental information as input, and transform it into appropriate output responses. Much of science is dedicated to building models of such systems-algorithmic abstractions of their input-output behavior that allow us to simulate how such systems can behave in the future, conditioned on what has transpired in the past. Here, we show that classical models cannot avoid inefficiency-storing past information that is unnecessary for correct future simulation. We construct quantum models that mitigate this waste, whenever it is physically possible to do so. This suggests that the complexity of general input-output processes depends fundamentally on what sort of information theory we use to describe them.

An efficient reversible privacy-preserving data mining technology over data streams.

PubMed

Lin, Chen-Yi; Kao, Yuan-Hung; Lee, Wei-Bin; Chen, Rong-Chang

2016-01-01

With the popularity of smart handheld devices and the emergence of cloud computing, users and companies can save various data, which may contain private data, to the cloud. Topics relating to data security have therefore received much attention. This study focuses on data stream environments and uses the concept of a sliding window to design a reversible privacy-preserving technology to process continuous data in real time, known as a continuous reversible privacy-preserving (CRP) algorithm. Data with CRP algorithm protection can be accurately recovered through a data recovery process. In addition, by using an embedded watermark, the integrity of the data can be verified. The results from the experiments show that, compared to existing algorithms, CRP is better at preserving knowledge and is more effective in terms of reducing information loss and privacy disclosure risk. In addition, it takes far less time for CRP to process continuous data than existing algorithms. As a result, CRP is confirmed as suitable for data stream environments and fulfills the requirements of being lightweight and energy-efficient for smart handheld devices.

A gossip based information fusion protocol for distributed frequent itemset mining

NASA Astrophysics Data System (ADS)

Sohrabi, Mohammad Karim

2018-07-01

The computational complexity, huge memory space requirement, and time-consuming nature of frequent pattern mining process are the most important motivations for distribution and parallelization of this mining process. On the other hand, the emergence of distributed computational and operational environments, which causes the production and maintenance of data on different distributed data sources, makes the parallelization and distribution of the knowledge discovery process inevitable. In this paper, a gossip based distributed itemset mining (GDIM) algorithm is proposed to extract frequent itemsets, which are special types of frequent patterns, in a wireless sensor network environment. In this algorithm, local frequent itemsets of each sensor are extracted using a bit-wise horizontal approach (LHPM) from the nodes which are clustered using a leach-based protocol. Heads of clusters exploit a gossip based protocol in order to communicate each other to find the patterns which their global support is equal to or more than the specified support threshold. Experimental results show that the proposed algorithm outperforms the best existing gossip based algorithm in term of execution time.

Predictive Rate-Distortion for Infinite-Order Markov Processes

NASA Astrophysics Data System (ADS)

Marzen, Sarah E.; Crutchfield, James P.

2016-06-01

Predictive rate-distortion analysis suffers from the curse of dimensionality: clustering arbitrarily long pasts to retain information about arbitrarily long futures requires resources that typically grow exponentially with length. The challenge is compounded for infinite-order Markov processes, since conditioning on finite sequences cannot capture all of their past dependencies. Spectral arguments confirm a popular intuition: algorithms that cluster finite-length sequences fail dramatically when the underlying process has long-range temporal correlations and can fail even for processes generated by finite-memory hidden Markov models. We circumvent the curse of dimensionality in rate-distortion analysis of finite- and infinite-order processes by casting predictive rate-distortion objective functions in terms of the forward- and reverse-time causal states of computational mechanics. Examples demonstrate that the resulting algorithms yield substantial improvements.

Towards human-computer synergetic analysis of large-scale biological data.

PubMed

Singh, Rahul; Yang, Hui; Dalziel, Ben; Asarnow, Daniel; Murad, William; Foote, David; Gormley, Matthew; Stillman, Jonathan; Fisher, Susan

2013-01-01

Advances in technology have led to the generation of massive amounts of complex and multifarious biological data in areas ranging from genomics to structural biology. The volume and complexity of such data leads to significant challenges in terms of its analysis, especially when one seeks to generate hypotheses or explore the underlying biological processes. At the state-of-the-art, the application of automated algorithms followed by perusal and analysis of the results by an expert continues to be the predominant paradigm for analyzing biological data. This paradigm works well in many problem domains. However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological processes is poorly understood and exploratory analysis is crucial, what is needed is to integrate domain expertise during the data analysis process and use it to drive the analysis itself. In context of the aforementioned background, the results presented in this paper describe advancements along two methodological directions. First, given the context of biological data, we utilize and extend a design approach called experiential computing from multimedia information system design. This paradigm combines information visualization and human-computer interaction with algorithms for exploratory analysis of large-scale and complex data. In the proposed approach, emphasis is laid on: (1) allowing users to directly visualize, interact, experience, and explore the data through interoperable visualization-based and algorithmic components, (2) supporting unified query and presentation spaces to facilitate experimentation and exploration, (3) providing external contextual information by assimilating relevant supplementary data, and (4) encouraging user-directed information visualization, data exploration, and hypotheses formulation. Second, to illustrate the proposed design paradigm and measure its efficacy, we describe two prototype web applications. The first, called XMAS (Experiential Microarray Analysis System) is designed for analysis of time-series transcriptional data. The second system, called PSPACE (Protein Space Explorer) is designed for holistic analysis of structural and structure-function relationships using interactive low-dimensional maps of the protein structure space. Both these systems promote and facilitate human-computer synergy, where cognitive elements such as domain knowledge, contextual reasoning, and purpose-driven exploration, are integrated with a host of powerful algorithmic operations that support large-scale data analysis, multifaceted data visualization, and multi-source information integration. The proposed design philosophy, combines visualization, algorithmic components and cognitive expertise into a seamless processing-analysis-exploration framework that facilitates sense-making, exploration, and discovery. Using XMAS, we present case studies that analyze transcriptional data from two highly complex domains: gene expression in the placenta during human pregnancy and reaction of marine organisms to heat stress. With PSPACE, we demonstrate how complex structure-function relationships can be explored. These results demonstrate the novelty, advantages, and distinctions of the proposed paradigm. Furthermore, the results also highlight how domain insights can be combined with algorithms to discover meaningful knowledge and formulate evidence-based hypotheses during the data analysis process. Finally, user studies against comparable systems indicate that both XMAS and PSPACE deliver results with better interpretability while placing lower cognitive loads on the users. XMAS is available at: http://tintin.sfsu.edu:8080/xmas. PSPACE is available at: http://pspace.info/.

Towards human-computer synergetic analysis of large-scale biological data

PubMed Central

2013-01-01

Background Advances in technology have led to the generation of massive amounts of complex and multifarious biological data in areas ranging from genomics to structural biology. The volume and complexity of such data leads to significant challenges in terms of its analysis, especially when one seeks to generate hypotheses or explore the underlying biological processes. At the state-of-the-art, the application of automated algorithms followed by perusal and analysis of the results by an expert continues to be the predominant paradigm for analyzing biological data. This paradigm works well in many problem domains. However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological processes is poorly understood and exploratory analysis is crucial, what is needed is to integrate domain expertise during the data analysis process and use it to drive the analysis itself. Results In context of the aforementioned background, the results presented in this paper describe advancements along two methodological directions. First, given the context of biological data, we utilize and extend a design approach called experiential computing from multimedia information system design. This paradigm combines information visualization and human-computer interaction with algorithms for exploratory analysis of large-scale and complex data. In the proposed approach, emphasis is laid on: (1) allowing users to directly visualize, interact, experience, and explore the data through interoperable visualization-based and algorithmic components, (2) supporting unified query and presentation spaces to facilitate experimentation and exploration, (3) providing external contextual information by assimilating relevant supplementary data, and (4) encouraging user-directed information visualization, data exploration, and hypotheses formulation. Second, to illustrate the proposed design paradigm and measure its efficacy, we describe two prototype web applications. The first, called XMAS (Experiential Microarray Analysis System) is designed for analysis of time-series transcriptional data. The second system, called PSPACE (Protein Space Explorer) is designed for holistic analysis of structural and structure-function relationships using interactive low-dimensional maps of the protein structure space. Both these systems promote and facilitate human-computer synergy, where cognitive elements such as domain knowledge, contextual reasoning, and purpose-driven exploration, are integrated with a host of powerful algorithmic operations that support large-scale data analysis, multifaceted data visualization, and multi-source information integration. Conclusions The proposed design philosophy, combines visualization, algorithmic components and cognitive expertise into a seamless processing-analysis-exploration framework that facilitates sense-making, exploration, and discovery. Using XMAS, we present case studies that analyze transcriptional data from two highly complex domains: gene expression in the placenta during human pregnancy and reaction of marine organisms to heat stress. With PSPACE, we demonstrate how complex structure-function relationships can be explored. These results demonstrate the novelty, advantages, and distinctions of the proposed paradigm. Furthermore, the results also highlight how domain insights can be combined with algorithms to discover meaningful knowledge and formulate evidence-based hypotheses during the data analysis process. Finally, user studies against comparable systems indicate that both XMAS and PSPACE deliver results with better interpretability while placing lower cognitive loads on the users. XMAS is available at: http://tintin.sfsu.edu:8080/xmas. PSPACE is available at: http://pspace.info/. PMID:24267485

VNIR hyperspectral background characterization methods in adverse weather conditions

NASA Astrophysics Data System (ADS)

Romano, João M.; Rosario, Dalton; Roth, Luz

2009-05-01

Hyperspectral technology is currently being used by the military to detect regions of interest where potential targets may be located. Weather variability, however, may affect the ability for an algorithm to discriminate possible targets from background clutter. Nonetheless, different background characterization approaches may facilitate the ability for an algorithm to discriminate potential targets over a variety of weather conditions. In a previous paper, we introduced a new autonomous target size invariant background characterization process, the Autonomous Background Characterization (ABC) or also known as the Parallel Random Sampling (PRS) method, features a random sampling stage, a parallel process to mitigate the inclusion by chance of target samples into clutter background classes during random sampling; and a fusion of results at the end. In this paper, we will demonstrate how different background characterization approaches are able to improve performance of algorithms over a variety of challenging weather conditions. By using the Mahalanobis distance as the standard algorithm for this study, we compare the performance of different characterization methods such as: the global information, 2 stage global information, and our proposed method, ABC, using data that was collected under a variety of adverse weather conditions. For this study, we used ARDEC's Hyperspectral VNIR Adverse Weather data collection comprised of heavy, light, and transitional fog, light and heavy rain, and low light conditions.

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach.

PubMed

Abduallah, Yasser; Turki, Turki; Byron, Kevin; Du, Zongxuan; Cervantes-Cervantes, Miguel; Wang, Jason T L

2017-01-01

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.

Design and analysis of large-scale biological rhythm studies: a comparison of algorithms for detecting periodic signals in biological data

PubMed Central

Deckard, Anastasia; Anafi, Ron C.; Hogenesch, John B.; Haase, Steven B.; Harer, John

2013-01-01

Motivation: To discover and study periodic processes in biological systems, we sought to identify periodic patterns in their gene expression data. We surveyed a large number of available methods for identifying periodicity in time series data and chose representatives of different mathematical perspectives that performed well on both synthetic data and biological data. Synthetic data were used to evaluate how each algorithm responds to different curve shapes, periods, phase shifts, noise levels and sampling rates. The biological datasets we tested represent a variety of periodic processes from different organisms, including the cell cycle and metabolic cycle in Saccharomyces cerevisiae, circadian rhythms in Mus musculus and the root clock in Arabidopsis thaliana. Results: From these results, we discovered that each algorithm had different strengths. Based on our findings, we make recommendations for selecting and applying these methods depending on the nature of the data and the periodic patterns of interest. Additionally, these results can also be used to inform the design of large-scale biological rhythm experiments so that the resulting data can be used with these algorithms to detect periodic signals more effectively. Contact: anastasia.deckard@duke.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24058056

The validation of the Yonsei CArbon Retrieval algorithm with improved aerosol information using GOSAT measurements

NASA Astrophysics Data System (ADS)

Jung, Yeonjin; Kim, Jhoon; Kim, Woogyung; Boesch, Hartmut; Goo, Tae-Young; Cho, Chunho

2017-04-01

Although several CO2 retrieval algorithms have been developed to improve our understanding about carbon cycle, limitations in spatial coverage and uncertainties due to aerosols and thin cirrus clouds are still remained as a problem for monitoring CO2 concentration globally. Based on an optimal estimation method, the Yonsei CArbon Retrieval (YCAR) algorithm was developed to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) using the Greenhouse Gases Observing SATellite (GOSAT) measurements with optimized a priori CO2 profiles and aerosol models over East Asia. In previous studies, the aerosol optical properties (AOP) are the most important factors in CO2 retrievals since AOPs are assumed as fixed parameters during retrieval process, resulting in significant XCO2 retrieval error up to 2.5 ppm. In this study, to reduce these errors caused by inaccurate aerosol optical information, the YCAR algorithm improved with taking into account aerosol optical properties as well as aerosol vertical distribution simultaneously. The CO2 retrievals with two difference aerosol approaches have been analyzed using the GOSAT spectra and have been evaluated throughout the comparison with collocated ground-based observations at several Total Carbon Column Observing Network (TCCON) sites. The improved YCAR algorithm has biases of 0.59±0.48 ppm and 2.16±0.87 ppm at Saga and Tsukuba sites, respectively, with smaller biases and higher correlation coefficients compared to the GOSAT operational algorithm. In addition, the XCO2 retrievals will be validated at other TCCON sites and error analysis will be evaluated. These results reveal that considering better aerosol information can improve the accuracy of CO2 retrieval algorithm and provide more useful XCO2 information with reduced uncertainties. This study would be expected to provide useful information in estimating carbon sources and sinks.

A Review on Sensor, Signal, and Information Processing Algorithms (PREPRINT)

DTIC Science & Technology

2010-01-01

processing [214], ambi- guity surface averaging [215], optimum uncertain field tracking, and optimal minimum variance track - before - detect [216]. In [217, 218...2) (2001) 739–746. [216] S. L. Tantum, L. W. Nolte, J. L. Krolik, K. Harmanci, The performance of matched-field track - before - detect methods using

Enabling Incremental Query Re-Optimization.

PubMed

Liu, Mengmeng; Ives, Zachary G; Loo, Boon Thau

2016-01-01

As declarative query processing techniques expand to the Web, data streams, network routers, and cloud platforms, there is an increasing need to re-plan execution in the presence of unanticipated performance changes. New runtime information may affect which query plan we prefer to run. Adaptive techniques require innovation both in terms of the algorithms used to estimate costs , and in terms of the search algorithm that finds the best plan. We investigate how to build a cost-based optimizer that recomputes the optimal plan incrementally given new cost information, much as a stream engine constantly updates its outputs given new data. Our implementation especially shows benefits for stream processing workloads. It lays the foundations upon which a variety of novel adaptive optimization algorithms can be built. We start by leveraging the recently proposed approach of formulating query plan enumeration as a set of recursive datalog queries ; we develop a variety of novel optimization approaches to ensure effective pruning in both static and incremental cases. We further show that the lessons learned in the declarative implementation can be equally applied to more traditional optimizer implementations.

Enabling Incremental Query Re-Optimization

PubMed Central

Liu, Mengmeng; Ives, Zachary G.; Loo, Boon Thau

2017-01-01

As declarative query processing techniques expand to the Web, data streams, network routers, and cloud platforms, there is an increasing need to re-plan execution in the presence of unanticipated performance changes. New runtime information may affect which query plan we prefer to run. Adaptive techniques require innovation both in terms of the algorithms used to estimate costs, and in terms of the search algorithm that finds the best plan. We investigate how to build a cost-based optimizer that recomputes the optimal plan incrementally given new cost information, much as a stream engine constantly updates its outputs given new data. Our implementation especially shows benefits for stream processing workloads. It lays the foundations upon which a variety of novel adaptive optimization algorithms can be built. We start by leveraging the recently proposed approach of formulating query plan enumeration as a set of recursive datalog queries; we develop a variety of novel optimization approaches to ensure effective pruning in both static and incremental cases. We further show that the lessons learned in the declarative implementation can be equally applied to more traditional optimizer implementations. PMID:28659658

Robust head pose estimation via supervised manifold learning.

PubMed

Wang, Chao; Song, Xubo

2014-05-01

Head poses can be automatically estimated using manifold learning algorithms, with the assumption that with the pose being the only variable, the face images should lie in a smooth and low-dimensional manifold. However, this estimation approach is challenging due to other appearance variations related to identity, head location in image, background clutter, facial expression, and illumination. To address the problem, we propose to incorporate supervised information (pose angles of training samples) into the process of manifold learning. The process has three stages: neighborhood construction, graph weight computation and projection learning. For the first two stages, we redefine inter-point distance for neighborhood construction as well as graph weight by constraining them with the pose angle information. For Stage 3, we present a supervised neighborhood-based linear feature transformation algorithm to keep the data points with similar pose angles close together but the data points with dissimilar pose angles far apart. The experimental results show that our method has higher estimation accuracy than the other state-of-art algorithms and is robust to identity and illumination variations. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fast fringe pattern phase demodulation using FIR Hilbert transformers

NASA Astrophysics Data System (ADS)

Gdeisat, Munther; Burton, David; Lilley, Francis; Arevalillo-Herráez, Miguel

2016-01-01

This paper suggests the use of FIR Hilbert transformers to extract the phase of fringe patterns. This method is computationally faster than any known spatial method that produces wrapped phase maps. Also, the algorithm does not require any parameters to be adjusted which are dependent upon the specific fringe pattern that is being processed, or upon the particular setup of the optical fringe projection system that is being used. It is therefore particularly suitable for full algorithmic automation. The accuracy and validity of the suggested method has been tested using both computer-generated and real fringe patterns. This novel algorithm has been proposed for its advantages in terms of computational processing speed as it is the fastest available method to extract the wrapped phase information from a fringe pattern.

Acceleration of planes segmentation using normals from previous frame

NASA Astrophysics Data System (ADS)

Gritsenko, Pavel; Gritsenko, Igor; Seidakhmet, Askar; Abduraimov, Azizbek

2017-12-01

One of the major problem in integration process of robots is to make them able to function in a human environment. In terms of computer vision, the major feature of human made rooms is the presence of planes [1, 2, 20, 21, 23]. In this article, we will present an algorithm dedicated to increase speed of a plane segmentation. The algorithm uses information about location of a plane and its normal vector to speed up the segmentation process in the next frame. In conjunction with it, we will address such aspects of ICP SLAM as performance and map representation.

An interactive ontology-driven information system for simulating background radiation and generating scenarios for testing special nuclear materials detection algorithms

DOE PAGES

Sorokine, Alexandre; Schlicher, Bob G.; Ward, Richard C.; ...

2015-05-22

This paper describes an original approach to generating scenarios for the purpose of testing the algorithms used to detect special nuclear materials (SNM) that incorporates the use of ontologies. Separating the signal of SNM from the background requires sophisticated algorithms. To assist in developing such algorithms, there is a need for scenarios that capture a very wide range of variables affecting the detection process, depending on the type of detector being used. To provide such a cpability, we developed an ontology-driven information system (ODIS) for generating scenarios that can be used in creating scenarios for testing of algorithms for SNMmore » detection. The ontology-driven scenario generator (ODSG) is an ODIS based on information supplied by subject matter experts and other documentation. The details of the creation of the ontology, the development of the ontology-driven information system, and the design of the web user interface (UI) are presented along with specific examples of scenarios generated using the ODSG. We demonstrate that the paradigm behind the ODSG is capable of addressing the problem of semantic complexity at both the user and developer levels. Compared to traditional approaches, an ODIS provides benefits such as faithful representation of the users' domain conceptualization, simplified management of very large and semantically diverse datasets, and the ability to handle frequent changes to the application and the UI. Furthermore, the approach makes possible the generation of a much larger number of specific scenarios based on limited user-supplied information« less

Random bits, true and unbiased, from atmospheric turbulence

PubMed Central

Marangon, Davide G.; Vallone, Giuseppe; Villoresi, Paolo

2014-01-01

Random numbers represent a fundamental ingredient for secure communications and numerical simulation as well as to games and in general to Information Science. Physical processes with intrinsic unpredictability may be exploited to generate genuine random numbers. The optical propagation in strong atmospheric turbulence is here taken to this purpose, by observing a laser beam after a 143 km free-space path. In addition, we developed an algorithm to extract the randomness of the beam images at the receiver without post-processing. The numbers passed very selective randomness tests for qualification as genuine random numbers. The extracting algorithm can be easily generalized to random images generated by different physical processes. PMID:24976499

Pan-sharpening algorithm to remove thin cloud via mask dodging and nonsampled shift-invariant shearlet transform

NASA Astrophysics Data System (ADS)

Shi, Cheng; Liu, Fang; Li, Ling-Ling; Hao, Hong-Xia

2014-01-01

The goal of pan-sharpening is to get an image with higher spatial resolution and better spectral information. However, the resolution of the pan-sharpened image is seriously affected by the thin clouds. For a single image, filtering algorithms are widely used to remove clouds. These kinds of methods can remove clouds effectively, but the detail lost in the cloud removal image is also serious. To solve this problem, a pan-sharpening algorithm to remove thin cloud via mask dodging and nonsampled shift-invariant shearlet transform (NSST) is proposed. For the low-resolution multispectral (LR MS) and high-resolution panchromatic images with thin clouds, a mask dodging method is used to remove clouds. For the cloud removal LR MS image, an adaptive principal component analysis transform is proposed to balance the spectral information and spatial resolution in the pan-sharpened image. Since the clouds removal process causes the detail loss problem, a weight matrix is designed to enhance the details of the cloud regions in the pan-sharpening process, but noncloud regions remain unchanged. And the details of the image are obtained by NSST. Experimental results over visible and evaluation metrics demonstrate that the proposed method can keep better spectral information and spatial resolution, especially for the images with thin clouds.

An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles

NASA Astrophysics Data System (ADS)

Liu, Yahui; Fan, Xiaoqian; Lv, Chen; Wu, Jian; Li, Liang; Ding, Dawei

2018-02-01

Information fusion method of INS/GPS navigation system based on filtering technology is a research focus at present. In order to improve the precision of navigation information, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposed to restrain noise in this paper. The algorithm continuously updates the measurement noise variance and processes noise variance of the system by collecting the estimated and measured values, and this method can suppress white noise. Because a measured value closer to the current time would more accurately reflect the characteristics of the noise, an attenuation factor is introduced to increase the weight of the current value, in order to deal with the noise variance caused by environment disturbance. To validate the effectiveness of the proposed algorithm, a series of road tests are carried out in urban environment. The GPS and IMU data of the experiments were collected and processed by dSPACE and MATLAB/Simulink. Based on the test results, the accuracy of the proposed algorithm is 20% higher than that of a traditional Adaptive Kalman Filter. It also shows that the precision of the integrated navigation can be improved due to the reduction of the influence of environment noise.

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

Thorne, N; Kassaee, A

Purpose: To develop an algorithm which can calculate the Full Width Half Maximum (FWHM) of a Proton Pencil Beam from a 2D dimensional ion chamber array (IBA Matrixx) with limited spatial resolution ( 7.6 mm inter chamber distance). The algorithm would allow beam FWHM measurements to be taken during daily QA without an appreciable time increase. Methods: Combinations of 147 MeV single spot beams were delivered onto an IBA Matrixx and concurrently on EBT3 films for a standard. Data were collected around the Bragg Peak region and evaluated by a custom MATLAB script based on our algorithm using a leastmore » squared analysis. A set of artificial data, modified with random noise, was also processed to test for robustness. Results: The Matlab script processed Matixx data shows acceptable agreement (within 5%) with film measurements with no single measurement differing by more than 1.8 mm. In cases where the spots show some degree of asymmetry, the algorithm is able to resolve the differences. The algorithm was able to process artificial data with noise up to 15% of the maximum value. Time assays of each measurement took less than 3 minutes to perform, indicating that such measurements may be efficiently added to daily QA treatment. Conclusion: The developed algorithm can be implemented in daily QA program for Proton Pencil Beam scanning beams (PBS) with Matrixx to extract spot size and position information. The developed algorithm may be extended to small field sizes in photon clinic.« less

Abbreviation definition identification based on automatic precision estimates.

PubMed

Sohn, Sunghwan; Comeau, Donald C; Kim, Won; Wilbur, W John

2008-09-25

The rapid growth of biomedical literature presents challenges for automatic text processing, and one of the challenges is abbreviation identification. The presence of unrecognized abbreviations in text hinders indexing algorithms and adversely affects information retrieval and extraction. Automatic abbreviation definition identification can help resolve these issues. However, abbreviations and their definitions identified by an automatic process are of uncertain validity. Due to the size of databases such as MEDLINE only a small fraction of abbreviation-definition pairs can be examined manually. An automatic way to estimate the accuracy of abbreviation-definition pairs extracted from text is needed. In this paper we propose an abbreviation definition identification algorithm that employs a variety of strategies to identify the most probable abbreviation definition. In addition our algorithm produces an accuracy estimate, pseudo-precision, for each strategy without using a human-judged gold standard. The pseudo-precisions determine the order in which the algorithm applies the strategies in seeking to identify the definition of an abbreviation. On the Medstract corpus our algorithm produced 97% precision and 85% recall which is higher than previously reported results. We also annotated 1250 randomly selected MEDLINE records as a gold standard. On this set we achieved 96.5% precision and 83.2% recall. This compares favourably with the well known Schwartz and Hearst algorithm. We developed an algorithm for abbreviation identification that uses a variety of strategies to identify the most probable definition for an abbreviation and also produces an estimated accuracy of the result. This process is purely automatic.

SMV⊥: Simplex of maximal volume based upon the Gram-Schmidt process

NASA Astrophysics Data System (ADS)

Salazar-Vazquez, Jairo; Mendez-Vazquez, Andres

2015-10-01

In recent years, different algorithms for Hyperspectral Image (HI) analysis have been introduced. The high spectral resolution of these images allows to develop different algorithms for target detection, material mapping, and material identification for applications in Agriculture, Security and Defense, Industry, etc. Therefore, from the computer science's point of view, there is fertile field of research for improving and developing algorithms in HI analysis. In some applications, the spectral pixels of a HI can be classified using laboratory spectral signatures. Nevertheless, for many others, there is no enough available prior information or spectral signatures, making any analysis a difficult task. One of the most popular algorithms for the HI analysis is the N-FINDR because it is easy to understand and provides a way to unmix the original HI in the respective material compositions. The N-FINDR is computationally expensive and its performance depends on a random initialization process. This paper proposes a novel idea to reduce the complexity of the N-FINDR by implementing a bottom-up approach based in an observation from linear algebra and the use of the Gram-Schmidt process. Therefore, the Simplex of Maximal Volume Perpendicular (SMV⊥) algorithm is proposed for fast endmember extraction in hyperspectral imagery. This novel algorithm has complexity O(n) with respect to the number of pixels. In addition, the evidence shows that SMV⊥ calculates a bigger volume, and has lower computational time complexity than other poular algorithms on synthetic and real scenarios.

