[The maintenance of automatic analysers and associated documentation].

PubMed

Adjidé, V; Fournier, P; Vassault, A

2010-12-01

The maintenance of automatic analysers and associated documentation taking part in the requirements of the ISO 15189 Standard and the French regulation as well have to be defined in the laboratory policy. The management of the periodic maintenance and documentation shall be implemented and fulfilled. The organisation of corrective maintenance has to be managed to avoid interruption of the task of the laboratory. The different recommendations concern the identification of materials including automatic analysers, the environmental conditions to take into account, the documentation provided by the manufacturer and documents prepared by the laboratory including procedures for maintenance.

A review of automatic patient identification options for public health care centers with restricted budgets.

PubMed

García-Betances, Rebeca I; Huerta, Mónica K

2012-01-01

A comparative review is presented of available technologies suitable for automatic reading of patient identification bracelet tags. Existing technologies' backgrounds, characteristics, advantages and disadvantages, are described in relation to their possible use by public health care centers with budgetary limitations. A comparative assessment is presented of suitable automatic identification systems based on graphic codes, both one- (1D) and two-dimensional (2D), printed on labels, as well as those based on radio frequency identification (RFID) tags. The analysis looks at the tradeoffs of these technologies to provide guidance to hospital administrator looking to deploy patient identification technology. The results suggest that affordable automatic patient identification systems can be easily and inexpensively implemented using 2D code printed on low cost bracelet labels, which can then be read and automatically decoded by ordinary mobile smart phones. Because of mobile smart phones' present versatility and ubiquity, the implantation and operation of 2D code, and especially Quick Response® (QR) Code, technology emerges as a very attractive alternative to automate the patients' identification processes in low-budget situations.

A Review of Automatic Patient Identification Options for Public Health Care Centers with Restricted Budgets

PubMed Central

García-Betances, Rebeca I.; Huerta, Mónica K.

2012-01-01

A comparative review is presented of available technologies suitable for automatic reading of patient identification bracelet tags. Existing technologies’ backgrounds, characteristics, advantages and disadvantages, are described in relation to their possible use by public health care centers with budgetary limitations. A comparative assessment is presented of suitable automatic identification systems based on graphic codes, both one- (1D) and two-dimensional (2D), printed on labels, as well as those based on radio frequency identification (RFID) tags. The analysis looks at the tradeoffs of these technologies to provide guidance to hospital administrator looking to deploy patient identification technology. The results suggest that affordable automatic patient identification systems can be easily and inexpensively implemented using 2D code printed on low cost bracelet labels, which can then be read and automatically decoded by ordinary mobile smart phones. Because of mobile smart phones’ present versatility and ubiquity, the implantation and operation of 2D code, and especially Quick Response® (QR) Code, technology emerges as a very attractive alternative to automate the patients’ identification processes in low-budget situations. PMID:23569629

Auto identification technology and its impact on patient safety in the Operating Room of the Future.

PubMed

Egan, Marie T; Sandberg, Warren S

2007-03-01

Automatic identification technologies, such as bar coding and radio frequency identification, are ubiquitous in everyday life but virtually nonexistent in the operating room. User expectations, based on everyday experience with automatic identification technologies, have generated much anticipation that these systems will improve readiness, workflow, and safety in the operating room, with minimal training requirements. We report, in narrative form, a multi-year experience with various automatic identification technologies in the Operating Room of the Future Project at Massachusetts General Hospital. In each case, the additional human labor required to make these ;labor-saving' technologies function in the medical environment has proved to be their undoing. We conclude that while automatic identification technologies show promise, significant barriers to realizing their potential still exist. Nevertheless, overcoming these obstacles is necessary if the vision of an operating room of the future in which all processes are monitored, controlled, and optimized is to be achieved.

Tidal analysis and Arrival Process Mining Using Automatic Identification System (AIS) Data

DTIC Science & Technology

2017-01-01

files, organized by location. The data were processed using the Python programming language (van Rossum and Drake 2001), the Pandas data analysis...ER D C/ CH L TR -1 7- 2 Coastal Inlets Research Program Tidal Analysis and Arrival Process Mining Using Automatic Identification System...17-2 January 2017 Tidal Analysis and Arrival Process Mining Using Automatic Identification System (AIS) Data Brandan M. Scully Coastal and

Forensic Odontology: Automatic Identification of Persons Comparing Antemortem and Postmortem Panoramic Radiographs Using Computer Vision.

PubMed

Heinrich, Andreas; Güttler, Felix; Wendt, Sebastian; Schenkl, Sebastian; Hubig, Michael; Wagner, Rebecca; Mall, Gita; Teichgräber, Ulf

2018-06-18

 In forensic odontology the comparison between antemortem and postmortem panoramic radiographs (PRs) is a reliable method for person identification. The purpose of this study was to improve and automate identification of unknown people by comparison between antemortem and postmortem PR using computer vision.  The study includes 43 467 PRs from 24 545 patients (46 % females/54 % males). All PRs were filtered and evaluated with Matlab R2014b including the toolboxes image processing and computer vision system. The matching process used the SURF feature to find the corresponding points between two PRs (unknown person and database entry) out of the whole database.  From 40 randomly selected persons, 34 persons (85 %) could be reliably identified by corresponding PR matching points between an already existing scan in the database and the most recent PR. The systematic matching yielded a maximum of 259 points for a successful identification between two different PRs of the same person and a maximum of 12 corresponding matching points for other non-identical persons in the database. Hence 12 matching points are the threshold for reliable assignment.  Operating with an automatic PR system and computer vision could be a successful and reliable tool for identification purposes. The applied method distinguishes itself by virtue of its fast and reliable identification of persons by PR. This Identification method is suitable even if dental characteristics were removed or added in the past. The system seems to be robust for large amounts of data.   · Computer vision allows an automated antemortem and postmortem comparison of panoramic radiographs (PRs) for person identification.. · The present method is able to find identical matching partners among huge datasets (big data) in a short computing time.. · The identification method is suitable even if dental characteristics were removed or added.. · Heinrich A, Güttler F, Wendt S et al. Forensic Odontology: Automatic Identification of Persons Comparing Antemortem and Postmortem Panoramic Radiographs Using Computer Vision. Fortschr Röntgenstr 2018; DOI: 10.1055/a-0632-4744. © Georg Thieme Verlag KG Stuttgart · New York.

Natural language processing of spoken diet records (SDRs).

PubMed

Lacson, Ronilda; Long, William

2006-01-01

Dietary assessment is a fundamental aspect of nutritional evaluation that is essential for management of obesity as well as for assessing dietary impact on chronic diseases. Various methods have been used for dietary assessment including written records, 24-hour recalls, and food frequency questionnaires. The use of mobile phones to provide real-time dietary records provides potential advantages for accessibility, ease of use and automated documentation. However, understanding even a perfect transcript of spoken dietary records (SDRs) is challenging for people. This work presents a first step towards automatic analysis of SDRs. Our approach consists of four steps - identification of food items, identification of food quantifiers, classification of food quantifiers and temporal annotation. Our method enables automatic extraction of dietary information from SDRs, which in turn allows automated mapping to a Diet History Questionnaire dietary database. Our model has an accuracy of 90%. This work demonstrates the feasibility of automatically processing SDRs.

Preparing a collection of radiology examinations for distribution and retrieval.

PubMed

Demner-Fushman, Dina; Kohli, Marc D; Rosenman, Marc B; Shooshan, Sonya E; Rodriguez, Laritza; Antani, Sameer; Thoma, George R; McDonald, Clement J

2016-03-01

Clinical documents made available for secondary use play an increasingly important role in discovery of clinical knowledge, development of research methods, and education. An important step in facilitating secondary use of clinical document collections is easy access to descriptions and samples that represent the content of the collections. This paper presents an approach to developing a collection of radiology examinations, including both the images and radiologist narrative reports, and making them publicly available in a searchable database. The authors collected 3996 radiology reports from the Indiana Network for Patient Care and 8121 associated images from the hospitals' picture archiving systems. The images and reports were de-identified automatically and then the automatic de-identification was manually verified. The authors coded the key findings of the reports and empirically assessed the benefits of manual coding on retrieval. The automatic de-identification of the narrative was aggressive and achieved 100% precision at the cost of rendering a few findings uninterpretable. Automatic de-identification of images was not quite as perfect. Images for two of 3996 patients (0.05%) showed protected health information. Manual encoding of findings improved retrieval precision. Stringent de-identification methods can remove all identifiers from text radiology reports. DICOM de-identification of images does not remove all identifying information and needs special attention to images scanned from film. Adding manual coding to the radiologist narrative reports significantly improved relevancy of the retrieved clinical documents. The de-identified Indiana chest X-ray collection is available for searching and downloading from the National Library of Medicine (http://openi.nlm.nih.gov/). Published by Oxford University Press on behalf of the American Medical Informatics Association 2015. This work is written by US Government employees and is in the public domain in the US.

47 CFR 80.275 - Technical Requirements for Class A Automatic Identification System (AIS) equipment.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Compulsory Ships § 80.275 Technical Requirements for Class A Automatic Identification System (AIS) equipment. (a) Prior to submitting a certification application for a Class A AIS device, the following... Identification System (AIS) equipment. 80.275 Section 80.275 Telecommunication FEDERAL COMMUNICATIONS COMMISSION...

47 CFR 80.275 - Technical Requirements for Class A Automatic Identification System (AIS) equipment.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Compulsory Ships § 80.275 Technical Requirements for Class A Automatic Identification System (AIS) equipment. (a) Prior to submitting a certification application for a Class A AIS device, the following... Identification System (AIS) equipment. 80.275 Section 80.275 Telecommunication FEDERAL COMMUNICATIONS COMMISSION...

47 CFR 80.275 - Technical Requirements for Class A Automatic Identification System (AIS) equipment.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Compulsory Ships § 80.275 Technical Requirements for Class A Automatic Identification System (AIS) equipment. (a) Prior to submitting a certification application for a Class A AIS device, the following... Identification System (AIS) equipment. 80.275 Section 80.275 Telecommunication FEDERAL COMMUNICATIONS COMMISSION...

47 CFR 80.275 - Technical Requirements for Class A Automatic Identification System (AIS) equipment.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Compulsory Ships § 80.275 Technical Requirements for Class A Automatic Identification System (AIS) equipment. (a) Prior to submitting a certification application for a Class A AIS device, the following... Identification System (AIS) equipment. 80.275 Section 80.275 Telecommunication FEDERAL COMMUNICATIONS COMMISSION...

Automatic contact in DYNA3D for vehicle crashworthiness

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

Whirley, R.G.; Engelmann, B.E.

1993-07-15

This paper presents a new formulation for the automatic definition and treatment of mechanical contact in explicit nonlinear finite element analysis. Automatic contact offers the benefits of significantly reduced model construction time and fewer opportunities for user error, but faces significant challenges in reliability and computational costs. This paper discusses in detail a new four-step automatic contact algorithm. Key aspects of the proposed method include automatic identification of adjacent and opposite surfaces in the global search phase, and the use of a smoothly varying surface normal which allows a consistent treatment of shell intersection and corner contact conditions without ad-hocmore » rules. The paper concludes with three examples which illustrate the performance of the newly proposed algorithm in the public DYNA3D code.« less

Suspect/foil identification in actual crimes and in the laboratory: a reality monitoring analysis.

PubMed

Behrman, Bruce W; Richards, Regina E

2005-06-01

Four reality monitoring variables were used to discriminate suspect from foil identifications in 183 actual criminal cases. Four hundred sixty-one identification attempts based on five and six-person lineups were analyzed. These identification attempts resulted in 238 suspect identifications and 68 foil identifications. Confidence, automatic processing, eliminative processing and feature use comprised the set of reality monitoring variables. Thirty-five verbal confidence phrases taken from police reports were assigned numerical values on a 10-point confidence scale. Automatic processing identifications were those that occurred "immediately" or "without hesitation." Eliminative processing identifications occurred when witnesses compared or eliminated persons in the lineups. Confidence, automatic processing and eliminative processing were significant predictors, but feature use was not. Confidence was the most effective discriminator. In cases that involved substantial evidence extrinsic to the identification 43% of the suspect identifications were made with high confidence, whereas only 10% of the foil identifications were made with high confidence. The results of a laboratory study using the same predictors generally paralleled the archival results. Forensic implications are discussed.

Automatic Car Identification - an Evaluation

DOT National Transportation Integrated Search

1972-03-01

In response to a Federal Railroad Administration request, the Transportation Systems Center evaluated the Automatic Car Identification System (ACI) used on the nation's railroads. The ACI scanner was found to be adequate for reliable data output whil...

Image portion identification methods, image parsing methods, image parsing systems, and articles of manufacture

DOEpatents

Lassahn, Gordon D.; Lancaster, Gregory D.; Apel, William A.; Thompson, Vicki S.

2013-01-08

Image portion identification methods, image parsing methods, image parsing systems, and articles of manufacture are described. According to one embodiment, an image portion identification method includes accessing data regarding an image depicting a plurality of biological substrates corresponding to at least one biological sample and indicating presence of at least one biological indicator within the biological sample and, using processing circuitry, automatically identifying a portion of the image depicting one of the biological substrates but not others of the biological substrates.

An automatic system to detect and extract texts in medical images for de-identification

NASA Astrophysics Data System (ADS)

Zhu, Yingxuan; Singh, P. D.; Siddiqui, Khan; Gillam, Michael

2010-03-01

Recently, there is an increasing need to share medical images for research purpose. In order to respect and preserve patient privacy, most of the medical images are de-identified with protected health information (PHI) before research sharing. Since manual de-identification is time-consuming and tedious, so an automatic de-identification system is necessary and helpful for the doctors to remove text from medical images. A lot of papers have been written about algorithms of text detection and extraction, however, little has been applied to de-identification of medical images. Since the de-identification system is designed for end-users, it should be effective, accurate and fast. This paper proposes an automatic system to detect and extract text from medical images for de-identification purposes, while keeping the anatomic structures intact. First, considering the text have a remarkable contrast with the background, a region variance based algorithm is used to detect the text regions. In post processing, geometric constraints are applied to the detected text regions to eliminate over-segmentation, e.g., lines and anatomic structures. After that, a region based level set method is used to extract text from the detected text regions. A GUI for the prototype application of the text detection and extraction system is implemented, which shows that our method can detect most of the text in the images. Experimental results validate that our method can detect and extract text in medical images with a 99% recall rate. Future research of this system includes algorithm improvement, performance evaluation, and computation optimization.

Motion estimation of subcellular structures from fluorescence microscopy images.

PubMed

Vallmitjana, A; Civera-Tregon, A; Hoenicka, J; Palau, F; Benitez, R

2017-07-01

We present an automatic image processing framework to study moving intracellular structures from live cell fluorescence microscopy. The system includes the identification of static and dynamic structures from time-lapse images using data clustering as well as the identification of the trajectory of moving objects with a probabilistic tracking algorithm. The method has been successfully applied to study mitochondrial movement in neurons. The approach provides excellent performance under different experimental conditions and is robust to common sources of noise including experimental, molecular and biological fluctuations.

Estimating spatial travel times using automatic vehicle identification data

DOT National Transportation Integrated Search

2001-01-01

Prepared ca. 2001. The paper describes an algorithm that was developed for estimating reliable and accurate average roadway link travel times using Automatic Vehicle Identification (AVI) data. The algorithm presented is unique in two aspects. First, ...

Automatic vasculature identification in coronary angiograms by adaptive geometrical tracking.

PubMed

Xiao, Ruoxiu; Yang, Jian; Goyal, Mahima; Liu, Yue; Wang, Yongtian

2013-01-01

As the uneven distribution of contrast agents and the perspective projection principle of X-ray, the vasculatures in angiographic image are with low contrast and are generally superposed with other organic tissues; therefore, it is very difficult to identify the vasculature and quantitatively estimate the blood flow directly from angiographic images. In this paper, we propose a fully automatic algorithm named adaptive geometrical vessel tracking (AGVT) for coronary artery identification in X-ray angiograms. Initially, the ridge enhancement (RE) image is obtained utilizing multiscale Hessian information. Then, automatic initialization procedures including seed points detection, and initial directions determination are performed on the RE image. The extracted ridge points can be adjusted to the geometrical centerline points adaptively through diameter estimation. Bifurcations are identified by discriminating connecting relationship of the tracked ridge points. Finally, all the tracked centerlines are merged and smoothed by classifying the connecting components on the vascular structures. Synthetic angiographic images and clinical angiograms are used to evaluate the performance of the proposed algorithm. The proposed algorithm is compared with other two vascular tracking techniques in terms of the efficiency and accuracy, which demonstrate successful applications of the proposed segmentation and extraction scheme in vasculature identification.

Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning.

PubMed

Go, Taesik; Byeon, Hyeokjun; Lee, Sang Joon

2018-04-30

Cell types of erythrocytes should be identified because they are closely related to their functionality and viability. Conventional methods for classifying erythrocytes are time consuming and labor intensive. Therefore, an automatic and accurate erythrocyte classification system is indispensable in healthcare and biomedical fields. In this study, we proposed a new label-free sensor for automatic identification of erythrocyte cell types using a digital in-line holographic microscopy (DIHM) combined with machine learning algorithms. A total of 12 features, including information on intensity distributions, morphological descriptors, and optical focusing characteristics, is quantitatively obtained from numerically reconstructed holographic images. All individual features for discocytes, echinocytes, and spherocytes are statistically different. To improve the performance of cell type identification, we adopted several machine learning algorithms, such as decision tree model, support vector machine, linear discriminant classification, and k-nearest neighbor classification. With the aid of these machine learning algorithms, the extracted features are effectively utilized to distinguish erythrocytes. Among the four tested algorithms, the decision tree model exhibits the best identification performance for the training sets (n = 440, 98.18%) and test sets (n = 190, 97.37%). This proposed methodology, which smartly combined DIHM and machine learning, would be helpful for sensing abnormal erythrocytes and computer-aided diagnosis of hematological diseases in clinic. Copyright © 2017 Elsevier B.V. All rights reserved.

The epidural needle guidance with an intelligent and automatic identification system for epidural anesthesia

NASA Astrophysics Data System (ADS)

Kao, Meng-Chun; Ting, Chien-Kun; Kuo, Wen-Chuan

2018-02-01

Incorrect placement of the needle causes medical complications in the epidural block, such as dural puncture or spinal cord injury. This study proposes a system which combines an optical coherence tomography (OCT) imaging probe with an automatic identification (AI) system to objectively identify the position of the epidural needle tip. The automatic identification system uses three features as image parameters to distinguish the different tissue by three classifiers. Finally, we found that the support vector machine (SVM) classifier has highest accuracy, specificity, and sensitivity, which reached to 95%, 98%, and 92%, respectively.

Automatic Publication of a MIS Product to GeoNetwork: Case of the AIS Indexer

DTIC Science & Technology

2012-11-01

installation and configuration The following instructions are for installing and configuring the software packages Java 1.6 and MySQL 5.5 which are...An Automatic Identification System (AIS) reception indexer Java application was developed in the summer of 2011, based on the work of Lapinski and...release; distribution unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT An Automatic Identification System (AIS) reception indexer Java application was

Associative priming in a masked perceptual identification task: evidence for automatic processes.

PubMed

Pecher, Diane; Zeelenberg, René; Raaijmakers, Jeroen G W

2002-10-01

Two experiments investigated the influence of automatic and strategic processes on associative priming effects in a perceptual identification task in which prime-target pairs are briefly presented and masked. In this paradigm, priming is defined as a higher percentage of correctly identified targets for related pairs than for unrelated pairs. In Experiment 1, priming was obtained for mediated word pairs. This mediated priming effect was affected neither by the presence of direct associations nor by the presentation time of the primes, indicating that automatic priming effects play a role in perceptual identification. Experiment 2 showed that the priming effect was not affected by the proportion (.90 vs. .10) of related pairs if primes were presented briefly to prevent their identification. However, a large proportion effect was found when primes were presented for 1000 ms so that they were clearly visible. These results indicate that priming in a masked perceptual identification task is the result of automatic processes and is not affected by strategies. The present paradigm provides a valuable alternative to more commonly used tasks such as lexical decision.

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.

Optical Automatic Car Identification (OACI) Field Test Program

DOT National Transportation Integrated Search

1976-05-01

The results of the Optical Automatic Car Identification (OACI) tests at Chicago conducted from August 16 to September 4, 1975 are presented. The main purpose of this test was to determine the suitability of optics as a principle of operation for an a...

Maritime over the Horizon Sensor Integration: High Frequency Surface-Wave-Radar and Automatic Identification System Data Integration Algorithm.

PubMed

Nikolic, Dejan; Stojkovic, Nikola; Lekic, Nikola

2018-04-09

To obtain the complete operational picture of the maritime situation in the Exclusive Economic Zone (EEZ) which lies over the horizon (OTH) requires the integration of data obtained from various sensors. These sensors include: high frequency surface-wave-radar (HFSWR), satellite automatic identification system (SAIS) and land automatic identification system (LAIS). The algorithm proposed in this paper utilizes radar tracks obtained from the network of HFSWRs, which are already processed by a multi-target tracking algorithm and associates SAIS and LAIS data to the corresponding radar tracks, thus forming an integrated data pair. During the integration process, all HFSWR targets in the vicinity of AIS data are evaluated and the one which has the highest matching factor is used for data association. On the other hand, if there is multiple AIS data in the vicinity of a single HFSWR track, the algorithm still makes only one data pair which consists of AIS and HFSWR data with the highest mutual matching factor. During the design and testing, special attention is given to the latency of AIS data, which could be very high in the EEZs of developing countries. The algorithm is designed, implemented and tested in a real working environment. The testing environment is located in the Gulf of Guinea and includes a network of HFSWRs consisting of two HFSWRs, several coastal sites with LAIS receivers and SAIS data provided by provider of SAIS data.

77 FR 64076 - Vessel Traffic Service Updates, Including Establishment of Vessel Traffic Service Requirements...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-18

... Port Arthur, Texas and Expansion of VTS Special Operating Area in Puget Sound AGENCY: Coast Guard, DHS... Service (VTS) regulations in 33 CFR part 161. The proposed revisions include adding the Maritime Mobile... would impose Automatic Identification System (AIS) equipment costs for owners and operators of the...