Algorithms for Port-of-Entry Inspection

DTIC Science & Technology

2007-05-29

Devdatt Lad, Rutgers University, Center for Advanced Information Processing Mingyu Li, Rutgers University, Statistics Francesco Longo, University of...Industrial and Systems Engineering graduate student Devdatt Lad, Rutgers University, Electrical & Computer Engineering, graduate student Mingyu Li

Clinical study of quantitative diagnosis of early cervical cancer based on the classification of acetowhitening kinetics

NASA Astrophysics Data System (ADS)

Wu, Tao; Cheung, Tak-Hong; Yim, So-Fan; Qu, Jianan Y.

2010-03-01

A quantitative colposcopic imaging system for the diagnosis of early cervical cancer is evaluated in a clinical study. This imaging technology based on 3-D active stereo vision and motion tracking extracts diagnostic information from the kinetics of acetowhitening process measured from the cervix of human subjects in vivo. Acetowhitening kinetics measured from 137 cervical sites of 57 subjects are analyzed and classified using multivariate statistical algorithms. Cross-validation methods are used to evaluate the performance of the diagnostic algorithms. The results show that an algorithm for screening precancer produced 95% sensitivity (SE) and 96% specificity (SP) for discriminating normal and human papillomavirus (HPV)-infected tissues from cervical intraepithelial neoplasia (CIN) lesions. For a diagnostic algorithm, 91% SE and 90% SP are achieved for discriminating normal tissue, HPV infected tissue, and low-grade CIN lesions from high-grade CIN lesions. The results demonstrate that the quantitative colposcopic imaging system could provide objective screening and diagnostic information for early detection of cervical cancer.

Separation of left and right lungs using 3D information of sequential CT images and a guided dynamic programming algorithm

PubMed Central

Park, Sang Cheol; Leader, Joseph Ken; Tan, Jun; Lee, Guee Sang; Kim, Soo Hyung; Na, In Seop; Zheng, Bin

2011-01-01

Objective this article presents a new computerized scheme that aims to accurately and robustly separate left and right lungs on CT examinations. Methods we developed and tested a method to separate the left and right lungs using sequential CT information and a guided dynamic programming algorithm using adaptively and automatically selected start point and end point with especially severe and multiple connections. Results the scheme successfully identified and separated all 827 connections on the total 4034 CT images in an independent testing dataset of CT examinations. The proposed scheme separated multiple connections regardless of their locations, and the guided dynamic programming algorithm reduced the computation time to approximately 4.6% in comparison with the traditional dynamic programming and avoided the permeation of the separation boundary into normal lung tissue. Conclusions The proposed method is able to robustly and accurately disconnect all connections between left and right lungs and the guided dynamic programming algorithm is able to remove redundant processing. PMID:21412104

Separation of left and right lungs using 3-dimensional information of sequential computed tomography images and a guided dynamic programming algorithm.

PubMed

Park, Sang Cheol; Leader, Joseph Ken; Tan, Jun; Lee, Guee Sang; Kim, Soo Hyung; Na, In Seop; Zheng, Bin

2011-01-01

This article presents a new computerized scheme that aims to accurately and robustly separate left and right lungs on computed tomography (CT) examinations. We developed and tested a method to separate the left and right lungs using sequential CT information and a guided dynamic programming algorithm using adaptively and automatically selected start point and end point with especially severe and multiple connections. The scheme successfully identified and separated all 827 connections on the total 4034 CT images in an independent testing data set of CT examinations. The proposed scheme separated multiple connections regardless of their locations, and the guided dynamic programming algorithm reduced the computation time to approximately 4.6% in comparison with the traditional dynamic programming and avoided the permeation of the separation boundary into normal lung tissue. The proposed method is able to robustly and accurately disconnect all connections between left and right lungs, and the guided dynamic programming algorithm is able to remove redundant processing.

An Improved Image Matching Method Based on Surf Algorithm

NASA Astrophysics Data System (ADS)

Chen, S. J.; Zheng, S. Z.; Xu, Z. G.; Guo, C. C.; Ma, X. L.

2018-04-01

Many state-of-the-art image matching methods, based on the feature matching, have been widely studied in the remote sensing field. These methods of feature matching which get highly operating efficiency, have a disadvantage of low accuracy and robustness. This paper proposes an improved image matching method which based on the SURF algorithm. The proposed method introduces color invariant transformation, information entropy theory and a series of constraint conditions to increase feature points detection and matching accuracy. First, the model of color invariant transformation is introduced for two matching images aiming at obtaining more color information during the matching process and information entropy theory is used to obtain the most information of two matching images. Then SURF algorithm is applied to detect and describe points from the images. Finally, constraint conditions which including Delaunay triangulation construction, similarity function and projective invariant are employed to eliminate the mismatches so as to improve matching precision. The proposed method has been validated on the remote sensing images and the result benefits from its high precision and robustness.

A new edge detection algorithm based on Canny idea

NASA Astrophysics Data System (ADS)

Feng, Yingke; Zhang, Jinmin; Wang, Siming

2017-10-01

The traditional Canny algorithm has poor self-adaptability threshold, and it is more sensitive to noise. In order to overcome these drawbacks, this paper proposed a new edge detection method based on Canny algorithm. Firstly, the media filtering and filtering based on the method of Euclidean distance are adopted to process it; secondly using the Frei-chen algorithm to calculate gradient amplitude; finally, using the Otsu algorithm to calculate partial gradient amplitude operation to get images of thresholds value, then find the average of all thresholds that had been calculated, half of the average is high threshold value, and the half of the high threshold value is low threshold value. Experiment results show that this new method can effectively suppress noise disturbance, keep the edge information, and also improve the edge detection accuracy.

Fast Ss-Ilm a Computationally Efficient Algorithm to Discover Socially Important Locations

NASA Astrophysics Data System (ADS)

Dokuz, A. S.; Celik, M.

2017-11-01

Socially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provide several valuable information about user behaviours on social media networking sites. However, discovering socially important locations are challenging due to data volume and dimensions, spatial and temporal calculations, location sparseness in social media datasets, and inefficiency of current algorithms. In the literature, several studies are conducted to discover important locations, however, the proposed approaches do not work in computationally efficient manner. In this study, we propose Fast SS-ILM algorithm by modifying the algorithm of SS-ILM to mine socially important locations efficiently. Experimental results show that proposed Fast SS-ILM algorithm decreases execution time of socially important locations discovery process up to 20 %.

Sensitivity-Based Guided Model Calibration

NASA Astrophysics Data System (ADS)

Semnani, M.; Asadzadeh, M.

2017-12-01

A common practice in automatic calibration of hydrologic models is applying the sensitivity analysis prior to the global optimization to reduce the number of decision variables (DVs) by identifying the most sensitive ones. This two-stage process aims to improve the optimization efficiency. However, Parameter sensitivity information can be used to enhance the ability of the optimization algorithms to find good quality solutions in a fewer number of solution evaluations. This improvement can be achieved by increasing the focus of optimization on sampling from the most sensitive parameters in each iteration. In this study, the selection process of the dynamically dimensioned search (DDS) optimization algorithm is enhanced by utilizing a sensitivity analysis method to put more emphasis on the most sensitive decision variables for perturbation. The performance of DDS with the sensitivity information is compared to the original version of DDS for different mathematical test functions and a model calibration case study. Overall, the results show that DDS with sensitivity information finds nearly the same solutions as original DDS, however, in a significantly fewer number of solution evaluations.

Automated Ontology Generation Using Spatial Reasoning

NASA Astrophysics Data System (ADS)

Coalter, Alton; Leopold, Jennifer L.

Recently there has been much interest in using ontologies to facilitate knowledge representation, integration, and reasoning. Correspondingly, the extent of the information embodied by an ontology is increasing beyond the conventional is_a and part_of relationships. To address these requirements, a vast amount of digitally available information may need to be considered when building ontologies, prompting a desire for software tools to automate at least part of the process. The main efforts in this direction have involved textual information retrieval and extraction methods. For some domains extension of the basic relationships could be enhanced further by the analysis of 2D and/or 3D images. For this type of media, image processing algorithms are more appropriate than textual analysis methods. Herein we present an algorithm that, given a collection of 3D image files, utilizes Qualitative Spatial Reasoning (QSR) to automate the creation of an ontology for the objects represented by the images, relating the objects in terms of is_a and part_of relationships and also through unambiguous Relational Connection Calculus (RCC) relations.

An Overview of the JPSS Ground Project Algorithm Integration Process

NASA Astrophysics Data System (ADS)

Vicente, G. A.; Williams, R.; Dorman, T. J.; Williamson, R. C.; Shaw, F. J.; Thomas, W. M.; Hung, L.; Griffin, A.; Meade, P.; Steadley, R. S.; Cember, R. P.

2015-12-01

The smooth transition, implementation and operationalization of scientific software's from the National Oceanic and Atmospheric Administration (NOAA) development teams to the Join Polar Satellite System (JPSS) Ground Segment requires a variety of experiences and expertise. This task has been accomplished by a dedicated group of scientist and engineers working in close collaboration with the NOAA Satellite and Information Services (NESDIS) Center for Satellite Applications and Research (STAR) science teams for the JPSS/Suomi-NPOES Preparatory Project (S-NPP) Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS), Visible Infrared Imaging Radiometer Suite (VIIRS) and Ozone Mapping and Profiler Suite (OMPS) instruments. The presentation purpose is to describe the JPSS project process for algorithm implementation from the very early delivering stages by the science teams to the full operationalization into the Interface Processing Segment (IDPS), the processing system that provides Environmental Data Records (EDR's) to NOAA. Special focus is given to the NASA Data Products Engineering and Services (DPES) Algorithm Integration Team (AIT) functional and regression test activities. In the functional testing phase, the AIT uses one or a few specific chunks of data (granules) selected by the NOAA STAR Calibration and Validation (cal/val) Teams to demonstrate that a small change in the code performs properly and does not disrupt the rest of the algorithm chain. In the regression testing phase, the modified code is placed into to the Government Resources for Algorithm Verification, Integration, Test and Evaluation (GRAVITE) Algorithm Development Area (ADA), a simulated and smaller version of the operational IDPS. Baseline files are swapped out, not edited and the whole code package runs in one full orbit of Science Data Records (SDR's) using Calibration Look Up Tables (Cal LUT's) for the time of the orbit. The purpose of the regression test is to identify unintended outcomes. Overall the presentation provides a general and easy to follow overview of the JPSS Algorithm Change Process (ACP) and is intended to facility the audience understanding of a very extensive and complex process.

Cargo identification algorithms facilitating unmanned/unattended inspection at high throughput portals

NASA Astrophysics Data System (ADS)

Chalmers, Alex

2007-10-01

A simple model is presented of a possible inspection regimen applied to each leg of a cargo containers' journey between its point of origin and destination. Several candidate modalities are proposed to be used at multiple remote locations to act as a pre-screen inspection as the target approaches a perimeter and as the primary inspection modality at the portal. Information from multiple data sets are fused to optimize the costs and performance of a network of such inspection systems. A series of image processing algorithms are presented that automatically process X-ray images of containerized cargo. The goal of this processing is to locate the container in a real time stream of traffic traversing a portal without impeding the flow of commerce. Such processing may facilitate the inclusion of unmanned/unattended inspection systems in such a network. Several samples of the processing applied to data collected from deployed systems are included. Simulated data from a notional cargo inspection system with multiple sensor modalities and advanced data fusion algorithms are also included to show the potential increased detection and throughput performance of such a configuration.

3D interactive surgical visualization system using mobile spatial information acquisition and autostereoscopic display.

PubMed

Fan, Zhencheng; Weng, Yitong; Chen, Guowen; Liao, Hongen

2017-07-01

Three-dimensional (3D) visualization of preoperative and intraoperative medical information becomes more and more important in minimally invasive surgery. We develop a 3D interactive surgical visualization system using mobile spatial information acquisition and autostereoscopic display for surgeons to observe surgical target intuitively. The spatial information of regions of interest (ROIs) is captured by the mobile device and transferred to a server for further image processing. Triangular patches of intraoperative data with texture are calculated with a dimension-reduced triangulation algorithm and a projection-weighted mapping algorithm. A point cloud selection-based warm-start iterative closest point (ICP) algorithm is also developed for fusion of the reconstructed 3D intraoperative image and the preoperative image. The fusion images are rendered for 3D autostereoscopic display using integral videography (IV) technology. Moreover, 3D visualization of medical image corresponding to observer's viewing direction is updated automatically using mutual information registration method. Experimental results show that the spatial position error between the IV-based 3D autostereoscopic fusion image and the actual object was 0.38±0.92mm (n=5). The system can be utilized in telemedicine, operating education, surgical planning, navigation, etc. to acquire spatial information conveniently and display surgical information intuitively. Copyright © 2017 Elsevier Inc. All rights reserved.

Standardized shrinking LORETA-FOCUSS (SSLOFO): a new algorithm for spatio-temporal EEG source reconstruction.

PubMed

Liu, Hesheng; Schimpf, Paul H; Dong, Guoya; Gao, Xiaorong; Yang, Fusheng; Gao, Shangkai

2005-10-01

This paper presents a new algorithm called Standardized Shrinking LORETA-FOCUSS (SSLOFO) for solving the electroencephalogram (EEG) inverse problem. Multiple techniques are combined in a single procedure to robustly reconstruct the underlying source distribution with high spatial resolution. This algorithm uses a recursive process which takes the smooth estimate of sLORETA as initialization and then employs the re-weighted minimum norm introduced by FOCUSS. An important technique called standardization is involved in the recursive process to enhance the localization ability. The algorithm is further improved by automatically adjusting the source space according to the estimate of the previous step, and by the inclusion of temporal information. Simulation studies are carried out on both spherical and realistic head models. The algorithm achieves very good localization ability on noise-free data. It is capable of recovering complex source configurations with arbitrary shapes and can produce high quality images of extended source distributions. We also characterized the performance with noisy data in a realistic head model. An important feature of this algorithm is that the temporal waveforms are clearly reconstructed, even for closely spaced sources. This provides a convenient way to estimate neural dynamics directly from the cortical sources.

An ant colony based algorithm for overlapping community detection in complex networks

NASA Astrophysics Data System (ADS)

Zhou, Xu; Liu, Yanheng; Zhang, Jindong; Liu, Tuming; Zhang, Di

2015-06-01

Community detection is of great importance to understand the structures and functions of networks. Overlap is a significant feature of networks and overlapping community detection has attracted an increasing attention. Many algorithms have been presented to detect overlapping communities. In this paper, we present an ant colony based overlapping community detection algorithm which mainly includes ants' location initialization, ants' movement and post processing phases. An ants' location initialization strategy is designed to identify initial location of ants and initialize label list stored in each node. During the ants' movement phase, the entire ants move according to the transition probability matrix, and a new heuristic information computation approach is redefined to measure similarity between two nodes. Every node keeps a label list through the cooperation made by ants until a termination criterion is reached. A post processing phase is executed on the label list to get final overlapping community structure naturally. We illustrate the capability of our algorithm by making experiments on both synthetic networks and real world networks. The results demonstrate that our algorithm will have better performance in finding overlapping communities and overlapping nodes in synthetic datasets and real world datasets comparing with state-of-the-art algorithms.

Compressing climate model simulations: reducing storage burden while preserving information

NASA Astrophysics Data System (ADS)

Hammerling, Dorit; Baker, Allison; Xu, Haiying; Clyne, John; Li, Samuel

2017-04-01

Climate models, which are run at high spatial and temporal resolutions, generate massive quantities of data. As our computing capabilities continue to increase, storing all of the generated data is becoming a bottleneck, which negatively affects scientific progress. It is thus important to develop methods for representing the full datasets by smaller compressed versions, which still preserve all the critical information and, as an added benefit, allow for faster read and write operations during analysis work. Traditional lossy compression algorithms, as for example used for image files, are not necessarily ideally suited for climate data. While visual appearance is relevant, climate data has additional critical features such as the preservation of extreme values and spatial and temporal gradients. Developing alternative metrics to quantify information loss in a manner that is meaningful to climate scientists is an ongoing process still in its early stages. We will provide an overview of current efforts to develop such metrics to assess existing algorithms and to guide the development of tailored compression algorithms to address this pressing challenge.

Asymptotic Cramer-Rao bounds for Morlet wavelet filter bank transforms of FM signals

NASA Astrophysics Data System (ADS)

Scheper, Richard

2002-03-01

Wavelet filter banks are potentially useful tools for analyzing and extracting information from frequency modulated (FM) signals in noise. Chief among the advantages of such filter banks is the tendency of wavelet transforms to concentrate signal energy while simultaneously dispersing noise energy over the time-frequency plane, thus raising the effective signal to noise ratio of filtered signals. Over the past decade, much effort has gone into devising new algorithms to extract the relevant information from transformed signals while identifying and discarding the transformed noise. Therefore, estimates of the ultimate performance bounds on such algorithms would serve as valuable benchmarks in the process of choosing optimal algorithms for given signal classes. Discussed here is the specific case of FM signals analyzed by Morlet wavelet filter banks. By making use of the stationary phase approximation of the Morlet transform, and assuming that the measured signals are well resolved digitally, the asymptotic form of the Fisher Information Matrix is derived. From this, Cramer-Rao bounds are analytically derived for simple cases.

Neurient: An Algorithm for Automatic Tracing of Confluent Neuronal Images to Determine Alignment

PubMed Central

Mitchel, J.A.; Martin, I.S.

2013-01-01

A goal of neural tissue engineering is the development and evaluation of materials that guide neuronal growth and alignment. However, the methods available to quantitatively evaluate the response of neurons to guidance materials are limited and/or expensive, and may require manual tracing to be performed by the researcher. We have developed an open source, automated Matlab-based algorithm, building on previously published methods, to trace and quantify alignment of fluorescent images of neurons in culture. The algorithm is divided into three phases, including computation of a lookup table which contains directional information for each image, location of a set of seed points which may lie along neurite centerlines, and tracing neurites starting with each seed point and indexing into the lookup table. This method was used to obtain quantitative alignment data for complex images of densely cultured neurons. Complete automation of tracing allows for unsupervised processing of large numbers of images. Following image processing with our algorithm, available metrics to quantify neurite alignment include angular histograms, percent of neurite segments in a given direction, and mean neurite angle. The alignment information obtained from traced images can be used to compare the response of neurons to a range of conditions. This tracing algorithm is freely available to the scientific community under the name Neurient, and its implementation in Matlab allows a wide range of researchers to use a standardized, open source method to quantitatively evaluate the alignment of dense neuronal cultures. PMID:23384629

DEVELOPMENT AND TESTING OF FAULT-DIAGNOSIS ALGORITHMS FOR REACTOR PLANT SYSTEMS

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

Grelle, Austin L.; Park, Young S.; Vilim, Richard B.

Argonne National Laboratory is further developing fault diagnosis algorithms for use by the operator of a nuclear plant to aid in improved monitoring of overall plant condition and performance. The objective is better management of plant upsets through more timely, informed decisions on control actions with the ultimate goal of improved plant safety, production, and cost management. Integration of these algorithms with visual aids for operators is taking place through a collaboration under the concept of an operator advisory system. This is a software entity whose purpose is to manage and distill the enormous amount of information an operator mustmore » process to understand the plant state, particularly in off-normal situations, and how the state trajectory will unfold in time. The fault diagnosis algorithms were exhaustively tested using computer simulations of twenty different faults introduced into the chemical and volume control system (CVCS) of a pressurized water reactor (PWR). The algorithms are unique in that each new application to a facility requires providing only the piping and instrumentation diagram (PID) and no other plant-specific information; a subject-matter expert is not needed to install and maintain each instance of an application. The testing approach followed accepted procedures for verifying and validating software. It was shown that the code satisfies its functional requirement which is to accept sensor information, identify process variable trends based on this sensor information, and then to return an accurate diagnosis based on chains of rules related to these trends. The validation and verification exercise made use of GPASS, a one-dimensional systems code, for simulating CVCS operation. Plant components were failed and the code generated the resulting plant response. Parametric studies with respect to the severity of the fault, the richness of the plant sensor set, and the accuracy of sensors were performed as part of the validation exercise. The background and overview of the software will be presented to give an overview of the approach. Following, the verification and validation effort using the GPASS code for simulation of plant transients including a sensitivity study on important parameters will be presented« less

Fitting-free algorithm for efficient quantification of collagen fiber alignment in SHG imaging applications.

PubMed

Hall, Gunnsteinn; Liang, Wenxuan; Li, Xingde

2017-10-01

Collagen fiber alignment derived from second harmonic generation (SHG) microscopy images can be important for disease diagnostics. Image processing algorithms are needed to robustly quantify the alignment in images with high sensitivity and reliability. Fourier transform (FT) magnitude, 2D power spectrum, and image autocorrelation have previously been used to extract fiber information from images by assuming a certain mathematical model (e.g. Gaussian distribution of the fiber-related parameters) and fitting. The fitting process is slow and fails to converge when the data is not Gaussian. Herein we present an efficient constant-time deterministic algorithm which characterizes the symmetricity of the FT magnitude image in terms of a single parameter, named the fiber alignment anisotropy R ranging from 0 (randomized fibers) to 1 (perfect alignment). This represents an important improvement of the technology and may bring us one step closer to utilizing the technology for various applications in real time. In addition, we present a digital image phantom-based framework for characterizing and validating the algorithm, as well as assessing the robustness of the algorithm against different perturbations.

An Automated Summarization Assessment Algorithm for Identifying Summarizing Strategies

PubMed Central

Abdi, Asad; Idris, Norisma; Alguliyev, Rasim M.; Aliguliyev, Ramiz M.

2016-01-01

Background Summarization is a process to select important information from a source text. Summarizing strategies are the core cognitive processes in summarization activity. Since summarization can be important as a tool to improve comprehension, it has attracted interest of teachers for teaching summary writing through direct instruction. To do this, they need to review and assess the students' summaries and these tasks are very time-consuming. Thus, a computer-assisted assessment can be used to help teachers to conduct this task more effectively. Design/Results This paper aims to propose an algorithm based on the combination of semantic relations between words and their syntactic composition to identify summarizing strategies employed by students in summary writing. An innovative aspect of our algorithm lies in its ability to identify summarizing strategies at the syntactic and semantic levels. The efficiency of the algorithm is measured in terms of Precision, Recall and F-measure. We then implemented the algorithm for the automated summarization assessment system that can be used to identify the summarizing strategies used by students in summary writing. PMID:26735139

From Data to Knowledge: GEOSS experience and the GEOSS Knowledge Base contribution to the GCI

NASA Astrophysics Data System (ADS)

Santoro, M.; Nativi, S.; Mazzetti, P., Sr.; Plag, H. P.

2016-12-01

According to systems theory, data is raw, it simply exists and has no significance beyond its existence; while, information is data that has been given meaning by way of relational connection. The appropriate collection of information, such that it contributes to understanding, is a process of knowledge creation.The Global Earth Observation System of Systems (GEOSS) developed by the Group on Earth Observations (GEO) is a set of coordinated, independent Earth observation, information and processing systems that interact and provide access to diverse information for a broad range of users in both public and private sectors. GEOSS links these systems to strengthen the monitoring of the state of the Earth. In the past ten years, the development of GEOSS has taught several lessons dealing with the need to move from (open) data to information and knowledge sharing. Advanced user-focused services require to move from a data-driven framework to a knowledge sharing platform. Such a platform needs to manage information and knowledge, in addition to datasets linked to them. For this scope, GEO has launched a specific task called "GEOSS Knowledge Base", which deals with resources, like user requirements, Sustainable Development Goals (SDGs), observation and processing ontologies, publications, guidelines, best practices, business processes/algorithms, definition of advanced concepts like Essential Variables (EVs), indicators, strategic goals, etc. In turn, information and knowledge (e.g. guidelines, best practices, user requirements, business processes, algorithms, etc.) can be used to generate additional information and knowledge from shared datasets. To fully utilize and leverage the GEOSS Knowledge Base, the current GEOSS Common Infrastructure (GCI) model will be extended and advanced to consider important concepts and implementation artifacts, such as data processing services and environmental/economic models as well as EVs, Primary Indicators, and SDGs. The new GCI model will link these concepts to the present dataset, observation and sensor concepts, enabling a set of very important new capabilities to be offered to GEOSS users.

Time-of-flight PET image reconstruction using origin ensembles.

PubMed

Wülker, Christian; Sitek, Arkadiusz; Prevrhal, Sven

2015-03-07

The origin ensemble (OE) algorithm is a novel statistical method for minimum-mean-square-error (MMSE) reconstruction of emission tomography data. This method allows one to perform reconstruction entirely in the image domain, i.e. without the use of forward and backprojection operations. We have investigated the OE algorithm in the context of list-mode (LM) time-of-flight (TOF) PET reconstruction. In this paper, we provide a general introduction to MMSE reconstruction, and a statistically rigorous derivation of the OE algorithm. We show how to efficiently incorporate TOF information into the reconstruction process, and how to correct for random coincidences and scattered events. To examine the feasibility of LM-TOF MMSE reconstruction with the OE algorithm, we applied MMSE-OE and standard maximum-likelihood expectation-maximization (ML-EM) reconstruction to LM-TOF phantom data with a count number typically registered in clinical PET examinations. We analyzed the convergence behavior of the OE algorithm, and compared reconstruction time and image quality to that of the EM algorithm. In summary, during the reconstruction process, MMSE-OE contrast recovery (CRV) remained approximately the same, while background variability (BV) gradually decreased with an increasing number of OE iterations. The final MMSE-OE images exhibited lower BV and a slightly lower CRV than the corresponding ML-EM images. The reconstruction time of the OE algorithm was approximately 1.3 times longer. At the same time, the OE algorithm can inherently provide a comprehensive statistical characterization of the acquired data. This characterization can be utilized for further data processing, e.g. in kinetic analysis and image registration, making the OE algorithm a promising approach in a variety of applications.

Time-of-flight PET image reconstruction using origin ensembles

NASA Astrophysics Data System (ADS)

Wülker, Christian; Sitek, Arkadiusz; Prevrhal, Sven

2015-03-01

The origin ensemble (OE) algorithm is a novel statistical method for minimum-mean-square-error (MMSE) reconstruction of emission tomography data. This method allows one to perform reconstruction entirely in the image domain, i.e. without the use of forward and backprojection operations. We have investigated the OE algorithm in the context of list-mode (LM) time-of-flight (TOF) PET reconstruction. In this paper, we provide a general introduction to MMSE reconstruction, and a statistically rigorous derivation of the OE algorithm. We show how to efficiently incorporate TOF information into the reconstruction process, and how to correct for random coincidences and scattered events. To examine the feasibility of LM-TOF MMSE reconstruction with the OE algorithm, we applied MMSE-OE and standard maximum-likelihood expectation-maximization (ML-EM) reconstruction to LM-TOF phantom data with a count number typically registered in clinical PET examinations. We analyzed the convergence behavior of the OE algorithm, and compared reconstruction time and image quality to that of the EM algorithm. In summary, during the reconstruction process, MMSE-OE contrast recovery (CRV) remained approximately the same, while background variability (BV) gradually decreased with an increasing number of OE iterations. The final MMSE-OE images exhibited lower BV and a slightly lower CRV than the corresponding ML-EM images. The reconstruction time of the OE algorithm was approximately 1.3 times longer. At the same time, the OE algorithm can inherently provide a comprehensive statistical characterization of the acquired data. This characterization can be utilized for further data processing, e.g. in kinetic analysis and image registration, making the OE algorithm a promising approach in a variety of applications.

Fragmenting networks by targeting collective influencers at a mesoscopic level.

PubMed

Kobayashi, Teruyoshi; Masuda, Naoki

2016-11-25

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure.

Fragmenting networks by targeting collective influencers at a mesoscopic level

NASA Astrophysics Data System (ADS)

Kobayashi, Teruyoshi; Masuda, Naoki

2016-11-01

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure.

Fragmenting networks by targeting collective influencers at a mesoscopic level

PubMed Central

Kobayashi, Teruyoshi; Masuda, Naoki

2016-01-01

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure. PMID:27886251

A View from Above Without Leaving the Ground

NASA Technical Reports Server (NTRS)

2004-01-01

In order to deliver accurate geospatial data and imagery to the remote sensing community, NASA is constantly developing new image-processing algorithms while refining existing ones for technical improvement. For 8 years, the NASA Regional Applications Center at Florida International University has served as a test bed for implementing and validating many of these algorithms, helping the Space Program to fulfill its strategic and educational goals in the area of remote sensing. The algorithms in return have helped the NASA Regional Applications Center develop comprehensive semantic database systems for data management, as well as new tools for disseminating geospatial information via the Internet.

Parallel multi-join query optimization algorithm for distributed sensor network in the internet of things

NASA Astrophysics Data System (ADS)

Zheng, Yan

2015-03-01

Internet of things (IoT), focusing on providing users with information exchange and intelligent control, attracts a lot of attention of researchers from all over the world since the beginning of this century. IoT is consisted of large scale of sensor nodes and data processing units, and the most important features of IoT can be illustrated as energy confinement, efficient communication and high redundancy. With the sensor nodes increment, the communication efficiency and the available communication band width become bottle necks. Many research work is based on the instance which the number of joins is less. However, it is not proper to the increasing multi-join query in whole internet of things. To improve the communication efficiency between parallel units in the distributed sensor network, this paper proposed parallel query optimization algorithm based on distribution attributes cost graph. The storage information relations and the network communication cost are considered in this algorithm, and an optimized information changing rule is established. The experimental result shows that the algorithm has good performance, and it would effectively use the resource of each node in the distributed sensor network. Therefore, executive efficiency of multi-join query between different nodes could be improved.

Robust fundamental frequency estimation in sustained vowels: Detailed algorithmic comparisons and information fusion with adaptive Kalman filtering

PubMed Central

Tsanas, Athanasios; Zañartu, Matías; Little, Max A.; Fox, Cynthia; Ramig, Lorraine O.; Clifford, Gari D.

2014-01-01

There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F0) of speech signals. This study examines ten F0 estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F0 in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F0 estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F0 estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F0 estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F0 estimation is required. PMID:24815269

Wind speed vector restoration algorithm

NASA Astrophysics Data System (ADS)

Baranov, Nikolay; Petrov, Gleb; Shiriaev, Ilia

2018-04-01

Impulse wind lidar (IWL) signal processing software developed by JSC «BANS» recovers full wind speed vector by radial projections and provides wind parameters information up to 2 km distance. Increasing accuracy and speed of wind parameters calculation signal processing technics have been studied in this research. Measurements results of IWL and continuous scanning lidar were compared. Also, IWL data processing modeling results have been analyzed.

Managed traffic evacuation using distributed sensor processing

NASA Astrophysics Data System (ADS)

Ramuhalli, Pradeep; Biswas, Subir

2005-05-01

This paper presents an integrated sensor network and distributed event processing architecture for managed in-building traffic evacuation during natural and human-caused disasters, including earthquakes, fire and biological/chemical terrorist attacks. The proposed wireless sensor network protocols and distributed event processing mechanisms offer a new distributed paradigm for improving reliability in building evacuation and disaster management. The networking component of the system is constructed using distributed wireless sensors for measuring environmental parameters such as temperature, humidity, and detecting unusual events such as smoke, structural failures, vibration, biological/chemical or nuclear agents. Distributed event processing algorithms will be executed by these sensor nodes to detect the propagation pattern of the disaster and to measure the concentration and activity of human traffic in different parts of the building. Based on this information, dynamic evacuation decisions are taken for maximizing the evacuation speed and minimizing unwanted incidents such as human exposure to harmful agents and stampedes near exits. A set of audio-visual indicators and actuators are used for aiding the automated evacuation process. In this paper we develop integrated protocols, algorithms and their simulation models for the proposed sensor networking and the distributed event processing framework. Also, efficient harnessing of the individually low, but collectively massive, processing abilities of the sensor nodes is a powerful concept behind our proposed distributed event processing algorithms. Results obtained through simulation in this paper are used for a detailed characterization of the proposed evacuation management system and its associated algorithmic components.

Evaluation of thresholding techniques for segmenting scaffold images in tissue engineering

NASA Astrophysics Data System (ADS)

Rajagopalan, Srinivasan; Yaszemski, Michael J.; Robb, Richard A.

2004-05-01

Tissue engineering attempts to address the ever widening gap between the demand and supply of organ and tissue transplants using natural and biomimetic scaffolds. The regeneration of specific tissues aided by synthetic materials is dependent on the structural and morphometric properties of the scaffold. These properties can be derived non-destructively using quantitative analysis of high resolution microCT scans of scaffolds. Thresholding of the scanned images into polymeric and porous phase is central to the outcome of the subsequent structural and morphometric analysis. Visual thresholding of scaffolds produced using stochastic processes is inaccurate. Depending on the algorithmic assumptions made, automatic thresholding might also be inaccurate. Hence there is a need to analyze the performance of different techniques and propose alternate ones, if needed. This paper provides a quantitative comparison of different thresholding techniques for segmenting scaffold images. The thresholding algorithms examined include those that exploit spatial information, locally adaptive characteristics, histogram entropy information, histogram shape information, and clustering of gray-level information. The performance of different techniques was evaluated using established criteria, including misclassification error, edge mismatch, relative foreground error, and region non-uniformity. Algorithms that exploit local image characteristics seem to perform much better than those using global information.

Gaussian process tomography for soft x-ray spectroscopy at WEST without equilibrium information

NASA Astrophysics Data System (ADS)

Wang, T.; Mazon, D.; Svensson, J.; Li, D.; Jardin, A.; Verdoolaege, G.

2018-06-01

Gaussian process tomography (GPT) is a recently developed tomography method based on the Bayesian probability theory [J. Svensson, JET Internal Report EFDA-JET-PR(11)24, 2011 and Li et al., Rev. Sci. Instrum. 84, 083506 (2013)]. By modeling the soft X-ray (SXR) emissivity field in a poloidal cross section as a Gaussian process, the Bayesian SXR tomography can be carried out in a robust and extremely fast way. Owing to the short execution time of the algorithm, GPT is an important candidate for providing real-time reconstructions with a view to impurity transport and fast magnetohydrodynamic control. In addition, the Bayesian formalism allows quantifying uncertainty on the inferred parameters. In this paper, the GPT technique is validated using a synthetic data set expected from the WEST tokamak, and the results are shown of its application to the reconstruction of SXR emissivity profiles measured on Tore Supra. The method is compared with the standard algorithm based on minimization of the Fisher information.

Improved chemical identification from sensor arrays using intelligent algorithms

NASA Astrophysics Data System (ADS)

Roppel, Thaddeus A.; Wilson, Denise M.

2001-02-01

Intelligent signal processing algorithms are shown to improve identification rates significantly in chemical sensor arrays. This paper focuses on the use of independently derived sensor status information to modify the processing of sensor array data by using a fast, easily-implemented "best-match" approach to filling in missing sensor data. Most fault conditions of interest (e.g., stuck high, stuck low, sudden jumps, excess noise, etc.) can be detected relatively simply by adjunct data processing, or by on-board circuitry. The objective then is to devise, implement, and test methods for using this information to improve the identification rates in the presence of faulted sensors. In one typical example studied, utilizing separately derived, a-priori knowledge about the health of the sensors in the array improved the chemical identification rate by an artificial neural network from below 10 percent correct to over 99 percent correct. While this study focuses experimentally on chemical sensor arrays, the results are readily extensible to other types of sensor platforms.

Implementation of dictionary pair learning algorithm for image quality improvement

NASA Astrophysics Data System (ADS)

Vimala, C.; Aruna Priya, P.

2018-04-01

This paper proposes an image denoising on dictionary pair learning algorithm. Visual information is transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmissions is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image.

A Parallel Ghosting Algorithm for The Flexible Distributed Mesh Database

DOE PAGES

Mubarak, Misbah; Seol, Seegyoung; Lu, Qiukai; ...

2013-01-01

Critical to the scalability of parallel adaptive simulations are parallel control functions including load balancing, reduced inter-process communication and optimal data decomposition. In distributed meshes, many mesh-based applications frequently access neighborhood information for computational purposes which must be transmitted efficiently to avoid parallel performance degradation when the neighbors are on different processors. This article presents a parallel algorithm of creating and deleting data copies, referred to as ghost copies, which localize neighborhood data for computation purposes while minimizing inter-process communication. The key characteristics of the algorithm are: (1) It can create ghost copies of any permissible topological order in amore » 1D, 2D or 3D mesh based on selected adjacencies. (2) It exploits neighborhood communication patterns during the ghost creation process thus eliminating all-to-all communication. (3) For applications that need neighbors of neighbors, the algorithm can create n number of ghost layers up to a point where the whole partitioned mesh can be ghosted. Strong and weak scaling results are presented for the IBM BG/P and Cray XE6 architectures up to a core count of 32,768 processors. The algorithm also leads to scalable results when used in a parallel super-convergent patch recovery error estimator, an application that frequently accesses neighborhood data to carry out computation.« less

Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes.

PubMed

Molina, Iñigo; Martinez, Estibaliz; Morillo, Carmen; Velasco, Jesus; Jara, Alvaro

2016-09-30

In this work a parametric multi-sensor Bayesian data fusion approach and a Support Vector Machine (SVM) are used for a Change Detection problem. For this purpose two sets of SPOT5-PAN images have been used, which are in turn used for Change Detection Indices (CDIs) calculation. For minimizing radiometric differences, a methodology based on zonal "invariant features" is suggested. The choice of one or the other CDI for a change detection process is a subjective task as each CDI is probably more or less sensitive to certain types of changes. Likewise, this idea might be employed to create and improve a "change map", which can be accomplished by means of the CDI's informational content. For this purpose, information metrics such as the Shannon Entropy and "Specific Information" have been used to weight the changes and no-changes categories contained in a certain CDI and thus introduced in the Bayesian information fusion algorithm. Furthermore, the parameters of the probability density functions (pdf's) that best fit the involved categories have also been estimated. Conversely, these considerations are not necessary for mapping procedures based on the discriminant functions of a SVM. This work has confirmed the capabilities of probabilistic information fusion procedure under these circumstances.

Electronics design of the airborne stabilized platform attitude acquisition module

NASA Astrophysics Data System (ADS)

Xu, Jiang; Wei, Guiling; Cheng, Yong; Li, Baolin; Bu, Hongyi; Wang, Hao; Zhang, Zhanwei; Li, Xingni

2014-02-01

We present an attitude acquisition module electronics design for the airborne stabilized platform. The design scheme, which is based on Integrated MEMS sensor ADIS16405, develops the attitude information processing algorithms and the hardware circuit. The hardware circuits with a small volume of only 44.9 x 43.6 x 24.6 mm3, has the characteristics of lightweight, modularization and digitalization. The interface design of the PC software uses the combination plane chart with track line to receive the attitude information and display. Attitude calculation uses the Kalman filtering algorithm to improve the measurement accuracy of the module in the dynamic environment.

Information theoretic analysis of canny edge detection in visual communication

NASA Astrophysics Data System (ADS)

Jiang, Bo; Rahman, Zia-ur

2011-06-01

In general edge detection evaluation, the edge detectors are examined, analyzed, and compared either visually or with a metric for specific an application. This analysis is usually independent of the characteristics of the image-gathering, transmission and display processes that do impact the quality of the acquired image and thus, the resulting edge image. We propose a new information theoretic analysis of edge detection that unites the different components of the visual communication channel and assesses edge detection algorithms in an integrated manner based on Shannon's information theory. The edge detection algorithm here is considered to achieve high performance only if the information rate from the scene to the edge approaches the maximum possible. Thus, by setting initial conditions of the visual communication system as constant, different edge detection algorithms could be evaluated. This analysis is normally limited to linear shift-invariant filters so in order to examine the Canny edge operator in our proposed system, we need to estimate its "power spectral density" (PSD). Since the Canny operator is non-linear and shift variant, we perform the estimation for a set of different system environment conditions using simulations. In our paper we will first introduce the PSD of the Canny operator for a range of system parameters. Then, using the estimated PSD, we will assess the Canny operator using information theoretic analysis. The information-theoretic metric is also used to compare the performance of the Canny operator with other edge-detection operators. This also provides a simple tool for selecting appropriate edgedetection algorithms based on system parameters, and for adjusting their parameters to maximize information throughput.

DynAMo: A Modular Platform for Monitoring Process, Outcome, and Algorithm-Based Treatment Planning in Psychotherapy

PubMed Central

Laireiter, Anton Rupert

2017-01-01

Background In recent years, the assessment of mental disorders has become more and more personalized. Modern advancements such as Internet-enabled mobile phones and increased computing capacity make it possible to tap sources of information that have long been unavailable to mental health practitioners. Objective Software packages that combine algorithm-based treatment planning, process monitoring, and outcome monitoring are scarce. The objective of this study was to assess whether the DynAMo Web application can fill this gap by providing a software solution that can be used by both researchers to conduct state-of-the-art psychotherapy process research and clinicians to plan treatments and monitor psychotherapeutic processes. Methods In this paper, we report on the current state of a Web application that can be used for assessing the temporal structure of mental disorders using information on their temporal and synchronous associations. A treatment planning algorithm automatically interprets the data and delivers priority scores of symptoms to practitioners. The application is also capable of monitoring psychotherapeutic processes during therapy and of monitoring treatment outcomes. This application was developed using the R programming language (R Core Team, Vienna) and the Shiny Web application framework (RStudio, Inc, Boston). It is made entirely from open-source software packages and thus is easily extensible. Results The capabilities of the proposed application are demonstrated. Case illustrations are provided to exemplify its usefulness in clinical practice. Conclusions With the broad availability of Internet-enabled mobile phones and similar devices, collecting data on psychopathology and psychotherapeutic processes has become easier than ever. The proposed application is a valuable tool for capturing, processing, and visualizing these data. The combination of dynamic assessment and process- and outcome monitoring has the potential to improve the efficacy and effectiveness of psychotherapy. PMID:28729233

The concept of ageing in evolutionary algorithms: Discussion and inspirations for human ageing.

PubMed

Dimopoulos, Christos; Papageorgis, Panagiotis; Boustras, George; Efstathiades, Christodoulos

2017-04-01

This paper discusses the concept of ageing as this applies to the operation of Evolutionary Algorithms, and examines its relationship to the concept of ageing as this is understood for human beings. Evolutionary Algorithms constitute a family of search algorithms which base their operation on an analogy from the evolution of species in nature. The paper initially provides the necessary knowledge on the operation of Evolutionary Algorithms, focusing on the use of ageing strategies during the implementation of the evolutionary process. Background knowledge on the concept of ageing, as this is defined scientifically for biological systems, is subsequently presented. Based on this information, the paper provides a comparison between the two ageing concepts, and discusses the philosophical inspirations which can be drawn for human ageing based on the operation of Evolutionary Algorithms. Copyright © 2017 Elsevier B.V. All rights reserved.

SDIA: A dynamic situation driven information fusion algorithm for cloud environment

NASA Astrophysics Data System (ADS)

Guo, Shuhang; Wang, Tong; Wang, Jian

2017-09-01

Information fusion is an important issue in information integration domain. In order to form an extensive information fusion technology under the complex and diverse situations, a new information fusion algorithm is proposed. Firstly, a fuzzy evaluation model of tag utility was proposed that can be used to count the tag entropy. Secondly, a ubiquitous situation tag tree model is proposed to define multidimensional structure of information situation. Thirdly, the similarity matching between the situation models is classified into three types: the tree inclusion, the tree embedding, and the tree compatibility. Next, in order to reduce the time complexity of the tree compatible matching algorithm, a fast and ordered tree matching algorithm is proposed based on the node entropy, which is used to support the information fusion by ubiquitous situation. Since the algorithm revolve from the graph theory of disordered tree matching algorithm, it can improve the information fusion present recall rate and precision rate in the situation. The information fusion algorithm is compared with the star and the random tree matching algorithm, and the difference between the three algorithms is analyzed in the view of isomorphism, which proves the innovation and applicability of the algorithm.

The Role of Visualization in Computer Science Education

ERIC Educational Resources Information Center

Fouh, Eric; Akbar, Monika; Shaffer, Clifford A.

2012-01-01

Computer science core instruction attempts to provide a detailed understanding of dynamic processes such as the working of an algorithm or the flow of information between computing entities. Such dynamic processes are not well explained by static media such as text and images, and are difficult to convey in lecture. The authors survey the history…

Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers

ERIC Educational Resources Information Center

Anaya, Leticia H.

2011-01-01

In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed.…

Mapping ephemeral stream networks in desert environments using very-high-spatial-resolution multispectral remote sensing

DOE PAGES

Hamada, Yuki; O'Connor, Ben L.; Orr, Andrew B.; ...

2016-03-26

In this paper, understanding the spatial patterns of ephemeral streams is crucial for understanding how hydrologic processes influence the abundance and distribution of wildlife habitats in desert regions. Available methods for mapping ephemeral streams at the watershed scale typically underestimate the size of channel networks. Although remote sensing is an effective means of collecting data and obtaining information on large, inaccessible areas, conventional techniques for extracting channel features are not sufficient in regions that have small topographic gradients and subtle target-background spectral contrast. By using very high resolution multispectral imagery, we developed a new algorithm that applies landscape information tomore » map ephemeral channels in desert regions of the Southwestern United States where utility-scale solar energy development is occurring. Knowledge about landscape features and structures was integrated into the algorithm using a series of spectral transformation and spatial statistical operations to integrate information about landscape features and structures. The algorithm extracted ephemeral stream channels at a local scale, with the result that approximately 900% more ephemeral streams was identified than what were identified by using the U.S. Geological Survey’s National Hydrography Dataset. The accuracy of the algorithm in detecting channel areas was as high as 92%, and its accuracy in delineating channel center lines was 91% when compared to a subset of channel networks that were digitized by using the very high resolution imagery. Although the algorithm captured stream channels in desert landscapes across various channel sizes and forms, it often underestimated stream headwaters and channels obscured by bright soils and sparse vegetation. While further improvement is warranted, the algorithm provides an effective means of obtaining detailed information about ephemeral streams, and it could make a significant contribution toward improving the hydrological modelling of desert environments.« less

Mapping ephemeral stream networks in desert environments using very-high-spatial-resolution multispectral remote sensing

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

Hamada, Yuki; O'Connor, Ben L.; Orr, Andrew B.

In this paper, understanding the spatial patterns of ephemeral streams is crucial for understanding how hydrologic processes influence the abundance and distribution of wildlife habitats in desert regions. Available methods for mapping ephemeral streams at the watershed scale typically underestimate the size of channel networks. Although remote sensing is an effective means of collecting data and obtaining information on large, inaccessible areas, conventional techniques for extracting channel features are not sufficient in regions that have small topographic gradients and subtle target-background spectral contrast. By using very high resolution multispectral imagery, we developed a new algorithm that applies landscape information tomore » map ephemeral channels in desert regions of the Southwestern United States where utility-scale solar energy development is occurring. Knowledge about landscape features and structures was integrated into the algorithm using a series of spectral transformation and spatial statistical operations to integrate information about landscape features and structures. The algorithm extracted ephemeral stream channels at a local scale, with the result that approximately 900% more ephemeral streams was identified than what were identified by using the U.S. Geological Survey’s National Hydrography Dataset. The accuracy of the algorithm in detecting channel areas was as high as 92%, and its accuracy in delineating channel center lines was 91% when compared to a subset of channel networks that were digitized by using the very high resolution imagery. Although the algorithm captured stream channels in desert landscapes across various channel sizes and forms, it often underestimated stream headwaters and channels obscured by bright soils and sparse vegetation. While further improvement is warranted, the algorithm provides an effective means of obtaining detailed information about ephemeral streams, and it could make a significant contribution toward improving the hydrological modelling of desert environments.« less

Pairwise gene GO-based measures for biclustering of high-dimensional expression data.

PubMed

Nepomuceno, Juan A; Troncoso, Alicia; Nepomuceno-Chamorro, Isabel A; Aguilar-Ruiz, Jesús S

2018-01-01

Biclustering algorithms search for groups of genes that share the same behavior under a subset of samples in gene expression data. Nowadays, the biological knowledge available in public repositories can be used to drive these algorithms to find biclusters composed of groups of genes functionally coherent. On the other hand, a distance among genes can be defined according to their information stored in Gene Ontology (GO). Gene pairwise GO semantic similarity measures report a value for each pair of genes which establishes their functional similarity. A scatter search-based algorithm that optimizes a merit function that integrates GO information is studied in this paper. This merit function uses a term that addresses the information through a GO measure. The effect of two possible different gene pairwise GO measures on the performance of the algorithm is analyzed. Firstly, three well known yeast datasets with approximately one thousand of genes are studied. Secondly, a group of human datasets related to clinical data of cancer is also explored by the algorithm. Most of these data are high-dimensional datasets composed of a huge number of genes. The resultant biclusters reveal groups of genes linked by a same functionality when the search procedure is driven by one of the proposed GO measures. Furthermore, a qualitative biological study of a group of biclusters show their relevance from a cancer disease perspective. It can be concluded that the integration of biological information improves the performance of the biclustering process. The two different GO measures studied show an improvement in the results obtained for the yeast dataset. However, if datasets are composed of a huge number of genes, only one of them really improves the algorithm performance. This second case constitutes a clear option to explore interesting datasets from a clinical point of view.

Advances in feature selection methods for hyperspectral image processing in food industry applications: a review.

PubMed

Dai, Qiong; Cheng, Jun-Hu; Sun, Da-Wen; Zeng, Xin-An

2015-01-01

There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods.

A Collaborative Secure Localization Algorithm Based on Trust Model in Underwater Wireless Sensor Networks

PubMed Central

Han, Guangjie; Liu, Li; Jiang, Jinfang; Shu, Lei; Rodrigues, Joel J.P.C.

2016-01-01

Localization is one of the hottest research topics in Underwater Wireless Sensor Networks (UWSNs), since many important applications of UWSNs, e.g., event sensing, target tracking and monitoring, require location information of sensor nodes. Nowadays, a large number of localization algorithms have been proposed for UWSNs. How to improve location accuracy are well studied. However, few of them take location reliability or security into consideration. In this paper, we propose a Collaborative Secure Localization algorithm based on Trust model (CSLT) for UWSNs to ensure location security. Based on the trust model, the secure localization process can be divided into the following five sub-processes: trust evaluation of anchor nodes, initial localization of unknown nodes, trust evaluation of reference nodes, selection of reference node, and secondary localization of unknown node. Simulation results demonstrate that the proposed CSLT algorithm performs better than the compared related works in terms of location security, average localization accuracy and localization ratio. PMID:26891300

Content-aware dark image enhancement through channel division.

PubMed

Rivera, Adin Ramirez; Ryu, Byungyong; Chae, Oksam

2012-09-01

The current contrast enhancement algorithms occasionally result in artifacts, overenhancement, and unnatural effects in the processed images. These drawbacks increase for images taken under poor illumination conditions. In this paper, we propose a content-aware algorithm that enhances dark images, sharpens edges, reveals details in textured regions, and preserves the smoothness of flat regions. The algorithm produces an ad hoc transformation for each image, adapting the mapping functions to each image's characteristics to produce the maximum enhancement. We analyze the contrast of the image in the boundary and textured regions, and group the information with common characteristics. These groups model the relations within the image, from which we extract the transformation functions. The results are then adaptively mixed, by considering the human vision system characteristics, to boost the details in the image. Results show that the algorithm can automatically process a wide range of images-e.g., mixed shadow and bright areas, outdoor and indoor lighting, and face images-without introducing artifacts, which is an improvement over many existing methods.

Arc-welding quality assurance by means of embedded fiber sensor and spectral processing combining feature selection and neural networks

NASA Astrophysics Data System (ADS)

Mirapeix, J.; García-Allende, P. B.; Cobo, A.; Conde, O.; López-Higuera, J. M.

2007-07-01

A new spectral processing technique designed for its application in the on-line detection and classification of arc-welding defects is presented in this paper. A non-invasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed by means of two consecutive stages. A compression algorithm is first applied to the data allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in a previous paper, giving rise to an improvement in the performance of the monitoring system.

An Out-of-Core GPU based dimensionality reduction algorithm for Big Mass Spectrometry Data and its application in bottom-up Proteomics.

PubMed

Awan, Muaaz Gul; Saeed, Fahad

2017-08-01

Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and computational operations inside GPU. These novel data structures include Binary Spectra and Quantized Indexed Spectra (QIS) . The former helps in communicating essential information between CPU and GPU using minimum amount of data while latter enables us to store and process complex 3-D data structure into a 1-D array structure while maintaining the integrity of MS data. Our proposed algorithm also takes into account the limited memory of GPUs and switches between in-core and out-of-core modes based upon the size of input data. G-MSR achieves a peak speed-up of 386x over its sequential counterpart and is shown to process over a million spectra in just 32 seconds. The code for this algorithm is available as a GPL open-source at GitHub at the following link: https://github.com/pcdslab/G-MSR.