Hybrid neuro-fuzzy approach for automatic vehicle license plate recognition

NASA Astrophysics Data System (ADS)

Lee, Hsi-Chieh; Jong, Chung-Shi

1998-03-01

Most currently available vehicle identification systems use techniques such as R.F., microwave, or infrared to help identifying the vehicle. Transponders are usually installed in the vehicle in order to transmit the corresponding information to the sensory system. It is considered expensive to install a transponder in each vehicle and the malfunction of the transponder will result in the failure of the vehicle identification system. In this study, novel hybrid approach is proposed for automatic vehicle license plate recognition. A system prototype is built which can be used independently or cooperating with current vehicle identification system in identifying a vehicle. The prototype consists of four major modules including the module for license plate region identification, the module for character extraction from the license plate, the module for character recognition, and the module for the SimNet neuro-fuzzy system. To test the performance of the proposed system, three hundred and eighty vehicle image samples are taken by a digital camera. The license plate recognition success rate of the prototype is approximately 91% while the character recognition success rate of the prototype is approximately 97%.

Method and apparatus for data decoding and processing

DOEpatents

Hunter, Timothy M.; Levy, Arthur J.

1992-01-01

A system and technique is disclosed for automatically controlling the decoding and digitizaiton of an analog tape. The system includes the use of a tape data format which includes a plurality of digital codes recorded on the analog tape in a predetermined proximity to a period of recorded analog data. The codes associated with each period of analog data include digital identification codes prior to the analog data, a start of data code coincident with the analog data recording, and an end of data code subsequent to the associated period of recorded analog data. The formatted tape is decoded in a processing and digitization system which includes an analog tape player coupled to a digitizer to transmit analog information from the recorded tape over at least one channel to the digitizer. At the same time, the tape player is coupled to a decoder and interface system which detects and decodes the digital codes on the tape corresponding to each period of recorded analog data and controls tape movement and digitizer initiation in response to preprogramed modes. A host computer is also coupled to the decoder and interface system and the digitizer and programmed to initiate specific modes of data decoding through the decoder and interface system including the automatic compilation and storage of digital identification information and digitized data for the period of recorded analog data corresponding to the digital identification data, compilation and storage of selected digitized data representing periods of recorded analog data, and compilation of digital identification information related to each of the periods of recorded analog data.

A discrete optimization approach for locating automatic vehicle identification readers for the provision of roadway travel times

DOT National Transportation Integrated Search

2002-11-01

This paper develops an algorithm for optimally locating surveillance technologies with an emphasis on Automatic Vehicle Identification tag readers by maximizing the benefit that would accrue from measuring travel times on a transportation network. Th...

47 CFR 25.281 - Automatic Transmitter Identification System (ATIS).

Code of Federal Regulations, 2010 CFR

2010-10-01

... identified through the use of an automatic transmitter identification system as specified below. (a.... (3) The ATIS signal as a minimum shall consist of the following: (i) The FCC assigned earth station... (ATIS). 25.281 Section 25.281 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON...

47 CFR 25.281 - Automatic Transmitter Identification System (ATIS).

Code of Federal Regulations, 2013 CFR

2013-10-01

... identified through the use of an automatic transmitter identification system as specified below. (a.... (3) The ATIS signal as a minimum shall consist of the following: (i) The FCC assigned earth station... (ATIS). 25.281 Section 25.281 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON...

47 CFR 25.281 - Automatic Transmitter Identification System (ATIS).

Code of Federal Regulations, 2012 CFR

2012-10-01

... identified through the use of an automatic transmitter identification system as specified below. (a.... (3) The ATIS signal as a minimum shall consist of the following: (i) The FCC assigned earth station... (ATIS). 25.281 Section 25.281 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON...

47 CFR 25.281 - Automatic Transmitter Identification System (ATIS).

Code of Federal Regulations, 2011 CFR

2011-10-01

... identified through the use of an automatic transmitter identification system as specified below. (a.... (3) The ATIS signal as a minimum shall consist of the following: (i) The FCC assigned earth station... (ATIS). 25.281 Section 25.281 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON...

Tier-scalable reconnaissance: the future in autonomous C4ISR systems has arrived: progress towards an outdoor testbed

NASA Astrophysics Data System (ADS)

Fink, Wolfgang; Brooks, Alexander J.-W.; Tarbell, Mark A.; Dohm, James M.

2017-05-01

Autonomous reconnaissance missions are called for in extreme environments, as well as in potentially hazardous (e.g., the theatre, disaster-stricken areas, etc.) or inaccessible operational areas (e.g., planetary surfaces, space). Such future missions will require increasing degrees of operational autonomy, especially when following up on transient events. Operational autonomy encompasses: (1) Automatic characterization of operational areas from different vantages (i.e., spaceborne, airborne, surface, subsurface); (2) automatic sensor deployment and data gathering; (3) automatic feature extraction including anomaly detection and region-of-interest identification; (4) automatic target prediction and prioritization; (5) and subsequent automatic (re-)deployment and navigation of robotic agents. This paper reports on progress towards several aspects of autonomous C4ISR systems, including: Caltech-patented and NASA award-winning multi-tiered mission paradigm, robotic platform development (air, ground, water-based), robotic behavior motifs as the building blocks for autonomous tele-commanding, and autonomous decision making based on a Caltech-patented framework comprising sensor-data-fusion (feature-vectors), anomaly detection (clustering and principal component analysis), and target prioritization (hypothetical probing).

Automatically identifying health outcome information in MEDLINE records.

PubMed

Demner-Fushman, Dina; Few, Barbara; Hauser, Susan E; Thoma, George

2006-01-01

Understanding the effect of a given intervention on the patient's health outcome is one of the key elements in providing optimal patient care. This study presents a methodology for automatic identification of outcomes-related information in medical text and evaluates its potential in satisfying clinical information needs related to health care outcomes. An annotation scheme based on an evidence-based medicine model for critical appraisal of evidence was developed and used to annotate 633 MEDLINE citations. Textual, structural, and meta-information features essential to outcome identification were learned from the created collection and used to develop an automatic system. Accuracy of automatic outcome identification was assessed in an intrinsic evaluation and in an extrinsic evaluation, in which ranking of MEDLINE search results obtained using PubMed Clinical Queries relied on identified outcome statements. The accuracy and positive predictive value of outcome identification were calculated. Effectiveness of the outcome-based ranking was measured using mean average precision and precision at rank 10. Automatic outcome identification achieved 88% to 93% accuracy. The positive predictive value of individual sentences identified as outcomes ranged from 30% to 37%. Outcome-based ranking improved retrieval accuracy, tripling mean average precision and achieving 389% improvement in precision at rank 10. Preliminary results in outcome-based document ranking show potential validity of the evidence-based medicine-model approach in timely delivery of information critical to clinical decision support at the point of service.

21 CFR 892.1900 - Automatic radiographic film processor.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Automatic radiographic film processor. 892.1900... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.1900 Automatic radiographic film processor. (a) Identification. An automatic radiographic film processor is a device intended to be used to...

21 CFR 892.1900 - Automatic radiographic film processor.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Automatic radiographic film processor. 892.1900... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.1900 Automatic radiographic film processor. (a) Identification. An automatic radiographic film processor is a device intended to be used to...

21 CFR 892.1900 - Automatic radiographic film processor.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Automatic radiographic film processor. 892.1900... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.1900 Automatic radiographic film processor. (a) Identification. An automatic radiographic film processor is a device intended to be used to...

21 CFR 892.1900 - Automatic radiographic film processor.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Automatic radiographic film processor. 892.1900... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.1900 Automatic radiographic film processor. (a) Identification. An automatic radiographic film processor is a device intended to be used to...

21 CFR 892.1900 - Automatic radiographic film processor.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automatic radiographic film processor. 892.1900... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.1900 Automatic radiographic film processor. (a) Identification. An automatic radiographic film processor is a device intended to be used to...

47 CFR 80.275 - Technical Requirements for Class A Automatic Identification System (AIS) equipment.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 5 2010-10-01 2010-10-01 false Technical Requirements for Class A Automatic Identification System (AIS) equipment. 80.275 Section 80.275 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) SAFETY AND SPECIAL RADIO SERVICES STATIONS IN THE MARITIME SERVICES Equipment Authorization for Compulsory Ships § 80.275...

RFID: A Revolution in Automatic Data Recognition

ERIC Educational Resources Information Center

Deal, Walter F., III

2004-01-01

Radio frequency identification, or RFID, is a generic term for technologies that use radio waves to automatically identify people or objects. There are several methods of identification, but the most common is to store a serial number that identifies a person or object, and perhaps other information, on a microchip that is attached to an antenna…

33 CFR 164.43 - Automatic Identification System Shipborne Equipment-Prince William Sound.

Code of Federal Regulations, 2010 CFR

2010-07-01

... (AISSE) system consisting of a: (1) Twelve-channel all-in-view Differential Global Positioning System (d... to indicate to shipboard personnel that the U.S. Coast Guard dGPS system cannot provide the required... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false Automatic Identification System...

Identification of the chemical constituents of Chinese medicine Yi-Xin-Shu capsule by molecular feature orientated precursor ion selection and tandem mass spectrometry structure elucidation.

PubMed

Wang, Hong-ping; Chen, Chang; Liu, Yan; Yang, Hong-Jun; Wu, Hong-Wei; Xiao, Hong-Bin

2015-11-01

The incomplete identification of the chemical components of traditional Chinese medicinal formula has been one of the bottlenecks in the modernization of traditional Chinese medicine. Tandem mass spectrometry has been widely used for the identification of chemical substances. Current automatic tandem mass spectrometry acquisition, where precursor ions were selected according to their signal intensity, encounters a drawback in chemical substances identification when samples contain many overlapping signals. Compounds in minor or trace amounts could not be identified because most tandem mass spectrometry information was lost. Herein, a molecular feature orientated precursor ion selection and tandem mass spectrometry structure elucidation method for complex Chinese medicine chemical constituent analysis was developed. The precursor ions were selected according to their two-dimensional characteristics of retention times and mass-to-charge ratio ranges from herbal compounds, so that all precursor ions from herbal compounds were included and more minor chemical constituents in Chinese medicine were identified. Compared to the conventional automatic tandem mass spectrometry setups, the approach is novel and can overcome the drawback for chemical substances identification. As an example, 276 compounds from the Chinese Medicine of Yi-Xin-Shu capsule were identified. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Multiple layer identification label using stacked identification symbols

NASA Technical Reports Server (NTRS)

Schramm, Harry F. (Inventor)

2005-01-01

An automatic identification system and method are provided which employ a machine readable multiple layer label. The label has a plurality of machine readable marking layers stacked one upon another. Each of the marking layers encodes an identification symbol detectable using one or more sensing technologies. The various marking layers may comprise the same marking material or each marking layer may comprise a different medium having characteristics detectable by a different sensing technology. These sensing technologies include x-ray, radar, capacitance, thermal, magnetic and ultrasonic. A complete symbol may be encoded within each marking layer or a symbol may be segmented into fragments which are then divided within a single marking layer or encoded across multiple marking layers.

49 CFR 395.15 - Automatic on-board recording devices.

Code of Federal Regulations, 2010 CFR

2010-10-01

... information concerning on-board system sensor failures and identification of edited data. Such support systems... driving today; (iv) Total hours on duty for the 7 consecutive day period, including today; (v) Total hours...-driver operation; (7) The on-board recording device/system identifies sensor failures and edited data...

Accurately Predicting Future Reading Difficulty for Bilingual Latino Children at Risk for Language Impairment

ERIC Educational Resources Information Center

Petersen, Douglas B.; Gillam, Ronald B.

2013-01-01

Sixty-three bilingual Latino children who were at risk for language impairment were administered reading-related measures in English and Spanish (letter identification, phonological awareness, rapid automatized naming, and sentence repetition) and descriptive measures including English language proficiency (ELP), language ability (LA),…

Automated segmentation of myocardial scar in late enhancement MRI using combined intensity and spatial information.

PubMed

Tao, Qian; Milles, Julien; Zeppenfeld, Katja; Lamb, Hildo J; Bax, Jeroen J; Reiber, Johan H C; van der Geest, Rob J

2010-08-01

Accurate assessment of the size and distribution of a myocardial infarction (MI) from late gadolinium enhancement (LGE) MRI is of significant prognostic value for postinfarction patients. In this paper, an automatic MI identification method combining both intensity and spatial information is presented in a clear framework of (i) initialization, (ii) false acceptance removal, and (iii) false rejection removal. The method was validated on LGE MR images of 20 chronic postinfarction patients, using manually traced MI contours from two independent observers as reference. Good agreement was observed between automatic and manual MI identification. Validation results showed that the average Dice indices, which describe the percentage of overlap between two regions, were 0.83 +/- 0.07 and 0.79 +/- 0.08 between the automatic identification and the manual tracing from observer 1 and observer 2, and the errors in estimated infarct percentage were 0.0 +/- 1.9% and 3.8 +/- 4.7% compared with observer 1 and observer 2. The difference between the automatic method and manual tracing is in the order of interobserver variation. In conclusion, the developed automatic method is accurate and robust in MI delineation, providing an objective tool for quantitative assessment of MI in LGE MR imaging.

Ontology-based automatic identification of public health-related Turkish tweets.

PubMed

Küçük, Emine Ela; Yapar, Kürşad; Küçük, Dilek; Küçük, Doğan

2017-04-01

Social media analysis, such as the analysis of tweets, is a promising research topic for tracking public health concerns including epidemics. In this paper, we present an ontology-based approach to automatically identify public health-related Turkish tweets. The system is based on a public health ontology that we have constructed through a semi-automated procedure. The ontology concepts are expanded through a linguistically motivated relaxation scheme as the last stage of ontology development, before being integrated into our system to increase its coverage. The ultimate lexical resource which includes the terms corresponding to the ontology concepts is used to filter the Twitter stream so that a plausible tweet subset, including mostly public-health related tweets, can be obtained. Experiments are carried out on two million genuine tweets and promising precision rates are obtained. Also implemented within the course of the current study is a Web-based interface, to track the results of this identification system, to be used by the related public health staff. Hence, the current social media analysis study has both technical and practical contributions to the significant domain of public health. Copyright © 2017 Elsevier Ltd. All rights reserved.

Longitudinal Stability of Pre-Reading Skill Profiles of Kindergarten Children: Implications for Early Screening and Theories of Reading

ERIC Educational Resources Information Center

Ozernov-Palchik, Ola; Norton, Elizabeth S.; Sideridis, Georgios; Beach, Sara D.; Wolf, Maryanne; Gabrieli, John D. E.; Gaab, Nadine

2017-01-01

Research suggests that early identification of developmental dyslexia is important for mitigating the negative effects of dyslexia, including reduced educational attainment and increased socioemotional difficulties. The strongest pre-literacy predictors of dyslexia are rapid automatized naming (RAN), phonological awareness (PA), letter knowledge,…

21 CFR 801.40 - Form of a unique device identifier.

Code of Federal Regulations, 2014 CFR

2014-04-01

...) Automatic identification and data capture (AIDC) technology. (b) The UDI must include a device identifier... evident upon visual examination of the label or device package, the label or device package must disclose... label and device packages is deemed to meet all requirements of subpart B of this part. The UPC will...

77 FR 5481 - Codex Alimentarius Commission: Meeting of the Codex Committee on Contaminants in Food

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-03

... expedite entry into the building and its parking area. You should also bring photo identification and plan for adequate time to pass through security screening systems. If you require parking, please include... automatic and customized access to selected food safety news and information. This service is available at...

OPTICAL correlation identification technology applied in underwater laser imaging target identification

NASA Astrophysics Data System (ADS)

Yao, Guang-tao; Zhang, Xiao-hui; Ge, Wei-long

2012-01-01

The underwater laser imaging detection is an effective method of detecting short distance target underwater as an important complement of sonar detection. With the development of underwater laser imaging technology and underwater vehicle technology, the underwater automatic target identification has gotten more and more attention, and is a research difficulty in the area of underwater optical imaging information processing. Today, underwater automatic target identification based on optical imaging is usually realized with the method of digital circuit software programming. The algorithm realization and control of this method is very flexible. However, the optical imaging information is 2D image even 3D image, the amount of imaging processing information is abundant, so the electronic hardware with pure digital algorithm will need long identification time and is hard to meet the demands of real-time identification. If adopt computer parallel processing, the identification speed can be improved, but it will increase complexity, size and power consumption. This paper attempts to apply optical correlation identification technology to realize underwater automatic target identification. The optics correlation identification technology utilizes the Fourier transform characteristic of Fourier lens which can accomplish Fourier transform of image information in the level of nanosecond, and optical space interconnection calculation has the features of parallel, high speed, large capacity and high resolution, combines the flexibility of calculation and control of digital circuit method to realize optoelectronic hybrid identification mode. We reduce theoretical formulation of correlation identification and analyze the principle of optical correlation identification, and write MATLAB simulation program. We adopt single frame image obtained in underwater range gating laser imaging to identify, and through identifying and locating the different positions of target, we can improve the speed and orientation efficiency of target identification effectively, and validate the feasibility of this method primarily.

Automatic Identification of Subtechniques in Skating-Style Roller Skiing Using Inertial Sensors

PubMed Central

Sakurai, Yoshihisa; Fujita, Zenya; Ishige, Yusuke

2016-01-01

This study aims to develop and validate an automated system for identifying skating-style cross-country subtechniques using inertial sensors. In the first experiment, the performance of a male cross-country skier was used to develop an automated identification system. In the second, eight male and seven female college cross-country skiers participated to validate the developed identification system. Each subject wore inertial sensors on both wrists and both roller skis, and a small video camera on a backpack. All subjects skied through a 3450 m roller ski course using a skating style at their maximum speed. The adopted subtechniques were identified by the automated method based on the data obtained from the sensors, as well as by visual observations from a video recording of the same ski run. The system correctly identified 6418 subtechniques from a total of 6768 cycles, which indicates an accuracy of 94.8%. The precisions of the automatic system for identifying the V1R, V1L, V2R, V2L, V2AR, and V2AL subtechniques were 87.6%, 87.0%, 97.5%, 97.8%, 92.1%, and 92.0%, respectively. Most incorrect identification cases occurred during a subtechnique identification that included a transition and turn event. Identification accuracy can be improved by separately identifying transition and turn events. This system could be used to evaluate each skier’s subtechniques in course conditions. PMID:27049388

Automatic identification of physical activity types and sedentary behaviors from triaxial accelerometer: laboratory-based calibrations are not enough.

PubMed

Bastian, Thomas; Maire, Aurélia; Dugas, Julien; Ataya, Abbas; Villars, Clément; Gris, Florence; Perrin, Emilie; Caritu, Yanis; Doron, Maeva; Blanc, Stéphane; Jallon, Pierre; Simon, Chantal

2015-03-15

"Objective" methods to monitor physical activity and sedentary patterns in free-living conditions are necessary to further our understanding of their impacts on health. In recent years, many software solutions capable of automatically identifying activity types from portable accelerometry data have been developed, with promising results in controlled conditions, but virtually no reports on field tests. An automatic classification algorithm initially developed using laboratory-acquired data (59 subjects engaging in a set of 24 standardized activities) to discriminate between 8 activity classes (lying, slouching, sitting, standing, walking, running, and cycling) was applied to data collected in the field. Twenty volunteers equipped with a hip-worn triaxial accelerometer performed at their own pace an activity set that included, among others, activities such as walking the streets, running, cycling, and taking the bus. Performances of the laboratory-calibrated classification algorithm were compared with those of an alternative version of the same model including field-collected data in the learning set. Despite good results in laboratory conditions, the performances of the laboratory-calibrated algorithm (assessed by confusion matrices) decreased for several activities when applied to free-living data. Recalibrating the algorithm with data closer to real-life conditions and from an independent group of subjects proved useful, especially for the detection of sedentary behaviors while in transports, thereby improving the detection of overall sitting (sensitivity: laboratory model = 24.9%; recalibrated model = 95.7%). Automatic identification methods should be developed using data acquired in free-living conditions rather than data from standardized laboratory activity sets only, and their limits carefully tested before they are used in field studies. Copyright © 2015 the American Physiological Society.

Design and implementation of an identification system in construction site safety for proactive accident prevention.

PubMed

Yang, Huanjia; Chew, David A S; Wu, Weiwei; Zhou, Zhipeng; Li, Qiming

2012-09-01

Identifying accident precursors using real-time identity information has great potential to improve safety performance in construction industry, which is still suffering from day to day records of accident fatality and injury. Based on the requirements analysis for identifying precursor and the discussion of enabling technology solutions for acquiring and sharing real-time automatic identification information on construction site, this paper proposes an identification system design for proactive accident prevention to improve construction site safety. Firstly, a case study is conducted to analyze the automatic identification requirements for identifying accident precursors in construction site. Results show that it mainly consists of three aspects, namely access control, training and inspection information and operation authority. The system is then designed to fulfill these requirements based on ZigBee enabled wireless sensor network (WSN), radio frequency identification (RFID) technology and an integrated ZigBee RFID sensor network structure. At the same time, an information database is also designed and implemented, which includes 15 tables, 54 queries and several reports and forms. In the end, a demonstration system based on the proposed system design is developed as a proof of concept prototype. The contributions of this study include the requirement analysis and technical design of a real-time identity information tracking solution for proactive accident prevention on construction sites. The technical solution proposed in this paper has a significant importance in improving safety performance on construction sites. Moreover, this study can serve as a reference design for future system integrations where more functions, such as environment monitoring and location tracking, can be added. Copyright © 2011 Elsevier Ltd. All rights reserved.

System Identification and Automatic Mass Balancing of Ground-Based Three-Axis Spacecraft Simulator

DTIC Science & Technology

2006-08-01

commanded torque to move away from these singularity points. The introduction of this error may not degrade the performance for large slew angle ...trajectory has been generated and quaternion feedback control has been implemented for reference trajectory tracking. The testbed was reasonably well...System Identification and Automatic Mass Balancing of Ground-Based Three-Axis Spacecraft Simulator Jae-Jun Kim∗ and Brij N. Agrawal † Department of

A new methodology for automatic detection of reference points in 3D cephalometry: A pilot study.

PubMed

Ed-Dhahraouy, Mohammed; Riri, Hicham; Ezzahmouly, Manal; Bourzgui, Farid; El Moutaoukkil, Abdelmajid

2018-04-05

The aim of this study was to develop a new method for an automatic detection of reference points in 3D cephalometry to overcome the limits of 2D cephalometric analyses. A specific application was designed using the C++ language for automatic and manual identification of 21 (reference) points on the craniofacial structures. Our algorithm is based on the implementation of an anatomical and geometrical network adapted to the craniofacial structure. This network was constructed based on the anatomical knowledge of the 3D cephalometric (reference) points. The proposed algorithm was tested on five CBCT images. The proposed approach for the automatic 3D cephalometric identification was able to detect 21 points with a mean error of 2.32mm. In this pilot study, we propose an automated methodology for the identification of the 3D cephalometric (reference) points. A larger sample will be implemented in the future to assess the method validity and reliability. Copyright © 2018 CEO. Published by Elsevier Masson SAS. All rights reserved.

Validation of automatic landmark identification for atlas-based segmentation for radiation treatment planning of the head-and-neck region

NASA Astrophysics Data System (ADS)

Leavens, Claudia; Vik, Torbjørn; Schulz, Heinrich; Allaire, Stéphane; Kim, John; Dawson, Laura; O'Sullivan, Brian; Breen, Stephen; Jaffray, David; Pekar, Vladimir

2008-03-01

Manual contouring of target volumes and organs at risk in radiation therapy is extremely time-consuming, in particular for treating the head-and-neck area, where a single patient treatment plan can take several hours to contour. As radiation treatment delivery moves towards adaptive treatment, the need for more efficient segmentation techniques will increase. We are developing a method for automatic model-based segmentation of the head and neck. This process can be broken down into three main steps: i) automatic landmark identification in the image dataset of interest, ii) automatic landmark-based initialization of deformable surface models to the patient image dataset, and iii) adaptation of the deformable models to the patient-specific anatomical boundaries of interest. In this paper, we focus on the validation of the first step of this method, quantifying the results of our automatic landmark identification method. We use an image atlas formed by applying thin-plate spline (TPS) interpolation to ten atlas datasets, using 27 manually identified landmarks in each atlas/training dataset. The principal variation modes returned by principal component analysis (PCA) of the landmark positions were used by an automatic registration algorithm, which sought the corresponding landmarks in the clinical dataset of interest using a controlled random search algorithm. Applying a run time of 60 seconds to the random search, a root mean square (rms) distance to the ground-truth landmark position of 9.5 +/- 0.6 mm was calculated for the identified landmarks. Automatic segmentation of the brain, mandible and brain stem, using the detected landmarks, is demonstrated.

Aviation Careers Series: Airline Non-Flying Careers

DOT National Transportation Integrated Search

1996-01-01

TRAVLINK demonstrated the use of Automatic Vehicle Location (AVL), ComputerAided dispatch (CAD), and Automatic Vehicle Identification (AVI) systems on Metropolitan Council Transit Operations (MCTO) buses in Minneapolis, Minnesota and western suburbs,...

Automatic limb identification and sleeping parameters assessment for pressure ulcer prevention.

PubMed

Baran Pouyan, Maziyar; Birjandtalab, Javad; Nourani, Mehrdad; Matthew Pompeo, M D

2016-08-01

Pressure ulcers (PUs) are common among vulnerable patients such as elderly, bedridden and diabetic. PUs are very painful for patients and costly for hospitals and nursing homes. Assessment of sleeping parameters on at-risk limbs is critical for ulcer prevention. An effective assessment depends on automatic identification and tracking of at-risk limbs. An accurate limb identification can be used to analyze the pressure distribution and assess risk for each limb. In this paper, we propose a graph-based clustering approach to extract the body limbs from the pressure data collected by a commercial pressure map system. A robust signature-based technique is employed to automatically label each limb. Finally, an assessment technique is applied to evaluate the experienced stress by each limb over time. The experimental results indicate high performance and more than 94% average accuracy of the proposed approach. Copyright © 2016 Elsevier Ltd. All rights reserved.

Tier-scalable reconnaissance: the challenge of sensor optimization, sensor deployment, sensor fusion, and sensor interoperability

NASA Astrophysics Data System (ADS)

Fink, Wolfgang; George, Thomas; Tarbell, Mark A.

2007-04-01

Robotic reconnaissance operations are called for in extreme environments, not only those such as space, including planetary atmospheres, surfaces, and subsurfaces, but also in potentially hazardous or inaccessible operational areas on Earth, such as mine fields, battlefield environments, enemy occupied territories, terrorist infiltrated environments, or areas that have been exposed to biochemical agents or radiation. Real time reconnaissance enables the identification and characterization of transient events. A fundamentally new mission concept for tier-scalable reconnaissance of operational areas, originated by Fink et al., is aimed at replacing the engineering and safety constrained mission designs of the past. The tier-scalable paradigm integrates multi-tier (orbit atmosphere surface/subsurface) and multi-agent (satellite UAV/blimp surface/subsurface sensing platforms) hierarchical mission architectures, introducing not only mission redundancy and safety, but also enabling and optimizing intelligent, less constrained, and distributed reconnaissance in real time. Given the mass, size, and power constraints faced by such a multi-platform approach, this is an ideal application scenario for a diverse set of MEMS sensors. To support such mission architectures, a high degree of operational autonomy is required. Essential elements of such operational autonomy are: (1) automatic mapping of an operational area from different vantage points (including vehicle health monitoring); (2) automatic feature extraction and target/region-of-interest identification within the mapped operational area; and (3) automatic target prioritization for close-up examination. These requirements imply the optimal deployment of MEMS sensors and sensor platforms, sensor fusion, and sensor interoperability.

Fast and automatic thermographic material identification for the recycling process

NASA Astrophysics Data System (ADS)

Haferkamp, Heinz; Burmester, Ingo

1998-03-01

Within the framework of the future closed loop recycling process the automatic and economical sorting of plastics is a decisive element. The at the present time available identification and sorting systems are not yet suitable for the sorting of technical plastics since essential demands, as the realization of high recognition reliability and identification rates considering the variety of technical plastics, can not be guaranteed. Therefore the Laser Zentrum Hannover e.V. in cooperation with the Hoerotron GmbH and the Preussag Noell GmbH has carried out investigations on a rapid thermographic and laser-supported material- identification-system for automatic material-sorting- systems. The automatic identification of different engineering plastics coming from electronic or automotive waste is possible. Identification rates up to 10 parts per second are allowed by the effort from fast IR line scanners. The procedure is based on the following principle: within a few milliseconds a spot on the relevant sample is heated by a CO2 laser. The samples different and specific chemical and physical material properties cause different temperature distributions on their surfaces that are measured by a fast IR-linescan system. This 'thermal impulse response' has to be analyzed by means of a computer system. Investigations have shown that it is possible to analyze more than 18 different sorts of plastics at a frequency of 10 Hz. Crucial for the development of such a system is the rapid processing of imaging data, the minimization of interferences caused by oscillating samples geometries, and a wide range of possible additives in plastics in question. One possible application area is sorting of plastics coming from car- and electronic waste recycling.

Identification of Velcro rales based on Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Chen, Xue; Shao, Jie; Long, Yingjiao; Que, Chengli; Zhang, Jue; Fang, Jing

2014-05-01

Velcro rales, as a kind of crackles, are relatively specific for lung fibrosis and usually the first clinical clue of interstitial lung disease (ILD). We proposed an automatic analytic tool based on Hilbert-Huang transform (HHT) for the computerized identification of Velcro rales. In particular, HHT was utilized to extract the energy weight in various frequency bands (EW) of crackles and to calculate the portion of crackles during late inspiration. Support vector machine (SVM) based on the HHT-derived measures was used to differentiate Velcro rales from other crackles. We found that there were significant differences in the extracted parameters between Velcro rales and other crackles, including EW, EW and the proportion of crackles that appeared during the late inspiration. The discrimination results obtained from SVM achieved a concordance rate up to 92.20%±1.80% as confirmed by the diagnosis from experienced physicians. For practical purpose, the proposed approach may have potential applications to improve the sensitivity and accuracy of auscultation and conduct automatic ILD diagnose system.

Crescent Evaluation : appendix D : crescent computer system components evaluation report

DOT National Transportation Integrated Search

1994-02-01

In 1990, Lockheed Integrated Systems Company (LISC) was awarded a contract, under the Crescent Demonstration Project, to demonstrate the integration of Weigh In Motion (WIM), Automatic Vehicle Classification (AVC) and Automatic Vehicle Identification...

Automatic Molar Extraction from Dental Panoramic Radiographs for Forensic Personal Identification

NASA Astrophysics Data System (ADS)

Samopa, Febriliyan; Asano, Akira; Taguchi, Akira

Measurement of an individual molar provides rich information for forensic personal identification. We propose a computer-based system for extracting an individual molar from dental panoramic radiographs. A molar is obtained by extracting the region-of-interest, separating the maxilla and mandible, and extracting the boundaries between teeth. The proposed system is almost fully automatic; all that the user has to do is clicking three points on the boundary between the maxilla and the mandible.

Automatic identification of the reference system based on the fourth ventricular landmarks in T1-weighted MR images.

PubMed

Fu, Yili; Gao, Wenpeng; Chen, Xiaoguang; Zhu, Minwei; Shen, Weigao; Wang, Shuguo

2010-01-01

The reference system based on the fourth ventricular landmarks (including the fastigial point and ventricular floor plane) is used in medical image analysis of the brain stem. The objective of this study was to develop a rapid, robust, and accurate method for the automatic identification of this reference system on T1-weighted magnetic resonance images. The fully automated method developed in this study consisted of four stages: preprocessing of the data set, expectation-maximization algorithm-based extraction of the fourth ventricle in the region of interest, a coarse-to-fine strategy for identifying the fastigial point, and localization of the base point. The method was evaluated on 27 Brain Web data sets qualitatively and 18 Internet Brain Segmentation Repository data sets and 30 clinical scans quantitatively. The results of qualitative evaluation indicated that the method was robust to rotation, landmark variation, noise, and inhomogeneity. The results of quantitative evaluation indicated that the method was able to identify the reference system with an accuracy of 0.7 +/- 0.2 mm for the fastigial point and 1.1 +/- 0.3 mm for the base point. It took <6 seconds for the method to identify the related landmarks on a personal computer with an Intel Core 2 6300 processor and 2 GB of random-access memory. The proposed method for the automatic identification of the reference system based on the fourth ventricular landmarks was shown to be rapid, robust, and accurate. The method has potentially utility in image registration and computer-aided surgery.

Port-of-entry advanced sorting system (PASS) operational test

DOT National Transportation Integrated Search

1998-12-01

In 1992 the Oregon Department of Transportation undertook an operational test of the Port-of-Entry Advanced Sorting System (PASS), which uses a two-way communication automatic vehicle identification system, integrated with weigh-in-motion, automatic ...

Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices.

PubMed

Pires, Ivan Miguel; Garcia, Nuno M; Pombo, Nuno; Flórez-Revuelta, Francisco; Spinsante, Susanna

2018-02-21

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking into account several concepts, including data acquisition, data processing, data fusion, and pattern recognition. These concepts can be mapped onto different modules of the framework. The proposed framework should perform the identification of ADL without Internet connection, performing these tasks locally on the mobile device, taking in account the hardware and software limitations of these devices. The main purpose of this paper is to present a new approach for the creation of a framework for the recognition of ADL, analyzing the allowed sensors available in the mobile devices, and the existing methods available in the literature.

Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices

PubMed Central

Pombo, Nuno

2018-01-01

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking into account several concepts, including data acquisition, data processing, data fusion, and pattern recognition. These concepts can be mapped onto different modules of the framework. The proposed framework should perform the identification of ADL without Internet connection, performing these tasks locally on the mobile device, taking in account the hardware and software limitations of these devices. The main purpose of this paper is to present a new approach for the creation of a framework for the recognition of ADL, analyzing the allowed sensors available in the mobile devices, and the existing methods available in the literature. PMID:29466316

Crop identification technology assessment for remote sensing (CITARS). Volume 10: Interpretation of results

NASA Technical Reports Server (NTRS)

Bizzell, R. M.; Feiveson, A. H.; Hall, F. G.; Bauer, M. E.; Davis, B. J.; Malila, W. A.; Rice, D. P.

1975-01-01

The CITARS was an experiment designed to quantitatively evaluate crop identification performance for corn and soybeans in various environments using a well-defined set of automatic data processing (ADP) techniques. Each technique was applied to data acquired to recognize and estimate proportions of corn and soybeans. The CITARS documentation summarizes, interprets, and discusses the crop identification performances obtained using (1) different ADP procedures; (2) a linear versus a quadratic classifier; (3) prior probability information derived from historic data; (4) local versus nonlocal recognition training statistics and the associated use of preprocessing; (5) multitemporal data; (6) classification bias and mixed pixels in proportion estimation; and (7) data with differnt site characteristics, including crop, soil, atmospheric effects, and stages of crop maturity.

Assigning unique identification numbers to new user accounts and groups in a computing environment with multiple registries

DOEpatents

DeRobertis, Christopher V.; Lu, Yantian T.

2010-02-23

A method, system, and program storage device for creating a new user account or user group with a unique identification number in a computing environment having multiple user registries is provided. In response to receiving a command to create a new user account or user group, an operating system of a clustered computing environment automatically checks multiple registries configured for the operating system to determine whether a candidate identification number for the new user account or user group has been assigned already to one or more existing user accounts or groups, respectively. The operating system automatically assigns the candidate identification number to the new user account or user group created in a target user registry if the checking indicates that the candidate identification number has not been assigned already to any of the existing user accounts or user groups, respectively.

Port-of-entry Advanced Sorting System (PASS) operational test : final report

DOT National Transportation Integrated Search

1998-12-01

In 1992 the Oregon Department of Transportation undertook an operational test of the Port-of-Entry Advanced Sorting System (PASS), which uses a two-way communication automatic vehicle identification system, integrated with weigh-in-motion, automatic ...

Understanding ITS/CVO Technology Applications, Student Manual, Course 3

DOT National Transportation Integrated Search

1999-01-01

WEIGHT-IN-MOTION OR WIM, COMMERCIAL VEHICLE INFORMATION SYSTEMS AND NETWORK OR CVISN, AUTOMATIC VEHICLE IDENTIFICATION OR AVI, AUTOMATIC LOCATION OR AVL, ELECTRONIC DATA INTERCHANGE OR EDI, GLOBAL POSITIONING SYSTEM OR GPS, INTERNET OR WORLD WIDE WEB...

Astrometrica: Astrometric data reduction of CCD images

NASA Astrophysics Data System (ADS)

Raab, Herbert

2012-03-01

Astrometrica is an interactive software tool for scientific grade astrometric data reduction of CCD images. The current version of the software is for the Windows 32bit operating system family. Astrometrica reads FITS (8, 16 and 32 bit integer files) and SBIG image files. The size of the images is limited only by available memory. It also offers automatic image calibration (Dark Frame and Flat Field correction), automatic reference star identification, automatic moving object detection and identification, and access to new-generation star catalogs (PPMXL, UCAC 3 and CMC-14), in addition to online help and other features. Astrometrica is shareware, available for use for a limited period of time (100 days) for free; special arrangements can be made for educational projects.

Automatic identification of bullet signatures based on consecutive matching striae (CMS) criteria.

PubMed

Chu, Wei; Thompson, Robert M; Song, John; Vorburger, Theodore V

2013-09-10

The consecutive matching striae (CMS) numeric criteria for firearm and toolmark identifications have been widely accepted by forensic examiners, although there have been questions concerning its observer subjectivity and limited statistical support. In this paper, based on signal processing and extraction, a model for the automatic and objective counting of CMS is proposed. The position and shape information of the striae on the bullet land is represented by a feature profile, which is used for determining the CMS number automatically. Rapid counting of CMS number provides a basis for ballistics correlations with large databases and further statistical and probability analysis. Experimental results in this report using bullets fired from ten consecutively manufactured barrels support this developed model. Published by Elsevier Ireland Ltd.

Experiments in automatic word class and word sense identification for information retrieval

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

Gauch, S.; Futrelle, R.P.

Automatic identification of related words and automatic detection of word senses are two long-standing goals of researchers in natural language processing. Word class information and word sense identification may enhance the performance of information retrieval system4ms. Large online corpora and increased computational capabilities make new techniques based on corpus linguisitics feasible. Corpus-based analysis is especially needed for corpora from specialized fields for which no electronic dictionaries or thesauri exist. The methods described here use a combination of mutual information and word context to establish word similarities. Then, unsupervised classification is done using clustering in the word space, identifying word classesmore » without pretagging. We also describe an extension of the method to handle the difficult problems of disambiguation and of determining part-of-speech and semantic information for low-frequency words. The method is powerful enough to produce high-quality results on a small corpus of 200,000 words from abstracts in a field of molecular biology.« less

47 CFR 80.231 - Technical Requirements for Class B Automatic Identification System (AIS) equipment.

Code of Federal Regulations, 2010 CFR

2010-10-01

... data in the device shall also be included in the user's manual for the device. The entry of static data... Communications Commission to input an MMSI that has not been properly assigned to the end user, or to otherwise... shall the entry of static data into a Class B AIS device be performed by the user of the device or the...

Fiber sensors for molecular detection

NASA Astrophysics Data System (ADS)

Gu, Claire; Yang, Xuan; Zhang, Jin; Newhouse, Rebecca; Cao, Liangcai

2010-11-01

The demand on sensors for detecting chemical and biological agents is greater than ever before, including medical, environmental, food safety, military, and security applications. At present, most detection or sensing techniques tend to be either non-molecular specific, bulky, expensive, relatively inaccurate, or unable to provide real time data. Clearly, alternative sensing technologies are urgently needed. Recently, we have been working to develop a compact fiber optic surface enhanced Raman scattering (SERS) sensor system that integrates various novel ideas to achieve compactness, high sensitivity and consistency, molecular specificity, and automatic preliminary identification capabilities. The unique sensor architecture is expected to bring SERS sensors to practical applications due to a combination of 1) novel SERS substrates that provide the high sensitivity and consistency, molecular specificity, and applicability to a wide range of compounds; 2) a unique hollow core optical fiber probe with double SERS substrate structure that provides the compactness, reliability, low cost, and ease of sampling; and 3) an innovative matched spectral filter set that provides automatic preliminary molecule identification. In this paper, we will review the principle of operation and some of the important milestones of fiber SERS sensor development with emphasis on our recent work to integrate photonic crystal fiber SERS probes with a portable Raman spectrometer and to demonstrate a matched spectral filter for molecule identification.

Human Activity Recognition in AAL Environments Using Random Projections.

PubMed

Damaševičius, Robertas; Vasiljevas, Mindaugas; Šalkevičius, Justas; Woźniak, Marcin

2016-01-01

Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented.

Human Activity Recognition in AAL Environments Using Random Projections

PubMed Central

Damaševičius, Robertas; Vasiljevas, Mindaugas; Šalkevičius, Justas; Woźniak, Marcin

2016-01-01

Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented. PMID:27413392

Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.

PubMed

Zdravevski, Eftim; Risteska Stojkoska, Biljana; Standl, Marie; Schulz, Holger

2017-01-01

Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position. The study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. Classification was performed using only data from the hip accelerometer, using only data from ankle accelerometer and using data from both accelerometers. The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of correctly identified jogging periods related to the total time including the missed ones, was up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious benefit of using two accelerometers, rather almost the same performance could be achieved from either accelerometer position. Machine learning techniques can be used for automatic activity recognition, as they provide very accurate activity recognition, significantly more accurate than when keeping a diary. Identification of jogging periods in adolescents can be performed using only one accelerometer. Performance-wise there is no significant benefit from using accelerometers on both locations.

Implementing electronic identification for performance recording in sheep: II. Cost-benefit analysis in meat and dairy farms.

PubMed

Ait-Saidi, A; Caja, G; Salama, A A K; Milán, M J

2014-12-01

Costs and secondary benefits of implementing electronic identification (e-ID) for performance recording (i.e., lambing, body weight, inventory, and milk yield) in dairy and meat ewes were assessed by using the results from a previous study in which manual (M), semiautomatic (SA), and automatic (AU) data collection systems were compared. Ewes were identified with visual ear tags and electronic rumen boluses. The M system used visual identification, on-paper data recording, and manual data uploading to a computer. The SA system used e-ID with a handheld reader in which performances were typed and automatic uploaded to a computer. The use of a personal digital assistant (PDA) for recording and automatic data uploading, which transformed M in a SA system, was also considered. The AU system was only used for BW recording and consisted of e-ID, automatic data recording in an electronic scale, and uploading to a computer. The cost-benefit study was applied to 2 reference sheep farms of 700 meat ewes, under extensive or intensive production systems, and of 400 dairy ewes, practicing once- or twice-a-day machine milkings. Sensitivity analyses under voluntary and mandatory e-ID scenarios were also included. Benefits of using e-ID for SA or AU performance recording mainly depended on sheep farm purpose, number of test days per year, handheld reader and PDA prices, and flock size. Implementing e-ID for SA and AU performance recording saved approximately 50% of the time required by the M system, and increased the reliability of the data collected. Use of e-ID increased the cost of performance recording in a voluntary e-ID scenario, paying only partially the investment made (15 to 70%). For the mandatory e-ID scenario, in which the cost of e-ID devices was not included, savings paid 100% of the extra costs needed for using e-ID in all farm types and conditions. In both scenarios, the reader price was the most important extra cost (40 to 90%) for implementing e-ID in sheep farms. Calculated extra costs of using the PDA covered more than 100% of the implementation costs in all type of sheep farms, indicating that this device was cost-effective for sheep-performance recording. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A tool for developing an automatic insect identification system based on wing outlines

PubMed Central

Yang, He-Ping; Ma, Chun-Sen; Wen, Hui; Zhan, Qing-Bin; Wang, Xin-Li

2015-01-01

For some insect groups, wing outline is an important character for species identification. We have constructed a program as the integral part of an automated system to identify insects based on wing outlines (DAIIS). This program includes two main functions: (1) outline digitization and Elliptic Fourier transformation and (2) classifier model training by pattern recognition of support vector machines and model validation. To demonstrate the utility of this program, a sample of 120 owlflies (Neuroptera: Ascalaphidae) was split into training and validation sets. After training, the sample was sorted into seven species using this tool. In five repeated experiments, the mean accuracy for identification of each species ranged from 90% to 98%. The accuracy increased to 99% when the samples were first divided into two groups based on features of their compound eyes. DAIIS can therefore be a useful tool for developing a system of automated insect identification. PMID:26251292

Automatic digital image analysis for identification of mitotic cells in synchronous mammalian cell cultures.