Quantitative optical imaging and sensing by joint design of point spread functions and estimation algorithms

NASA Astrophysics Data System (ADS)

Quirin, Sean Albert

The joint application of tailored optical Point Spread Functions (PSF) and estimation methods is an important tool for designing quantitative imaging and sensing solutions. By enhancing the information transfer encoded by the optical waves into an image, matched post-processing algorithms are able to complete tasks with improved performance relative to conventional designs. In this thesis, new engineered PSF solutions with image processing algorithms are introduced and demonstrated for quantitative imaging using information-efficient signal processing tools and/or optical-efficient experimental implementations. The use of a 3D engineered PSF, the Double-Helix (DH-PSF), is applied as one solution for three-dimensional, super-resolution fluorescence microscopy. The DH-PSF is a tailored PSF which was engineered to have enhanced information transfer for the task of localizing point sources in three dimensions. Both an information- and optical-efficient implementation of the DH-PSF microscope are demonstrated here for the first time. This microscope is applied to image single-molecules and micro-tubules located within a biological sample. A joint imaging/axial-ranging modality is demonstrated for application to quantifying sources of extended transverse and axial extent. The proposed implementation has improved optical-efficiency relative to prior designs due to the use of serialized cycling through select engineered PSFs. This system is demonstrated for passive-ranging, extended Depth-of-Field imaging and digital refocusing of random objects under broadband illumination. Although the serialized engineered PSF solution is an improvement over prior designs for the joint imaging/passive-ranging modality, it requires the use of multiple PSFs---a potentially significant constraint. Therefore an alternative design is proposed, the Single-Helix PSF, where only one engineered PSF is necessary and the chromatic behavior of objects under broadband illumination provides the necessary information transfer. The matched estimation algorithms are introduced along with an optically-efficient experimental system to image and passively estimate the distance to a test object. An engineered PSF solution is proposed for improving the sensitivity of optical wave-front sensing using a Shack-Hartmann Wave-front Sensor (SHWFS). The performance limits of the classical SHWFS design are evaluated and the engineered PSF system design is demonstrated to enhance performance. This system is fabricated and the mechanism for additional information transfer is identified.

Locating the source of diffusion in complex networks by time-reversal backward spreading.

PubMed

Shen, Zhesi; Cao, Shinan; Wang, Wen-Xu; Di, Zengru; Stanley, H Eugene

2016-03-01

Locating the source that triggers a dynamical process is a fundamental but challenging problem in complex networks, ranging from epidemic spreading in society and on the Internet to cancer metastasis in the human body. An accurate localization of the source is inherently limited by our ability to simultaneously access the information of all nodes in a large-scale complex network. This thus raises two critical questions: how do we locate the source from incomplete information and can we achieve full localization of sources at any possible location from a given set of observable nodes. Here we develop a time-reversal backward spreading algorithm to locate the source of a diffusion-like process efficiently and propose a general locatability condition. We test the algorithm by employing epidemic spreading and consensus dynamics as typical dynamical processes and apply it to the H1N1 pandemic in China. We find that the sources can be precisely located in arbitrary networks insofar as the locatability condition is assured. Our tools greatly improve our ability to locate the source of diffusion in complex networks based on limited accessibility of nodal information. Moreover, they have implications for controlling a variety of dynamical processes taking place on complex networks, such as inhibiting epidemics, slowing the spread of rumors, pollution control, and environmental protection.

Millimeter wave scattering characteristics and radar cross section measurements of common roadway objects

NASA Astrophysics Data System (ADS)

Zoratti, Paul K.; Gilbert, R. Kent; Majewski, Ronald; Ference, Jack

1995-12-01

Development of automotive collision warning systems has progressed rapidly over the past several years. A key enabling technology for these systems is millimeter-wave radar. This paper addresses a very critical millimeter-wave radar sensing issue for automotive radar, namely the scattering characteristics of common roadway objects such as vehicles, roadsigns, and bridge overpass structures. The data presented in this paper were collected on ERIM's Fine Resolution Radar Imaging Rotary Platform Facility and processed with ERIM's image processing tools. The value of this approach is that it provides system developers with a 2D radar image from which information about individual point scatterers `within a single target' can be extracted. This information on scattering characteristics will be utilized to refine threat assessment processing algorithms and automotive radar hardware configurations. (1) By evaluating the scattering characteristics identified in the radar image, radar signatures as a function of aspect angle for common roadway objects can be established. These signatures will aid in the refinement of threat assessment processing algorithms. (2) Utilizing ERIM's image manipulation tools, total RCS and RCS as a function of range and azimuth can be extracted from the radar image data. This RCS information will be essential in defining the operational envelope (e.g. dynamic range) within which any radar sensor hardware must be designed.

Locating the source of diffusion in complex networks by time-reversal backward spreading

NASA Astrophysics Data System (ADS)

Shen, Zhesi; Cao, Shinan; Wang, Wen-Xu; Di, Zengru; Stanley, H. Eugene

2016-03-01

Locating the source that triggers a dynamical process is a fundamental but challenging problem in complex networks, ranging from epidemic spreading in society and on the Internet to cancer metastasis in the human body. An accurate localization of the source is inherently limited by our ability to simultaneously access the information of all nodes in a large-scale complex network. This thus raises two critical questions: how do we locate the source from incomplete information and can we achieve full localization of sources at any possible location from a given set of observable nodes. Here we develop a time-reversal backward spreading algorithm to locate the source of a diffusion-like process efficiently and propose a general locatability condition. We test the algorithm by employing epidemic spreading and consensus dynamics as typical dynamical processes and apply it to the H1N1 pandemic in China. We find that the sources can be precisely located in arbitrary networks insofar as the locatability condition is assured. Our tools greatly improve our ability to locate the source of diffusion in complex networks based on limited accessibility of nodal information. Moreover, they have implications for controlling a variety of dynamical processes taking place on complex networks, such as inhibiting epidemics, slowing the spread of rumors, pollution control, and environmental protection.

Binomial probability distribution model-based protein identification algorithm for tandem mass spectrometry utilizing peak intensity information.

PubMed

Xiao, Chuan-Le; Chen, Xiao-Zhou; Du, Yang-Li; Sun, Xuesong; Zhang, Gong; He, Qing-Yu

2013-01-04

Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/ .

Information filtering in evolving online networks

NASA Astrophysics Data System (ADS)

Chen, Bo-Lun; Li, Fen-Fen; Zhang, Yong-Jun; Ma, Jia-Lin

2018-02-01

Recommender systems use the records of users' activities and profiles of both users and products to predict users' preferences in the future. Considerable works towards recommendation algorithms have been published to solve the problems such as accuracy, diversity, congestion, cold-start, novelty, coverage and so on. However, most of these research did not consider the temporal effects of the information included in the users' historical data. For example, the segmentation of the training set and test set was completely random, which was entirely different from the real scenario in recommender systems. More seriously, all the objects are treated as the same, regardless of the new, the popular or obsoleted products, so do the users. These data processing methods always lose useful information and mislead the understanding of the system's state. In this paper, we detailed analyzed the difference of the network structure between the traditional random division method and the temporal division method on two benchmark data sets, Netflix and MovieLens. Then three classical recommendation algorithms, Global Ranking method, Collaborative Filtering and Mass Diffusion method, were employed. The results show that all these algorithms became worse in all four key indicators, ranking score, precision, popularity and diversity, in the temporal scenario. Finally, we design a new recommendation algorithm based on both users' and objects' first appearance time in the system. Experimental results showed that the new algorithm can greatly improve the accuracy and other metrics.

The artificial retina for track reconstruction at the LHC crossing rate

NASA Astrophysics Data System (ADS)

Abba, A.; Bedeschi, F.; Citterio, M.; Caponio, F.; Cusimano, A.; Geraci, A.; Marino, P.; Morello, M. J.; Neri, N.; Punzi, G.; Piucci, A.; Ristori, L.; Spinella, F.; Stracka, S.; Tonelli, D.

2016-04-01

We present the results of an R&D study for a specialized processor capable of precisely reconstructing events with hundreds of charged-particle tracks in pixel and silicon strip detectors at 40 MHz, thus suitable for processing LHC events at the full crossing frequency. For this purpose we design and test a massively parallel pattern-recognition algorithm, inspired to the current understanding of the mechanisms adopted by the primary visual cortex of mammals in the early stages of visual-information processing. The detailed geometry and charged-particle's activity of a large tracking detector are simulated and used to assess the performance of the artificial retina algorithm. We find that high-quality tracking in large detectors is possible with sub-microsecond latencies when the algorithm is implemented in modern, high-speed, high-bandwidth FPGA devices.

Single-scale center-surround Retinex based restoration of low-illumination images with edge enhancement

NASA Astrophysics Data System (ADS)

Kwok, Ngaiming; Shi, Haiyan; Peng, Yeping; Wu, Hongkun; Li, Ruowei; Liu, Shilong; Rahman, Md Arifur

2018-04-01

Restoring images captured under low-illuminations is an essential front-end process for most image based applications. The Center-Surround Retinex algorithm has been a popular approach employed to improve image brightness. However, this algorithm in its basic form, is known to produce color degradations. In order to mitigate this problem, here the Single-Scale Retinex algorithm is modifid as an edge extractor while illumination is recovered through a non-linear intensity mapping stage. The derived edges are then integrated with the mapped image to produce the enhanced output. Furthermore, in reducing color distortion, the process is conducted in the magnitude sorted domain instead of the conventional Red-Green-Blue (RGB) color channels. Experimental results had shown that improvements with regard to mean brightness, colorfulness, saturation, and information content can be obtained.

Description of texts of auxiliary programs for processing video information. Part 2: SUODH program of automated separation of quasihomogeneous formations

NASA Technical Reports Server (NTRS)

Borisenko, V. I.; Chesalin, L. S.

1980-01-01

The algorithm, block diagram, complete text, and instructions are given for the use of a computer program to separate formations whose spectral characteristics are constant on the average. The initial material for operating the computer program presented is video information in a standard color-superposition format.

Mechanisms of face perception

PubMed Central

Tsao, Doris Y.

2009-01-01

Faces are among the most informative stimuli we ever perceive: Even a split-second glimpse of a person's face tells us their identity, sex, mood, age, race, and direction of attention. The specialness of face processing is acknowledged in the artificial vision community, where contests for face recognition algorithms abound. Neurological evidence strongly implicates a dedicated machinery for face processing in the human brain, to explain the double dissociability of face and object recognition deficits. Furthermore, it has recently become clear that macaques too have specialized neural machinery for processing faces. Here we propose a unifying hypothesis, deduced from computational, neurological, fMRI, and single-unit experiments: that what makes face processing special is that it is gated by an obligatory detection process. We will clarify this idea in concrete algorithmic terms, and show how it can explain a variety of phenomena associated with face processing. PMID:18558862

Optical information-processing systems and architectures II; Proceedings of the Meeting, San Diego, CA, July 9-13, 1990

NASA Astrophysics Data System (ADS)

Javidi, Bahram

The present conference discusses topics in the fields of neural networks, acoustooptic signal processing, pattern recognition, phase-only processing, nonlinear signal processing, image processing, optical computing, and optical information processing. Attention is given to the optical implementation of an inner-product neural associative memory, optoelectronic associative recall via motionless-head/parallel-readout optical disk, a compact real-time acoustooptic image correlator, a multidimensional synthetic estimation filter, and a light-efficient joint transform optical correlator. Also discussed are a high-resolution spatial light modulator, compact real-time interferometric Fourier-transform processors, a fast decorrelation algorithm for permutation arrays, the optical interconnection of optical modules, and carry-free optical binary adders.

Waveform Design and Diversity for Advanced Space-Time Adaptive Processing and Multiple Input Multiple Output Systems

DTIC Science & Technology

2012-08-01

It suggests that a smart use of some a-priori information about the operating environment, when processing the received signal and designing the...random variable with the same variance of the backscattering target amplitude αT , and D ( αT , α G T ) is the Kullback − Leibler divergence, see [65...MI . Proof. See Appendix 3.6.6. Thus, we can use the optimization procedure of Algorithm 4 to optimize the Mutual Information between the target

Volumetric brain tumour detection from MRI using visual saliency.

PubMed

Mitra, Somosmita; Banerjee, Subhashis; Hayashi, Yoichi

2017-01-01

Medical image processing has become a major player in the world of automatic tumour region detection and is tantamount to the incipient stages of computer aided design. Saliency detection is a crucial application of medical image processing, and serves in its potential aid to medical practitioners by making the affected area stand out in the foreground from the rest of the background image. The algorithm developed here is a new approach to the detection of saliency in a three dimensional multi channel MR image sequence for the glioblastoma multiforme (a form of malignant brain tumour). First we enhance the three channels, FLAIR (Fluid Attenuated Inversion Recovery), T2 and T1C (contrast enhanced with gadolinium) to generate a pseudo coloured RGB image. This is then converted to the CIE L*a*b* color space. Processing on cubes of sizes k = 4, 8, 16, the L*a*b* 3D image is then compressed into volumetric units; each representing the neighbourhood information of the surrounding 64 voxels for k = 4, 512 voxels for k = 8 and 4096 voxels for k = 16, respectively. The spatial distance of these voxels are then compared along the three major axes to generate the novel 3D saliency map of a 3D image, which unambiguously highlights the tumour region. The algorithm operates along the three major axes to maximise the computation efficiency while minimising loss of valuable 3D information. Thus the 3D multichannel MR image saliency detection algorithm is useful in generating a uniform and logistically correct 3D saliency map with pragmatic applicability in Computer Aided Detection (CADe). Assignment of uniform importance to all three axes proves to be an important factor in volumetric processing, which helps in noise reduction and reduces the possibility of compromising essential information. The effectiveness of the algorithm was evaluated over the BRATS MICCAI 2015 dataset having 274 glioma cases, consisting both of high grade and low grade GBM. The results were compared with that of the 2D saliency detection algorithm taken over the entire sequence of brain data. For all comparisons, the Area Under the receiver operator characteristic (ROC) Curve (AUC) has been found to be more than 0.99 ± 0.01 over various tumour types, structures and locations.

Adaptive Grouping Cloud Model Shuffled Frog Leaping Algorithm for Solving Continuous Optimization Problems

PubMed Central

Liu, Haorui; Yi, Fengyan; Yang, Heli

2016-01-01

The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The algorithm divides the definition domain into several groups and gives each group a set of frogs. Frogs of each region search in their memeplex, and in the search process the algorithm uses the “elite strategy” to update the location information of existing elite frogs through cloud model algorithm. This method narrows the searching space and it can effectively improve the situation of a local optimum; thus convergence speed and accuracy can be significantly improved. The results of computer simulation confirm this conclusion. PMID:26819584

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

PubMed Central

Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein

2017-01-01

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

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

PubMed

Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein

2017-01-01

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

Segmentation of remotely sensed data using parallel region growing

NASA Technical Reports Server (NTRS)

Tilton, J. C.; Cox, S. C.

1983-01-01

The improved spatial resolution of the new earth resources satellites will increase the need for effective utilization of spatial information in machine processing of remotely sensed data. One promising technique is scene segmentation by region growing. Region growing can use spatial information in two ways: only spatially adjacent regions merge together, and merging criteria can be based on region-wide spatial features. A simple region growing approach is described in which the similarity criterion is based on region mean and variance (a simple spatial feature). An effective way to implement region growing for remote sensing is as an iterative parallel process on a large parallel processor. A straightforward parallel pixel-based implementation of the algorithm is explored and its efficiency is compared with sequential pixel-based, sequential region-based, and parallel region-based implementations. Experimental results from on aircraft scanner data set are presented, as is a discussioon of proposed improvements to the segmentation algorithm.

Estimation of road profile variability from measured vehicle responses

NASA Astrophysics Data System (ADS)

Fauriat, W.; Mattrand, C.; Gayton, N.; Beakou, A.; Cembrzynski, T.

2016-05-01

When assessing the statistical variability of fatigue loads acting throughout the life of a vehicle, the question of the variability of road roughness naturally arises, as both quantities are strongly related. For car manufacturers, gathering information on the environment in which vehicles evolve is a long and costly but necessary process to adapt their products to durability requirements. In the present paper, a data processing algorithm is proposed in order to estimate the road profiles covered by a given vehicle, from the dynamic responses measured on this vehicle. The algorithm based on Kalman filtering theory aims at solving a so-called inverse problem, in a stochastic framework. It is validated using experimental data obtained from simulations and real measurements. The proposed method is subsequently applied to extract valuable statistical information on road roughness from an existing load characterisation campaign carried out by Renault within one of its markets.

Use of laser range finders and range image analysis in automated assembly tasks

NASA Technical Reports Server (NTRS)

Alvertos, Nicolas; Dcunha, Ivan

1990-01-01

A proposition to study the effect of filtering processes on range images and to evaluate the performance of two different laser range mappers is made. Median filtering was utilized to remove noise from the range images. First and second order derivatives are then utilized to locate the similarities and dissimilarities between the processed and the original images. Range depth information is converted into spatial coordinates, and a set of coefficients which describe 3-D objects is generated using the algorithm developed in the second phase of this research. Range images of spheres and cylinders are used for experimental purposes. An algorithm was developed to compare the performance of two different laser range mappers based upon the range depth information of surfaces generated by each of the mappers. Furthermore, an approach based on 2-D analytic geometry is also proposed which serves as a basis for the recognition of regular 3-D geometric objects.

Comparison of two non-convex mixed-integer nonlinear programming algorithms applied to autoregressive moving average model structure and parameter estimation

NASA Astrophysics Data System (ADS)

Uilhoorn, F. E.

2016-10-01

In this article, the stochastic modelling approach proposed by Box and Jenkins is treated as a mixed-integer nonlinear programming (MINLP) problem solved with a mesh adaptive direct search and a real-coded genetic class of algorithms. The aim is to estimate the real-valued parameters and non-negative integer, correlated structure of stationary autoregressive moving average (ARMA) processes. The maximum likelihood function of the stationary ARMA process is embedded in Akaike's information criterion and the Bayesian information criterion, whereas the estimation procedure is based on Kalman filter recursions. The constraints imposed on the objective function enforce stability and invertibility. The best ARMA model is regarded as the global minimum of the non-convex MINLP problem. The robustness and computational performance of the MINLP solvers are compared with brute-force enumeration. Numerical experiments are done for existing time series and one new data set.

A Holistic Approach to Networked Information Systems Design and Analysis

DTIC Science & Technology

2016-04-15

attain quite substantial savings. 11. Optimal algorithms for energy harvesting in wireless networks. We use a Markov- decision-process (MDP) based...approach to obtain optimal policies for transmissions . The key advantage of our approach is that it holistically considers information and energy in a...Coding technique to minimize delays and the number of transmissions in Wireless Systems. As we approach an era of ubiquitous computing with information

Data mining in bioinformatics using Weka.

PubMed

Frank, Eibe; Hall, Mark; Trigg, Len; Holmes, Geoffrey; Witten, Ian H

2004-10-12

The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it. http://www.cs.waikato.ac.nz/ml/weka.

Automatic protein structure solution from weak X-ray data

NASA Astrophysics Data System (ADS)

Skubák, Pavol; Pannu, Navraj S.

2013-11-01

Determining new protein structures from X-ray diffraction data at low resolution or with a weak anomalous signal is a difficult and often an impossible task. Here we propose a multivariate algorithm that simultaneously combines the structure determination steps. In tests on over 140 real data sets from the protein data bank, we show that this combined approach can automatically build models where current algorithms fail, including an anisotropically diffracting 3.88 Å RNA polymerase II data set. The method seamlessly automates the process, is ideal for non-specialists and provides a mathematical framework for successfully combining various sources of information in image processing.

A Semantic Approach for Geospatial Information Extraction from Unstructured Documents

NASA Astrophysics Data System (ADS)

Sallaberry, Christian; Gaio, Mauro; Lesbegueries, Julien; Loustau, Pierre

Local cultural heritage document collections are characterized by their content, which is strongly attached to a territory and its land history (i.e., geographical references). Our contribution aims at making the content retrieval process more efficient whenever a query includes geographic criteria. We propose a core model for a formal representation of geographic information. It takes into account characteristics of different modes of expression, such as written language, captures of drawings, maps, photographs, etc. We have developed a prototype that fully implements geographic information extraction (IE) and geographic information retrieval (IR) processes. All PIV prototype processing resources are designed as Web Services. We propose a geographic IE process based on semantic treatment as a supplement to classical IE approaches. We implement geographic IR by using intersection computing algorithms that seek out any intersection between formal geocoded representations of geographic information in a user query and similar representations in document collection indexes.

Kernel Temporal Differences for Neural Decoding

PubMed Central

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

Data fusion for target tracking and classification with wireless sensor network

NASA Astrophysics Data System (ADS)

Pannetier, Benjamin; Doumerc, Robin; Moras, Julien; Dezert, Jean; Canevet, Loic

2016-10-01

In this paper, we address the problem of multiple ground target tracking and classification with information obtained from a unattended wireless sensor network. A multiple target tracking (MTT) algorithm, taking into account road and vegetation information, is proposed based on a centralized architecture. One of the key issue is how to adapt classical MTT approach to satisfy embedded processing. Based on track statistics, the classification algorithm uses estimated location, velocity and acceleration to help to classify targets. The algorithms enables tracking human and vehicles driving both on and off road. We integrate road or trail width and vegetation cover, as constraints in target motion models to improve performance of tracking under constraint with classification fusion. Our algorithm also presents different dynamic models, to palliate the maneuvers of targets. The tracking and classification algorithms are integrated into an operational platform (the fusion node). In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

An Algorithm to Compress Line-transition Data for Radiative-transfer Calculations

NASA Astrophysics Data System (ADS)

Cubillos, Patricio E.

2017-11-01

Molecular line-transition lists are an essential ingredient for radiative-transfer calculations. With recent databases now surpassing the billion-line mark, handling them has become computationally prohibitive, due to both the required processing power and memory. Here I present a temperature-dependent algorithm to separate strong from weak line transitions, reformatting the large majority of the weaker lines into a cross-section data file, and retaining the detailed line-by-line information of the fewer strong lines. For any given molecule over the 0.3-30 μm range, this algorithm reduces the number of lines to a few million, enabling faster radiative-transfer computations without a significant loss of information. The final compression rate depends on how densely populated the spectrum is. I validate this algorithm by comparing Exomol’s HCN extinction-coefficient spectra between the complete (65 million line transitions) and compressed (7.7 million) line lists. Over the 0.6-33 μm range, the average difference between extinction-coefficient values is less than 1%. A Python/C implementation of this algorithm is open-source and available at https://github.com/pcubillos/repack. So far, this code handles the Exomol and HITRAN line-transition format.

NASA GPM GV Science Requirements

NASA Technical Reports Server (NTRS)

Smith, E.

2003-01-01

An important scientific objective of the NASA portion of the GPM Mission is to generate quantitatively-based error characterization information along with the rainrate retrievals emanating from the GPM constellation of satellites. These data must serve four main purposes: (1) they must be of sufficient quality, uniformity, and timeliness to govern the observation weighting schemes used in the data assimilation modules of numerical weather prediction models; (2) they must extend over that portion of the globe accessible by the GPM core satellite to which the NASA GV program is focused - (approx.65 degree inclination); (3) they must have sufficient specificity to enable detection of physically-formulated microphysical and meteorological weaknesses in the standard physical level 2 rainrate algorithms to be used in the GPM Precipitation Processing System (PPS), i.e., algorithms which will have evolved from the TRMM standard physical level 2 algorithms; and (4) they must support the use of physical error modeling as a primary validation tool and as the eventual replacement of the conventional GV approach of statistically intercomparing surface rainrates fiom ground and satellite measurements. This approach to ground validation research represents a paradigm shift vis-&-vis the program developed for the TRMM mission, which conducted ground validation largely as a statistical intercomparison process between raingauge-derived or radar-derived rainrates and the TRMM satellite rainrate retrievals -- long after the original satellite retrievals were archived. This approach has been able to quantify averaged rainrate differences between the satellite algorithms and the ground instruments, but has not been able to explain causes of algorithm failures or produce error information directly compatible with the cost functions of data assimilation schemes. These schemes require periodic and near-realtime bias uncertainty (i.e., global space-time distributed conditional accuracy of the retrieved rainrates) and local error covariance structure (i.e., global space-time distributed error correlation information for the local 4-dimensional space-time domain -- or in simpler terms, the matrix form of precision error). This can only be accomplished by establishing a network of high quality-heavily instrumented supersites selectively distributed at a few oceanic, continental, and coastal sites. Economics and pragmatics dictate that the network must be made up of a relatively small number of sites (6-8) created through international cooperation. This presentation will address some of the details of the methodology behind the error characterization approach, some proposed solutions for expanding site-developed error properties to regional scales, a data processing and communications concept that would enable rapid implementation of algorithm improvement by the algorithm developers, and the likely available options for developing the supersite network.

Completing the Physical Representation of Quantum Algorithms Provides a Quantitative Explanation of Their Computational Speedup

NASA Astrophysics Data System (ADS)

Castagnoli, Giuseppe

2018-03-01

The usual representation of quantum algorithms, limited to the process of solving the problem, is physically incomplete. We complete it in three steps: (i) extending the representation to the process of setting the problem, (ii) relativizing the extended representation to the problem solver to whom the problem setting must be concealed, and (iii) symmetrizing the relativized representation for time reversal to represent the reversibility of the underlying physical process. The third steps projects the input state of the representation, where the problem solver is completely ignorant of the setting and thus the solution of the problem, on one where she knows half solution (half of the information specifying it when the solution is an unstructured bit string). Completing the physical representation shows that the number of computation steps (oracle queries) required to solve any oracle problem in an optimal quantum way should be that of a classical algorithm endowed with the advanced knowledge of half solution.