PubMed

Eccles, B A; Klevecz, R R

1986-06-01

Mitotic frequency in a synchronous culture of mammalian cells was determined fully automatically and in real time using low-intensity phase-contrast microscopy and a newvicon video camera connected to an EyeCom III image processor. Image samples, at a frequency of one per minute for 50 hours, were analyzed by first extracting the high-frequency picture components, then thresholding and probing for annular objects indicative of putative mitotic cells. Both the extraction of high-frequency components and the recognition of rings of varying radii and discontinuities employed novel algorithms. Spatial and temporal relationships between annuli were examined to discern the occurrences of mitoses, and such events were recorded in a computer data file. At present, the automatic analysis is suited for random cell proliferation rate measurements or cell cycle studies. The automatic identification of mitotic cells as described here provides a measure of the average proliferative activity of the cell population as a whole and eliminates more than eight hours of manual review per time-lapse video recording.

Tests with VHR images for the identification of olive trees and other fruit trees in the European Union

NASA Astrophysics Data System (ADS)

Masson, Josiane; Soille, Pierre; Mueller, Rick

2004-10-01

In the context of the Common Agricultural Policy (CAP) there is a strong interest of the European Commission for counting and individually locating fruit trees. An automatic counting algorithm developed by the JRC (OLICOUNT) was used in the past for olive trees only, on 1m black and white orthophotos but with limits in case of young trees or irregular groves. This study investigates the improvement of fruit tree identification using VHR images on a large set of data in three test sites, one in Creta (Greece; one in the south-east of France with a majority of olive trees and associated fruit trees, and the last one in Florida on citrus trees. OLICOUNT was compared with two other automatic tree counting, applications, one using the CRISP software on citrus trees and the other completely automatic based on regional minima (morphological image analysis). Additional investigation was undertaken to refine the methods. This paper describes the automatic methods and presents the results derived from the tests.

Research into automatic recognition of joints in human symmetrical movements

NASA Astrophysics Data System (ADS)

Fan, Yifang; Li, Zhiyu

2008-03-01

High speed photography is a major means of collecting data from human body movement. It enables the automatic identification of joints, which brings great significance to the research, treatment and recovery of injuries, the analysis to the diagnosis of sport techniques and the ergonomics. According to the features that when the adjacent joints of human body are in planetary motion, their distance remains the same, and according to the human body joint movement laws (such as the territory of the articular anatomy and the kinematic features), a new approach is introduced to process the image thresholding of joints filmed by the high speed camera, to automatically identify the joints and to automatically trace the joint points (by labeling markers at the joints). Based upon the closure of marking points, automatic identification can be achieved through thresholding treatment. Due to the screening frequency and the laws of human segment movement, when the marking points have been initialized, their automatic tracking can be achieved with the progressive sequential images.Then the testing results, the data from three-dimensional force platform and the characteristics that human body segment will only rotate around the closer ending segment when the segment has no boding force and only valid to the conservative force all tell that after being analyzed kinematically, the approach is approved to be valid.

Identification Of Cells With A Compact Microscope Imaging System With Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2006-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking mic?oscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

Image processing tool for automatic feature recognition and quantification

DOEpatents

Chen, Xing; Stoddard, Ryan J.

2017-05-02

A system for defining structures within an image is described. The system includes reading of an input file, preprocessing the input file while preserving metadata such as scale information and then detecting features of the input file. In one version the detection first uses an edge detector followed by identification of features using a Hough transform. The output of the process is identified elements within the image.

Face recognition for criminal identification: An implementation of principal component analysis for face recognition

NASA Astrophysics Data System (ADS)

Abdullah, Nurul Azma; Saidi, Md. Jamri; Rahman, Nurul Hidayah Ab; Wen, Chuah Chai; Hamid, Isredza Rahmi A.

2017-10-01

In practice, identification of criminal in Malaysia is done through thumbprint identification. However, this type of identification is constrained as most of criminal nowadays getting cleverer not to leave their thumbprint on the scene. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. However, because of limited software developed to automatically detect the similarity between photo in the footage and recorded photo of criminals, the law enforce thumbprint identification. In this paper, an automated facial recognition system for criminal database was proposed using known Principal Component Analysis approach. This system will be able to detect face and recognize face automatically. This will help the law enforcements to detect or recognize suspect of the case if no thumbprint present on the scene. The results show that about 80% of input photo can be matched with the template data.

Automatic identification of abstract online groups

DOEpatents

Engel, David W; Gregory, Michelle L; Bell, Eric B; Cowell, Andrew J; Piatt, Andrew W

2014-04-15

Online abstract groups, in which members aren't explicitly connected, can be automatically identified by computer-implemented methods. The methods involve harvesting records from social media and extracting content-based and structure-based features from each record. Each record includes a social-media posting and is associated with one or more entities. Each feature is stored on a data storage device and includes a computer-readable representation of an attribute of one or more records. The methods further involve grouping records into record groups according to the features of each record. Further still the methods involve calculating an n-dimensional surface representing each record group and defining an outlier as a record having feature-based distances measured from every n-dimensional surface that exceed a threshold value. Each of the n-dimensional surfaces is described by a footprint that characterizes the respective record group as an online abstract group.

RFID applications in transportation operation and intelligent transportation systems (ITS).

DOT National Transportation Integrated Search

2009-06-01

Radio frequency identification (RFID) transmits the identity of an object or a person wirelessly. It is grouped under : the broad category of automatic identification technologies with corresponding standards and established protocols. : RFID is suit...

MAC, A System for Automatically IPR Identification, Collection and Distribution

NASA Astrophysics Data System (ADS)

Serrão, Carlos

Controlling Intellectual Property Rights (IPR) in the Digital World is a very hard challenge. The facility to create multiple bit-by-bit identical copies from original IPR works creates the opportunities for digital piracy. One of the most affected industries by this fact is the Music Industry. The Music Industry has supported huge losses during the last few years due to this fact. Moreover, this fact is also affecting the way that music rights collecting and distributing societies are operating to assure a correct music IPR identification, collection and distribution. In this article a system for automating this IPR identification, collection and distribution is presented and described. This system makes usage of advanced automatic audio identification system based on audio fingerprinting technology. This paper will present the details of the system and present a use-case scenario where this system is being used.

Utilizing automatic identification tracking systems to compile operational field and structure data : [research summary].

DOT National Transportation Integrated Search

2014-05-01

Thefederallymandatedmaterialsclearanceprocessrequiresstatetransportation : agenciestosubjectallconstructionfieldsamplestoqualitycontrol/assurancetestingin : ordertopassstandardizedstateinspections....

Automatic blood vessel based-liver segmentation using the portal phase abdominal CT

NASA Astrophysics Data System (ADS)

Maklad, Ahmed S.; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Shimada, Mitsuo; Iinuma, Gen

2018-02-01

Liver segmentation is the basis for computer-based planning of hepatic surgical interventions. In diagnosis and analysis of hepatic diseases and surgery planning, automatic segmentation of liver has high importance. Blood vessel (BV) has showed high performance at liver segmentation. In our previous work, we developed a semi-automatic method that segments the liver through the portal phase abdominal CT images in two stages. First stage was interactive segmentation of abdominal blood vessels (ABVs) and subsequent classification into hepatic (HBVs) and non-hepatic (non-HBVs). This stage had 5 interactions that include selective threshold for bone segmentation, selecting two seed points for kidneys segmentation, selection of inferior vena cava (IVC) entrance for starting ABVs segmentation, identification of the portal vein (PV) entrance to the liver and the IVC-exit for classifying HBVs from other ABVs (non-HBVs). Second stage is automatic segmentation of the liver based on segmented ABVs as described in [4]. For full automation of our method we developed a method [5] that segments ABVs automatically tackling the first three interactions. In this paper, we propose full automation of classifying ABVs into HBVs and non- HBVs and consequently full automation of liver segmentation that we proposed in [4]. Results illustrate that the method is effective at segmentation of the liver through the portal abdominal CT images.

Automatic reference selection for quantitative EEG interpretation: identification of diffuse/localised activity and the active earlobe reference, iterative detection of the distribution of EEG rhythms.

PubMed

Wang, Bei; Wang, Xingyu; Ikeda, Akio; Nagamine, Takashi; Shibasaki, Hiroshi; Nakamura, Masatoshi

2014-01-01

EEG (Electroencephalograph) interpretation is important for the diagnosis of neurological disorders. The proper adjustment of the montage can highlight the EEG rhythm of interest and avoid false interpretation. The aim of this study was to develop an automatic reference selection method to identify a suitable reference. The results may contribute to the accurate inspection of the distribution of EEG rhythms for quantitative EEG interpretation. The method includes two pre-judgements and one iterative detection module. The diffuse case is initially identified by pre-judgement 1 when intermittent rhythmic waveforms occur over large areas along the scalp. The earlobe reference or averaged reference is adopted for the diffuse case due to the effect of the earlobe reference depending on pre-judgement 2. An iterative detection algorithm is developed for the localised case when the signal is distributed in a small area of the brain. The suitable averaged reference is finally determined based on the detected focal and distributed electrodes. The presented technique was applied to the pathological EEG recordings of nine patients. One example of the diffuse case is introduced by illustrating the results of the pre-judgements. The diffusely intermittent rhythmic slow wave is identified. The effect of active earlobe reference is analysed. Two examples of the localised case are presented, indicating the results of the iterative detection module. The focal and distributed electrodes are detected automatically during the repeating algorithm. The identification of diffuse and localised activity was satisfactory compared with the visual inspection. The EEG rhythm of interest can be highlighted using a suitable selected reference. The implementation of an automatic reference selection method is helpful to detect the distribution of an EEG rhythm, which can improve the accuracy of EEG interpretation during both visual inspection and automatic interpretation. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

Collaborative Study for Analysis of High Resolution Infrared Atmospheric Spectra Between NASA Langley Research Center and the University of Denver

NASA Technical Reports Server (NTRS)

Goldman, A.

2002-01-01

The Langley-D.U. collaboration on the analysis of high resolultion infrared atmospheric spectra covered a number of important studies of trace gases identification and quantification from field spectra, and spectral line parameters analysis. The collaborative work included: 1) Quantification and monitoring of trace gases from ground-based spectra available from various locations and seasons and from balloon flights; 2) Identification and preliminary quantification of several isotopic species, including oxygen and Sulfur isotopes; 3) Search for new species on the available spectra, including the use of selective coadding of ground-based spectra for high signal to noise; 4) Update of spectroscopic line parameters, by combining laboratory and atmospheric spectra with theoretical spectroscopy methods; 5) Study of trends and correlations of atmosphere trace constituents; and 6) Algorithms developments, retrievals intercomparisons and automatization of the analysis of NDSC spectra, for both column amounts and vertical profiles.

Automatic building identification under bomb damage conditions

NASA Astrophysics Data System (ADS)

Woodley, Robert; Noll, Warren; Barker, Joseph; Wunsch, Donald C., II

2009-05-01

Given the vast amount of image intelligence utilized in support of planning and executing military operations, a passive automated image processing capability for target identification is urgently required. Furthermore, transmitting large image streams from remote locations would quickly use available band width (BW) precipitating the need for processing to occur at the sensor location. This paper addresses the problem of automatic target recognition for battle damage assessment (BDA). We utilize an Adaptive Resonance Theory approach to cluster templates of target buildings. The results show that the network successfully classifies targets from non-targets in a virtual test bed environment.

The integrated manual and automatic control of complex flight systems

NASA Technical Reports Server (NTRS)

Schmidt, D. K.

1986-01-01

The topics of research in this program include pilot/vehicle analysis techniques, identification of pilot dynamics, and control and display synthesis techniques for optimizing aircraft handling qualities. The project activities are discussed. The current technical activity is directed at extending and validating the active display synthesis procedure, and the pilot/vehicle analysis of the NLR rate-command flight configurations in the landing task. Two papers published by the researchers are attached as appendices.

Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards: Smoke Detection Using an Autologistic Regression Classifier.

PubMed

Wolters, Mark A; Dean, C B

2017-01-01

Remote sensing images from Earth-orbiting satellites are a potentially rich data source for monitoring and cataloguing atmospheric health hazards that cover large geographic regions. A method is proposed for classifying such images into hazard and nonhazard regions using the autologistic regression model, which may be viewed as a spatial extension of logistic regression. The method includes a novel and simple approach to parameter estimation that makes it well suited to handling the large and high-dimensional datasets arising from satellite-borne instruments. The methodology is demonstrated on both simulated images and a real application to the identification of forest fire smoke.

An automatic microseismic or acoustic emission arrival identification scheme with deep recurrent neural networks

NASA Astrophysics Data System (ADS)

Zheng, Jing; Lu, Jiren; Peng, Suping; Jiang, Tianqi

2018-02-01

The conventional arrival pick-up algorithms cannot avoid the manual modification of the parameters for the simultaneous identification of multiple events under different signal-to-noise ratios (SNRs). Therefore, in order to automatically obtain the arrivals of multiple events with high precision under different SNRs, in this study an algorithm was proposed which had the ability to pick up the arrival of microseismic or acoustic emission events based on deep recurrent neural networks. The arrival identification was performed using two important steps, which included a training phase and a testing phase. The training process was mathematically modelled by deep recurrent neural networks using Long Short-Term Memory architecture. During the testing phase, the learned weights were utilized to identify the arrivals through the microseismic/acoustic emission data sets. The data sets were obtained by rock physics experiments of the acoustic emission. In order to obtain the data sets under different SNRs, this study added random noise to the raw experiments' data sets. The results showed that the outcome of the proposed method was able to attain an above 80 per cent hit-rate at SNR 0 dB, and an approximately 70 per cent hit-rate at SNR -5 dB, with an absolute error in 10 sampling points. These results indicated that the proposed method had high selection precision and robustness.

Investigation of an automatic trim algorithm for restructurable aircraft control

NASA Technical Reports Server (NTRS)

Weiss, J.; Eterno, J.; Grunberg, D.; Looze, D.; Ostroff, A.

1986-01-01

This paper develops and solves an automatic trim problem for restructurable aircraft control. The trim solution is applied as a feed-forward control to reject measurable disturbances following control element failures. Disturbance rejection and command following performances are recovered through the automatic feedback control redesign procedure described by Looze et al. (1985). For this project the existence of a failure detection mechanism is assumed, and methods to cope with potential detection and identification inaccuracies are addressed.

Automatic and rapid identification of glycopeptides by nano-UPLC-LTQ-FT-MS and proteomic search engine.

PubMed

Giménez, Estela; Gay, Marina; Vilaseca, Marta

2017-01-30

Here we demonstrate the potential of nano-UPLC-LTQ-FT-MS and the Byonic™ proteomic search engine for the separation, detection, and identification of N- and O-glycopeptide glycoforms in standard glycoproteins. The use of a BEH C18 nanoACQUITY column allowed the separation of the glycopeptides present in the glycoprotein digest and a baseline-resolution of the glycoforms of the same glycopeptide on the basis of the number of sialic acids. Moreover, we evaluated several acquisition strategies in order to improve the detection and characterization of glycopeptide glycoforms with the maximum number of identification percentages. The proposed strategy is simple to set up with the technology platforms commonly used in proteomic labs. The method allows the straightforward and rapid obtention of a general glycosylated map of a given protein, including glycosites and their corresponding glycosylated structures. The MS strategy selected in this work, based on a gas phase fractionation approach, led to 136 unique peptides from four standard proteins, which represented 78% of the total number of peptides identified. Moreover, the method does not require an extra glycopeptide enrichment step, thus preventing the bias that this step could cause towards certain glycopeptide species. Data are available via ProteomeXchange with identifier PXD003578. We propose a simple and high-throughput glycoproteomics-based methodology that allows the separation of glycopeptide glycoforms on the basis of the number of sialic acids, and their automatic and rapid identification without prior knowledge of protein glycosites or type and structure of the glycans. Copyright © 2016 Elsevier B.V. All rights reserved.

Automatic Identification and Organization of Index Terms for Interactive Browsing.

ERIC Educational Resources Information Center

Wacholder, Nina; Evans, David K.; Klavans, Judith L.

The potential of automatically generated indexes for information access has been recognized for several decades, but the quantity of text and the ambiguity of natural language processing have made progress at this task more difficult than was originally foreseen. Recently, a body of work on development of interactive systems to support phrase…

Automatic identification of artifacts in electrodermal activity data.

PubMed

Taylor, Sara; Jaques, Natasha; Chen, Weixuan; Fedor, Szymon; Sano, Akane; Picard, Rosalind

2015-01-01

Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.

Management of natural resources through automatic cartographic inventory

NASA Technical Reports Server (NTRS)

Rey, P.; Gourinard, Y.; Cambou, F. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Significant results of the ARNICA program from August 1972 - January 1973 have been: (1) establishment of image to object correspondence codes for all types of soil use and forestry in northern Spain; (2) establishment of a transfer procedure between qualitative (remote identification and remote interpretation) and quantitative (numerization, storage, automatic statistical cartography) use of images; (3) organization of microdensitometric data processing and automatic cartography software; and (4) development of a system for measuring reflectance simultaneous with imagery.

48 CFR 252.211-7003 - Item identification and valuation.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., used to retrieve data encoded on machine-readable media. Concatenated unique item identifier means— (1... (or controlling) authority for the enterprise identifier. Item means a single hardware article or a...-readable means an automatic identification technology media, such as bar codes, contact memory buttons...

48 CFR 252.211-7003 - Item identification and valuation.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., used to retrieve data encoded on machine-readable media. Concatenated unique item identifier means— (1... (or controlling) authority for the enterprise identifier. Item means a single hardware article or a...-readable means an automatic identification technology media, such as bar codes, contact memory buttons...

48 CFR 252.211-7003 - Item identification and valuation.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., used to retrieve data encoded on machine-readable media. Concatenated unique item identifier means— (1... (or controlling) authority for the enterprise identifier. Item means a single hardware article or a...-readable means an automatic identification technology media, such as bar codes, contact memory buttons...

Automatic Identification Technology (AIT): The Development of Functional Capability and Card Application Matrices

DTIC Science & Technology

1994-09-01

650 B.C. in Asia Minor, coins were developed and used in acquiring goods and services. In France, during the eighteenth century, paper money made its... counterfeited . [INFO94, p. 23] Other weaknesses of bar code technology include limited data storage capability based on the bar code symbology used when...extremely accurate, with calculated error rates as low as 1 in 100 trillion, and are difficult to counterfeit . Strong magnetic fields cannot erase RF

Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions

PubMed Central

Risteska Stojkoska, Biljana; Standl, Marie; Schulz, Holger

2017-01-01

Background Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position. Methods The study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. Classification was performed using only data from the hip accelerometer, using only data from ankle accelerometer and using data from both accelerometers. Results The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of correctly identified jogging periods related to the total time including the missed ones, was up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious benefit of using two accelerometers, rather almost the same performance could be achieved from either accelerometer position. Conclusions Machine learning techniques can be used for automatic activity recognition, as they provide very accurate activity recognition, significantly more accurate than when keeping a diary. Identification of jogging periods in adolescents can be performed using only one accelerometer. Performance-wise there is no significant benefit from using accelerometers on both locations. PMID:28880923

Automatic Adviser on Mobile Objects Status Identification and Classification

NASA Astrophysics Data System (ADS)

Shabelnikov, A. N.; Liabakh, N. N.; Gibner, Ya M.; Saryan, A. S.

2018-05-01

A mobile object status identification task is defined within the image discrimination theory. It is proposed to classify objects into three classes: object operation status; its maintenance is required and object should be removed from the production process. Two methods were developed to construct the separating boundaries between the designated classes: a) using statistical information on the research objects executed movement, b) basing on regulatory documents and expert commentary. Automatic Adviser operation simulation and the operation results analysis complex were synthesized. Research results are commented using a specific example of cuts rolling from the hump yard. The work was supported by Russian Fundamental Research Fund, project No. 17-20-01040.

Automatic measurement of images on astrometric plates

NASA Astrophysics Data System (ADS)

Ortiz Gil, A.; Lopez Garcia, A.; Martinez Gonzalez, J. M.; Yershov, V.

1994-04-01

We present some results on the process of automatic detection and measurement of objects in overlapped fields of astrometric plates. The main steps of our algorithm are the following: determination of the Scale and Tilt between charge coupled devices (CCD) and microscope coordinate systems and estimation of signal-to-noise ratio in each field;--image identification and improvement of its position and size;--image final centering;--image selection and storage. Several parameters allow the use of variable criteria for image identification, characterization and selection. Problems related with faint images and crowded fields will be approached by special techniques (morphological filters, histogram properties and fitting models).

Kocuria rosea, kocuria kristinae, leuconostoc mesenteroides as caries-causing representatives of oral microflora.

PubMed

Ananieva, Maiia M; Faustova, Mariia O; Basarab, Iaroslav O; Loban', Galina A

2017-01-01

Recently, opportunistic microflora are increasingly known to be involved in the development of pathological processes in various systems and organs. This situation promotes interest in their detailed study as causative agents of bacterial infections. To study the microbial species residing in carious cavities in acute profound caries. The study involved 14 people with a diagnosis of acute profound caries. Microbiological methods included determining species of microorganisms' cultures from carious cavities in acute profound caries. Final identification was carried out by automatic bacteriological analyzer Vitec-2compact bioMérieux. Among the bacteria isolated, Kocuria rosae, Kocuria kristinae, and Leuconostoc mesenteroides are the focus of the authors' attention due to their identification rate in the patients. These microbial species are little studied due to the lack of data on their cariogenic associations.The meticulous study of the microorganisms, isolated from carious cavities in patients with acute profound caries by automatic bacteriological analyzer Vitec-2 Systems bioMérieux, and findings on their biochemical properties allow us to conclude that Kocuria rosae, Kocuria kristinae, and Leuconostoc mesenteroides are among the microorganisms making up the microflora of carious cavities under acute profound caries and are involved in the development of the caries process.

CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction.

PubMed

Xu, Youjun; Wang, Shiwei; Hu, Qiwan; Gao, Shuaishi; Ma, Xiaomin; Zhang, Weilin; Shen, Yihang; Chen, Fangjin; Lai, Luhua; Pei, Jianfeng

2018-05-10

CavityPlus is a web server that offers protein cavity detection and various functional analyses. Using protein three-dimensional structural information as the input, CavityPlus applies CAVITY to detect potential binding sites on the surface of a given protein structure and rank them based on ligandability and druggability scores. These potential binding sites can be further analysed using three submodules, CavPharmer, CorrSite, and CovCys. CavPharmer uses a receptor-based pharmacophore modelling program, Pocket, to automatically extract pharmacophore features within cavities. CorrSite identifies potential allosteric ligand-binding sites based on motion correlation analyses between cavities. CovCys automatically detects druggable cysteine residues, which is especially useful to identify novel binding sites for designing covalent allosteric ligands. Overall, CavityPlus provides an integrated platform for analysing comprehensive properties of protein binding cavities. Such analyses are useful for many aspects of drug design and discovery, including target selection and identification, virtual screening, de novo drug design, and allosteric and covalent-binding drug design. The CavityPlus web server is freely available at http://repharma.pku.edu.cn/cavityplus or http://www.pkumdl.cn/cavityplus.

Computer-Aided Parallelizer and Optimizer

NASA Technical Reports Server (NTRS)

Jin, Haoqiang

2011-01-01

The Computer-Aided Parallelizer and Optimizer (CAPO) automates the insertion of compiler directives (see figure) to facilitate parallel processing on Shared Memory Parallel (SMP) machines. While CAPO currently is integrated seamlessly into CAPTools (developed at the University of Greenwich, now marketed as ParaWise), CAPO was independently developed at Ames Research Center as one of the components for the Legacy Code Modernization (LCM) project. The current version takes serial FORTRAN programs, performs interprocedural data dependence analysis, and generates OpenMP directives. Due to the widely supported OpenMP standard, the generated OpenMP codes have the potential to run on a wide range of SMP machines. CAPO relies on accurate interprocedural data dependence information currently provided by CAPTools. Compiler directives are generated through identification of parallel loops in the outermost level, construction of parallel regions around parallel loops and optimization of parallel regions, and insertion of directives with automatic identification of private, reduction, induction, and shared variables. Attempts also have been made to identify potential pipeline parallelism (implemented with point-to-point synchronization). Although directives are generated automatically, user interaction with the tool is still important for producing good parallel codes. A comprehensive graphical user interface is included for users to interact with the parallelization process.

Implementation of a high-speed face recognition system that uses an optical parallel correlator.

PubMed

Watanabe, Eriko; Kodate, Kashiko

2005-02-10

We implement a fully automatic fast face recognition system by using a 1000 frame/s optical parallel correlator designed and assembled by us. The operational speed for the 1:N (i.e., matching one image against N, where N refers to the number of images in the database) identification experiment (4000 face images) amounts to less than 1.5 s, including the preprocessing and postprocessing times. The binary real-only matched filter is devised for the sake of face recognition, and the system is optimized by the false-rejection rate (FRR) and the false-acceptance rate (FAR), according to 300 samples selected by the biometrics guideline. From trial 1:N identification experiments with the optical parallel correlator, we acquired low error rates of 2.6% FRR and 1.3% FAR. Facial images of people wearing thin glasses or heavy makeup that rendered identification difficult were identified with this system.

AUTOMOTIVE DIESEL MAINTENANCE 2. UNIT VII, AUTOMATIC TRANSMISSIONS--ALLISON, TORQUMATIC SERIES 5960 AND 6060 (PART I).

ERIC Educational Resources Information Center

Human Engineering Inst., Cleveland, OH.

THIS MODULE OF A 25-MODULE COURSE IS DESIGNED TO DEVELOP AN UNDERSTANDING OF THE OPERATION AND MAINTENANCE OF SPECIFIC MODELS OF AUTOMATIC TRANSMISSIONS USED ON DIESEL POWERED VEHICLES. TOPICS ARE (1) GENERAL SPECIFICATION DATA, (2) OPTIONS FOR VARIOUS APPLICATIONS, (3) ROAD TEST INSTRUCTIONS, (4) IDENTIFICATION AND SPECIFICATION DATA, (5) ALLISON…

Automatic Method of Pause Measurement for Normal and Dysarthric Speech

ERIC Educational Resources Information Center

Rosen, Kristin; Murdoch, Bruce; Folker, Joanne; Vogel, Adam; Cahill, Louise; Delatycki, Martin; Corben, Louise

2010-01-01

This study proposes an automatic method for the detection of pauses and identification of pause types in conversational speech for the purpose of measuring the effects of Friedreich's Ataxia (FRDA) on speech. Speech samples of [approximately] 3 minutes were recorded from 13 speakers with FRDA and 18 healthy controls. Pauses were measured from the…

21 CFR 820.200 - Servicing.

Code of Federal Regulations, 2010 CFR

2010-04-01

... reports with appropriate statistical methodology in accordance with § 820.100. (c) Each manufacturer who... chapter shall automatically consider the report a complaint and shall process it in accordance with the... device serviced; (2) Any device identification(s) and control number(s) used; (3) The date of service; (4...

48 CFR 252.211-7003 - Item unique identification and valuation.

Code of Federal Regulations, 2014 CFR

2014-10-01

... reader or interrogator, used to retrieve data encoded on machine-readable media. Concatenated unique item... identifier. Item means a single hardware article or a single unit formed by a grouping of subassemblies... manufactured under identical conditions. Machine-readable means an automatic identification technology media...

78 FR 63159 - Amendment to Certification of Nebraska's Central Filing System

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-23

... system for Nebraska to permit the conversion of all debtor social security and taxpayer identification... automatically convert social security numbers and taxpayer identification numbers into ten number unique... certified central filing systems is available through the Internet on the GIPSA Web site ( http://www.gipsa...

Semi-automated identification of leopard frogs

USGS Publications Warehouse

Petrovska-Delacrétaz, Dijana; Edwards, Aaron; Chiasson, John; Chollet, Gérard; Pilliod, David S.

2014-01-01

Principal component analysis is used to implement a semi-automatic recognition system to identify recaptured northern leopard frogs (Lithobates pipiens). Results of both open set and closed set experiments are given. The presented algorithm is shown to provide accurate identification of 209 individual leopard frogs from a total set of 1386 images.

33 CFR 169.235 - What exemptions are there from reporting?

Code of Federal Regulations, 2012 CFR

2012-07-01

... SECURITY (CONTINUED) PORTS AND WATERWAYS SAFETY SHIP REPORTING SYSTEMS Transmission of Long Range Identification and Tracking Information § 169.235 What exemptions are there from reporting? A ship is exempt from this subpart if it is— (a) Fitted with an operating automatic identification system (AIS), under 33 CFR...

33 CFR 164.46 - Automatic Identification System (AIS).

Code of Federal Regulations, 2014 CFR

2014-07-01

... (AIS). 164.46 Section 164.46 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND... Identification System (AIS). (a) The following vessels must have a properly installed, operational, type approved AIS as of the date specified: (1) Self-propelled vessels of 65 feet or more in length, other than...

33 CFR 169.235 - What exemptions are there from reporting?

Code of Federal Regulations, 2011 CFR

2011-07-01

... SECURITY (CONTINUED) PORTS AND WATERWAYS SAFETY SHIP REPORTING SYSTEMS Transmission of Long Range Identification and Tracking Information § 169.235 What exemptions are there from reporting? A ship is exempt from this subpart if it is— (a) Fitted with an operating automatic identification system (AIS), under 33 CFR...

33 CFR 164.46 - Automatic Identification System (AIS).

Code of Federal Regulations, 2013 CFR

2013-07-01

... (AIS). 164.46 Section 164.46 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND... Identification System (AIS). (a) The following vessels must have a properly installed, operational, type approved AIS as of the date specified: (1) Self-propelled vessels of 65 feet or more in length, other than...

33 CFR 164.46 - Automatic Identification System (AIS).

Code of Federal Regulations, 2010 CFR

2010-07-01

... (AIS). 164.46 Section 164.46 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND... Identification System (AIS). (a) The following vessels must have a properly installed, operational, type approved AIS as of the date specified: (1) Self-propelled vessels of 65 feet or more in length, other than...

33 CFR 169.235 - What exemptions are there from reporting?

Code of Federal Regulations, 2013 CFR

2013-07-01

... SECURITY (CONTINUED) PORTS AND WATERWAYS SAFETY SHIP REPORTING SYSTEMS Transmission of Long Range Identification and Tracking Information § 169.235 What exemptions are there from reporting? A ship is exempt from this subpart if it is— (a) Fitted with an operating automatic identification system (AIS), under 33 CFR...

33 CFR 164.46 - Automatic Identification System (AIS).

Code of Federal Regulations, 2011 CFR

2011-07-01

... (AIS). 164.46 Section 164.46 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND... Identification System (AIS). (a) The following vessels must have a properly installed, operational, type approved AIS as of the date specified: (1) Self-propelled vessels of 65 feet or more in length, other than...

33 CFR 169.235 - What exemptions are there from reporting?

Code of Federal Regulations, 2014 CFR

2014-07-01

... SECURITY (CONTINUED) PORTS AND WATERWAYS SAFETY SHIP REPORTING SYSTEMS Transmission of Long Range Identification and Tracking Information § 169.235 What exemptions are there from reporting? A ship is exempt from this subpart if it is— (a) Fitted with an operating automatic identification system (AIS), under 33 CFR...

33 CFR 164.46 - Automatic Identification System (AIS).

Code of Federal Regulations, 2012 CFR

2012-07-01

... (AIS). 164.46 Section 164.46 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND... Identification System (AIS). (a) The following vessels must have a properly installed, operational, type approved AIS as of the date specified: (1) Self-propelled vessels of 65 feet or more in length, other than...

Report: Unsupervised identification of malaria parasites using computer vision.

PubMed

Khan, Najeed Ahmed; Pervaz, Hassan; Latif, Arsalan; Musharaff, Ayesha

2017-01-01

Malaria in human is a serious and fatal tropical disease. This disease results from Anopheles mosquitoes that are infected by Plasmodium species. The clinical diagnosis of malaria based on the history, symptoms and clinical findings must always be confirmed by laboratory diagnosis. Laboratory diagnosis of malaria involves identification of malaria parasite or its antigen / products in the blood of the patient. Manual diagnosis of malaria parasite by the pathologists has proven to become cumbersome. Therefore, there is a need of automatic, efficient and accurate identification of malaria parasite. In this paper, we proposed a computer vision based approach to identify the malaria parasite from light microscopy images. This research deals with the challenges involved in the automatic detection of malaria parasite tissues. Our proposed method is based on the pixel-based approach. We used K-means clustering (unsupervised approach) for the segmentation to identify malaria parasite tissues.

Automatic network coupling analysis for dynamical systems based on detailed kinetic models.

PubMed

Lebiedz, Dirk; Kammerer, Julia; Brandt-Pollmann, Ulrich

2005-10-01

We introduce a numerical complexity reduction method for the automatic identification and analysis of dynamic network decompositions in (bio)chemical kinetics based on error-controlled computation of a minimal model dimension represented by the number of (locally) active dynamical modes. Our algorithm exploits a generalized sensitivity analysis along state trajectories and subsequent singular value decomposition of sensitivity matrices for the identification of these dominant dynamical modes. It allows for a dynamic coupling analysis of (bio)chemical species in kinetic models that can be exploited for the piecewise computation of a minimal model on small time intervals and offers valuable functional insight into highly nonlinear reaction mechanisms and network dynamics. We present results for the identification of network decompositions in a simple oscillatory chemical reaction, time scale separation based model reduction in a Michaelis-Menten enzyme system and network decomposition of a detailed model for the oscillatory peroxidase-oxidase enzyme system.

Space-Based Identification of Archaeological Illegal Excavations and a New Automatic Method for Looting Feature Extraction in Desert Areas

NASA Astrophysics Data System (ADS)

Lasaponara, Rosa; Masini, Nicola

2018-06-01

The identification and quantification of disturbance of archaeological sites has been generally approached by visual inspection of optical aerial or satellite pictures. In this paper, we briefly summarize the state of the art of the traditionally satellite-based approaches for looting identification and propose a new automatic method for archaeological looting feature extraction approach (ALFEA). It is based on three steps: the enhancement using spatial autocorrelation, unsupervised classification, and segmentation. ALFEA has been applied to Google Earth images of two test areas, selected in desert environs in Syria (Dura Europos), and in Peru (Cahuachi-Nasca). The reliability of ALFEA was assessed through field surveys in Peru and visual inspection for the Syrian case study. Results from the evaluation procedure showed satisfactory performance from both of the two analysed test cases with a rate of success higher than 90%.

Salient Feature Identification and Analysis using Kernel-Based Classification Techniques for Synthetic Aperture Radar Automatic Target Recognition

DTIC Science & Technology

2014-03-27

and machine learning for a range of research including such topics as medical imaging [10] and handwriting recognition [11]. The type of feature...1989. [11] C. Bahlmann, B. Haasdonk, and H. Burkhardt, “Online handwriting recognition with support vector machines-a kernel approach,” in Eighth...International Workshop on Frontiers in Handwriting Recognition, pp. 49–54, IEEE, 2002. [12] C. Cortes and V. Vapnik, “Support-vector networks,” Machine

Marine Ship Automatic Identification System (AIS) for Enhanced Coastal Security Capabilities: An Oil Spill Tracking Application

DTIC Science & Technology

2007-09-01

in port, harbor or waterway incidents; and, oil or oily wastes illegally dumped at sea, including illegal discharge of oily bilge or ballast waters ...quantities of oily waste and oily bilge water and sludge at sea using specially installed pipes, which they were careful to have removed and hidden...detailing specifics for oil and bilge water handling equipment, oil hold washing protocols, and a 15 part per million discharge limit of oil content in

Development of a Korotkov sound processor for automatic identification of auscultatory events. I - Specification of preprocessing bandpass filters

NASA Technical Reports Server (NTRS)

Golden, D. P., Jr.; Wolthuis, R. A.; Hoffler, G. W.; Gowen, R. J.

1974-01-01

Frequency bands that best discriminate the Korotkov sounds at systole and at diastole from the sounds immediately preceding these events are defined. Korotkov sound data were recorded from five normotensive subjects during orthostatic stress (lower body negative pressure) and bicycle ergometry. A spectral analysis of the seven Korotkov sounds centered about the systolic and diastolic auscultatory events revealed that a maximum increase in amplitude at the systolic transition occurred in the 18-26-Hz band, while a maximum decrease in amplitude at the diastolic transition occurred in the 40-60-Hz band. These findings were remarkably consistent across subjects and test conditions. These passbands are included in the design specifications for an automatic blood pressure measuring system used in conjuction with medical experiments during NASA's Skylab program.

Automatic photointerpretation for plant species and stress identification (ERTS-A1)

NASA Technical Reports Server (NTRS)

Swanlund, G. D. (Principal Investigator); Kirvida, L.; Johnson, G. R.

1973-01-01

The author has identified the following significant results. Automatic stratification of forested land from ERTS-1 data provides a valuable tool for resource management. The results are useful for wood product yield estimates, recreation and wildlife management, forest inventory, and forest condition monitoring. Automatic procedures based on both multispectral and spatial features are evaluated. With five classes, training and testing on the same samples, classification accuracy of 74 percent was achieved using the MSS multispectral features. When adding texture computed from 8 x 8 arrays, classification accuracy of 90 percent was obtained.

Open Dataset for the Automatic Recognition of Sedentary Behaviors.

PubMed

Possos, William; Cruz, Robinson; Cerón, Jesús D; López, Diego M; Sierra-Torres, Carlos H

2017-01-01

Sedentarism is associated with the development of noncommunicable diseases (NCD) such as cardiovascular diseases (CVD), type 2 diabetes, and cancer. Therefore, the identification of specific sedentary behaviors (TV viewing, sitting at work, driving, relaxing, etc.) is especially relevant for planning personalized prevention programs. To build and evaluate a public a dataset for the automatic recognition (classification) of sedentary behaviors. The dataset included data from 30 subjects, who performed 23 sedentary behaviors while wearing a commercial wearable on the wrist, a smartphone on the hip and another in the thigh. Bluetooth Low Energy (BLE) beacons were used in order to improve the automatic classification of different sedentary behaviors. The study also compared six well know data mining classification techniques in order to identify the more precise method of solving the classification problem of the 23 defined behaviors. A better classification accuracy was obtained using the Random Forest algorithm and when data were collected from the phone on the hip. Furthermore, the use of beacons as a reference for obtaining the symbolic location of the individual improved the precision of the classification.

Automatic identification of watercourses in flat and engineered landscapes by computing the skeleton of a LiDAR point cloud

NASA Astrophysics Data System (ADS)

Broersen, Tom; Peters, Ravi; Ledoux, Hugo

2017-09-01

Drainage networks play a crucial role in protecting land against floods. It is therefore important to have an accurate map of the watercourses that form the drainage network. Previous work on the automatic identification of watercourses was typically based on grids, focused on natural landscapes, and used mostly the slope and curvature of the terrain. We focus in this paper on areas that are characterised by low-lying, flat, and engineered landscapes; these are characteristic to the Netherlands for instance. We propose a new methodology to identify watercourses automatically from elevation data, it uses solely a raw classified LiDAR point cloud as input. We show that by computing twice a skeleton of the point cloud-once in 2D and once in 3D-and that by using the properties of the skeletons we can identify most of the watercourses. We have implemented our methodology and tested it for three different soil types around Utrecht, the Netherlands. We were able to detect 98% of the watercourses for one soil type, and around 75% for the worst case, when we compared to a reference dataset that was obtained semi-automatically.

Research on the Additional Secondary Phase Factor for Automatic Identification System Signals Transmitted over a Rough Sea Surface

PubMed Central

Zhang, Shufang; Sun, Xiaowen

2018-01-01

This paper investigates the Additional Secondary Phase Factor (ASF) characteristics of Automatic Identification System (AIS) signals spreading over a rough sea surface. According to the change of the ASFs for AIS signals in different signal form, the influences of the different propagation conditions on the ASFs are analyzed. The expression, numerical calculation, and simulation analysis of the ASFs of AIS signal are performed in the rough sea surface. The results contribute to the high-accuracy propagation delay measurement of AIS signals spreading over the rough sea surface as, well as providing a reference for reliable communication link design in marine engineering for Very High Frequency (VHF) signals. PMID:29462995

Identification of mycobacterium tuberculosis in sputum smear slide using automatic scanning microscope

NASA Astrophysics Data System (ADS)

Rulaningtyas, Riries; Suksmono, Andriyan B.; Mengko, Tati L. R.; Saptawati, Putri

2015-04-01

Sputum smear observation has an important role in tuberculosis (TB) disease diagnosis, because it needs accurate identification to avoid high errors diagnosis. In development countries, sputum smear slide observation is commonly done with conventional light microscope from Ziehl-Neelsen stained tissue and it doesn't need high cost to maintain the microscope. The clinicians do manual screening process for sputum smear slide which is time consuming and needs highly training to detect the presence of TB bacilli (mycobacterium tuberculosis) accurately, especially for negative slide and slide with less number of TB bacilli. For helping the clinicians, we propose automatic scanning microscope with automatic identification of TB bacilli. The designed system modified the field movement of light microscope with stepper motor which was controlled by microcontroller. Every sputum smear field was captured by camera. After that some image processing techniques were done for the sputum smear images. The color threshold was used for background subtraction with hue canal in HSV color space. Sobel edge detection algorithm was used for TB bacilli image segmentation. We used feature extraction based on shape for bacilli analyzing and then neural network classified TB bacilli or not. The results indicated identification of TB bacilli that we have done worked well and detected TB bacilli accurately in sputum smear slide with normal staining, but not worked well in over staining and less staining tissue slide. However, overall the designed system can help the clinicians in sputum smear observation becomes more easily.

Designing and Implementing a Retrospective Earthquake Detection Framework at the U.S. Geological Survey National Earthquake Information Center

NASA Astrophysics Data System (ADS)

Patton, J.; Yeck, W.; Benz, H.

2017-12-01

The U.S. Geological Survey National Earthquake Information Center (USGS NEIC) is implementing and integrating new signal detection methods such as subspace correlation, continuous beamforming, multi-band picking and automatic phase identification into near-real-time monitoring operations. Leveraging the additional information from these techniques help the NEIC utilize a large and varied network on local to global scales. The NEIC is developing an ordered, rapid, robust, and decentralized framework for distributing seismic detection data as well as a set of formalized formatting standards. These frameworks and standards enable the NEIC to implement a seismic event detection framework that supports basic tasks, including automatic arrival time picking, social media based event detections, and automatic association of different seismic detection data into seismic earthquake events. In addition, this framework enables retrospective detection processing such as automated S-wave arrival time picking given a detected event, discrimination and classification of detected events by type, back-azimuth and slowness calculations, and ensuring aftershock and induced sequence detection completeness. These processes and infrastructure improve the NEIC's capabilities, accuracy, and speed of response. In addition, this same infrastructure provides an improved and convenient structure to support access to automatic detection data for both research and algorithmic development.

A Review on Automatic Mammographic Density and Parenchymal Segmentation

PubMed Central

He, Wenda; Juette, Arne; Denton, Erika R. E.; Oliver, Arnau

2015-01-01

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models. PMID:26171249

Automatic poisson peak harvesting for high throughput protein identification.

PubMed

Breen, E J; Hopwood, F G; Williams, K L; Wilkins, M R

2000-06-01

High throughput identification of proteins by peptide mass fingerprinting requires an efficient means of picking peaks from mass spectra. Here, we report the development of a peak harvester to automatically pick monoisotopic peaks from spectra generated on matrix-assisted laser desorption/ionisation time of flight (MALDI-TOF) mass spectrometers. The peak harvester uses advanced mathematical morphology and watershed algorithms to first process spectra to stick representations. Subsequently, Poisson modelling is applied to determine which peak in an isotopically resolved group represents the monoisotopic mass of a peptide. We illustrate the features of the peak harvester with mass spectra of standard peptides, digests of gel-separated bovine serum albumin, and with Escherictia coli proteins prepared by two-dimensional polyacrylamide gel electrophoresis. In all cases, the peak harvester proved effective in its ability to pick similar monoisotopic peaks as an experienced human operator, and also proved effective in the identification of monoisotopic masses in cases where isotopic distributions of peptides were overlapping. The peak harvester can be operated in an interactive mode, or can be completely automated and linked through to peptide mass fingerprinting protein identification tools to achieve high throughput automated protein identification.

An automatic method to detect and track the glottal gap from high speed videoendoscopic images.

PubMed

Andrade-Miranda, Gustavo; Godino-Llorente, Juan I; Moro-Velázquez, Laureano; Gómez-García, Jorge Andrés

2015-10-29

The image-based analysis of the vocal folds vibration plays an important role in the diagnosis of voice disorders. The analysis is based not only on the direct observation of the video sequences, but also in an objective characterization of the phonation process by means of features extracted from the recorded images. However, such analysis is based on a previous accurate identification of the glottal gap, which is the most challenging step for a further automatic assessment of the vocal folds vibration. In this work, a complete framework to automatically segment and track the glottal area (or glottal gap) is proposed. The algorithm identifies a region of interest that is adapted along time, and combine active contours and watershed transform for the final delineation of the glottis and also an automatic procedure for synthesize different videokymograms is proposed. Thanks to the ROI implementation, our technique is robust to the camera shifting and also the objective test proved the effectiveness and performance of the approach in the most challenging scenarios that it is when exist an inappropriate closure of the vocal folds. The novelties of the proposed algorithm relies on the used of temporal information for identify an adaptive ROI and the use of watershed merging combined with active contours for the glottis delimitation. Additionally, an automatic procedure for synthesize multiline VKG by the identification of the glottal main axis is developed.