A Vehicle Management End-to-End Testing and Analysis Platform for Validation of Mission and Fault Management Algorithms to Reduce Risk for NASA's Space Launch System

NASA Technical Reports Server (NTRS)

Trevino, Luis; Johnson, Stephen B.; Patterson, Jonathan; Teare, David

2015-01-01

The development of the Space Launch System (SLS) launch vehicle requires cross discipline teams with extensive knowledge of launch vehicle subsystems, information theory, and autonomous algorithms dealing with all operations from pre-launch through on orbit operations. The characteristics of these systems must be matched with the autonomous algorithm monitoring and mitigation capabilities for accurate control and response to abnormal conditions throughout all vehicle mission flight phases, including precipitating safing actions and crew aborts. This presents a large complex systems engineering challenge being addressed in part by focusing on the specific subsystems handling of off-nominal mission and fault tolerance. Using traditional model based system and software engineering design principles from the Unified Modeling Language (UML), the Mission and Fault Management (M&FM) algorithms are crafted and vetted in specialized Integrated Development Teams composed of multiple development disciplines. NASA also has formed an M&FM team for addressing fault management early in the development lifecycle. This team has developed a dedicated Vehicle Management End-to-End Testbed (VMET) that integrates specific M&FM algorithms, specialized nominal and off-nominal test cases, and vendor-supplied physics-based launch vehicle subsystem models. The flexibility of VMET enables thorough testing of the M&FM algorithms by providing configurable suites of both nominal and off-nominal test cases to validate the algorithms utilizing actual subsystem models. The intent is to validate the algorithms and substantiate them with performance baselines for each of the vehicle subsystems in an independent platform exterior to flight software test processes. In any software development process there is inherent risk in the interpretation and implementation of concepts into software through requirements and test processes. Risk reduction is addressed by working with other organizations such as S&MA, Structures and Environments, GNC, Orion, the Crew Office, Flight Operations, and Ground Operations by assessing performance of the M&FM algorithms in terms of their ability to reduce Loss of Mission and Loss of Crew probabilities. In addition, through state machine and diagnostic modeling, analysis efforts investigate a broader suite of failure effects and detection and responses that can be tested in VMET and confirm that responses do not create additional risks or cause undesired states through interactive dynamic effects with other algorithms and systems. VMET further contributes to risk reduction by prototyping and exercising the M&FM algorithms early in their implementation and without any inherent hindrances such as meeting FSW processor scheduling constraints due to their target platform - ARINC 653 partitioned OS, resource limitations, and other factors related to integration with other subsystems not directly involved with M&FM. The plan for VMET encompasses testing the original M&FM algorithms coded in the same C++ language and state machine architectural concepts as that used by Flight Software. This enables the development of performance standards and test cases to characterize the M&FM algorithms and sets a benchmark from which to measure the effectiveness of M&FM algorithms performance in the FSW development and test processes. This paper is outlined in a systematic fashion analogous to a lifecycle process flow for engineering development of algorithms into software and testing. Section I describes the NASA SLS M&FM context, presenting the current infrastructure, leading principles, methods, and participants. Section II defines the testing philosophy of the M&FM algorithms as related to VMET followed by section III, which presents the modeling methods of the algorithms to be tested and validated in VMET. Its details are then further presented in section IV followed by Section V presenting integration, test status, and state analysis. Finally, section VI addresses the summary and forward directions followed by the appendices presenting relevant information on terminology and documentation.

Concepts for a global resources information system

NASA Technical Reports Server (NTRS)

Billingsley, F. C.; Urena, J. L.

1984-01-01

The objective of the Global Resources Information System (GRIS) is to establish an effective and efficient information management system to meet the data access requirements of NASA and NASA-related scientists conducting large-scale, multi-disciplinary, multi-mission scientific investigations. Using standard interfaces and operating guidelines, diverse data systems can be integrated to provide the capabilities to access and process multiple geographically dispersed data sets and to develop the necessary procedures and algorithms to derive global resource information.

Human/autonomy collaboration for the automated generation of intelligence products

NASA Astrophysics Data System (ADS)

DiBona, Phil; Schlachter, Jason; Kuter, Ugur; Goldman, Robert

2017-05-01

Intelligence Analysis remains a manual process despite trends toward autonomy in information processing. Analysts need agile decision-­-support tools that can adapt to the evolving information needs of the mission, allowing the analyst to pose novel analytic questions. Our research enables the analysts to only provide a constrained English specification of what the intelligence product should be. Using HTN planning, the autonomy discovers, decides, and generates a workflow of algorithms to create the intelligence product. Therefore, the analyst can quickly and naturally communicate to the autonomy what information product is needed, rather than how to create it.

Spectral implementation of some quantum algorithms by one- and two-dimensional nuclear magnetic resonance

NASA Astrophysics Data System (ADS)

Das, Ranabir; Kumar, Anil

2004-10-01

Quantum information processing has been effectively demonstrated on a small number of qubits by nuclear magnetic resonance. An important subroutine in any computing is the readout of the output. "Spectral implementation" originally suggested by Z. L. Madi, R. Bruschweiler, and R. R. Ernst [J. Chem. Phys. 109, 10603 (1999)], provides an elegant method of readout with the use of an extra "observer" qubit. At the end of computation, detection of the observer qubit provides the output via the multiplet structure of its spectrum. In spectral implementation by two-dimensional experiment the observer qubit retains the memory of input state during computation, thereby providing correlated information on input and output, in the same spectrum. Spectral implementation of Grover's search algorithm, approximate quantum counting, a modified version of Berstein-Vazirani problem, and Hogg's algorithm are demonstrated here in three- and four-qubit systems.

Integration of a Self-Coherence Algorithm into DISAT for Forced Oscillation Detection

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

Follum, James D.; Tuffner, Francis K.; Amidan, Brett G.

2015-03-03

With the increasing number of phasor measurement units on the power system, behaviors typically not observable on the power system are becoming more apparent. Oscillatory behavior on the power system, notably forced oscillations, are one such behavior. However, the large amounts of data coming from the PMUs makes manually detecting and locating these oscillations difficult. To automate portions of the process, an oscillation detection routine was coded into the Data Integrity and Situational Awareness Tool (DISAT) framework. Integration into the DISAT framework allows forced oscillations to be detected and information about the event provided to operational engineers. The oscillation detectionmore » algorithm integrates with the data handling and atypical data detecting capabilities of DISAT, building off of a standard library of functions. This report details that integration with information on the algorithm, some implementation issues, and some sample results from the western United States’ power grid.« less

Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface.

PubMed

Friedenberg, David A; Bouton, Chad E; Annetta, Nicholas V; Skomrock, Nicholas; Mingming Zhang; Schwemmer, Michael; Bockbrader, Marcia A; Mysiw, W Jerry; Rezai, Ali R; Bresler, Herbert S; Sharma, Gaurav

2016-08-01

Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.

A hybrid dynamic harmony search algorithm for identical parallel machines scheduling

NASA Astrophysics Data System (ADS)

Chen, Jing; Pan, Quan-Ke; Wang, Ling; Li, Jun-Qing

2012-02-01

In this article, a dynamic harmony search (DHS) algorithm is proposed for the identical parallel machines scheduling problem with the objective to minimize makespan. First, an encoding scheme based on a list scheduling rule is developed to convert the continuous harmony vectors to discrete job assignments. Second, the whole harmony memory (HM) is divided into multiple small-sized sub-HMs, and each sub-HM performs evolution independently and exchanges information with others periodically by using a regrouping schedule. Third, a novel improvisation process is applied to generate a new harmony by making use of the information of harmony vectors in each sub-HM. Moreover, a local search strategy is presented and incorporated into the DHS algorithm to find promising solutions. Simulation results show that the hybrid DHS (DHS_LS) is very competitive in comparison to its competitors in terms of mean performance and average computational time.

High-dimensional cluster analysis with the Masked EM Algorithm

PubMed Central

Kadir, Shabnam N.; Goodman, Dan F. M.; Harris, Kenneth D.

2014-01-01

Cluster analysis faces two problems in high dimensions: first, the “curse of dimensionality” that can lead to overfitting and poor generalization performance; and second, the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of “spike sorting” for next-generation high channel-count neural probes. In this problem, only a small subset of features provide information about the cluster member-ship of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective. We introduce a “Masked EM” algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data, and to real-world high-channel-count spike sorting data. PMID:25149694

Robust Adaptive Thresholder For Document Scanning Applications

NASA Astrophysics Data System (ADS)

Hsing, To R.

1982-12-01

In document scanning applications, thresholding is used to obtain binary data from a scanner. However, due to: (1) a wide range of different color backgrounds; (2) density variations of printed text information; and (3) the shading effect caused by the optical systems, the use of adaptive thresholding to enhance the useful information is highly desired. This paper describes a new robust adaptive thresholder for obtaining valid binary images. It is basically a memory type algorithm which can dynamically update the black and white reference level to optimize a local adaptive threshold function. The results of high image quality from different types of simulate test patterns can be obtained by this algorithm. The software algorithm is described and experiment results are present to describe the procedures. Results also show that the techniques described here can be used for real-time signal processing in the varied applications.

Attribute Utility Motivated k-anonymization of Datasets to Support the Heterogeneous Needs of Biomedical Researchers

PubMed Central

Ye, Huimin; Chen, Elizabeth S.

2011-01-01

In order to support the increasing need to share electronic health data for research purposes, various methods have been proposed for privacy preservation including k-anonymity. Many k-anonymity models provide the same level of anoymization regardless of practical need, which may decrease the utility of the dataset for a particular research study. In this study, we explore extensions to the k-anonymity algorithm that aim to satisfy the heterogeneous needs of different researchers while preserving privacy as well as utility of the dataset. The proposed algorithm, Attribute Utility Motivated k-anonymization (AUM), involves analyzing the characteristics of attributes and utilizing them to minimize information loss during the anonymization process. Through comparison with two existing algorithms, Mondrian and Incognito, preliminary results indicate that AUM may preserve more information from original datasets thus providing higher quality results with lower distortion. PMID:22195223

The comparison and analysis of extracting video key frame

NASA Astrophysics Data System (ADS)

Ouyang, S. Z.; Zhong, L.; Luo, R. Q.

2018-05-01

Video key frame extraction is an important part of the large data processing. Based on the previous work in key frame extraction, we summarized four important key frame extraction algorithms, and these methods are largely developed by comparing the differences between each of two frames. If the difference exceeds a threshold value, take the corresponding frame as two different keyframes. After the research, the key frame extraction based on the amount of mutual trust is proposed, the introduction of information entropy, by selecting the appropriate threshold values into the initial class, and finally take a similar mean mutual information as a candidate key frame. On this paper, several algorithms is used to extract the key frame of tunnel traffic videos. Then, with the analysis to the experimental results and comparisons between the pros and cons of these algorithms, the basis of practical applications is well provided.

Identification of Patients with Family History of Pancreatic Cancer--Investigation of an NLP System Portability.

PubMed

Mehrabi, Saeed; Krishnan, Anand; Roch, Alexandra M; Schmidt, Heidi; Li, DingCheng; Kesterson, Joe; Beesley, Chris; Dexter, Paul; Schmidt, Max; Palakal, Mathew; Liu, Hongfang

2015-01-01

In this study we have developed a rule-based natural language processing (NLP) system to identify patients with family history of pancreatic cancer. The algorithm was developed in a Unstructured Information Management Architecture (UIMA) framework and consisted of section segmentation, relation discovery, and negation detection. The system was evaluated on data from two institutions. The family history identification precision was consistent across the institutions shifting from 88.9% on Indiana University (IU) dataset to 87.8% on Mayo Clinic dataset. Customizing the algorithm on the the Mayo Clinic data, increased its precision to 88.1%. The family member relation discovery achieved precision, recall, and F-measure of 75.3%, 91.6% and 82.6% respectively. Negation detection resulted in precision of 99.1%. The results show that rule-based NLP approaches for specific information extraction tasks are portable across institutions; however customization of the algorithm on the new dataset improves its performance.

Standalone Mobile Application for Shipping Services Based on Geographic Information System and A-Star Algorithm

NASA Astrophysics Data System (ADS)

Gunawan, D.; Marzuki, I.; Candra, A.

2018-03-01

Geographic Information Systems (GIS) plays an essential role in shipping service related application. By utilizing GIS, the courier can find the route to deliver goods for its customer. This research proposes a standalone mobile application to provide the shortest route to the destinations by utilizing geographic information systems with A-Star algorithm. This application is intended to be used although the area has no Internet network available. The developed application can handle several drop off points then calculates the shortest route that passes through all the drop off points. According to the conducted testing, the number of drop off points that can be calculated is influenced by the specification of the smartphone. More destinations require more smartphone resources and time to process.

Optimization of IBF parameters based on adaptive tool-path algorithm

NASA Astrophysics Data System (ADS)

Deng, Wen Hui; Chen, Xian Hua; Jin, Hui Liang; Zhong, Bo; Hou, Jin; Li, An Qi

2018-03-01

As a kind of Computer Controlled Optical Surfacing(CCOS) technology. Ion Beam Figuring(IBF) has obvious advantages in the control of surface accuracy, surface roughness and subsurface damage. The superiority and characteristics of IBF in optical component processing are analyzed from the point of view of removal mechanism. For getting more effective and automatic tool path with the information of dwell time, a novel algorithm is proposed in this thesis. Based on the removal functions made through our IBF equipment and the adaptive tool-path, optimized parameters are obtained through analysis the residual error that would be created in the polishing process. A Φ600 mm plane reflector element was used to be a simulation instance. The simulation result shows that after four combinations of processing, the surface accuracy of PV (Peak Valley) value and the RMS (Root Mean Square) value was reduced to 4.81 nm and 0.495 nm from 110.22 nm and 13.998 nm respectively in the 98% aperture. The result shows that the algorithm and optimized parameters provide a good theoretical for high precision processing of IBF.

Hierarchical Discriminant Analysis.

PubMed

Lu, Di; Ding, Chuntao; Xu, Jinliang; Wang, Shangguang

2018-01-18

The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA). It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.

Optical Method For Monitoring Tool Control For Green Burnishing With Using Of Algorithms With Adaptive Settings

NASA Astrophysics Data System (ADS)

Lukyanov, A. A.; Grigoriev, S. N.; Bobrovskij, I. N.; Melnikov, P. A.; Bobrovskij, N. M.

2017-05-01

With regard to the complexity of the new technology and increase its reliability requirements laboriousness of control operations in industrial quality control systems increases significantly. The importance of quality management control due to the fact that its promotes the correct use of production conditions, the relevant requirements are required. Digital image processing allows to reach a new technological level of production (new technological way). The most complicated automated interpretation of information is the basis for decision-making in the management of production processes. In the case of surface analysis of tools used for processing with the using of metalworking fluids (MWF) it is more complicated. The authors suggest new algorithm for optical inspection of the wear of the cylinder tool for burnishing, which used in surface plastic deformation without using of MWF. The main advantage of proposed algorithm is the possibility of automatic recognition of images of burnisher tool with the subsequent allocation of its boundaries, finding a working surface and automatically allocating the defects and wear area. Software that implements the algorithm was developed by the authors in Matlab programming environment, but can be implemented using other programming languages.

Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data

DOE PAGES

Weekley, R. Andrew; Goodrich, R. Kent; Cornman, Larry B.

2016-04-06

An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinatemore » system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.« less

Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations.

PubMed

Meyer, C R; Boes, J L; Kim, B; Bland, P H; Zasadny, K R; Kison, P V; Koral, K; Frey, K A; Wahl, R L

1997-04-01

This paper applies and evaluates an automatic mutual information-based registration algorithm across a broad spectrum of multimodal volume data sets. The algorithm requires little or no pre-processing, minimal user input and easily implements either affine, i.e. linear or thin-plate spline (TPS) warped registrations. We have evaluated the algorithm in phantom studies as well as in selected cases where few other algorithms could perform as well, if at all, to demonstrate the value of this new method. Pairs of multimodal gray-scale volume data sets were registered by iteratively changing registration parameters to maximize mutual information. Quantitative registration errors were assessed in registrations of a thorax phantom using PET/CT and in the National Library of Medicine's Visible Male using MRI T2-/T1-weighted acquisitions. Registrations of diverse clinical data sets were demonstrated including rotate-translate mapping of PET/MRI brain scans with significant missing data, full affine mapping of thoracic PET/CT and rotate-translate mapping of abdominal SPECT/CT. A five-point thin-plate spline (TPS) warped registration of thoracic PET/CT is also demonstrated. The registration algorithm converged in times ranging between 3.5 and 31 min for affine clinical registrations and 57 min for TPS warping. Mean error vector lengths for rotate-translate registrations were measured to be subvoxel in phantoms. More importantly the rotate-translate algorithm performs well even with missing data. The demonstrated clinical fusions are qualitatively excellent at all levels. We conclude that such automatic, rapid, robust algorithms significantly increase the likelihood that multimodality registrations will be routinely used to aid clinical diagnoses and post-therapeutic assessment in the near future.

Utility-preserving anonymization for health data publishing.

PubMed

Lee, Hyukki; Kim, Soohyung; Kim, Jong Wook; Chung, Yon Dohn

2017-07-11

Publishing raw electronic health records (EHRs) may be considered as a breach of the privacy of individuals because they usually contain sensitive information. A common practice for the privacy-preserving data publishing is to anonymize the data before publishing, and thus satisfy privacy models such as k-anonymity. Among various anonymization techniques, generalization is the most commonly used in medical/health data processing. Generalization inevitably causes information loss, and thus, various methods have been proposed to reduce information loss. However, existing generalization-based data anonymization methods cannot avoid excessive information loss and preserve data utility. We propose a utility-preserving anonymization for privacy preserving data publishing (PPDP). To preserve data utility, the proposed method comprises three parts: (1) utility-preserving model, (2) counterfeit record insertion, (3) catalog of the counterfeit records. We also propose an anonymization algorithm using the proposed method. Our anonymization algorithm applies full-domain generalization algorithm. We evaluate our method in comparison with existence method on two aspects, information loss measured through various quality metrics and error rate of analysis result. With all different types of quality metrics, our proposed method show the lower information loss than the existing method. In the real-world EHRs analysis, analysis results show small portion of error between the anonymized data through the proposed method and original data. We propose a new utility-preserving anonymization method and an anonymization algorithm using the proposed method. Through experiments on various datasets, we show that the utility of EHRs anonymized by the proposed method is significantly better than those anonymized by previous approaches.

Parameterization of typhoon-induced ocean cooling using temperature equation and machine learning algorithms: an example of typhoon Soulik (2013)

NASA Astrophysics Data System (ADS)

Wei, Jun; Jiang, Guo-Qing; Liu, Xin

2017-09-01

This study proposed three algorithms that can potentially be used to provide sea surface temperature (SST) conditions for typhoon prediction models. Different from traditional data assimilation approaches, which provide prescribed initial/boundary conditions, our proposed algorithms aim to resolve a flow-dependent SST feedback between growing typhoons and oceans in the future time. Two of these algorithms are based on linear temperature equations (TE-based), and the other is based on an innovative technique involving machine learning (ML-based). The algorithms are then implemented into a Weather Research and Forecasting model for the simulation of typhoon to assess their effectiveness, and the results show significant improvement in simulated storm intensities by including ocean cooling feedback. The TE-based algorithm I considers wind-induced ocean vertical mixing and upwelling processes only, and thus obtained a synoptic and relatively smooth sea surface temperature cooling. The TE-based algorithm II incorporates not only typhoon winds but also ocean information, and thus resolves more cooling features. The ML-based algorithm is based on a neural network, consisting of multiple layers of input variables and neurons, and produces the best estimate of the cooling structure, in terms of its amplitude and position. Sensitivity analysis indicated that the typhoon-induced ocean cooling is a nonlinear process involving interactions of multiple atmospheric and oceanic variables. Therefore, with an appropriate selection of input variables and neuron sizes, the ML-based algorithm appears to be more efficient in prognosing the typhoon-induced ocean cooling and in predicting typhoon intensity than those algorithms based on linear regression methods.

RGB-to-RGBG conversion algorithm with adaptive weighting factors based on edge detection and minimal square error.

PubMed

Huang, Chengqiang; Yang, Youchang; Wu, Bo; Yu, Weize

2018-06-01

The sub-pixel arrangement of the RGBG panel and the image with RGB format are different and the algorithm that converts RGB to RGBG is urgently needed to display an image with RGB arrangement on the RGBG panel. However, the information loss is still large although color fringing artifacts are weakened in the published papers that study this conversion. In this paper, an RGB-to-RGBG conversion algorithm with adaptive weighting factors based on edge detection and minimal square error (EDMSE) is proposed. The main points of innovation include the following: (1) the edge detection is first proposed to distinguish image details with serious color fringing artifacts and image details which are prone to be lost in the process of RGB-RGBG conversion; (2) for image details with serious color fringing artifacts, the weighting factor 0.5 is applied to weaken the color fringing artifacts; and (3) for image details that are prone to be lost in the process of RGB-RGBG conversion, a special mechanism to minimize square error is proposed. The experiment shows that the color fringing artifacts are slightly improved by EDMSE, and the values of MSE of the image processed are 19.6% and 7% smaller than those of the image processed by the direct assignment and weighting factor algorithm, respectively. The proposed algorithm is implemented on a field programmable gate array to enable the image display on the RGBG panel.

Comparison of Open Source Compression Algorithms on Vhr Remote Sensing Images for Efficient Storage Hierarchy

NASA Astrophysics Data System (ADS)

Akoguz, A.; Bozkurt, S.; Gozutok, A. A.; Alp, G.; Turan, E. G.; Bogaz, M.; Kent, S.

2016-06-01

High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file sizes increase even more and then it gets a lot harder to store and consume much more time to transmit the data from one source to another; hence, it should be taken into account that to save even more space with file compression of the raw and various levels of processed data is a necessity for archiving stations to save more space. Lossless data compression algorithms that will be examined in this study aim to provide compression without any loss of data holding spectral information. Within this objective, well-known open source programs supporting related compression algorithms have been implemented on processed GeoTIFF images of Airbus Defence & Spaces SPOT 6 & 7 satellites having 1.5 m. of GSD, which were acquired and stored by ITU Center for Satellite Communications and Remote Sensing (ITU CSCRS), with the algorithms Lempel-Ziv-Welch (LZW), Lempel-Ziv-Markov chain Algorithm (LZMA & LZMA2), Lempel-Ziv-Oberhumer (LZO), Deflate & Deflate 64, Prediction by Partial Matching (PPMd or PPM2), Burrows-Wheeler Transform (BWT) in order to observe compression performances of these algorithms over sample datasets in terms of how much of the image data can be compressed by ensuring lossless compression.

The influence of digital filter type, amplitude normalisation method, and co-contraction algorithm on clinically relevant surface electromyography data during clinical movement assessments.

PubMed

Devaprakash, Daniel; Weir, Gillian J; Dunne, James J; Alderson, Jacqueline A; Donnelly, Cyril J

2016-12-01

There is a large and growing body of surface electromyography (sEMG) research using laboratory-specific signal processing procedures (i.e., digital filter type and amplitude normalisation protocols) and data analyses methods (i.e., co-contraction algorithms) to acquire practically meaningful information from these data. As a result, the ability to compare sEMG results between studies is, and continues to be challenging. The aim of this study was to determine if digital filter type, amplitude normalisation method, and co-contraction algorithm could influence the practical or clinical interpretation of processed sEMG data. Sixteen elite female athletes were recruited. During data collection, sEMG data was recorded from nine lower limb muscles while completing a series of calibration and clinical movement assessment trials (running and sidestepping). Three analyses were conducted: (1) signal processing with two different digital filter types (Butterworth or critically damped), (2) three amplitude normalisation methods, and (3) three co-contraction ratio algorithms. Results showed the choice of digital filter did not influence the clinical interpretation of sEMG; however, choice of amplitude normalisation method and co-contraction algorithm did influence the clinical interpretation of the running and sidestepping task. Care is recommended when choosing amplitude normalisation method and co-contraction algorithms if researchers/clinicians are interested in comparing sEMG data between studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

[An Extraction and Recognition Method of the Distributed Optical Fiber Vibration Signal Based on EMD-AWPP and HOSA-SVM Algorithm].

PubMed

Zhang, Yanjun; Liu, Wen-zhe; Fu, Xing-hu; Bi, Wei-hong

2016-02-01

Given that the traditional signal processing methods can not effectively distinguish the different vibration intrusion signal, a feature extraction and recognition method of the vibration information is proposed based on EMD-AWPP and HOSA-SVM, using for high precision signal recognition of distributed fiber optic intrusion detection system. When dealing with different types of vibration, the method firstly utilizes the adaptive wavelet processing algorithm based on empirical mode decomposition effect to reduce the abnormal value influence of sensing signal and improve the accuracy of signal feature extraction. Not only the low frequency part of the signal is decomposed, but also the high frequency part the details of the signal disposed better by time-frequency localization process. Secondly, it uses the bispectrum and bicoherence spectrum to accurately extract the feature vector which contains different types of intrusion vibration. Finally, based on the BPNN reference model, the recognition parameters of SVM after the implementation of the particle swarm optimization can distinguish signals of different intrusion vibration, which endows the identification model stronger adaptive and self-learning ability. It overcomes the shortcomings, such as easy to fall into local optimum. The simulation experiment results showed that this new method can effectively extract the feature vector of sensing information, eliminate the influence of random noise and reduce the effects of outliers for different types of invasion source. The predicted category identifies with the output category and the accurate rate of vibration identification can reach above 95%. So it is better than BPNN recognition algorithm and improves the accuracy of the information analysis effectively.

SamSelect: a sample sequence selection algorithm for quorum planted motif search on large DNA datasets.

PubMed

Yu, Qiang; Wei, Dingbang; Huo, Hongwei

2018-06-18

Given a set of t n-length DNA sequences, q satisfying 0 < q ≤ 1, and l and d satisfying 0 ≤ d < l < n, the quorum planted motif search (qPMS) finds l-length strings that occur in at least qt input sequences with up to d mismatches and is mainly used to locate transcription factor binding sites in DNA sequences. Existing qPMS algorithms have been able to efficiently process small standard datasets (e.g., t = 20 and n = 600), but they are too time consuming to process large DNA datasets, such as ChIP-seq datasets that contain thousands of sequences or more. We analyze the effects of t and q on the time performance of qPMS algorithms and find that a large t or a small q causes a longer computation time. Based on this information, we improve the time performance of existing qPMS algorithms by selecting a sample sequence set D' with a small t and a large q from the large input dataset D and then executing qPMS algorithms on D'. A sample sequence selection algorithm named SamSelect is proposed. The experimental results on both simulated and real data show (1) that SamSelect can select D' efficiently and (2) that the qPMS algorithms executed on D' can find implanted or real motifs in a significantly shorter time than when executed on D. We improve the ability of existing qPMS algorithms to process large DNA datasets from the perspective of selecting high-quality sample sequence sets so that the qPMS algorithms can find motifs in a short time in the selected sample sequence set D', rather than take an unfeasibly long time to search the original sequence set D. Our motif discovery method is an approximate algorithm.

Dilated contour extraction and component labeling algorithm for object vector representation

NASA Astrophysics Data System (ADS)

Skourikhine, Alexei N.