Developing a standard for de-identifying electronic patient records written in Swedish: precision, recall and F-measure in a manual and computerized annotation trial.

PubMed

Velupillai, Sumithra; Dalianis, Hercules; Hassel, Martin; Nilsson, Gunnar H

2009-12-01

Electronic patient records (EPRs) contain a large amount of information written in free text. This information is considered very valuable for research but is also very sensitive since the free text parts may contain information that could reveal the identity of a patient. Therefore, methods for de-identifying EPRs are needed. The work presented here aims to perform a manual and automatic Protected Health Information (PHI)-annotation trial for EPRs written in Swedish. This study consists of two main parts: the initial creation of a manually PHI-annotated gold standard, and the porting and evaluation of an existing de-identification software written for American English to Swedish in a preliminary automatic de-identification trial. Results are measured with precision, recall and F-measure. This study reports fairly high Inter-Annotator Agreement (IAA) results on the manually created gold standard, especially for specific tags such as names. The average IAA over all tags was 0.65 F-measure (0.84 F-measure highest pairwise agreement). For name tags the average IAA was 0.80 F-measure (0.91 F-measure highest pairwise agreement). Porting a de-identification software written for American English to Swedish directly was unfortunately non-trivial, yielding poor results. Developing gold standard sets as well as automatic systems for de-identification tasks in Swedish is feasible. However, discussions and definitions on identifiable information is needed, as well as further developments both on the tag sets and the annotation guidelines, in order to get a reliable gold standard. A completely new de-identification software needs to be developed.

Automatic topics segmentation for TV news video

NASA Astrophysics Data System (ADS)

Hmayda, Mounira; Ejbali, Ridha; Zaied, Mourad

2017-03-01

Automatic identification of television programs in the TV stream is an important task for operating archives. This article proposes a new spatio-temporal approach to identify the programs in TV stream into two main steps: First, a reference catalogue for video features visual jingles built. We operate the features that characterize the instances of the same program type to identify the different types of programs in the flow of television. The role of video features is to represent the visual invariants for each visual jingle using appropriate automatic descriptors for each television program. On the other hand, programs in television streams are identified by examining the similarity of the video signal for visual grammars in the catalogue. The main idea of the identification process is to compare the visual similarity of the video signal features in the flow of television to the catalogue. After presenting the proposed approach, the paper overviews encouraging experimental results on several streams extracted from different channels and compounds of several programs.

An automatic identification procedure to promote the use of FES-cycling training for hemiparetic patients.

PubMed

Ambrosini, Emilia; Ferrante, Simona; Schauer, Thomas; Ferrigno, Giancarlo; Molteni, Franco; Pedrocchi, Alessandra

2014-01-01

Cycling induced by Functional Electrical Stimulation (FES) training currently requires a manual setting of different parameters, which is a time-consuming and scarcely repeatable procedure. We proposed an automatic procedure for setting session-specific parameters optimized for hemiparetic patients. This procedure consisted of the identification of the stimulation strategy as the angular ranges during which FES drove the motion, the comparison between the identified strategy and the physiological muscular activation strategy, and the setting of the pulse amplitude and duration of each stimulated muscle. Preliminary trials on 10 healthy volunteers helped define the procedure. Feasibility tests on 8 hemiparetic patients (5 stroke, 3 traumatic brain injury) were performed. The procedure maximized the motor output within the tolerance constraint, identified a biomimetic strategy in 6 patients, and always lasted less than 5 minutes. Its reasonable duration and automatic nature make the procedure usable at the beginning of every training session, potentially enhancing the performance of FES-cycling training.

Evaluation of the automatic optical authentication technologies for control systems of objects

NASA Astrophysics Data System (ADS)

Averkin, Vladimir V.; Volegov, Peter L.; Podgornov, Vladimir A.

2000-03-01

The report considers the evaluation of the automatic optical authentication technologies for the automated integrated system of physical protection, control and accounting of nuclear materials at RFNC-VNIITF, and for providing of the nuclear materials nonproliferation regime. The report presents the nuclear object authentication objectives and strategies, the methodology of the automatic optical authentication and results of the development of pattern recognition techniques carried out under the ISTC project #772 with the purpose of identification of unique features of surface structure of a controlled object and effects of its random treatment. The current decision of following functional control tasks is described in the report: confirmation of the item authenticity (proof of the absence of its substitution by an item of similar shape), control over unforeseen change of item state, control over unauthorized access to the item. The most important distinctive feature of all techniques is not comprehensive description of some properties of controlled item, but unique identification of item using minimum necessary set of parameters, properly comprising identification attribute of the item. The main emphasis in the technical approach is made on the development of rather simple technological methods for the first time intended for use in the systems of physical protection, control and accounting of nuclear materials. The developed authentication devices and system are described.

MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomic datasets.

PubMed

Edmands, William M B; Barupal, Dinesh K; Scalbert, Augustin

2015-03-01

MetMSLine represents a complete collection of functions in the R programming language as an accessible GUI for biomarker discovery in large-scale liquid-chromatography high-resolution mass spectral datasets from acquisition through to final metabolite identification forming a backend to output from any peak-picking software such as XCMS. MetMSLine automatically creates subdirectories, data tables and relevant figures at the following steps: (i) signal smoothing, normalization, filtration and noise transformation (PreProc.QC.LSC.R); (ii) PCA and automatic outlier removal (Auto.PCA.R); (iii) automatic regression, biomarker selection, hierarchical clustering and cluster ion/artefact identification (Auto.MV.Regress.R); (iv) Biomarker-MS/MS fragmentation spectra matching and fragment/neutral loss annotation (Auto.MS.MS.match.R) and (v) semi-targeted metabolite identification based on a list of theoretical masses obtained from public databases (DBAnnotate.R). All source code and suggested parameters are available in an un-encapsulated layout on http://wmbedmands.github.io/MetMSLine/. Readme files and a synthetic dataset of both X-variables (simulated LC-MS data), Y-variables (simulated continuous variables) and metabolite theoretical masses are also available on our GitHub repository. © The Author 2014. Published by Oxford University Press.

MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC–MS metabolomic datasets

PubMed Central

Edmands, William M. B.; Barupal, Dinesh K.; Scalbert, Augustin

2015-01-01

Summary: MetMSLine represents a complete collection of functions in the R programming language as an accessible GUI for biomarker discovery in large-scale liquid-chromatography high-resolution mass spectral datasets from acquisition through to final metabolite identification forming a backend to output from any peak-picking software such as XCMS. MetMSLine automatically creates subdirectories, data tables and relevant figures at the following steps: (i) signal smoothing, normalization, filtration and noise transformation (PreProc.QC.LSC.R); (ii) PCA and automatic outlier removal (Auto.PCA.R); (iii) automatic regression, biomarker selection, hierarchical clustering and cluster ion/artefact identification (Auto.MV.Regress.R); (iv) Biomarker—MS/MS fragmentation spectra matching and fragment/neutral loss annotation (Auto.MS.MS.match.R) and (v) semi-targeted metabolite identification based on a list of theoretical masses obtained from public databases (DBAnnotate.R). Availability and implementation: All source code and suggested parameters are available in an un-encapsulated layout on http://wmbedmands.github.io/MetMSLine/. Readme files and a synthetic dataset of both X-variables (simulated LC–MS data), Y-variables (simulated continuous variables) and metabolite theoretical masses are also available on our GitHub repository. Contact: ScalbertA@iarc.fr PMID:25348215

Parametric diagnosis of the adaptive gas path in the automatic control system of the aircraft engine

NASA Astrophysics Data System (ADS)

Kuznetsova, T. A.

2017-01-01

The paper dwells on the adaptive multimode mathematical model of the gas-turbine aircraft engine (GTE) embedded in the automatic control system (ACS). The mathematical model is based on the throttle performances, and is characterized by high accuracy of engine parameters identification in stationary and dynamic modes. The proposed on-board engine model is the state space linearized low-level simulation. The engine health is identified by the influence of the coefficient matrix. The influence coefficient is determined by the GTE high-level mathematical model based on measurements of gas-dynamic parameters. In the automatic control algorithm, the sum of squares of the deviation between the parameters of the mathematical model and real GTE is minimized. The proposed mathematical model is effectively used for gas path defects detecting in on-line GTE health monitoring. The accuracy of the on-board mathematical model embedded in ACS determines the quality of adaptive control and reliability of the engine. To improve the accuracy of identification solutions and sustainability provision, the numerical method of Monte Carlo was used. The parametric diagnostic algorithm based on the LPτ - sequence was developed and tested. Analysis of the results suggests that the application of the developed algorithms allows achieving higher identification accuracy and reliability than similar models used in practice.

Automated detection of retinal landmarks for the identification of clinically relevant regions in fundus photography

NASA Astrophysics Data System (ADS)

Ometto, Giovanni; Calivá, Francesco; Al-Diri, Bashir; Bek, Toke; Hunter, Andrew

2016-03-01

Automatic, quick and reliable identification of retinal landmarks from fundus photography is key for measurements used in research, diagnosis, screening and treating of common diseases affecting the eyes. This study presents a fast method for the detection of the centre of mass of the vascular arcades, optic nerve head (ONH) and fovea, used in the definition of five clinically relevant areas in use for screening programmes for diabetic retinopathy (DR). Thirty-eight fundus photographs showing 7203 DR lesions were analysed to find the landmarks manually by two retina-experts and automatically by the proposed method. The automatic identification of the ONH and fovea were performed using template matching based on normalised cross correlation. The centre of mass of the arcades was obtained by fitting an ellipse on sample coordinates of the main vessels. The coordinates were obtained by processing the image with hessian filtering followed by shape analyses and finally sampling the results. The regions obtained manually and automatically were used to count the retinal lesions falling within, and to evaluate the method. 92.7% of the lesions were falling within the same regions based on the landmarks selected by the two experts. 91.7% and 89.0% were counted in the same areas identified by the method and the first and second expert respectively. The inter-repeatability of the proposed method and the experts is comparable, while the 100% intra-repeatability makes the algorithm a valuable tool in tasks like analyses in real-time, of large datasets and of intra-patient variability.

6 CFR 37.19 - Machine readable technology on the driver's license or identification card.

Code of Federal Regulations, 2011 CFR

2011-01-01

..., States must use the ISO/IEC 15438:2006(E) Information Technology—Automatic identification and data... 6 Domestic Security 1 2011-01-01 2011-01-01 false Machine readable technology on the driver's..., Verification, and Card Issuance Requirements § 37.19 Machine readable technology on the driver's license or...

6 CFR 37.19 - Machine readable technology on the driver's license or identification card.

Code of Federal Regulations, 2010 CFR

2010-01-01

..., States must use the ISO/IEC 15438:2006(E) Information Technology—Automatic identification and data... 6 Domestic Security 1 2010-01-01 2010-01-01 false Machine readable technology on the driver's..., Verification, and Card Issuance Requirements § 37.19 Machine readable technology on the driver's license or...

Caller I.D. and ANI: The Technology and the Controversy.

ERIC Educational Resources Information Center

Bertot, John C.

1992-01-01

Examines telephone caller identification (caller-I.D.) and Automatic Number Identification (ANI) technology and discusses policy and privacy issues at the state and federal levels of government. A comparative analysis of state caller-I.D. adoption policies is presented, caller-I.D. blocking is discussed, costs are reported, and legal aspects of…

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish.

PubMed

Vieira, Manuel; Fonseca, Paulo J; Amorim, M Clara P; Teixeira, Carlos J C

2015-12-01

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.

Automated Dispersion and Orientation Analysis for Carbon Nanotube Reinforced Polymer Composites

PubMed Central

Gao, Yi; Li, Zhuo; Lin, Ziyin; Zhu, Liangjia; Tannenbaum, Allen; Bouix, Sylvain; Wong, C.P.

2012-01-01

The properties of carbon nanotube (CNT)/polymer composites are strongly dependent on the dispersion and orientation of CNTs in the host matrix. Quantification of the dispersion and orientation of CNTs by microstructure observation and image analysis has been demonstrated as a useful way to understand the structure-property relationship of CNT/polymer composites. However, due to the various morphologies and large amount of CNTs in one image, automatic and accurate identification of CNTs has become the bottleneck for dispersion/orientation analysis. To solve this problem, shape identification is performed for each pixel in the filler identification step, so that individual CNT can be exacted from images automatically. The improved filler identification enables more accurate analysis of CNT dispersion and orientation. The obtained dispersion index and orientation index of both synthetic and real images from model compounds correspond well with the observations. Moreover, these indices help to explain the electrical properties of CNT/Silicone composite, which is used as a model compound. This method can also be extended to other polymer composites with high aspect ratio fillers. PMID:23060008

Independent component analysis-based algorithm for automatic identification of Raman spectra applied to artistic pigments and pigment mixtures.

PubMed

González-Vidal, Juan José; Pérez-Pueyo, Rosanna; Soneira, María José; Ruiz-Moreno, Sergio

2015-03-01

A new method has been developed to automatically identify Raman spectra, whether they correspond to single- or multicomponent spectra. The method requires no user input or judgment. There are thus no parameters to be tweaked. Furthermore, it provides a reliability factor on the resulting identification, with the aim of becoming a useful support tool for the analyst in the decision-making process. The method relies on the multivariate techniques of principal component analysis (PCA) and independent component analysis (ICA), and on some metrics. It has been developed for the application of automated spectral analysis, where the analyzed spectrum is provided by a spectrometer that has no previous knowledge of the analyzed sample, meaning that the number of components in the sample is unknown. We describe the details of this method and demonstrate its efficiency by identifying both simulated spectra and real spectra. The method has been applied to artistic pigment identification. The reliable and consistent results that were obtained make the methodology a helpful tool suitable for the identification of pigments in artwork or in paint in general.

Automatic identification of inertial sensor placement on human body segments during walking

PubMed Central

2013-01-01

Background Current inertial motion capture systems are rarely used in biomedical applications. The attachment and connection of the sensors with cables is often a complex and time consuming task. Moreover, it is prone to errors, because each sensor has to be attached to a predefined body segment. By using wireless inertial sensors and automatic identification of their positions on the human body, the complexity of the set-up can be reduced and incorrect attachments are avoided. We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is attached is identified automatically. Methods Walking data was recorded from ten healthy subjects using an Xsens MVN Biomech system with full-body configuration (17 inertial sensors). Subjects were asked to walk for about 6 seconds at normal walking speed (about 5 km/h). After rotating the sensor data to a global coordinate frame with x-axis in walking direction, y-axis pointing left and z-axis vertical, RMS, mean, and correlation coefficient features were extracted from x-, y- and z-components and magnitudes of the accelerations, angular velocities and angular accelerations. As a classifier, a decision tree based on the C4.5 algorithm was developed using Weka (Waikato Environment for Knowledge Analysis). Results and conclusions After testing the algorithm with 10-fold cross-validation using 31 walking trials (involving 527 sensors), 514 sensors were correctly classified (97.5%). When a decision tree for a lower body plus trunk configuration (8 inertial sensors) was trained and tested using 10-fold cross-validation, 100% of the sensors were correctly identified. This decision tree was also tested on walking trials of 7 patients (17 walking trials) after anterior cruciate ligament reconstruction, which also resulted in 100% correct identification, thus illustrating the robustness of the method. PMID:23517757

Automatic identification of inertial sensor placement on human body segments during walking.

PubMed

Weenk, Dirk; van Beijnum, Bert-Jan F; Baten, Chris T M; Hermens, Hermie J; Veltink, Peter H

2013-03-21

Current inertial motion capture systems are rarely used in biomedical applications. The attachment and connection of the sensors with cables is often a complex and time consuming task. Moreover, it is prone to errors, because each sensor has to be attached to a predefined body segment. By using wireless inertial sensors and automatic identification of their positions on the human body, the complexity of the set-up can be reduced and incorrect attachments are avoided.We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is attached is identified automatically. Walking data was recorded from ten healthy subjects using an Xsens MVN Biomech system with full-body configuration (17 inertial sensors). Subjects were asked to walk for about 6 seconds at normal walking speed (about 5 km/h). After rotating the sensor data to a global coordinate frame with x-axis in walking direction, y-axis pointing left and z-axis vertical, RMS, mean, and correlation coefficient features were extracted from x-, y- and z-components and magnitudes of the accelerations, angular velocities and angular accelerations. As a classifier, a decision tree based on the C4.5 algorithm was developed using Weka (Waikato Environment for Knowledge Analysis). After testing the algorithm with 10-fold cross-validation using 31 walking trials (involving 527 sensors), 514 sensors were correctly classified (97.5%). When a decision tree for a lower body plus trunk configuration (8 inertial sensors) was trained and tested using 10-fold cross-validation, 100% of the sensors were correctly identified. This decision tree was also tested on walking trials of 7 patients (17 walking trials) after anterior cruciate ligament reconstruction, which also resulted in 100% correct identification, thus illustrating the robustness of the method.

Flow Pattern Identification of Horizontal Two-Phase Refrigerant Flow Using Neural Networks

DTIC Science & Technology

2015-12-31

AFRL-RQ-WP-TP-2016-0079 FLOW PATTERN IDENTIFICATION OF HORIZONTAL TWO-PHASE REFRIGERANT FLOW USING NEURAL NETWORKS (POSTPRINT) Abdeel J...Journal Article Postprint 01 October 2013 – 22 June 2015 4. TITLE AND SUBTITLE FLOW PATTERN IDENTIFICATION OF HORIZONTAL TWO-PHASE REFRIGERANT FLOW USING...networks were used to automatically identify two-phase flow patterns for refrigerant R-134a flowing in a horizontal tube. In laboratory experiments

Farm-specific economic value of automatic lameness detection systems in dairy cattle: From concepts to operational simulations.

PubMed

Van De Gucht, Tim; Saeys, Wouter; Van Meensel, Jef; Van Nuffel, Annelies; Vangeyte, Jurgen; Lauwers, Ludwig

2018-01-01

Although prototypes of automatic lameness detection systems for dairy cattle exist, information about their economic value is lacking. In this paper, a conceptual and operational framework for simulating the farm-specific economic value of automatic lameness detection systems was developed and tested on 4 system types: walkover pressure plates, walkover pressure mats, camera systems, and accelerometers. The conceptual framework maps essential factors that determine economic value (e.g., lameness prevalence, incidence and duration, lameness costs, detection performance, and their relationships). The operational simulation model links treatment costs and avoided losses with detection results and farm-specific information, such as herd size and lameness status. Results show that detection performance, herd size, discount rate, and system lifespan have a large influence on economic value. In addition, lameness prevalence influences the economic value, stressing the importance of an adequate prior estimation of the on-farm prevalence. The simulations provide first estimates for the upper limits for purchase prices of automatic detection systems. The framework allowed for identification of knowledge gaps obstructing more accurate economic value estimation. These include insights in cost reductions due to early detection and treatment, and links between specific lameness causes and their related losses. Because this model provides insight in the trade-offs between automatic detection systems' performance and investment price, it is a valuable tool to guide future research and developments. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Collaborative Study of Analysis of High Resolution Infrared Atmospheric Spectra Between NASA Langley Research Center and the University of Denver

NASA Technical Reports Server (NTRS)

Goldman, Aaron

1999-01-01

The Langley-D.U. collaboration on the analysis of high resolution infrared atmospheric spectra covered a number of important studies of trace gases identification and quantification from field spectra, and spectral line parameters analysis. The collaborative work included: Quantification and monitoring of trace gases from ground-based spectra available from various locations and seasons and from balloon flights. Studies toward identification and quantification of isotopic species, mostly oxygen and Sulfur isotopes. Search for new species on the available spectra. Update of spectroscopic line parameters, by combining laboratory and atmospheric spectra with theoretical spectroscopy methods. Study of trends of atmosphere trace constituents. Algorithms developments, retrievals intercomparisons and automatization of the analysis of NDSC spectra, for both column amounts and vertical profiles.

Development of automatic body condition scoring using a low-cost 3-dimensional Kinect camera.

PubMed

Spoliansky, Roii; Edan, Yael; Parmet, Yisrael; Halachmi, Ilan

2016-09-01

Body condition scoring (BCS) is a farm-management tool for estimating dairy cows' energy reserves. Today, BCS is performed manually by experts. This paper presents a 3-dimensional algorithm that provides a topographical understanding of the cow's body to estimate BCS. An automatic BCS system consisting of a Kinect camera (Microsoft Corp., Redmond, WA) triggered by a passive infrared motion detector was designed and implemented. Image processing and regression algorithms were developed and included the following steps: (1) image restoration, the removal of noise; (2) object recognition and separation, identification and separation of the cows; (3) movie and image selection, selection of movies and frames that include the relevant data; (4) image rotation, alignment of the cow parallel to the x-axis; and (5) image cropping and normalization, removal of irrelevant data, setting the image size to 150×200 pixels, and normalizing image values. All steps were performed automatically, including image selection and classification. Fourteen individual features per cow, derived from the cows' topography, were automatically extracted from the movies and from the farm's herd-management records. These features appear to be measurable in a commercial farm. Manual BCS was performed by a trained expert and compared with the output of the training set. A regression model was developed, correlating the features with the manual BCS references. Data were acquired for 4 d, resulting in a database of 422 movies of 101 cows. Movies containing cows' back ends were automatically selected (389 movies). The data were divided into a training set of 81 cows and a test set of 20 cows; both sets included the identical full range of BCS classes. Accuracy tests gave a mean absolute error of 0.26, median absolute error of 0.19, and coefficient of determination of 0.75, with 100% correct classification within 1 step and 91% correct classification within a half step for BCS classes. Results indicated good repeatability, with all standard deviations under 0.33. The algorithm is independent of the background and requires 10 cows for training with approximately 30 movies of 4 s each. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Automatic integration of data from dissimilar sensors

NASA Astrophysics Data System (ADS)

Citrin, W. I.; Proue, R. W.; Thomas, J. W.

The present investigation is concerned with the automatic integration of radar and electronic support measures (ESM) sensor data, and with the development of a method for the automatical integration of identification friend or foe (IFF) and radar sensor data. On the basis of the two considered proojects, significant advances have been made in the areas of sensor data integration. It is pointed out that the log likelihood approach in sensor data correlation is appropriate for both similar and dissimilar sensor data. Attention is given to the real time integration of radar and ESM sensor data, and a radar ESM correlation simulation program.

Gradual cut detection using low-level vision for digital video

NASA Astrophysics Data System (ADS)

Lee, Jae-Hyun; Choi, Yeun-Sung; Jang, Ok-bae

1996-09-01

Digital video computing and organization is one of the important issues in multimedia system, signal compression, or database. Video should be segmented into shots to be used for identification and indexing. This approach requires a suitable method to automatically locate cut points in order to separate shot in a video. Automatic cut detection to isolate shots in a video has received considerable attention due to many practical applications; our video database, browsing, authoring system, retrieval and movie. Previous studies are based on a set of difference mechanisms and they measured the content changes between video frames. But they could not detect more special effects which include dissolve, wipe, fade-in, fade-out, and structured flashing. In this paper, a new cut detection method for gradual transition based on computer vision techniques is proposed. And then, experimental results applied to commercial video are presented and evaluated.

An automatic and efficient pipeline for disease gene identification through utilizing family-based sequencing data.

PubMed

Song, Dandan; Li, Ning; Liao, Lejian

2015-01-01

Due to the generation of enormous amounts of data at both lower costs as well as in shorter times, whole-exome sequencing technologies provide dramatic opportunities for identifying disease genes implicated in Mendelian disorders. Since upwards of thousands genomic variants can be sequenced in each exome, it is challenging to filter pathogenic variants in protein coding regions and reduce the number of missing true variants. Therefore, an automatic and efficient pipeline for finding disease variants in Mendelian disorders is designed by exploiting a combination of variants filtering steps to analyze the family-based exome sequencing approach. Recent studies on the Freeman-Sheldon disease are revisited and show that the proposed method outperforms other existing candidate gene identification methods.

Early-type galaxies: Automated reduction and analysis of ROSAT PSPC data

NASA Technical Reports Server (NTRS)

Mackie, G.; Fabbiano, G.; Harnden, F. R., Jr.; Kim, D.-W.; Maggio, A.; Micela, G.; Sciortino, S.; Ciliegi, P.

1996-01-01

Preliminary results of early-type galaxies that will be part of a galaxy catalog to be derived from the complete Rosat data base are presented. The stored data were reduced and analyzed by an automatic pipeline. This pipeline is based on a command language scrip. The important features of the pipeline include new data time screening in order to maximize the signal to noise ratio of faint point-like sources, source detection via a wavelet algorithm, and the identification of sources with objects from existing catalogs. The pipeline outputs include reduced images, contour maps, surface brightness profiles, spectra, color and hardness ratios.

47 CFR 15.251 - Operation within the bands 2.9-3.26 GHz, 3.267-3.332 GHz, 3.339-3.3458 GHz, and 3.358-3.6 GHz.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., and 3.358-3.6 GHz. (a) Operation under the provisions of this section is limited to automatic vehicle identification systems (AVIS) which use swept frequency techniques for the purpose of automatically identifying transportation vehicles. (b) The field strength anywhere within the frequency range swept by the signal shall not...

47 CFR 15.251 - Operation within the bands 2.9-3.26 GHz, 3.267-3.332 GHz, 3.339-3.3458 GHz, and 3.358-3.6 GHz.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., and 3.358-3.6 GHz. (a) Operation under the provisions of this section is limited to automatic vehicle identification systems (AVIS) which use swept frequency techniques for the purpose of automatically identifying transportation vehicles. (b) The field strength anywhere within the frequency range swept by the signal shall not...

47 CFR 15.251 - Operation within the bands 2.9-3.26 GHz, 3.267-3.332 GHz, 3.339-3.3458 GHz, and 3.358-3.6 GHz.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., and 3.358-3.6 GHz. (a) Operation under the provisions of this section is limited to automatic vehicle identification systems (AVIS) which use swept frequency techniques for the purpose of automatically identifying transportation vehicles. (b) The field strength anywhere within the frequency range swept by the signal shall not...

47 CFR 15.251 - Operation within the bands 2.9-3.26 GHz, 3.267-3.332 GHz, 3.339-3.3458 GHz, and 3.358-3.6 GHz.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., and 3.358-3.6 GHz. (a) Operation under the provisions of this section is limited to automatic vehicle identification systems (AVIS) which use swept frequency techniques for the purpose of automatically identifying transportation vehicles. (b) The field strength anywhere within the frequency range swept by the signal shall not...

Director, Operational Test and Evaluation FY 2004 Annual Report

DTIC Science & Technology

2004-01-01

HIGH) Space Based Radar (SBR) Sensor Fuzed Weapon (SFW) P3I (CBU-97/B) Small Diameter Bomb (SDB) Secure Mobile Anti-Jam Reliable Tactical Terminal...detection, identification, and sampling capability for both fixed-site and mobile operations. The system must automatically detect and identify up to ten...staffing within the Services. SYSTEM DESCRIPTION AND MISSION The Services envision JCAD as a hand-held device that automatically detects, identifies, and

Evaluating current automatic de-identification methods with Veteran's health administration clinical documents.

PubMed

Ferrández, Oscar; South, Brett R; Shen, Shuying; Friedlin, F Jeffrey; Samore, Matthew H; Meystre, Stéphane M

2012-07-27

The increased use and adoption of Electronic Health Records (EHR) causes a tremendous growth in digital information useful for clinicians, researchers and many other operational purposes. However, this information is rich in Protected Health Information (PHI), which severely restricts its access and possible uses. A number of investigators have developed methods for automatically de-identifying EHR documents by removing PHI, as specified in the Health Insurance Portability and Accountability Act "Safe Harbor" method.This study focuses on the evaluation of existing automated text de-identification methods and tools, as applied to Veterans Health Administration (VHA) clinical documents, to assess which methods perform better with each category of PHI found in our clinical notes; and when new methods are needed to improve performance. We installed and evaluated five text de-identification systems "out-of-the-box" using a corpus of VHA clinical documents. The systems based on machine learning methods were trained with the 2006 i2b2 de-identification corpora and evaluated with our VHA corpus, and also evaluated with a ten-fold cross-validation experiment using our VHA corpus. We counted exact, partial, and fully contained matches with reference annotations, considering each PHI type separately, or only one unique 'PHI' category. Performance of the systems was assessed using recall (equivalent to sensitivity) and precision (equivalent to positive predictive value) metrics, as well as the F(2)-measure. Overall, systems based on rules and pattern matching achieved better recall, and precision was always better with systems based on machine learning approaches. The highest "out-of-the-box" F(2)-measure was 67% for partial matches; the best precision and recall were 95% and 78%, respectively. Finally, the ten-fold cross validation experiment allowed for an increase of the F(2)-measure to 79% with partial matches. The "out-of-the-box" evaluation of text de-identification systems provided us with compelling insight about the best methods for de-identification of VHA clinical documents. The errors analysis demonstrated an important need for customization to PHI formats specific to VHA documents. This study informed the planning and development of a "best-of-breed" automatic de-identification application for VHA clinical text.

Smart sensors II; Proceedings of the Seminar, San Diego, CA, July 31, August 1, 1980

NASA Astrophysics Data System (ADS)

Barbe, D. F.

1980-01-01

Topics discussed include technology for smart sensors, smart sensors for tracking and surveillance, and techniques and algorithms for smart sensors. Papers are presented on the application of very large scale integrated circuits to smart sensors, imaging charge-coupled devices for deep-space surveillance, ultra-precise star tracking using charge coupled devices, and automatic target identification of blurred images with super-resolution features. Attention is also given to smart sensors for terminal homing, algorithms for estimating image position, and the computational efficiency of multiple image registration algorithms.

Automatic quality assessment and peak identification of auditory brainstem responses with fitted parametric peaks.

PubMed

Valderrama, Joaquin T; de la Torre, Angel; Alvarez, Isaac; Segura, Jose Carlos; Thornton, A Roger D; Sainz, Manuel; Vargas, Jose Luis

2014-05-01

The recording of the auditory brainstem response (ABR) is used worldwide for hearing screening purposes. In this process, a precise estimation of the most relevant components is essential for an accurate interpretation of these signals. This evaluation is usually carried out subjectively by an audiologist. However, the use of automatic methods for this purpose is being encouraged nowadays in order to reduce human evaluation biases and ensure uniformity among test conditions, patients, and screening personnel. This article describes a new method that performs automatic quality assessment and identification of the peaks, the fitted parametric peaks (FPP). This method is based on the use of synthesized peaks that are adjusted to the ABR response. The FPP is validated, on one hand, by an analysis of amplitudes and latencies measured manually by an audiologist and automatically by the FPP method in ABR signals recorded at different stimulation rates; and on the other hand, contrasting the performance of the FPP method with the automatic evaluation techniques based on the correlation coefficient, FSP, and cross correlation with a predefined template waveform by comparing the automatic evaluations of the quality of these methods with subjective evaluations provided by five experienced evaluators on a set of ABR signals of different quality. The results of this study suggest (a) that the FPP method can be used to provide an accurate parameterization of the peaks in terms of amplitude, latency, and width, and (b) that the FPP remains as the method that best approaches the averaged subjective quality evaluation, as well as provides the best results in terms of sensitivity and specificity in ABR signals validation. The significance of these findings and the clinical value of the FPP method are highlighted on this paper. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Crop Identification Technolgy Assessment for Remote Sensing (CITARS). Volume 1: Task design plan

NASA Technical Reports Server (NTRS)

Hall, F. G.; Bizzell, R. M.

1975-01-01

A plan for quantifying the crop identification performances resulting from the remote identification of corn, soybeans, and wheat is described. Steps for the conversion of multispectral data tapes to classification results are specified. The crop identification performances resulting from the use of several basic types of automatic data processing techniques are compared and examined for significant differences. The techniques are evaluated also for changes in geographic location, time of the year, management practices, and other physical factors. The results of the Crop Identification Technology Assessment for Remote Sensing task will be applied extensively in the Large Area Crop Inventory Experiment.

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification

PubMed Central

Bradbury, Kyle; Saboo, Raghav; L. Johnson, Timothy; Malof, Jordan M.; Devarajan, Arjun; Zhang, Wuming; M. Collins, Leslie; G. Newell, Richard

2016-01-01

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment. PMID:27922592

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification

NASA Astrophysics Data System (ADS)

Bradbury, Kyle; Saboo, Raghav; L. Johnson, Timothy; Malof, Jordan M.; Devarajan, Arjun; Zhang, Wuming; M. Collins, Leslie; G. Newell, Richard

2016-12-01

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification.

PubMed

Bradbury, Kyle; Saboo, Raghav; L Johnson, Timothy; Malof, Jordan M; Devarajan, Arjun; Zhang, Wuming; M Collins, Leslie; G Newell, Richard

2016-12-06

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

Automatic topic identification of health-related messages in online health community using text classification.

PubMed

Lu, Yingjie

2013-01-01

To facilitate patient involvement in online health community and obtain informative support and emotional support they need, a topic identification approach was proposed in this paper for identifying automatically topics of the health-related messages in online health community, thus assisting patients in reaching the most relevant messages for their queries efficiently. Feature-based classification framework was presented for automatic topic identification in our study. We first collected the messages related to some predefined topics in a online health community. Then we combined three different types of features, n-gram-based features, domain-specific features and sentiment features to build four feature sets for health-related text representation. Finally, three different text classification techniques, C4.5, Naïve Bayes and SVM were adopted to evaluate our topic classification model. By comparing different feature sets and different classification techniques, we found that n-gram-based features, domain-specific features and sentiment features were all considered to be effective in distinguishing different types of health-related topics. In addition, feature reduction technique based on information gain was also effective to improve the topic classification performance. In terms of classification techniques, SVM outperformed C4.5 and Naïve Bayes significantly. The experimental results demonstrated that the proposed approach could identify the topics of online health-related messages efficiently.

Semi-automatic forensic approach using mandibular midline lingual structures as fingerprint: a pilot study.

PubMed

Shaheen, E; Mowafy, B; Politis, C; Jacobs, R

2017-12-01

Previous research proposed the use of the mandibular midline neurovascular canal structures as a forensic finger print. In their observer study, an average correct identification of 95% was reached which triggered this study. To present a semi-automatic computer recognition approach to replace the observers and to validate the accuracy of this newly proposed method. Imaging data from Computer Tomography (CT) and Cone Beam Computer Tomography (CBCT) of mandibles scanned at two different moments were collected to simulate an AM and PM situation where the first scan presented AM and the second scan was used to simulate PM. Ten cases with 20 scans were used to build a classifier which relies on voxel based matching and results with classification into one of two groups: "Unmatched" and "Matched". This protocol was then tested using five other scans out of the database. Unpaired t-testing was applied and accuracy of the computerized approach was determined. A significant difference was found between the "Unmatched" and "Matched" classes with means of 0.41 and 0.86 respectively. Furthermore, the testing phase showed an accuracy of 100%. The validation of this method pushes this protocol further to a fully automatic identification procedure for victim identification based on the mandibular midline canals structures only in cases with available AM and PM CBCT/CT data.

Supporting the education evidence portal via text mining

PubMed Central

Ananiadou, Sophia; Thompson, Paul; Thomas, James; Mu, Tingting; Oliver, Sandy; Rickinson, Mark; Sasaki, Yutaka; Weissenbacher, Davy; McNaught, John

2010-01-01

The UK Education Evidence Portal (eep) provides a single, searchable, point of access to the contents of the websites of 33 organizations relating to education, with the aim of revolutionizing work practices for the education community. Use of the portal alleviates the need to spend time searching multiple resources to find relevant information. However, the combined content of the websites of interest is still very large (over 500 000 documents and growing). This means that searches using the portal can produce very large numbers of hits. As users often have limited time, they would benefit from enhanced methods of performing searches and viewing results, allowing them to drill down to information of interest more efficiently, without having to sift through potentially long lists of irrelevant documents. The Joint Information Systems Committee (JISC)-funded ASSIST project has produced a prototype web interface to demonstrate the applicability of integrating a number of text-mining tools and methods into the eep, to facilitate an enhanced searching, browsing and document-viewing experience. New features include automatic classification of documents according to a taxonomy, automatic clustering of search results according to similar document content, and automatic identification and highlighting of key terms within documents. PMID:20643679

Automatic Keyword Identification by Artificial Neural Networks Compared to Manual Identification by Users of Filtering Systems.

ERIC Educational Resources Information Center

Boger, Zvi; Kuflik, Tsvi; Shoval, Peretz; Shapira, Bracha

2001-01-01

Discussion of information filtering (IF) and information retrieval focuses on the use of an artificial neural network (ANN) as an alternative method for both IF and term selection and compares its effectiveness to that of traditional methods. Results show that the ANN relevance prediction out-performs the prediction of an IF system. (Author/LRW)

Automatic Processing and the Unitization of Two Features.

DTIC Science & Technology

1980-02-01

experiment, LaBerge (1973) showed that with practice two features could be automatically unitized to form a novel character. We wish to address a...different from a search for a target which requires identification of one of the features alone. Page 2 Indeed, LaBerge (1973) used a similar implicit...perception? Journal of Experimental Child Psychology, 1978, 26, 498-507. LaBerge , D. Attention and the measurement of perceptual learning. Memory and

An evaluation of automatic coronary artery calcium scoring methods with cardiac CT using the orCaScore framework.

PubMed

Wolterink, Jelmer M; Leiner, Tim; de Vos, Bob D; Coatrieux, Jean-Louis; Kelm, B Michael; Kondo, Satoshi; Salgado, Rodrigo A; Shahzad, Rahil; Shu, Huazhong; Snoeren, Miranda; Takx, Richard A P; van Vliet, Lucas J; van Walsum, Theo; Willems, Tineke P; Yang, Guanyu; Zheng, Yefeng; Viergever, Max A; Išgum, Ivana

2016-05-01

The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) events. In clinical practice, CAC is manually identified and automatically quantified in cardiac CT using commercially available software. This is a tedious and time-consuming process in large-scale studies. Therefore, a number of automatic methods that require no interaction and semiautomatic methods that require very limited interaction for the identification of CAC in cardiac CT have been proposed. Thus far, a comparison of their performance has been lacking. The objective of this study was to perform an independent evaluation of (semi)automatic methods for CAC scoring in cardiac CT using a publicly available standardized framework. Cardiac CT exams of 72 patients distributed over four CVD risk categories were provided for (semi)automatic CAC scoring. Each exam consisted of a noncontrast-enhanced calcium scoring CT (CSCT) and a corresponding coronary CT angiography (CCTA) scan. The exams were acquired in four different hospitals using state-of-the-art equipment from four major CT scanner vendors. The data were divided into 32 training exams and 40 test exams. A reference standard for CAC in CSCT was defined by consensus of two experts following a clinical protocol. The framework organizers evaluated the performance of (semi)automatic methods on test CSCT scans, per lesion, artery, and patient. Five (semi)automatic methods were evaluated. Four methods used both CSCT and CCTA to identify CAC, and one method used only CSCT. The evaluated methods correctly detected between 52% and 94% of CAC lesions with positive predictive values between 65% and 96%. Lesions in distal coronary arteries were most commonly missed and aortic calcifications close to the coronary ostia were the most common false positive errors. The majority (between 88% and 98%) of correctly identified CAC lesions were assigned to the correct artery. Linearly weighted Cohen's kappa for patient CVD risk categorization by the evaluated methods ranged from 0.80 to 1.00. A publicly available standardized framework for the evaluation of (semi)automatic methods for CAC identification in cardiac CT is described. An evaluation of five (semi)automatic methods within this framework shows that automatic per patient CVD risk categorization is feasible. CAC lesions at ambiguous locations such as the coronary ostia remain challenging, but their detection had limited impact on CVD risk determination.

Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

NASA Astrophysics Data System (ADS)

Sa, Qila; Wang, Zhihui

2018-03-01

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

An Analysis of Serial Number Tracking Automatic Identification Technology as Used in Naval Aviation Programs

NASA Astrophysics Data System (ADS)

Csorba, Robert

2002-09-01

The Government Accounting Office found that the Navy, between 1996 and 1998, lost 3 billion in materiel in-transit. This thesis explores the benefits and cost of automatic identification and serial number tracking technologies under consideration by the Naval Supply Systems Command and the Naval Air Systems Command. Detailed cost-savings estimates are made for each aircraft type in the Navy inventory. Project and item managers of repairable components using Serial Number Tracking were surveyed as to the value of this system. It concludes that two thirds of the in-transit losses can be avoided with implementation of effective information technology-based logistics and maintenance tracking systems. Recommendations are made for specific steps and components of such an implementation. Suggestions are made for further research.

Organics in water contamination analyzer, phase 1

NASA Technical Reports Server (NTRS)

1986-01-01

The requirements which would result in identifying the components of an automatic analytical system for the analysis of specific organic compounds in the space station potable water supply are defined. The gas chromatographic system for such an analysis is limited to commercially available off-the-shelf hardware and includes the sample inlet, an ionization detector, capillary columns as well as computerized compound identification. The sampling system will be a special variation of the purge and trap Tenax mode using six-port valves and a 500 microliter water sample. Capillary columns used for the separating of contaminants will be bonded phase fused silica with a silicone stationary phase. Two detectors can be used: photoionization and far ultraviolet, since they are sensitive and compatible with capillary columns. A computer system evaluation and program with the principle of compound identification based on the retention index is presented.

77 FR 28923 - Shipping Coordinating Committee; Notice of Committee Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-16

... new symbols for Automatic Identification System (AIS) aids to navigation --Casualty analysis..., parking in the vicinity of the building is extremely limited. Additional information regarding this and...

Automated and simultaneous fovea center localization and macula segmentation using the new dynamic identification and classification of edges model.

PubMed

Onal, Sinan; Chen, Xin; Satamraju, Veeresh; Balasooriya, Maduka; Dabil-Karacal, Humeyra

2016-07-01

Detecting the position of retinal structures, including the fovea center and macula, in retinal images plays a key role in diagnosing eye diseases such as optic nerve hypoplasia, amblyopia, diabetic retinopathy, and macular edema. However, current detection methods are unreliable for infants or certain ethnic populations. Thus, a methodology is proposed here that may be useful for infants and across ethnicities that automatically localizes the fovea center and segments the macula on digital fundus images. First, dark structures and bright artifacts are removed from the input image using preprocessing operations, and the resulting image is transformed to polar space. Second, the fovea center is identified, and the macula region is segmented using the proposed dynamic identification and classification of edges (DICE) model. The performance of the method was evaluated using 1200 fundus images obtained from the relatively large, diverse, and publicly available Messidor database. In 96.1% of these 1200 cases, the distance between the fovea center identified manually by ophthalmologists and automatically using the proposed method remained within 0 to 8 pixels. The dice similarity index comparing the manually obtained results with those of the model for macula segmentation was 96.12% for these 1200 cases. Thus, the proposed method displayed a high degree of accuracy. The methodology using the DICE model is unique and advantageous over previously reported methods because it simultaneously determines the fovea center and segments the macula region without using any structural information, such as optic disc or blood vessel location, and it may prove useful for all populations, including infants.

Automatic identification of alpine mass movements based on seismic and infrasound signals

NASA Astrophysics Data System (ADS)

Schimmel, Andreas; Hübl, Johannes

2017-04-01

The automatic detection and identification of alpine mass movements like debris flows, debris floods or landslides gets increasing importance for mitigation measures in the densely populated and intensively used alpine regions. Since this mass movement processes emits characteristically seismic and acoustic waves in the low frequency range this events can be detected and identified based on this signals. So already several approaches for detection and warning systems based on seismic or infrasound signals has been developed. But a combination of both methods, which can increase detection probability and reduce false alarms is currently used very rarely and can serve as a promising method for developing an automatic detection and identification system. So this work presents an approach for a detection and identification system based on a combination of seismic and infrasound sensors, which can detect sediment related mass movements from a remote location unaffected by the process. The system is based on one infrasound sensor and one geophone which are placed co-located and a microcontroller where a specially designed detection algorithm is executed which can detect mass movements in real time directly at the sensor site. Further this work tries to get out more information from the seismic and infrasound spectrum produced by different sediment related mass movements to identify the process type and estimate the magnitude of the event. The system is currently installed and tested on five test sites in Austria, two in Italy and one in Switzerland as well as one in Germany. This high number of test sites is used to get a large database of very different events which will be the basis for a new identification method for alpine mass movements. These tests shows promising results and so this system provides an easy to install and inexpensive approach for a detection and warning system.