2005-08-01

Object boundary extraction from binary images is important for many applications, e.g., image vectorization, automatic interpretation of images containing segmentation results, printed and handwritten documents and drawings, maps, and AutoCAD drawings. Efficient and reliable contour extraction is also important for pattern recognition due to its impact on shape-based object characterization and recognition. The presented contour tracing and component labeling algorithm produces dilated (sub-pixel) contours associated with corresponding regions. The algorithm has the following features: (1) it always produces non-intersecting, non-degenerate contours, including the case of one-pixel wide objects; (2) it associates the outer and inner (i.e., around hole) contours with the corresponding regions during the process of contour tracing in a single pass over the image; (3) it maintains desired connectivity of object regions as specified by 8-neighbor or 4-neighbor connectivity of adjacent pixels; (4) it avoids degenerate regions in both background and foreground; (5) it allows an easy augmentation that will provide information about the containment relations among regions; (6) it has a time complexity that is dominantly linear in the number of contour points. This early component labeling (contour-region association) enables subsequent efficient object-based processing of the image information.

Informed-Proteomics: open-source software package for top-down proteomics

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

Park, Jungkap; Piehowski, Paul D.; Wilkins, Christopher

Top-down proteomics involves the analysis of intact proteins. This approach is very attractive as it allows for analyzing proteins in their endogenous form without proteolysis, preserving valuable information about post-translation modifications, isoforms, proteolytic processing or their combinations collectively called proteoforms. Moreover, the quality of the top-down LC-MS/MS datasets is rapidly increasing due to advances in the liquid chromatography and mass spectrometry instrumentation and sample processing protocols. However, the top-down mass spectra are substantially more complex compare to the more conventional bottom-up data. To take full advantage of the increasing quality of the top-down LC-MS/MS datasets there is an urgent needmore » to develop algorithms and software tools for confident proteoform identification and quantification. In this study we present a new open source software suite for top-down proteomics analysis consisting of an LC-MS feature finding algorithm, a database search algorithm, and an interactive results viewer. The presented tool along with several other popular tools were evaluated using human-in-mouse xenograft luminal and basal breast tumor samples that are known to have significant differences in protein abundance based on bottom-up analysis.« less

Sub-pixel mineral mapping using EO-1 Hyperion hyperspectral data

NASA Astrophysics Data System (ADS)

Kumar, C.; Shetty, A.; Raval, S.; Champatiray, P. K.; Sharma, R.

2014-11-01

This study describes the utility of Earth Observation (EO)-1 Hyperion data for sub-pixel mineral investigation using Mixture Tuned Target Constrained Interference Minimized Filter (MTTCIMF) algorithm in hostile mountainous terrain of Rajsamand district of Rajasthan, which hosts economic mineralization such as lead, zinc, and copper etc. The study encompasses pre-processing, data reduction, Pixel Purity Index (PPI) and endmember extraction from reflectance image of surface minerals such as illite, montmorillonite, phlogopite, dolomite and chlorite. These endmembers were then assessed with USGS mineral spectral library and lab spectra of rock samples collected from field for spectral inspection. Subsequently, MTTCIMF algorithm was implemented on processed image to obtain mineral distribution map of each detected mineral. A virtual verification method has been adopted to evaluate the classified image, which uses directly image information to evaluate the result and confirm the overall accuracy and kappa coefficient of 68 % and 0.6 respectively. The sub-pixel level mineral information with reasonable accuracy could be a valuable guide to geological and exploration community for expensive ground and/or lab experiments to discover economic deposits. Thus, the study demonstrates the feasibility of Hyperion data for sub-pixel mineral mapping using MTTCIMF algorithm with cost and time effective approach.

Automated global structure extraction for effective local building block processing in XCS.

PubMed

Butz, Martin V; Pelikan, Martin; Llorà, Xavier; Goldberg, David E

2006-01-01

Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are specialized, propagated, and recombined to provide increasingly accurate subsolutions. Recently, it was shown that, as in conventional genetic algorithms (GAs), some problems require efficient processing of subsets of features to find problem solutions efficiently. In such problems, standard variation operators of genetic and evolutionary algorithms used in LCSs suffer from potential disruption of groups of interacting features, resulting in poor performance. This paper introduces efficient crossover operators to XCS by incorporating techniques derived from competent GAs: the extended compact GA (ECGA) and the Bayesian optimization algorithm (BOA). Instead of simple crossover operators such as uniform crossover or one-point crossover, ECGA or BOA-derived mechanisms are used to build a probabilistic model of the global population and to generate offspring classifiers locally using the model. Several offspring generation variations are introduced and evaluated. The results show that it is possible to achieve performance similar to runs with an informed crossover operator that is specifically designed to yield ideal problem-dependent exploration, exploiting provided problem structure information. Thus, we create the first competent LCSs, XCS/ECGA and XCS/BOA, that detect dependency structures online and propagate corresponding lower-level dependency structures effectively without any information about these structures given in advance.

Building a Reference Resolution System Using Human Language Processing for Inspiration

ERIC Educational Resources Information Center

Watters, Shana Kay

2010-01-01

For over 30 years, reference resolution, the process of determining what a noun phrase including a pronoun refers to in written and spoken language, has been an important and on-going area of research. Most existing pronominal reference resolution algorithms and systems are designed to use syntactic information and surface features (e.g. number…

Sampling Operations on Big Data

DTIC Science & Technology

2015-11-29

gories. These include edge sampling methods where edges are selected by a predetermined criteria; snowball sampling methods where algorithms start... Sampling Operations on Big Data Vijay Gadepally, Taylor Herr, Luke Johnson, Lauren Milechin, Maja Milosavljevic, Benjamin A. Miller Lincoln...process and disseminate information for discovery and exploration under real-time constraints. Common signal processing operations such as sampling and

Research on HDR image fusion algorithm based on Laplace pyramid weight transform with extreme low-light CMOS

NASA Astrophysics Data System (ADS)

Guan, Wen; Li, Li; Jin, Weiqi; Qiu, Su; Zou, Yan

2015-10-01

Extreme-Low-Light CMOS has been widely applied in the field of night-vision as a new type of solid image sensor. But if the illumination in the scene has drastic changes or the illumination is too strong, Extreme-Low-Light CMOS can't both clearly present the high-light scene and low-light region. According to the partial saturation problem in the field of night-vision, a HDR image fusion algorithm based on the Laplace Pyramid was researched. The overall gray value and the contrast of the low light image is very low. We choose the fusion strategy based on regional average gradient for the top layer of the long exposure image and short exposure image, which has rich brightness and textural features. The remained layers which represent the edge feature information of the target are based on the fusion strategy based on regional energy. In the process of source image reconstruction with Laplacian pyramid image, we compare the fusion results with four kinds of basal images. The algorithm is tested using Matlab and compared with the different fusion strategies. We use information entropy, average gradient and standard deviation these three objective evaluation parameters for the further analysis of the fusion result. Different low illumination environment experiments show that the algorithm in this paper can rapidly get wide dynamic range while keeping high entropy. Through the verification of this algorithm features, there is a further application prospect of the optimized algorithm. Keywords: high dynamic range imaging, image fusion, multi-exposure image, weight coefficient, information fusion, Laplacian pyramid transform.

A Hybrid Ant Colony Optimization Algorithm for the Extended Capacitated Arc Routing Problem.

PubMed

Li-Ning Xing; Rohlfshagen, P; Ying-Wu Chen; Xin Yao

2011-08-01

The capacitated arc routing problem (CARP) is representative of numerous practical applications, and in order to widen its scope, we consider an extended version of this problem that entails both total service time and fixed investment costs. We subsequently propose a hybrid ant colony optimization (ACO) algorithm (HACOA) to solve instances of the extended CARP. This approach is characterized by the exploitation of heuristic information, adaptive parameters, and local optimization techniques: Two kinds of heuristic information, arc cluster information and arc priority information, are obtained continuously from the solutions sampled to guide the subsequent optimization process. The adaptive parameters ease the burden of choosing initial values and facilitate improved and more robust results. Finally, local optimization, based on the two-opt heuristic, is employed to improve the overall performance of the proposed algorithm. The resulting HACOA is tested on four sets of benchmark problems containing a total of 87 instances with up to 140 nodes and 380 arcs. In order to evaluate the effectiveness of the proposed method, some existing capacitated arc routing heuristics are extended to cope with the extended version of this problem; the experimental results indicate that the proposed ACO method outperforms these heuristics.

Control of Finite-State, Finite Memory Stochastic Systems

NASA Technical Reports Server (NTRS)

Sandell, Nils R.

1974-01-01

A generalized problem of stochastic control is discussed in which multiple controllers with different data bases are present. The vehicle for the investigation is the finite state, finite memory (FSFM) stochastic control problem. Optimality conditions are obtained by deriving an equivalent deterministic optimal control problem. A FSFM minimum principle is obtained via the equivalent deterministic problem. The minimum principle suggests the development of a numerical optimization algorithm, the min-H algorithm. The relationship between the sufficiency of the minimum principle and the informational properties of the problem are investigated. A problem of hypothesis testing with 1-bit memory is investigated to illustrate the application of control theoretic techniques to information processing problems.

Model-based vision for space applications

NASA Technical Reports Server (NTRS)

Chaconas, Karen; Nashman, Marilyn; Lumia, Ronald

1992-01-01

This paper describes a method for tracking moving image features by combining spatial and temporal edge information with model based feature information. The algorithm updates the two-dimensional position of object features by correlating predicted model features with current image data. The results of the correlation process are used to compute an updated model. The algorithm makes use of a high temporal sampling rate with respect to spatial changes of the image features and operates in a real-time multiprocessing environment. Preliminary results demonstrate successful tracking for image feature velocities between 1.1 and 4.5 pixels every image frame. This work has applications for docking, assembly, retrieval of floating objects and a host of other space-related tasks.

Models of formation and some algorithms of hyperspectral image processing

NASA Astrophysics Data System (ADS)

Achmetov, R. N.; Stratilatov, N. R.; Yudakov, A. A.; Vezenov, V. I.; Eremeev, V. V.

2014-12-01

Algorithms and information technologies for processing Earth hyperspectral imagery are presented. Several new approaches are discussed. Peculiar properties of processing the hyperspectral imagery, such as multifold signal-to-noise reduction, atmospheric distortions, access to spectral characteristics of every image point, and high dimensionality of data, were studied. Different measures of similarity between individual hyperspectral image points and the effect of additive uncorrelated noise on these measures were analyzed. It was shown that these measures are substantially affected by noise, and a new measure free of this disadvantage was proposed. The problem of detecting the observed scene object boundaries, based on comparing the spectral characteristics of image points, is considered. It was shown that contours are processed much better when spectral characteristics are used instead of energy brightness. A statistical approach to the correction of atmospheric distortions, which makes it possible to solve the stated problem based on analysis of a distorted image in contrast to analytical multiparametric models, was proposed. Several algorithms used to integrate spectral zonal images with data from other survey systems, which make it possible to image observed scene objects with a higher quality, are considered. Quality characteristics of hyperspectral data processing were proposed and studied.

A fast algorithm for vertex-frequency representations of signals on graphs

PubMed Central

Jestrović, Iva; Coyle, James L.; Sejdić, Ervin

2016-01-01

The windowed Fourier transform (short time Fourier transform) and the S-transform are widely used signal processing tools for extracting frequency information from non-stationary signals. Previously, the windowed Fourier transform had been adopted for signals on graphs and has been shown to be very useful for extracting vertex-frequency information from graphs. However, high computational complexity makes these algorithms impractical. We sought to develop a fast windowed graph Fourier transform and a fast graph S-transform requiring significantly shorter computation time. The proposed schemes have been tested with synthetic test graph signals and real graph signals derived from electroencephalography recordings made during swallowing. The results showed that the proposed schemes provide significantly lower computation time in comparison with the standard windowed graph Fourier transform and the fast graph S-transform. Also, the results showed that noise has no effect on the results of the algorithm for the fast windowed graph Fourier transform or on the graph S-transform. Finally, we showed that graphs can be reconstructed from the vertex-frequency representations obtained with the proposed algorithms. PMID:28479645

Post-processing interstitialcy diffusion from molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Bhardwaj, U.; Bukkuru, S.; Warrier, M.

2016-01-01

An algorithm to rigorously trace the interstitialcy diffusion trajectory in crystals is developed. The algorithm incorporates unsupervised learning and graph optimization which obviate the need to input extra domain specific information depending on crystal or temperature of the simulation. The algorithm is implemented in a flexible framework as a post-processor to molecular dynamics (MD) simulations. We describe in detail the reduction of interstitialcy diffusion into known computational problems of unsupervised clustering and graph optimization. We also discuss the steps, computational efficiency and key components of the algorithm. Using the algorithm, thermal interstitialcy diffusion from low to near-melting point temperatures is studied. We encapsulate the algorithms in a modular framework with functionality to calculate diffusion coefficients, migration energies and other trajectory properties. The study validates the algorithm by establishing the conformity of output parameters with experimental values and provides detailed insights for the interstitialcy diffusion mechanism. The algorithm along with the help of supporting visualizations and analysis gives convincing details and a new approach to quantifying diffusion jumps, jump-lengths, time between jumps and to identify interstitials from lattice atoms.

ECG Sensor Card with Evolving RBP Algorithms for Human Verification.

PubMed

Tseng, Kuo-Kun; Huang, Huang-Nan; Zeng, Fufu; Tu, Shu-Yi

2015-08-21

It is known that cardiac and respiratory rhythms in electrocardiograms (ECGs) are highly nonlinear and non-stationary. As a result, most traditional time-domain algorithms are inadequate for characterizing the complex dynamics of the ECG. This paper proposes a new ECG sensor card and a statistical-based ECG algorithm, with the aid of a reduced binary pattern (RBP), with the aim of achieving faster ECG human identity recognition with high accuracy. The proposed algorithm has one advantage that previous ECG algorithms lack-the waveform complex information and de-noising preprocessing can be bypassed; therefore, it is more suitable for non-stationary ECG signals. Experimental results tested on two public ECG databases (MIT-BIH) from MIT University confirm that the proposed scheme is feasible with excellent accuracy, low complexity, and speedy processing. To be more specific, the advanced RBP algorithm achieves high accuracy in human identity recognition and is executed at least nine times faster than previous algorithms. Moreover, based on the test results from a long-term ECG database, the evolving RBP algorithm also demonstrates superior capability in handling long-term and non-stationary ECG signals.

Post-processing interstitialcy diffusion from molecular dynamics simulations

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

Bhardwaj, U., E-mail: haptork@gmail.com; Bukkuru, S.; Warrier, M.

2016-01-15

An algorithm to rigorously trace the interstitialcy diffusion trajectory in crystals is developed. The algorithm incorporates unsupervised learning and graph optimization which obviate the need to input extra domain specific information depending on crystal or temperature of the simulation. The algorithm is implemented in a flexible framework as a post-processor to molecular dynamics (MD) simulations. We describe in detail the reduction of interstitialcy diffusion into known computational problems of unsupervised clustering and graph optimization. We also discuss the steps, computational efficiency and key components of the algorithm. Using the algorithm, thermal interstitialcy diffusion from low to near-melting point temperatures ismore » studied. We encapsulate the algorithms in a modular framework with functionality to calculate diffusion coefficients, migration energies and other trajectory properties. The study validates the algorithm by establishing the conformity of output parameters with experimental values and provides detailed insights for the interstitialcy diffusion mechanism. The algorithm along with the help of supporting visualizations and analysis gives convincing details and a new approach to quantifying diffusion jumps, jump-lengths, time between jumps and to identify interstitials from lattice atoms. -- Graphical abstract:.« less

Raster Data Partitioning for Supporting Distributed GIS Processing

NASA Astrophysics Data System (ADS)

Nguyen Thai, B.; Olasz, A.

2015-08-01

In the geospatial sector big data concept also has already impact. Several studies facing originally computer science techniques applied in GIS processing of huge amount of geospatial data. In other research studies geospatial data is considered as it were always been big data (Lee and Kang, 2015). Nevertheless, we can prove data acquisition methods have been improved substantially not only the amount, but the resolution of raw data in spectral, spatial and temporal aspects as well. A significant portion of big data is geospatial data, and the size of such data is growing rapidly at least by 20% every year (Dasgupta, 2013). The produced increasing volume of raw data, in different format, representation and purpose the wealth of information derived from this data sets represents only valuable results. However, the computing capability and processing speed rather tackle with limitations, even if semi-automatic or automatic procedures are aimed on complex geospatial data (Kristóf et al., 2014). In late times, distributed computing has reached many interdisciplinary areas of computer science inclusive of remote sensing and geographic information processing approaches. Cloud computing even more requires appropriate processing algorithms to be distributed and handle geospatial big data. Map-Reduce programming model and distributed file systems have proven their capabilities to process non GIS big data. But sometimes it's inconvenient or inefficient to rewrite existing algorithms to Map-Reduce programming model, also GIS data can not be partitioned as text-based data by line or by bytes. Hence, we would like to find an alternative solution for data partitioning, data distribution and execution of existing algorithms without rewriting or with only minor modifications. This paper focuses on technical overview of currently available distributed computing environments, as well as GIS data (raster data) partitioning, distribution and distributed processing of GIS algorithms. A proof of concept implementation have been made for raster data partitioning, distribution and processing. The first results on performance have been compared against commercial software ERDAS IMAGINE 2011 and 2014. Partitioning methods heavily depend on application areas, therefore we may consider data partitioning as a preprocessing step before applying processing services on data. As a proof of concept we have implemented a simple tile-based partitioning method splitting an image into smaller grids (NxM tiles) and comparing the processing time to existing methods by NDVI calculation. The concept is demonstrated using own development open source processing framework.

Reconstruction of elasticity: a stochastic model-based approach in ultrasound elastography

PubMed Central

2013-01-01

Background The convectional strain-based algorithm has been widely utilized in clinical practice. It can only provide the information of relative information of tissue stiffness. However, the exact information of tissue stiffness should be valuable for clinical diagnosis and treatment. Methods In this study we propose a reconstruction strategy to recover the mechanical properties of the tissue. After the discrepancies between the biomechanical model and data are modeled as the process noise, and the biomechanical model constraint is transformed into a state space representation the reconstruction of elasticity can be accomplished through one filtering identification process, which is to recursively estimate the material properties and kinematic functions from ultrasound data according to the minimum mean square error (MMSE) criteria. In the implementation of this model-based algorithm, the linear isotropic elasticity is adopted as the biomechanical constraint. The estimation of kinematic functions (i.e., the full displacement and velocity field), and the distribution of Young’s modulus are computed simultaneously through an extended Kalman filter (EKF). Results In the following experiments the accuracy and robustness of this filtering framework is first evaluated on synthetic data in controlled conditions, and the performance of this framework is then evaluated in the real data collected from elastography phantom and patients using the ultrasound system. Quantitative analysis verifies that strain fields estimated by our filtering strategy are more closer to the ground truth. The distribution of Young’s modulus is also well estimated. Further, the effects of measurement noise and process noise have been investigated as well. Conclusions The advantage of this model-based algorithm over the conventional strain-based algorithm is its potential of providing the distribution of elasticity under a proper biomechanical model constraint. We address the model-data discrepancy and measurement noise by introducing process noise and measurement noise in our framework, and then the absolute values of Young’s modulus are estimated through the EFK in the MMSE sense. However, the initial conditions, and the mesh strategy will affect the performance, i.e., the convergence rate, and computational cost, etc. PMID:23937814

Reconstruction of elasticity: a stochastic model-based approach in ultrasound elastography.

PubMed

Lu, Minhua; Zhang, Heye; Wang, Jun; Yuan, Jinwei; Hu, Zhenghui; Liu, Huafeng

2013-08-10

The convectional strain-based algorithm has been widely utilized in clinical practice. It can only provide the information of relative information of tissue stiffness. However, the exact information of tissue stiffness should be valuable for clinical diagnosis and treatment. In this study we propose a reconstruction strategy to recover the mechanical properties of the tissue. After the discrepancies between the biomechanical model and data are modeled as the process noise, and the biomechanical model constraint is transformed into a state space representation the reconstruction of elasticity can be accomplished through one filtering identification process, which is to recursively estimate the material properties and kinematic functions from ultrasound data according to the minimum mean square error (MMSE) criteria. In the implementation of this model-based algorithm, the linear isotropic elasticity is adopted as the biomechanical constraint. The estimation of kinematic functions (i.e., the full displacement and velocity field), and the distribution of Young's modulus are computed simultaneously through an extended Kalman filter (EKF). In the following experiments the accuracy and robustness of this filtering framework is first evaluated on synthetic data in controlled conditions, and the performance of this framework is then evaluated in the real data collected from elastography phantom and patients using the ultrasound system. Quantitative analysis verifies that strain fields estimated by our filtering strategy are more closer to the ground truth. The distribution of Young's modulus is also well estimated. Further, the effects of measurement noise and process noise have been investigated as well. The advantage of this model-based algorithm over the conventional strain-based algorithm is its potential of providing the distribution of elasticity under a proper biomechanical model constraint. We address the model-data discrepancy and measurement noise by introducing process noise and measurement noise in our framework, and then the absolute values of Young's modulus are estimated through the EFK in the MMSE sense. However, the initial conditions, and the mesh strategy will affect the performance, i.e., the convergence rate, and computational cost, etc.

A meta-learning system based on genetic algorithms

NASA Astrophysics Data System (ADS)

Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain

2004-04-01

The design of an efficient machine learning process through self-adaptation is a great challenge. The goal of meta-learning is to build a self-adaptive learning system that is constantly adapting to its specific (and dynamic) environment. To that end, the meta-learning mechanism must improve its bias dynamically by updating the current learning strategy in accordance with its available experiences or meta-knowledge. We suggest using genetic algorithms as the basis of an adaptive system. In this work, we propose a meta-learning system based on a combination of the a priori and a posteriori concepts. A priori refers to input information and knowledge available at the beginning in order to built and evolve one or more sets of parameters by exploiting the context of the system"s information. The self-learning component is based on genetic algorithms and neural Darwinism. A posteriori refers to the implicit knowledge discovered by estimation of the future states of parameters and is also applied to the finding of optimal parameters values. The in-progress research presented here suggests a framework for the discovery of knowledge that can support human experts in their intelligence information assessment tasks. The conclusion presents avenues for further research in genetic algorithms and their capability to learn to learn.

Multi-viewpoint Image Array Virtual Viewpoint Rapid Generation Algorithm Based on Image Layering

NASA Astrophysics Data System (ADS)

Jiang, Lu; Piao, Yan

2018-04-01

The use of multi-view image array combined with virtual viewpoint generation technology to record 3D scene information in large scenes has become one of the key technologies for the development of integrated imaging. This paper presents a virtual viewpoint rendering method based on image layering algorithm. Firstly, the depth information of reference viewpoint image is quickly obtained. During this process, SAD is chosen as the similarity measure function. Then layer the reference image and calculate the parallax based on the depth information. Through the relative distance between the virtual viewpoint and the reference viewpoint, the image layers are weighted and panned. Finally the virtual viewpoint image is rendered layer by layer according to the distance between the image layers and the viewer. This method avoids the disadvantages of the algorithm DIBR, such as high-precision requirements of depth map and complex mapping operations. Experiments show that, this algorithm can achieve the synthesis of virtual viewpoints in any position within 2×2 viewpoints range, and the rendering speed is also very impressive. The average result proved that this method can get satisfactory image quality. The average SSIM value of the results relative to real viewpoint images can reaches 0.9525, the PSNR value can reaches 38.353 and the image histogram similarity can reaches 93.77%.

Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer

NASA Astrophysics Data System (ADS)

Bredfeldt, Jeremy S.; Liu, Yuming; Pehlke, Carolyn A.; Conklin, Matthew W.; Szulczewski, Joseph M.; Inman, David R.; Keely, Patricia J.; Nowak, Robert D.; Mackie, Thomas R.; Eliceiri, Kevin W.

2014-01-01

Second-harmonic generation (SHG) imaging can help reveal interactions between collagen fibers and cancer cells. Quantitative analysis of SHG images of collagen fibers is challenged by the heterogeneity of collagen structures and low signal-to-noise ratio often found while imaging collagen in tissue. The role of collagen in breast cancer progression can be assessed post acquisition via enhanced computation. To facilitate this, we have implemented and evaluated four algorithms for extracting fiber information, such as number, length, and curvature, from a variety of SHG images of collagen in breast tissue. The image-processing algorithms included a Gaussian filter, SPIRAL-TV filter, Tubeness filter, and curvelet-denoising filter. Fibers are then extracted using an automated tracking algorithm called fiber extraction (FIRE). We evaluated the algorithm performance by comparing length, angle and position of the automatically extracted fibers with those of manually extracted fibers in twenty-five SHG images of breast cancer. We found that the curvelet-denoising filter followed by FIRE, a process we call CT-FIRE, outperforms the other algorithms under investigation. CT-FIRE was then successfully applied to track collagen fiber shape changes over time in an in vivo mouse model for breast cancer.

Validation of Coevolving Residue Algorithms via Pipeline Sensitivity Analysis: ELSC and OMES and ZNMI, Oh My!

PubMed Central

Brown, Christopher A.; Brown, Kevin S.

2010-01-01

Correlated amino acid substitution algorithms attempt to discover groups of residues that co-fluctuate due to either structural or functional constraints. Although these algorithms could inform both ab initio protein folding calculations and evolutionary studies, their utility for these purposes has been hindered by a lack of confidence in their predictions due to hard to control sources of error. To complicate matters further, naive users are confronted with a multitude of methods to choose from, in addition to the mechanics of assembling and pruning a dataset. We first introduce a new pair scoring method, called ZNMI (Z-scored-product Normalized Mutual Information), which drastically improves the performance of mutual information for co-fluctuating residue prediction. Second and more important, we recast the process of finding coevolving residues in proteins as a data-processing pipeline inspired by the medical imaging literature. We construct an ensemble of alignment partitions that can be used in a cross-validation scheme to assess the effects of choices made during the procedure on the resulting predictions. This pipeline sensitivity study gives a measure of reproducibility (how similar are the predictions given perturbations to the pipeline?) and accuracy (are residue pairs with large couplings on average close in tertiary structure?). We choose a handful of published methods, along with ZNMI, and compare their reproducibility and accuracy on three diverse protein families. We find that (i) of the algorithms tested, while none appear to be both highly reproducible and accurate, ZNMI is one of the most accurate by far and (ii) while users should be wary of predictions drawn from a single alignment, considering an ensemble of sub-alignments can help to determine both highly accurate and reproducible couplings. Our cross-validation approach should be of interest both to developers and end users of algorithms that try to detect correlated amino acid substitutions. PMID:20531955

Information fusion via isocortex-based Area 37 modeling

NASA Astrophysics Data System (ADS)

Peterson, James K.

2004-08-01

A simplified model of information processing in the brain can be constructed using primary sensory input from two modalities (auditory and visual) and recurrent connections to the limbic subsystem. Information fusion would then occur in Area 37 of the temporal cortex. The creation of meta concepts from the low order primary inputs is managed by models of isocortex processing. Isocortex algorithms are used to model parietal (auditory), occipital (visual), temporal (polymodal fusion) cortex and the limbic system. Each of these four modules is constructed out of five cortical stacks in which each stack consists of three vertically oriented six layer isocortex models. The input to output training of each cortical model uses the OCOS (on center - off surround) and FFP (folded feedback pathway) circuitry of (Grossberg, 1) which is inherently a recurrent network type of learning characterized by the identification of perceptual groups. Models of this sort are thus closely related to cognitive models as it is difficult to divorce the sensory processing subsystems from the higher level processing in the associative cortex. The overall software architecture presented is biologically based and is presented as a potential architectural prototype for the development of novel sensory fusion strategies. The algorithms are motivated to some degree by specific data from projects on musical composition and autonomous fine art painting programs, but only in the sense that these projects use two specific types of auditory and visual cortex data. Hence, the architectures are presented for an artificial information processing system which utilizes two disparate sensory sources. The exact nature of the two primary sensory input streams is irrelevant.

Production of τ τ jj final states at the LHC and the TauSpinner algorithm: the spin-2 case

NASA Astrophysics Data System (ADS)

Bahmani, M.; Kalinowski, J.; Kotlarski, W.; Richter-Wąs, E.; Wąs, Z.

2018-01-01

The TauSpinner algorithm is a tool that allows one to modify the physics model of the Monte Carlo generated samples due to the changed assumptions of event production dynamics, but without the need of re-generating events. With the help of weights τ -lepton production or decay processes can be modified accordingly to a new physics model. In a recent paper a new version TauSpinner ver.2.0.0 has been presented which includes a provision for introducing non-standard states and couplings and study their effects in the vector-boson-fusion processes by exploiting the spin correlations of τ -lepton pair decay products in processes where final states include also two hard jets. In the present paper we document how this can be achieved taking as an example the non-standard spin-2 state that couples to Standard Model particles and tree-level matrix elements with complete helicity information included for the parton-parton scattering amplitudes into a τ -lepton pair and two outgoing partons. This implementation is prepared as the external (user-provided) routine for the TauSpinner algorithm. It exploits amplitudes generated by MadGraph5 and adapted to the TauSpinner algorithm format. Consistency tests of the implemented matrix elements, re-weighting algorithm and numerical results for observables sensitive to τ polarisation are presented.

Design and demonstration of automated data analysis algorithms for ultrasonic inspection of complex composite panels with bonds

NASA Astrophysics Data System (ADS)

Aldrin, John C.; Forsyth, David S.; Welter, John T.

2016-02-01

To address the data review burden and improve the reliability of the ultrasonic inspection of large composite structures, automated data analysis (ADA) algorithms have been developed to make calls on indications that satisfy the detection criteria and minimize false calls. The original design followed standard procedures for analyzing signals for time-of-flight indications and backwall amplitude dropout. However, certain complex panels with varying shape, ply drops and the presence of bonds can complicate this interpretation process. In this paper, enhancements to the automated data analysis algorithms are introduced to address these challenges. To estimate the thickness of the part and presence of bonds without prior information, an algorithm tracks potential backwall or bond-line signals, and evaluates a combination of spatial, amplitude, and time-of-flight metrics to identify bonded sections. Once part boundaries, thickness transitions and bonded regions are identified, feature extraction algorithms are applied to multiple sets of through-thickness and backwall C-scan images, for evaluation of both first layer through thickness and layers under bonds. ADA processing results are presented for a variety of complex test specimens with inserted materials and other test discontinuities. Lastly, enhancements to the ADA software interface are presented, which improve the software usability for final data review by the inspectors and support the certification process.

Area collapse algorithm computing new curve of 2D geometric objects

NASA Astrophysics Data System (ADS)

Buczek, Michał Mateusz

2017-06-01

The processing of cartographic data demands human involvement. Up-to-date algorithms try to automate a part of this process. The goal is to obtain a digital model, or additional information about shape and topology of input geometric objects. A topological skeleton is one of the most important tools in the branch of science called shape analysis. It represents topological and geometrical characteristics of input data. Its plot depends on using algorithms such as medial axis, skeletonization, erosion, thinning, area collapse and many others. Area collapse, also known as dimension change, replaces input data with lower-dimensional geometric objects like, for example, a polygon with a polygonal chain, a line segment with a point. The goal of this paper is to introduce a new algorithm for the automatic calculation of polygonal chains representing a 2D polygon. The output is entirely contained within the area of the input polygon, and it has a linear plot without branches. The computational process is automatic and repeatable. The requirements of input data are discussed. The author analyzes results based on the method of computing ends of output polygonal chains. Additional methods to improve results are explored. The algorithm was tested on real-world cartographic data received from BDOT/GESUT databases, and on point clouds from laser scanning. An implementation for computing hatching of embankment is described.

Time resolving beam position measurement and analysis of beam unstable movement in PSR

NASA Astrophysics Data System (ADS)

Aleksandrov, A. V.

2000-11-01

Precise measurement of beam centroid movement is very important for understanding the fast transverse instability in the Los Alamos Proton Storage Ring (PSR). Proton bunch in the PSR is long thus different parts of the bunch can have different betatron phase and move differently therefore time resolving position measurement is needed. Wide band strip line BPM can be adequate if proper processing algorithm is used. In this work we present the results of the analysis of unstable transverse beam motion using time resolving processing algorithm. Suggested algorithm allows to calculate transverse position of different parts of the beam on each turn, then beam centroid movement on successive turns can be developed in series of plane travelling waves in the beam frame of reference thus providing important information on instability development. Some general features of fast transverse instability, unknown before, are discovered.

Linear segmentation algorithm for detecting layer boundary with lidar.

PubMed

Mao, Feiyue; Gong, Wei; Logan, Timothy

2013-11-04

The automatic detection of aerosol- and cloud-layer boundary (base and top) is important in atmospheric lidar data processing, because the boundary information is not only useful for environment and climate studies, but can also be used as input for further data processing. Previous methods have demonstrated limitations in defining the base and top, window-size setting, and have neglected the in-layer attenuation. To overcome these limitations, we present a new layer detection scheme for up-looking lidars based on linear segmentation with a reasonable threshold setting, boundary selecting, and false positive removing strategies. Preliminary results from both real and simulated data show that this algorithm cannot only detect the layer-base as accurate as the simple multi-scale method, but can also detect the layer-top more accurately than that of the simple multi-scale method. Our algorithm can be directly applied to uncalibrated data without requiring any additional measurements or window size selections.

Analytical optimal pulse shapes obtained with the aid of genetic algorithms

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

Guerrero, Rubén D., E-mail: rdguerrerom@unal.edu.co; Arango, Carlos A.; Reyes, Andrés

2015-09-28

We propose a methodology to design optimal pulses for achieving quantum optimal control on molecular systems. Our approach constrains pulse shapes to linear combinations of a fixed number of experimentally relevant pulse functions. Quantum optimal control is obtained by maximizing a multi-target fitness function using genetic algorithms. As a first application of the methodology, we generated an optimal pulse that successfully maximized the yield on a selected dissociation channel of a diatomic molecule. Our pulse is obtained as a linear combination of linearly chirped pulse functions. Data recorded along the evolution of the genetic algorithm contained important information regarding themore » interplay between radiative and diabatic processes. We performed a principal component analysis on these data to retrieve the most relevant processes along the optimal path. Our proposed methodology could be useful for performing quantum optimal control on more complex systems by employing a wider variety of pulse shape functions.« less

Exploration of available feature detection and identification systems and their performance on radiographs

NASA Astrophysics Data System (ADS)

Wantuch, Andrew C.; Vita, Joshua A.; Jimenez, Edward S.; Bray, Iliana E.

2016-10-01

Despite object detection, recognition, and identification being very active areas of computer vision research, many of the available tools to aid in these processes are designed with only photographs in mind. Although some algorithms used specifically for feature detection and identification may not take explicit advantage of the colors available in the image, they still under-perform on radiographs, which are grayscale images. We are especially interested in the robustness of these algorithms, specifically their performance on a preexisting database of X-ray radiographs in compressed JPEG form, with multiple ways of describing pixel information. We will review various aspects of the performance of available feature detection and identification systems, including MATLABs Computer Vision toolbox, VLFeat, and OpenCV on our non-ideal database. In the process, we will explore possible reasons for the algorithms' lessened ability to detect and identify features from the X-ray radiographs.

Bio-inspired multi-mode optic flow sensors for micro air vehicles

NASA Astrophysics Data System (ADS)

Park, Seokjun; Choi, Jaehyuk; Cho, Jihyun; Yoon, Euisik

2013-06-01

Monitoring wide-field surrounding information is essential for vision-based autonomous navigation in micro-air-vehicles (MAV). Our image-cube (iCube) module, which consists of multiple sensors that are facing different angles in 3-D space, can be applied to the wide-field of view optic flows estimation (μ-Compound eyes) and to attitude control (μ- Ocelli) in the Micro Autonomous Systems and Technology (MAST) platforms. In this paper, we report an analog/digital (A/D) mixed-mode optic-flow sensor, which generates both optic flows and normal images in different modes for μ- Compound eyes and μ-Ocelli applications. The sensor employs a time-stamp based optic flow algorithm which is modified from the conventional EMD (Elementary Motion Detector) algorithm to give an optimum partitioning of hardware blocks in analog and digital domains as well as adequate allocation of pixel-level, column-parallel, and chip-level signal processing. Temporal filtering, which may require huge hardware resources if implemented in digital domain, is remained in a pixel-level analog processing unit. The rest of the blocks, including feature detection and timestamp latching, are implemented using digital circuits in a column-parallel processing unit. Finally, time-stamp information is decoded into velocity from look-up tables, multiplications, and simple subtraction circuits in a chip-level processing unit, thus significantly reducing core digital processing power consumption. In the normal image mode, the sensor generates 8-b digital images using single slope ADCs in the column unit. In the optic flow mode, the sensor estimates 8-b 1-D optic flows from the integrated mixed-mode algorithm core and 2-D optic flows with an external timestamp processing, respectively.

Study on the multi-sensors monitoring and information fusion technology of dangerous cargo container

NASA Astrophysics Data System (ADS)

Xu, Shibo; Zhang, Shuhui; Cao, Wensheng

2017-10-01

In this paper, monitoring system of dangerous cargo container based on multi-sensors is presented. In order to improve monitoring accuracy, multi-sensors will be applied inside of dangerous cargo container. Multi-sensors information fusion solution of monitoring dangerous cargo container is put forward, and information pre-processing, the fusion algorithm of homogenous sensors and information fusion based on BP neural network are illustrated, applying multi-sensors in the field of container monitoring has some novelty.

[The segmentation of urinary cells--a first step in the automated processing in urine cytology (author's transl)].

PubMed

Liedtke, C E; Aeikens, B

1980-01-01

By segmentation of cell images we understand the automated decomposition of microscopic cell scenes into nucleus, plasma and background. A segmentation is achieved by using information from the microscope image and prior knowledge about the content of the scene. Different algorithms have been investigated and applied to samples of urothelial cells. A particular algorithm based on a histogram approach which can be easily implemented in hardware is discussed in more detail.

Internet Protocol Security (IPSEC): Testing and Implications on IPv4 and IPv6 Networks

DTIC Science & Technology

2008-08-27

Message Authentication Code-Message Digest 5-96). Due to the processing power consumption and slowness of public key authentication methods, RSA ...MODP) group with a 768 -bit modulus 2. a MODP group with a 1024-bit modulus 3. an Elliptic Curve Group over GF[ 2n ] (EC2N) group with a 155-bit...nonces, digital signatures using the Digital Signature Algorithm, and the Rivest-Shamir- Adelman ( RSA ) algorithm. For more information about the

Quantum Image Steganography and Steganalysis Based On LSQu-Blocks Image Information Concealing Algorithm

NASA Astrophysics Data System (ADS)

A. AL-Salhi, Yahya E.; Lu, Songfeng

2016-08-01

Quantum steganography can solve some problems that are considered inefficient in image information concealing. It researches on Quantum image information concealing to have been widely exploited in recent years. Quantum image information concealing can be categorized into quantum image digital blocking, quantum image stereography, anonymity and other branches. Least significant bit (LSB) information concealing plays vital roles in the classical world because many image information concealing algorithms are designed based on it. Firstly, based on the novel enhanced quantum representation (NEQR), image uniform blocks clustering around the concrete the least significant Qu-block (LSQB) information concealing algorithm for quantum image steganography is presented. Secondly, a clustering algorithm is proposed to optimize the concealment of important data. Finally, we used Con-Steg algorithm to conceal the clustered image blocks. Information concealing located on the Fourier domain of an image can achieve the security of image information, thus we further discuss the Fourier domain LSQu-block information concealing algorithm for quantum image based on Quantum Fourier Transforms. In our algorithms, the corresponding unitary Transformations are designed to realize the aim of concealing the secret information to the least significant Qu-block representing color of the quantum cover image. Finally, the procedures of extracting the secret information are illustrated. Quantum image LSQu-block image information concealing algorithm can be applied in many fields according to different needs.

A novel clinical decision support algorithm for constructing complete medication histories.

PubMed

Long, Ju; Yuan, Michael Juntao

2017-07-01

A patient's complete medication history is a crucial element for physicians to develop a full understanding of the patient's medical conditions and treatment options. However, due to the fragmented nature of medical data, this process can be very time-consuming and often impossible for physicians to construct a complete medication history for complex patients. In this paper, we describe an accurate, computationally efficient and scalable algorithm to construct a medication history timeline. The algorithm is developed and validated based on 1 million random prescription records from a large national prescription data aggregator. Our evaluation shows that the algorithm can be scaled horizontally on-demand, making it suitable for future delivery in a cloud-computing environment. We also propose that this cloud-based medication history computation algorithm could be integrated into Electronic Medical Records, enabling informed clinical decision-making at the point of care. Copyright © 2017 Elsevier B.V. All rights reserved.

The model of encryption algorithm based on non-positional polynomial notations and constructed on an SP-network

NASA Astrophysics Data System (ADS)

Kapalova, N.; Haumen, A.

2018-05-01

This paper addresses to structures and properties of the cryptographic information protection algorithm model based on NPNs and constructed on an SP-network. The main task of the research is to increase the cryptostrength of the algorithm. In the paper, the transformation resulting in the improvement of the cryptographic strength of the algorithm is described in detail. The proposed model is based on an SP-network. The reasons for using the SP-network in this model are the conversion properties used in these networks. In the encryption process, transformations based on S-boxes and P-boxes are used. It is known that these transformations can withstand cryptanalysis. In addition, in the proposed model, transformations that satisfy the requirements of the "avalanche effect" are used. As a result of this work, a computer program that implements an encryption algorithm model based on the SP-network has been developed.

Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem

PubMed Central

Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi

2013-01-01

Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem. PMID:23935429

Image based book cover recognition and retrieval

NASA Astrophysics Data System (ADS)

Sukhadan, Kalyani; Vijayarajan, V.; Krishnamoorthi, A.; Bessie Amali, D. Geraldine

2017-11-01

In this we are developing a graphical user interface using MATLAB for the users to check the information related to books in real time. We are taking the photos of the book cover using GUI, then by using MSER algorithm it will automatically detect all the features from the input image, after this it will filter bifurcate non-text features which will be based on morphological difference between text and non-text regions. We implemented a text character alignment algorithm which will improve the accuracy of the original text detection. We will also have a look upon the built in MATLAB OCR recognition algorithm and an open source OCR which is commonly used to perform better detection results, post detection algorithm is implemented and natural language processing to perform word correction and false detection inhibition. Finally, the detection result will be linked to internet to perform online matching. More than 86% accuracy can be obtained by this algorithm.

Status Report on the First Round of the Development of the Advanced Encryption Standard

PubMed Central

Nechvatal, James; Barker, Elaine; Dodson, Donna; Dworkin, Morris; Foti, James; Roback, Edward

1999-01-01

In 1997, the National Institute of Standards and Technology (NIST) initiated a process to select a symmetric-key encryption algorithm to be used to protect sensitive (unclassified) Federal information in furtherance of NIST’s statutory responsibilities. In 1998, NIST announced the acceptance of 15 candidate algorithms and requested the assistance of the cryptographic research community in analyzing the candidates. This analysis included an initial examination of the security and efficiency characteristics for each algorithm. NIST has reviewed the results of this research and selected five algorithms (MARS, RC6™, Rijndael, Serpent and Twofish) as finalists. The research results and rationale for the selection of the finalists are documented in this report. The five finalists will be the subject of further study before the selection of one or more of these algorithms for inclusion in the Advanced Encryption Standard.

Falls event detection using triaxial accelerometry and barometric pressure measurement.

PubMed

Bianchi, Federico; Redmond, Stephen J; Narayanan, Michael R; Cerutti, Sergio; Celler, Branko G; Lovell, Nigel H

2009-01-01

A falls detection system, employing a Bluetooth-based wearable device, containing a triaxial accelerometer and a barometric pressure sensor, is described. The aim of this study is to evaluate the use of barometric pressure measurement, as a surrogate measure of altitude, to augment previously reported accelerometry-based falls detection algorithms. The accelerometry and barometric pressure signals obtained from the waist-mounted device are analyzed by a signal processing and classification algorithm to discriminate falls from activities of daily living. This falls detection algorithm has been compared to two existing algorithms which utilize accelerometry signals alone. A set of laboratory-based simulated falls, along with other tasks associated with activities of daily living (16 tests) were performed by 15 healthy volunteers (9 male and 6 female; age: 23.7 +/- 2.9 years; height: 1.74 +/- 0.11 m). The algorithm incorporating pressure information detected falls with the highest sensitivity (97.8%) and the highest specificity (96.7%).

GIGA: a simple, efficient algorithm for gene tree inference in the genomic age

PubMed Central

2010-01-01

Background Phylogenetic relationships between genes are not only of theoretical interest: they enable us to learn about human genes through the experimental work on their relatives in numerous model organisms from bacteria to fruit flies and mice. Yet the most commonly used computational algorithms for reconstructing gene trees can be inaccurate for numerous reasons, both algorithmic and biological. Additional information beyond gene sequence data has been shown to improve the accuracy of reconstructions, though at great computational cost. Results We describe a simple, fast algorithm for inferring gene phylogenies, which makes use of information that was not available prior to the genomic age: namely, a reliable species tree spanning much of the tree of life, and knowledge of the complete complement of genes in a species' genome. The algorithm, called GIGA, constructs trees agglomeratively from a distance matrix representation of sequences, using simple rules to incorporate this genomic age information. GIGA makes use of a novel conceptualization of gene trees as being composed of orthologous subtrees (containing only speciation events), which are joined by other evolutionary events such as gene duplication or horizontal gene transfer. An important innovation in GIGA is that, at every step in the agglomeration process, the tree is interpreted/reinterpreted in terms of the evolutionary events that created it. Remarkably, GIGA performs well even when using a very simple distance metric (pairwise sequence differences) and no distance averaging over clades during the tree construction process. Conclusions GIGA is efficient, allowing phylogenetic reconstruction of very large gene families and determination of orthologs on a large scale. It is exceptionally robust to adding more gene sequences, opening up the possibility of creating stable identifiers for referring to not only extant genes, but also their common ancestors. We compared trees produced by GIGA to those in the TreeFam database, and they were very similar in general, with most differences likely due to poor alignment quality. However, some remaining differences are algorithmic, and can be explained by the fact that GIGA tends to put a larger emphasis on minimizing gene duplication and deletion events. PMID:20534164

GIGA: a simple, efficient algorithm for gene tree inference in the genomic age.

PubMed

Thomas, Paul D

2010-06-09

Phylogenetic relationships between genes are not only of theoretical interest: they enable us to learn about human genes through the experimental work on their relatives in numerous model organisms from bacteria to fruit flies and mice. Yet the most commonly used computational algorithms for reconstructing gene trees can be inaccurate for numerous reasons, both algorithmic and biological. Additional information beyond gene sequence data has been shown to improve the accuracy of reconstructions, though at great computational cost. We describe a simple, fast algorithm for inferring gene phylogenies, which makes use of information that was not available prior to the genomic age: namely, a reliable species tree spanning much of the tree of life, and knowledge of the complete complement of genes in a species' genome. The algorithm, called GIGA, constructs trees agglomeratively from a distance matrix representation of sequences, using simple rules to incorporate this genomic age information. GIGA makes use of a novel conceptualization of gene trees as being composed of orthologous subtrees (containing only speciation events), which are joined by other evolutionary events such as gene duplication or horizontal gene transfer. An important innovation in GIGA is that, at every step in the agglomeration process, the tree is interpreted/reinterpreted in terms of the evolutionary events that created it. Remarkably, GIGA performs well even when using a very simple distance metric (pairwise sequence differences) and no distance averaging over clades during the tree construction process. GIGA is efficient, allowing phylogenetic reconstruction of very large gene families and determination of orthologs on a large scale. It is exceptionally robust to adding more gene sequences, opening up the possibility of creating stable identifiers for referring to not only extant genes, but also their common ancestors. We compared trees produced by GIGA to those in the TreeFam database, and they were very similar in general, with most differences likely due to poor alignment quality. However, some remaining differences are algorithmic, and can be explained by the fact that GIGA tends to put a larger emphasis on minimizing gene duplication and deletion events.

Phase retrieval using regularization method in intensity correlation imaging

NASA Astrophysics Data System (ADS)

Li, Xiyu; Gao, Xin; Tang, Jia; Lu, Changming; Wang, Jianli; Wang, Bin

2014-11-01

Intensity correlation imaging(ICI) method can obtain high resolution image with ground-based low precision mirrors, in the imaging process, phase retrieval algorithm should be used to reconstituted the object's image. But the algorithm now used(such as hybrid input-output algorithm) is sensitive to noise and easy to stagnate. However the signal-to-noise ratio of intensity interferometry is low especially in imaging astronomical objects. In this paper, we build the mathematical model of phase retrieval and simplified it into a constrained optimization problem of a multi-dimensional function. New error function was designed by noise distribution and prior information using regularization method. The simulation results show that the regularization method can improve the performance of phase retrieval algorithm and get better image especially in low SNR condition

On the performances of computer vision algorithms on mobile platforms

NASA Astrophysics Data System (ADS)

Battiato, S.; Farinella, G. M.; Messina, E.; Puglisi, G.; Ravì, D.; Capra, A.; Tomaselli, V.

2012-01-01

Computer Vision enables mobile devices to extract the meaning of the observed scene from the information acquired with the onboard sensor cameras. Nowadays, there is a growing interest in Computer Vision algorithms able to work on mobile platform (e.g., phone camera, point-and-shot-camera, etc.). Indeed, bringing Computer Vision capabilities on mobile devices open new opportunities in different application contexts. The implementation of vision algorithms on mobile devices is still a challenging task since these devices have poor image sensors and optics as well as limited processing power. In this paper we have considered different algorithms covering classic Computer Vision tasks: keypoint extraction, face detection, image segmentation. Several tests have been done to compare the performances of the involved mobile platforms: Nokia N900, LG Optimus One, Samsung Galaxy SII.

Information Fusion of Conflicting Input Data.

PubMed

Mönks, Uwe; Dörksen, Helene; Lohweg, Volker; Hübner, Michael

2016-10-29

Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the μ BalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible.

Information Fusion of Conflicting Input Data

PubMed Central

Mönks, Uwe; Dörksen, Helene; Lohweg, Volker; Hübner, Michael

2016-01-01

Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the μBalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible. PMID:27801874

An algorithm for automated ROI definition in water or epoxy-filled NEMA NU-2 image quality phantoms.

PubMed

Pierce, Larry A; Byrd, Darrin W; Elston, Brian F; Karp, Joel S; Sunderland, John J; Kinahan, Paul E

2016-01-08

Drawing regions of interest (ROIs) in positron emission tomography/computed tomography (PET/CT) scans of the National Electrical Manufacturers Association (NEMA) NU-2 Image Quality (IQ) phantom is a time-consuming process that allows for interuser variability in the measurements. In order to reduce operator effort and allow batch processing of IQ phantom images, we propose a fast, robust, automated algorithm for performing IQ phantom sphere localization and analysis. The algorithm is easily altered to accommodate different configurations of the IQ phantom. The proposed algorithm uses information from both the PET and CT image volumes in order to overcome the challenges of detecting the smallest spheres in the PET volume. This algorithm has been released as an open-source plug-in to the Osirix medical image viewing software package. We test the algorithm under various noise conditions, positions within the scanner, air bubbles in the phantom spheres, and scanner misalignment conditions. The proposed algorithm shows run-times between 3 and 4 min and has proven to be robust under all tested conditions, with expected sphere localization deviations of less than 0.2 mm and variations of PET ROI mean and maximum values on the order of 0.5% and 2%, respectively, over multiple PET acquisitions. We conclude that the proposed algorithm is stable when challenged with a variety of physical and imaging anomalies, and that the algorithm can be a valuable tool for those who use the NEMA NU-2 IQ phantom for PET/CT scanner acceptance testing and QA/QC.

Optical implementation of the synthetic discriminant function

NASA Astrophysics Data System (ADS)

Butler, S.; Riggins, J.

1984-10-01

Much attention is focused on the use of coherent optical pattern recognition (OPR) using matched spatial filters for robotics and intelligent systems. The OPR problem consists of three aspects -- information input, information processing, and information output. This paper discusses the information processing aspect which consists of choosing a filter to provide robust correlation with high efficiency. The filter should ideally be invariant to image shift, rotation and scale, provide a reasonable signal-to-noise (S/N) ratio and allow high throughput efficiency. The physical implementation of a spatial matched filter involves many choices. These include the use of conventional holograms or computer-generated holograms (CGH) and utilizing absorption or phase materials. Conventional holograms inherently modify the reference image by non-uniform emphasis of spatial frequencies. Proper use of film nonlinearity provides improved filter performance by emphasizing frequency ranges crucial to target discrimination. In the case of a CGH, the emphasis of the reference magnitude and phase can be controlled independently of the continuous tone or binary writing processes. This paper describes computer simulation and optical implementation of a geometrical shape and a Synthetic Discriminant Function (SDF) matched filter. The authors chose the binary Allebach-Keegan (AK) CGH algorithm to produce actual filters. The performances of these filters were measured to verify the simulation results. This paper provides a brief summary of the matched filter theory, the SDF, CGH algorithms, Phase-Only-Filtering, simulation procedures, and results.

Quantum communication and information processing

NASA Astrophysics Data System (ADS)

Beals, Travis Roland

Quantum computers enable dramatically more efficient algorithms for solving certain classes of computational problems, but, in doing so, they create new problems. In particular, Shor's Algorithm allows for efficient cryptanalysis of many public-key cryptosystems. As public key cryptography is a critical component of present-day electronic commerce, it is crucial that a working, secure replacement be found. Quantum key distribution (QKD), first developed by C.H. Bennett and G. Brassard, offers a partial solution, but many challenges remain, both in terms of hardware limitations and in designing cryptographic protocols for a viable large-scale quantum communication infrastructure. In Part I, I investigate optical lattice-based approaches to quantum information processing. I look at details of a proposal for an optical lattice-based quantum computer, which could potentially be used for both quantum communications and for more sophisticated quantum information processing. In Part III, I propose a method for converting and storing photonic quantum bits in the internal state of periodically-spaced neutral atoms by generating and manipulating a photonic band gap and associated defect states. In Part II, I present a cryptographic protocol which allows for the extension of present-day QKD networks over much longer distances without the development of new hardware. I also present a second, related protocol which effectively solves the authentication problem faced by a large QKD network, thus making QKD a viable, information-theoretic secure replacement for public key cryptosystems.

A Diagnosis-Prognosis Feedback Loop for Improved Performance Under Uncertainties

NASA Technical Reports Server (NTRS)

Leser, Patrick E.; Warner, James E.

2017-01-01

The feed-forward relationship between diagnosis and prognosis is the foundation of both aircraft structural health management and the digital twin concept. Measurements of structural response are obtained either in-situ with mounted sensor networks or offline using more traditional techniques (e.g., nondestructive evaluation). Diagnosis algorithms process this information to detect and quantify damage and then feed this data forward to a prognostic framework. A prognosis of the structure's future operational readiness (e.g., remaining useful life or residual strength) is then made and is used to inform mission- critical decision-making. Years of research have been devoted to improving the elements of this process, but the process itself has not changed significantly. Here, a new approach is proposed in which prognosis information is not only fed forward for decision-making, but it is also fed back to the forthcoming diagnosis. In this way, diagnosis algorithms can take advantage of a priori information about the expected state of health, rather than operating in an uninformed condition. As a feasibility test, a diagnosis-prognosis feedback loop of this manner is demonstrated. The approach is applied to a numerical example in which fatigue crack growth is simulated in a simple aluminum alloy test specimen. A prognosis was derived from a set of diagnoses which provided feedback to a subsequent set of diagnoses. Improvements in accuracy and a reduction in uncertainty in the prognosis- informed diagnoses were observed when compared with an uninformed diagnostic approach.

An energy-efficient and secure hybrid algorithm for wireless sensor networks using a mobile data collector

NASA Astrophysics Data System (ADS)

Dayananda, Karanam Ravichandran; Straub, Jeremy

2017-05-01

This paper proposes a new hybrid algorithm for security, which incorporates both distributed and hierarchal approaches. It uses a mobile data collector (MDC) to collect information in order to save energy of sensor nodes in a wireless sensor network (WSN) as, in most networks, these sensor nodes have limited energy. Wireless sensor networks are prone to security problems because, among other things, it is possible to use a rogue sensor node to eavesdrop on or alter the information being transmitted. To prevent this, this paper introduces a security algorithm for MDC-based WSNs. A key use of this algorithm is to protect the confidentiality of the information sent by the sensor nodes. The sensor nodes are deployed in a random fashion and form group structures called clusters. Each cluster has a cluster head. The cluster head collects data from the other nodes using the time-division multiple access protocol. The sensor nodes send their data to the cluster head for transmission to the base station node for further processing. The MDC acts as an intermediate node between the cluster head and base station. The MDC, using its dynamic acyclic graph path, collects the data from the cluster head and sends it to base station. This approach is useful for applications including warfighting, intelligent building and medicine. To assess the proposed system, the paper presents a comparison of its performance with other approaches and algorithms that can be used for similar purposes.

Automated analysis of information processing, kinetic independence and modular architecture in biochemical networks using MIDIA.

PubMed

Bowsher, Clive G

2011-02-15

Understanding the encoding and propagation of information by biochemical reaction networks and the relationship of such information processing properties to modular network structure is of fundamental importance in the study of cell signalling and regulation. However, a rigorous, automated approach for general biochemical networks has not been available, and high-throughput analysis has therefore been out of reach. Modularization Identification by Dynamic Independence Algorithms (MIDIA) is a user-friendly, extensible R package that performs automated analysis of how information is processed by biochemical networks. An important component is the algorithm's ability to identify exact network decompositions based on both the mass action kinetics and informational properties of the network. These modularizations are visualized using a tree structure from which important dynamic conditional independence properties can be directly read. Only partial stoichiometric information needs to be used as input to MIDIA, and neither simulations nor knowledge of rate parameters are required. When applied to a signalling network, for example, the method identifies the routes and species involved in the sequential propagation of information between its multiple inputs and outputs. These routes correspond to the relevant paths in the tree structure and may be further visualized using the Input-Output Path Matrix tool. MIDIA remains computationally feasible for the largest network reconstructions currently available and is straightforward to use with models written in Systems Biology Markup Language (SBML). The package is distributed under the GNU General Public License and is available, together with a link to browsable Supplementary Material, at http://code.google.com/p/midia. Further information is at www.maths.bris.ac.uk/~macgb/Software.html.

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

PubMed

Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

2015-01-01

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs

PubMed Central

Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

2015-01-01

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network. PMID:26571042

Phasor based single-molecule localization microscopy in 3D (pSMLM-3D): An algorithm for MHz localization rates using standard CPUs

NASA Astrophysics Data System (ADS)

Martens, Koen J. A.; Bader, Arjen N.; Baas, Sander; Rieger, Bernd; Hohlbein, Johannes

2018-03-01

We present a fast and model-free 2D and 3D single-molecule localization algorithm that allows more than 3 × 106 localizations per second to be calculated on a standard multi-core central processing unit with localization accuracies in line with the most accurate algorithms currently available. Our algorithm converts the region of interest around a point spread function to two phase vectors (phasors) by calculating the first Fourier coefficients in both the x- and y-direction. The angles of these phasors are used to localize the center of the single fluorescent emitter, and the ratio of the magnitudes of the two phasors is a measure for astigmatism, which can be used to obtain depth information (z-direction). Our approach can be used both as a stand-alone algorithm for maximizing localization speed and as a first estimator for more time consuming iterative algorithms.

EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal

NASA Astrophysics Data System (ADS)

Chen, Yong; Wu, Chun-ting; Liu, Huan-lin

2017-07-01

Noise may reduce the demodulation accuracy of fiber Bragg grating (FBG) sensing signal so as to affect the quality of sensing detection. Thus, the recovery of a signal from observed noisy data is necessary. In this paper, a precise self-adaptive algorithm of selecting relevant modes is proposed to remove the noise of signal. Empirical mode decomposition (EMD) is first used to decompose a signal into a set of modes. The pseudo modes cancellation is introduced to identify and eliminate false modes, and then the Mutual Information (MI) of partial modes is calculated. MI is used to estimate the critical point of high and low frequency components. Simulation results show that the proposed algorithm estimates the critical point more accurately than the traditional algorithms for FBG spectral signal. While, compared to the similar algorithms, the signal noise ratio of the signal can be improved more than 10 dB after processing by the proposed algorithm, and correlation coefficient can be increased by 0.5, so it demonstrates better de-noising effect.

RISMA: A Rule-based Interval State Machine Algorithm for Alerts Generation, Performance Analysis and Monitoring Real-Time Data Processing

NASA Astrophysics Data System (ADS)

Laban, Shaban; El-Desouky, Aly

2013-04-01

The monitoring of real-time systems is a challenging and complicated process. So, there is a continuous need to improve the monitoring process through the use of new intelligent techniques and algorithms for detecting exceptions, anomalous behaviours and generating the necessary alerts during the workflow monitoring of such systems. The interval-based or period-based theorems have been discussed, analysed, and used by many researches in Artificial Intelligence (AI), philosophy, and linguistics. As explained by Allen, there are 13 relations between any two intervals. Also, there have also been many studies of interval-based temporal reasoning and logics over the past decades. Interval-based theorems can be used for monitoring real-time interval-based data processing. However, increasing the number of processed intervals makes the implementation of such theorems a complex and time consuming process as the relationships between such intervals are increasing exponentially. To overcome the previous problem, this paper presents a Rule-based Interval State Machine Algorithm (RISMA) for processing, monitoring, and analysing the behaviour of interval-based data, received from real-time sensors. The proposed intelligent algorithm uses the Interval State Machine (ISM) approach to model any number of interval-based data into well-defined states as well as inferring them. An interval-based state transition model and methodology are presented to identify the relationships between the different states of the proposed algorithm. By using such model, the unlimited number of relationships between similar large numbers of intervals can be reduced to only 18 direct relationships using the proposed well-defined states. For testing the proposed algorithm, necessary inference rules and code have been designed and applied to the continuous data received in near real-time from the stations of International Monitoring System (IMS) by the International Data Centre (IDC) of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). The CLIPS expert system shell has been used as the main rule engine for implementing the algorithm rules. Python programming language and the module "PyCLIPS" are used for building the necessary code for algorithm implementation. More than 1.7 million intervals constitute the Concise List of Frames (CLF) from 20 different seismic stations have been used for evaluating the proposed algorithm and evaluating stations behaviour and performance. The initial results showed that proposed algorithm can help in better understanding of the operation and performance of those stations. Different important information, such as alerts and some station performance parameters, can be derived from the proposed algorithm. For IMS interval-based data and at any period of time it is possible to analyze station behavior, determine the missing data, generate necessary alerts, and to measure some of station performance attributes. The details of the proposed algorithm, methodology, implementation, experimental results, advantages, and limitations of this research are presented. Finally, future directions and recommendations are discussed.

Quantitative Aspects of Single Molecule Microscopy

PubMed Central

Ober, Raimund J.; Tahmasbi, Amir; Ram, Sripad; Lin, Zhiping; Ward, E. Sally

2015-01-01

Single molecule microscopy is a relatively new optical microscopy technique that allows the detection of individual molecules such as proteins in a cellular context. This technique has generated significant interest among biologists, biophysicists and biochemists, as it holds the promise to provide novel insights into subcellular processes and structures that otherwise cannot be gained through traditional experimental approaches. Single molecule experiments place stringent demands on experimental and algorithmic tools due to the low signal levels and the presence of significant extraneous noise sources. Consequently, this has necessitated the use of advanced statistical signal and image processing techniques for the design and analysis of single molecule experiments. In this tutorial paper, we provide an overview of single molecule microscopy from early works to current applications and challenges. Specific emphasis will be on the quantitative aspects of this imaging modality, in particular single molecule localization and resolvability, which will be discussed from an information theoretic perspective. We review the stochastic framework for image formation, different types of estimation techniques and expressions for the Fisher information matrix. We also discuss several open problems in the field that demand highly non-trivial signal processing algorithms. PMID:26167102

Monitoring Deformation in Graphene Through Hyperspectral Synchrotron Spectroscopy to Inform Fabrication

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

Winter, Allen Douglas; Rojas, Wudmir Y.; Williams, Adrienne D.

The promise from graphene to produce devices with high mobilities and detectors with fast response times is truncated in practice by strain and deformation originating during growth and subsequent processing. This work describes effects from graphene growth, multiple layer transfer, and substrate termination on out of plane deformation, critical to device performance. Synchrotron spectroscopy data was acquired with a state-of-the-art hyperspectral large-area detector to describe growth and processing with molecular sensitivity at wafer length scales. A study of methodologies used in data analysis discouraged dichroic ratio approaches in favor of orbital vector approximations and data mining algorithms. Orbital vector methodsmore » provide a physical insight into mobility-detrimental rippling by identifying ripple frequency as main actor, rather than intensity; which was confirmed by data mining algorithms, and in good agreement with electron scattering theories of corrugation in graphene. This work paves the way to efficient information from mechanical properties in graphene in a high throughput mode throughout growth and processing in a materials by design approach.« less

The Dynamic Process of Drug-GPCR Binding at Either Orthosteric or Allosteric Sites Evaluated by Metadynamics

PubMed Central

Schneider, Sebastian; Provasi, Davide; Filizola, Marta

2015-01-01

Major advances in G Protein-Coupled Receptor (GPCR) structural biology over the past few years have yielded a significant number of high-resolution crystal structures for several different receptor subtypes. This dramatic increase in GPCR structural information has underscored the use of automated docking algorithms for the discovery of novel ligands that can eventually be developed into improved therapeutics. However, these algorithms are often unable to discriminate between different, yet energetically similar, poses because of their relatively simple scoring functions. Here, we describe a metadynamics-based approach to study the dynamic process of ligand binding to/unbinding from GPCRs with a higher level of accuracy and yet satisfying efficiency. PMID:26260607

Spatio-temporal models of mental processes from fMRI.

PubMed

Janoos, Firdaus; Machiraju, Raghu; Singh, Shantanu; Morocz, Istvan Ákos

2011-07-15

Understanding the highly complex, spatially distributed and temporally organized phenomena entailed by mental processes using functional MRI is an important research problem in cognitive and clinical neuroscience. Conventional analysis methods focus on the spatial dimension of the data discarding the information about brain function contained in the temporal dimension. This paper presents a fully spatio-temporal multivariate analysis method using a state-space model (SSM) for brain function that yields not only spatial maps of activity but also its temporal structure along with spatially varying estimates of the hemodynamic response. Efficient algorithms for estimating the parameters along with quantitative validations are given. A novel low-dimensional feature-space for representing the data, based on a formal definition of functional similarity, is derived. Quantitative validation of the model and the estimation algorithms is provided with a simulation study. Using a real fMRI study for mental arithmetic, the ability of this neurophysiologically inspired model to represent the spatio-temporal information corresponding to mental processes is demonstrated. Moreover, by comparing the models across multiple subjects, natural patterns in mental processes organized according to different mental abilities are revealed. Copyright © 2011 Elsevier Inc. All rights reserved.

Non-rigid ultrasound image registration using generalized relaxation labeling process

NASA Astrophysics Data System (ADS)

Lee, Jong-Ha; Seong, Yeong Kyeong; Park, MoonHo; Woo, Kyoung-Gu; Ku, Jeonghun; Park, Hee-Jun

2013-03-01

This research proposes a novel non-rigid registration method for ultrasound images. The most predominant anatomical features in medical images are tissue boundaries, which appear as edges. In ultrasound images, however, other features can be identified as well due to the specular reflections that appear as bright lines superimposed on the ideal edge location. In this work, an image's local phase information (via the frequency domain) is used to find the ideal edge location. The generalized relaxation labeling process is then formulated to align the feature points extracted from the ideal edge location. In this work, the original relaxation labeling method was generalized by taking n compatibility coefficient values to improve non-rigid registration performance. This contextual information combined with a relaxation labeling process is used to search for a correspondence. Then the transformation is calculated by the thin plate spline (TPS) model. These two processes are iterated until the optimal correspondence and transformation are found. We have tested our proposed method and the state-of-the-art algorithms with synthetic data and bladder ultrasound images of in vivo human subjects. Experiments show that the proposed method improves registration performance significantly, as compared to other state-of-the-art non-rigid registration algorithms.

Acquisition of business intelligence from human experience in route planning

NASA Astrophysics Data System (ADS)

Bello Orgaz, Gema; Barrero, David F.; R-Moreno, María D.; Camacho, David

2015-04-01

The logistic sector raises a number of highly challenging problems. Probably one of the most important ones is the shipping planning, i.e. plan the routes that the shippers have to follow to deliver the goods. In this article, we present an artificial intelligence-based solution that has been designed to help a logistic company to improve its routes planning process. In order to achieve this goal, the solution uses the knowledge acquired by the company drivers to propose optimised routes. Hence, the proposed solution gathers the experience of the drivers, processes it and optimises the delivery process. The solution uses data mining to extract knowledge from the company information systems and prepares it for analysis with a case-based reasoning (CBR) algorithm. The CBR obtains critical business intelligence knowledge from the drivers experience that is needed by the planner. The design of the routes is done by a genetic algorithm that, given the processed information, optimises the routes following several objectives, such as minimise the distance or time. Experimentation shows that the proposed approach is able to find routes that improve, on average, the routes made by the human experts.

Sequence information signal processor for local and global string comparisons

DOEpatents

Peterson, John C.; Chow, Edward T.; Waterman, Michael S.; Hunkapillar, Timothy J.

1997-01-01

A sequence information signal processing integrated circuit chip designed to perform high speed calculation of a dynamic programming algorithm based upon the algorithm defined by Waterman and Smith. The signal processing chip of the present invention is designed to be a building block of a linear systolic array, the performance of which can be increased by connecting additional sequence information signal processing chips to the array. The chip provides a high speed, low cost linear array processor that can locate highly similar global sequences or segments thereof such as contiguous subsequences from two different DNA or protein sequences. The chip is implemented in a preferred embodiment using CMOS VLSI technology to provide the equivalent of about 400,000 transistors or 100,000 gates. Each chip provides 16 processing elements, and is designed to provide 16 bit, two's compliment operation for maximum score precision of between -32,768 and +32,767. It is designed to provide a comparison between sequences as long as 4,194,304 elements without external software and between sequences of unlimited numbers of elements with the aid of external software. Each sequence can be assigned different deletion and insertion weight functions. Each processor is provided with a similarity measure device which is independently variable. Thus, each processor can contribute to maximum value score calculation using a different similarity measure.

Advanced Oil Spill Detection Algorithms For Satellite Based Maritime Environment Monitoring

NASA Astrophysics Data System (ADS)

Radius, Andrea; Azevedo, Rui; Sapage, Tania; Carmo, Paulo

2013-12-01

During the last years, the increasing pollution occurrence and the alarming deterioration of the environmental health conditions of the sea, lead to the need of global monitoring capabilities, namely for marine environment management in terms of oil spill detection and indication of the suspected polluter. The sensitivity of Synthetic Aperture Radar (SAR) to the different phenomena on the sea, especially for oil spill and vessel detection, makes it a key instrument for global pollution monitoring. The SAR performances in maritime pollution monitoring are being operationally explored by a set of service providers on behalf of the European Maritime Safety Agency (EMSA), which has launched in 2007 the CleanSeaNet (CSN) project - a pan-European satellite based oil monitoring service. EDISOFT, which is from the beginning a service provider for CSN, is continuously investing in R&D activities that will ultimately lead to better algorithms and better performance on oil spill detection from SAR imagery. This strategy is being pursued through EDISOFT participation in the FP7 EC Sea-U project and in the Automatic Oil Spill Detection (AOSD) ESA project. The Sea-U project has the aim to improve the current state of oil spill detection algorithms, through the informative content maximization obtained with data fusion, the exploitation of different type of data/ sensors and the development of advanced image processing, segmentation and classification techniques. The AOSD project is closely related to the operational segment, because it is focused on the automation of the oil spill detection processing chain, integrating auxiliary data, like wind information, together with image and geometry analysis techniques. The synergy between these different objectives (R&D versus operational) allowed EDISOFT to develop oil spill detection software, that combines the operational automatic aspect, obtained through dedicated integration of the processing chain in the existing open source NEST software, with new detection, filtering and classification algorithms. Particularly, dedicated filtering algorithm development based on Wavelet filtering was exploited for the improvement of oil spill detection and classification. In this work we present the functionalities of the developed software and the main results in support of the developed algorithm validity.

On an algorithmic definition for the components of the minimal cell.

PubMed

Martínez, Octavio; Reyes-Valdés, M Humberto

2018-01-01

Living cells are highly complex systems comprising a multitude of elements that are engaged in the many convoluted processes observed during the cell cycle. However, not all elements and processes are essential for cell survival and reproduction under steady-state environmental conditions. To distinguish between essential from expendable cell components and thus define the 'minimal cell' and the corresponding 'minimal genome', we postulate that the synthesis of all cell elements can be represented as a finite set of binary operators, and within this framework we show that cell elements that depend on their previous existence to be synthesized are those that are essential for cell survival. An algorithm to distinguish essential cell elements is presented and demonstrated within an interactome. Data and functions implementing the algorithm are given as supporting information. We expect that this algorithmic approach will lead to the determination of the complete interactome of the minimal cell, which could then be experimentally validated. The assumptions behind this hypothesis as well as its consequences for experimental and theoretical biology are discussed.

Hybrid wavefront sensing and image correction algorithm for imaging through turbulent media

NASA Astrophysics Data System (ADS)

Wu, Chensheng; Robertson Rzasa, John; Ko, Jonathan; Davis, Christopher C.

2017-09-01

It is well known that passive image correction of turbulence distortions often involves using geometry-dependent deconvolution algorithms. On the other hand, active imaging techniques using adaptive optic correction should use the distorted wavefront information for guidance. Our work shows that a hybrid hardware-software approach is possible to obtain accurate and highly detailed images through turbulent media. The processing algorithm also takes much fewer iteration steps in comparison with conventional image processing algorithms. In our proposed approach, a plenoptic sensor is used as a wavefront sensor to guide post-stage image correction on a high-definition zoomable camera. Conversely, we show that given the ground truth of the highly detailed image and the plenoptic imaging result, we can generate an accurate prediction of the blurred image on a traditional zoomable camera. Similarly, the ground truth combined with the blurred image from the zoomable camera would provide the wavefront conditions. In application, our hybrid approach can be used as an effective way to conduct object recognition in a turbulent environment where the target has been significantly distorted or is even unrecognizable.

Colony image acquisition and segmentation

NASA Astrophysics Data System (ADS)

Wang, W. X.

2007-12-01

For counting of both colonies and plaques, there is a large number of applications including food, dairy, beverages, hygiene, environmental monitoring, water, toxicology, sterility testing, AMES testing, pharmaceuticals, paints, sterile fluids and fungal contamination. Recently, many researchers and developers have made efforts for this kind of systems. By investigation, some existing systems have some problems. The main problems are image acquisition and image segmentation. In order to acquire colony images with good quality, an illumination box was constructed as: the box includes front lightning and back lightning, which can be selected by users based on properties of colony dishes. With the illumination box, lightning can be uniform; colony dish can be put in the same place every time, which make image processing easy. The developed colony image segmentation algorithm consists of the sub-algorithms: (1) image classification; (2) image processing; and (3) colony delineation. The colony delineation algorithm main contain: the procedures based on grey level similarity, on boundary tracing, on shape information and colony excluding. In addition, a number of algorithms are developed for colony analysis. The system has been tested and satisfactory.

Pan-sharpening via compressed superresolution reconstruction and multidictionary learning

NASA Astrophysics Data System (ADS)

Shi, Cheng; Liu, Fang; Li, Lingling; Jiao, Licheng; Hao, Hongxia; Shang, Ronghua; Li, Yangyang

2018-01-01

In recent compressed sensing (CS)-based pan-sharpening algorithms, pan-sharpening performance is affected by two key problems. One is that there are always errors between the high-resolution panchromatic (HRP) image and the linear weighted high-resolution multispectral (HRM) image, resulting in spatial and spectral information lost. The other is that the dictionary construction process depends on the nontruth training samples. These problems have limited applications to CS-based pan-sharpening algorithm. To solve these two problems, we propose a pan-sharpening algorithm via compressed superresolution reconstruction and multidictionary learning. Through a two-stage implementation, compressed superresolution reconstruction model reduces the error effectively between the HRP and the linear weighted HRM images. Meanwhile, the multidictionary with ridgelet and curvelet is learned for both the two stages in the superresolution reconstruction process. Since ridgelet and curvelet can better capture the structure and directional characteristics, a better reconstruction result can be obtained. Experiments are done on the QuickBird and IKONOS satellites images. The results indicate that the proposed algorithm is competitive compared with the recent CS-based pan-sharpening methods and other well-known methods.

Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients

PubMed Central

Jin, Shuo; Li, Dengwang; Yin, Yong

2013-01-01

Accurate registration of  18F−FDG PET (positron emission tomography) and CT (computed tomography) images has important clinical significance in radiation oncology. PET and CT images are acquired from  18F−FDG PET/CT scanner, but the two acquisition processes are separate and take a long time. As a result, there are position errors in global and deformable errors in local caused by respiratory movement or organ peristalsis. The purpose of this work was to implement and validate a deformable CT to PET image registration method in esophageal cancer to eventually facilitate accurate positioning the tumor target on CT, and improve the accuracy of radiation therapy. Global registration was firstly utilized to preprocess position errors between PET and CT images, achieving the purpose of aligning these two images on the whole. Demons algorithm, based on optical flow field, has the features of fast process speed and high accuracy, and the gradient of mutual information‐based demons (GMI demons) algorithm adds an additional external force based on the gradient of mutual information (GMI) between two images, which is suitable for multimodality images registration. In this paper, GMI demons algorithm was used to achieve local deformable registration of PET and CT images, which can effectively reduce errors between internal organs. In addition, to speed up the registration process, maintain its robustness, and avoid the local extremum, multiresolution image pyramid structure was used before deformable registration. By quantitatively and qualitatively analyzing cases with esophageal cancer, the registration scheme proposed in this paper can improve registration accuracy and speed, which is helpful for precisely positioning tumor target and developing the radiation treatment planning in clinical radiation therapy application. PACS numbers: 87.57.nj, 87.57.Q‐, 87.57.uk PMID:23318381

Technologies for network-centric C4ISR

NASA Astrophysics Data System (ADS)

Dunkelberger, Kirk A.

2003-07-01

Three technologies form the heart of any network-centric command, control, communication, intelligence, surveillance, and reconnaissance (C4ISR) system: distributed processing, reconfigurable networking, and distributed resource management. Distributed processing, enabled by automated federation, mobile code, intelligent process allocation, dynamic multiprocessing groups, check pointing, and other capabilities creates a virtual peer-to-peer computing network across the force. Reconfigurable networking, consisting of content-based information exchange, dynamic ad-hoc routing, information operations (perception management) and other component technologies forms the interconnect fabric for fault tolerant inter processor and node communication. Distributed resource management, which provides the means for distributed cooperative sensor management, foe sensor utilization, opportunistic collection, symbiotic inductive/deductive reasoning and other applications provides the canonical algorithms for network-centric enterprises and warfare. This paper introduces these three core technologies and briefly discusses a sampling of their component technologies and their individual contributions to network-centric enterprises and warfare. Based on the implied requirements, two new algorithms are defined and characterized which provide critical building blocks for network centricity: distributed asynchronous auctioning and predictive dynamic source routing. The first provides a reliable, efficient, effective approach for near-optimal assignment problems; the algorithm has been demonstrated to be a viable implementation for ad-hoc command and control, object/sensor pairing, and weapon/target assignment. The second is founded on traditional dynamic source routing (from mobile ad-hoc networking), but leverages the results of ad-hoc command and control (from the contributed auctioning algorithm) into significant increases in connection reliability through forward prediction. Emphasis is placed on the advantages gained from the closed-loop interaction of the multiple technologies in the network-centric application environment.