Identification of Cichlid Fishes from Lake Malawi Using Computer Vision

PubMed Central

Joo, Deokjin; Kwan, Ye-seul; Song, Jongwoo; Pinho, Catarina; Hey, Jody; Won, Yong-Jin

2013-01-01

Background The explosively radiating evolution of cichlid fishes of Lake Malawi has yielded an amazing number of haplochromine species estimated as many as 500 to 800 with a surprising degree of diversity not only in color and stripe pattern but also in the shape of jaw and body among them. As these morphological diversities have been a central subject of adaptive speciation and taxonomic classification, such high diversity could serve as a foundation for automation of species identification of cichlids. Methodology/Principal Finding Here we demonstrate a method for automatic classification of the Lake Malawi cichlids based on computer vision and geometric morphometrics. For this end we developed a pipeline that integrates multiple image processing tools to automatically extract informative features of color and stripe patterns from a large set of photographic images of wild cichlids. The extracted information was evaluated by statistical classifiers Support Vector Machine and Random Forests. Both classifiers performed better when body shape information was added to the feature of color and stripe. Besides the coloration and stripe pattern, body shape variables boosted the accuracy of classification by about 10%. The programs were able to classify 594 live cichlid individuals belonging to 12 different classes (species and sexes) with an average accuracy of 78%, contrasting to a mere 42% success rate by human eyes. The variables that contributed most to the accuracy were body height and the hue of the most frequent color. Conclusions Computer vision showed a notable performance in extracting information from the color and stripe patterns of Lake Malawi cichlids although the information was not enough for errorless species identification. Our results indicate that there appears an unavoidable difficulty in automatic species identification of cichlid fishes, which may arise from short divergence times and gene flow between closely related species. PMID:24204918

Transmit: An Advanced Traffic Management System

DOT National Transportation Integrated Search

1995-11-27

TRANSCOM'S SYSTEM FOR MANAGING INCIDENTS AND TRAFFIC, KNOWN AS TRANSMIT, WAS INITIATED TO ESTABLISH THE FEASIBILITY OF USING AUTOMATIC VEHICLE IDENTIFICATION (AVI) EQUIPMENT FOR TRAFFIC MANAGEMENT AND SURVEILLANCE APPLICATIONS. AVI TECHNOLOGY SYSTEMS...

Master-slave control scheme in electric vehicle smart charging infrastructure.

PubMed

Chung, Ching-Yen; Chynoweth, Joshua; Chu, Chi-Cheng; Gadh, Rajit

2014-01-01

WINSmartEV is a software based plug-in electric vehicle (PEV) monitoring, control, and management system. It not only incorporates intelligence at every level so that charge scheduling can avoid grid bottlenecks, but it also multiplies the number of PEVs that can be plugged into a single circuit. This paper proposes, designs, and executes many upgrades to WINSmartEV. These upgrades include new hardware that makes the level 1 and level 2 chargers faster, more robust, and more scalable. It includes algorithms that provide a more optimal charge scheduling for the level 2 (EVSE) and an enhanced vehicle monitoring/identification module (VMM) system that can automatically identify PEVs and authorize charging.

Master-Slave Control Scheme in Electric Vehicle Smart Charging Infrastructure

PubMed Central

Chung, Ching-Yen; Chynoweth, Joshua; Chu, Chi-Cheng; Gadh, Rajit

2014-01-01

WINSmartEV is a software based plug-in electric vehicle (PEV) monitoring, control, and management system. It not only incorporates intelligence at every level so that charge scheduling can avoid grid bottlenecks, but it also multiplies the number of PEVs that can be plugged into a single circuit. This paper proposes, designs, and executes many upgrades to WINSmartEV. These upgrades include new hardware that makes the level 1 and level 2 chargers faster, more robust, and more scalable. It includes algorithms that provide a more optimal charge scheduling for the level 2 (EVSE) and an enhanced vehicle monitoring/identification module (VMM) system that can automatically identify PEVs and authorize charging. PMID:24982956

Investigations into the Properties, Conditions, and Effects of the Ionosphere

DTIC Science & Technology

1990-01-15

ionogram database to be used in testing trace-identification algorithms; d. Development of automatic trace-identification algorithms and autoscaling ...Scaler ( ARTIST ) and improvement of the ARTIST software; g. Maintenance and upgrade of the digital ionosondes at Argentia, Newfoundland, and Goose Bay...provided by the contractor; j. Upgrade of the ARTIST computer at the Danish Meteorological Institute/GL Qaanaaq site to provide digisonde tape-playback

An automated on-line turbulent flow liquid-chromatography technology coupled to a high resolution mass spectrometer LTQ-Orbitrap for suspect screening of antibiotic transformation products during microalgae wastewater treatment.

PubMed

Jaén-Gil, Adrián; Hom-Diaz, Andrea; Llorca, Marta; Vicent, Teresa; Blánquez, Paqui; Barceló, Damià; Rodríguez-Mozaz, Sara

2018-06-11

The evaluation of wastewater treatment capabilities in terms of removal of water pollutants is crucial when assessing water mitigation issues. Not only the monitoring of target pollutants becomes a critical point, but also the transformation products (TPs) generated. Since these TPs are very often unknown compounds, their study in both wastewater and natural environment is currently recognized as a tedious task and challenging research field. In this study, a novel automated suspect screening methodology was developed for a comprehensive assessment of the TPs generated from nine antibiotics during microalgae water treatment. Three macrolides (azithromycin, erythromycin, clarithromycin), three fluoroquinolones (ofloxacin, ciprofloxacin, norfloxacin) and three additional antibiotics (trimethoprim, pipemidic acid, sulfapyridine) were selected as target pollutants. The analysis of samples was carried out by direct injection in an on-line turbulent flow liquid chromatography-high resolution mass spectrometry (TFC-LC-LTQ-Orbitrap-MS/MS) system, followed by automatic data processing for compound identification. The screening methodology allowed the identification of 40 tentative TPs from a list of software predicted intermediates created automatically. Once known and unknown TPs were identified, degradation pathways were suggested considering the different mechanisms involved on their formation (biotic and abiotic). Results reveal microalgae ability for macrolide biotransformation, but not for other antibiotics such as for fluoroquinolones. Finally, the intermediates detected were included into an in-house library and applied to the identification of tentative TPs in real toilet wastewater treated in a microalgae based photobioreactor (PBR). The overall approach allowed a comprehensive overview of the performance of microalgae water treatment in a fast and reliable manner: it represents a useful tool for the rapid screening of wide range of compounds, reducing time invested in data analysis and providing reliable structural identification. Copyright © 2018 Elsevier B.V. All rights reserved.

Automatic identification and location technology of glass insulator self-shattering

NASA Astrophysics Data System (ADS)

Huang, Xinbo; Zhang, Huiying; Zhang, Ye

2017-11-01

The insulator of transmission lines is one of the most important infrastructures, which is vital to ensure the safe operation of transmission lines under complex and harsh operating conditions. The glass insulator often self-shatters but the available identification methods are inefficient and unreliable. Then, an automatic identification and localization technology of self-shattered glass insulators is proposed, which consists of the cameras installed on the tower video monitoring devices or the unmanned aerial vehicles, the 4G/OPGW network, and the monitoring center, where the identification and localization algorithm is embedded into the expert software. First, the images of insulators are captured by cameras, which are processed to identify the region of insulator string by the presented identification algorithm of insulator string. Second, according to the characteristics of the insulator string image, a mathematical model of the insulator string is established to estimate the direction and the length of the sliding blocks. Third, local binary pattern histograms of the template and the sliding block are extracted, by which the self-shattered insulator can be recognized and located. Finally, a series of experiments is fulfilled to verify the effectiveness of the algorithm. For single insulator images, Ac, Pr, and Rc of the algorithm are 94.5%, 92.38%, and 96.78%, respectively. For double insulator images, Ac, Pr, and Rc are 90.00%, 86.36%, and 93.23%, respectively.

78 FR 32699 - Shipping Coordinating Committee; Notice of Committee Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-31

...)) --Revision of the Guidelines for the onboard operational use of shipborne automatic identification systems... transportation is not generally available). However, parking in the vicinity of the building is limited...

Identification of Interesting Objects in Large Spectral Surveys Using Highly Parallelized Machine Learning

NASA Astrophysics Data System (ADS)

Škoda, Petr; Palička, Andrej; Koza, Jakub; Shakurova, Ksenia

2017-06-01

The current archives of LAMOST multi-object spectrograph contain millions of fully reduced spectra, from which the automatic pipelines have produced catalogues of many parameters of individual objects, including their approximate spectral classification. This is, however, mostly based on the global shape of the whole spectrum and on integral properties of spectra in given bandpasses, namely presence and equivalent width of prominent spectral lines, while for identification of some interesting object types (e.g. Be stars or quasars) the detailed shape of only a few lines is crucial. Here the machine learning is bringing a new methodology capable of improving the reliability of classification of such objects even in boundary cases. We present results of Spark-based semi-supervised machine learning of LAMOST spectra attempting to automatically identify the single and double-peak emission of Hα line typical for Be and B[e] stars. The labelled sample was obtained from archive of 2m Perek telescope at Ondřejov observatory. A simple physical model of spectrograph resolution was used in domain adaptation to LAMOST training domain. The resulting list of candidates contains dozens of Be stars (some are likely yet unknown), but also a bunch of interesting objects resembling spectra of quasars and even blazars, as well as many instrumental artefacts. The verification of a nature of interesting candidates benefited considerably from cross-matching and visualisation in the Virtual Observatory environment.

Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway.

PubMed

Shi, Qi; Abdel-Aty, Mohamed; Yu, Rongjie

2016-03-01

In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimating Ocean Currents from Automatic Identification System Based Ship Drift Measurements

NASA Astrophysics Data System (ADS)

Jakub, Thomas D.

Ship drift is a technique that has been used over the last century and a half to estimate ocean currents. Several of the shortcomings of the ship drift technique include obtaining the data from multiple ships, the time delay in getting those ship positions to a data center for processing and the limited resolution based on the amount of time between position measurements. These shortcomings can be overcome through the use of the Automatic Identification System (AIS). AIS enables more precise ocean current estimates, the option of finer resolution and more timely estimates. In this work, a demonstration of the use of AIS to compute ocean currents is performed. A corresponding error and sensitivity analysis is performed to help identify under which conditions errors will be smaller. A case study in San Francisco Bay with constant AIS message updates was compared against high frequency radar and demonstrated ocean current magnitude residuals of 19 cm/s for ship tracks in a high signal to noise environment. These ship tracks were only minutes long compared to the normally 12 to 24 hour ship tracks. The Gulf of Mexico case study demonstrated the ability to estimate ocean currents over longer baselines and identified the dependency of the estimates on the accuracy of time measurements. Ultimately, AIS measurements when combined with ship drift can provide another method of estimating ocean currents, particularly when other measurements techniques are not available.

Retrieval, automaticity, vocabulary elaboration, orthography (RAVE-O): a comprehensive, fluency-based reading intervention program.

PubMed

Wolf, M; Miller, L; Donnelly, K

2000-01-01

The most important implication of the double-deficit hypothesis (Wolf & Bowers, in this issue) concerns a new emphasis on fluency and automaticity in intervention for children with developmental reading disabilities. The RAVE-O (Retrieval, Automaticity, Vocabulary Elaboration, Orthography) program is an experimental, fluency-based approach to reading intervention that is designed to accompany a phonological analysis program. In an effort to address multiple possible sources of dysfluency in readers with disabilities, the program involves comprehensive emphases both on fluency in word attack, word identification, and comprehension and on automaticity in underlying componential processes (e.g., phonological, orthographic, semantic, and lexical retrieval skills). The goals, theoretical principles, and applied activities of the RAVE-O curriculum are described with particular stress on facilitating the development of rapid orthographic pattern recognition and on changing children's attitudes toward language.

Vision-Based Finger Detection, Tracking, and Event Identification Techniques for Multi-Touch Sensing and Display Systems

PubMed Central

Chen, Yen-Lin; Liang, Wen-Yew; Chiang, Chuan-Yen; Hsieh, Tung-Ju; Lee, Da-Cheng; Yuan, Shyan-Ming; Chang, Yang-Lang

2011-01-01

This study presents efficient vision-based finger detection, tracking, and event identification techniques and a low-cost hardware framework for multi-touch sensing and display applications. The proposed approach uses a fast bright-blob segmentation process based on automatic multilevel histogram thresholding to extract the pixels of touch blobs obtained from scattered infrared lights captured by a video camera. The advantage of this automatic multilevel thresholding approach is its robustness and adaptability when dealing with various ambient lighting conditions and spurious infrared noises. To extract the connected components of these touch blobs, a connected-component analysis procedure is applied to the bright pixels acquired by the previous stage. After extracting the touch blobs from each of the captured image frames, a blob tracking and event recognition process analyzes the spatial and temporal information of these touch blobs from consecutive frames to determine the possible touch events and actions performed by users. This process also refines the detection results and corrects for errors and occlusions caused by noise and errors during the blob extraction process. The proposed blob tracking and touch event recognition process includes two phases. First, the phase of blob tracking associates the motion correspondence of blobs in succeeding frames by analyzing their spatial and temporal features. The touch event recognition process can identify meaningful touch events based on the motion information of touch blobs, such as finger moving, rotating, pressing, hovering, and clicking actions. Experimental results demonstrate that the proposed vision-based finger detection, tracking, and event identification system is feasible and effective for multi-touch sensing applications in various operational environments and conditions. PMID:22163990

76 FR 19176 - Shipping Coordinating Committee; Notice of Committee Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-06

... (SOLAS) regulation V/22 --Development of policy and new symbols for Automatic Identification System (AIS... transportation is not generally available). However, parking in the vicinity of the building is extremely limited...

Better service, greater efficiency : transit management for demand response systems

DOT National Transportation Integrated Search

1999-01-01

This brochure briefly describes different technologies which can enhance demand response transit systems. It covers automated scheduling and dispatching, mobile data terminals, electronic identification cards, automatic vehicle location, and geograph...

Development of an optimal filter substrate for the identification of small microplastic particles in food by micro-Raman spectroscopy.

PubMed

Oßmann, Barbara E; Sarau, George; Schmitt, Sebastian W; Holtmannspötter, Heinrich; Christiansen, Silke H; Dicke, Wilhelm

2017-06-01

When analysing microplastics in food, due to toxicological reasons it is important to achieve clear identification of particles down to a size of at least 1 μm. One reliable, optical analytical technique allowing this is micro-Raman spectroscopy. After isolation of particles via filtration, analysis is typically performed directly on the filter surface. In order to obtain high qualitative Raman spectra, the material of the membrane filters should not show any interference in terms of background and Raman signals during spectrum acquisition. To facilitate the usage of automatic particle detection, membrane filters should also show specific optical properties. In this work, beside eight different, commercially available membrane filters, three newly designed metal-coated polycarbonate membrane filters were tested to fulfil these requirements. We found that aluminium-coated polycarbonate membrane filters had ideal characteristics as a substrate for micro-Raman spectroscopy. Its spectrum shows no or minimal interference with particle spectra, depending on the laser wavelength. Furthermore, automatic particle detection can be applied when analysing the filter surface under dark-field illumination. With this new membrane filter, analytics free of interference of microplastics down to a size of 1 μm becomes possible. Thus, an important size class of these contaminants can now be visualized and spectrally identified. Graphical abstract A newly developed aluminium coated polycarbonate membrane filter enables automatic particle detection and generation of high qualitative Raman spectra allowing identification of small microplastics.

Automated Drug Identification for Urban Hospitals

NASA Technical Reports Server (NTRS)

Shirley, Donna L.

1971-01-01

Many urban hospitals are becoming overloaded with drug abuse cases requiring chemical analysis for identification of drugs. In this paper, the requirements for chemical analysis of body fluids for drugs are determined and a system model for automated drug analysis is selected. The system as modeled, would perform chemical preparation of samples, gas-liquid chromatographic separation of drugs in the chemically prepared samples, infrared spectrophotometric analysis of the drugs, and would utilize automatic data processing and control for drug identification. Requirements of cost, maintainability, reliability, flexibility, and operability are considered.

Northeast Artificial Intelligence Consortium Annual Report 1986. Volume 4. Part A. Hierarchical Region-Based Approach to Automatic Photointerpretation. Part B. Application of AI Techniques to Image Segmentation and Region Identification

DTIC Science & Technology

1988-01-01

MONITORING ORGANIZATION Northeast Artificial (If applicaole)nelincCostum(AcRome Air Development Center (COCU) Inteligence Consortium (NAIC)I 6c. ADDRESS...f, Offell RADC-TR-88-1 1, Vol IV (of eight) Interim Technical ReportS June 1988 NORTHEAST ARTIFICIAL INTELLIGENCE CONSORTIUM ANNUAL REPORT 1986...13441-5700 EMENT NO NO NO ACCESSION NO62702F 5 8 71 " " over) I 58 27 13 " ൓ TITLE (Include Security Classification) NORTHEAST ARTIFICIAL INTELLIGENCE

Dexter: Data Extractor for scanned graphs

NASA Astrophysics Data System (ADS)

Demleitner, Markus

2011-12-01

The NASA Astrophysics Data System (ADS) now holds 1.3 million scanned pages, containing numerous plots and figures for which the original data sets are lost or inaccessible. The availability of scans of the figures can significantly ease the regeneration of the data sets. For this purpose, the ADS has developed Dexter, a Java applet that supports the user in this process. Dexter's basic functionality is to let the user manually digitize a plot by marking points and defining the coordinate transformation from the logical to the physical coordinate system. Advanced features include automatic identification of axes, tracing lines and finding points matching a template.

Rapid and automatic chemical identification of the medicinal flower buds of Lonicera plants by the benchtop and hand-held Fourier transform infrared spectroscopy

NASA Astrophysics Data System (ADS)

Chen, Jianbo; Guo, Baolin; Yan, Rui; Sun, Suqin; Zhou, Qun

2017-07-01

With the utilization of the hand-held equipment, Fourier transform infrared (FT-IR) spectroscopy is a promising analytical technique to minimize the time cost for the chemical identification of herbal materials. This research examines the feasibility of the hand-held FT-IR spectrometer for the on-site testing of herbal materials, using Lonicerae Japonicae Flos (LJF) and Lonicerae Flos (LF) as examples. Correlation-based linear discriminant models for LJF and LF are established based on the benchtop and hand-held FT-IR instruments. The benchtop FT-IR models can exactly recognize all articles of LJF and LF. Although a few LF articles are misjudged at the sub-class level, the hand-held FT-IR models are able to exactly discriminate LJF and LF. As a direct and label-free analytical technique, FT-IR spectroscopy has great potential in the rapid and automatic chemical identification of herbal materials either in laboratories or in fields. This is helpful to prevent the spread and use of adulterated herbal materials in time.

The busy social brain: evidence for automaticity and control in the neural systems supporting social cognition and action understanding.

PubMed

Spunt, Robert P; Lieberman, Matthew D

2013-01-01

Much social-cognitive processing is believed to occur automatically; however, the relative automaticity of the brain systems underlying social cognition remains largely undetermined. We used functional MRI to test for automaticity in the functioning of two brain systems that research has indicated are important for understanding other people's behavior: the mirror neuron system and the mentalizing system. Participants remembered either easy phone numbers (low cognitive load) or difficult phone numbers (high cognitive load) while observing actions after adopting one of four comprehension goals. For all four goals, mirror neuron system activation showed relatively little evidence of modulation by load; in contrast, the association of mentalizing system activation with the goal of inferring the actor's mental state was extinguished by increased cognitive load. These results support a dual-process model of the brain systems underlying action understanding and social cognition; the mirror neuron system supports automatic behavior identification, and the mentalizing system supports controlled social causal attribution.

Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration’s Adverse Event Reporting System Narratives

PubMed Central

Polepalli Ramesh, Balaji; Belknap, Steven M; Li, Zuofeng; Frid, Nadya; West, Dennis P

2014-01-01

Background The Food and Drug Administration’s (FDA) Adverse Event Reporting System (FAERS) is a repository of spontaneously-reported adverse drug events (ADEs) for FDA-approved prescription drugs. FAERS reports include both structured reports and unstructured narratives. The narratives often include essential information for evaluation of the severity, causality, and description of ADEs that are not present in the structured data. The timely identification of unknown toxicities of prescription drugs is an important, unsolved problem. Objective The objective of this study was to develop an annotated corpus of FAERS narratives and biomedical named entity tagger to automatically identify ADE related information in the FAERS narratives. Methods We developed an annotation guideline and annotate medication information and adverse event related entities on 122 FAERS narratives comprising approximately 23,000 word tokens. A named entity tagger using supervised machine learning approaches was built for detecting medication information and adverse event entities using various categories of features. Results The annotated corpus had an agreement of over .9 Cohen’s kappa for medication and adverse event entities. The best performing tagger achieves an overall performance of 0.73 F1 score for detection of medication, adverse event and other named entities. Conclusions In this study, we developed an annotated corpus of FAERS narratives and machine learning based models for automatically extracting medication and adverse event information from the FAERS narratives. Our study is an important step towards enriching the FAERS data for postmarketing pharmacovigilance. PMID:25600332

Automatic Adviser on stationary devices status identification and anticipated change

NASA Astrophysics Data System (ADS)

Shabelnikov, A. N.; Liabakh, N. N.; Gibner, Ya M.; Pushkarev, E. A.

2018-05-01

A task is defined to synthesize an Automatic Adviser to identify the automation systems stationary devices status using an autoregressive model of changing their key parameters. An applied model type was rationalized and the research objects monitoring process algorithm was developed. A complex of mobile objects status operation simulation and prediction results analysis was proposed. Research results are commented using a specific example of a hump yard compressor station. The work was supported by the Russian Fundamental Research Fund, project No. 17-20-01040.

Patient identification error among prostate needle core biopsy specimens--are we ready for a DNA time-out?

PubMed

Suba, Eric J; Pfeifer, John D; Raab, Stephen S

2007-10-01

Patient identification errors in surgical pathology often involve switches of prostate or breast needle core biopsy specimens among patients. We assessed strategies for decreasing the occurrence of these uncommon and yet potentially catastrophic events. Root cause analyses were performed following 3 cases of patient identification error involving prostate needle core biopsy specimens. Patient identification errors in surgical pathology result from slips and lapses of automatic human action that may occur at numerous steps during pre-laboratory, laboratory and post-laboratory work flow processes. Patient identification errors among prostate needle biopsies may be difficult to entirely prevent through the optimization of work flow processes. A DNA time-out, whereby DNA polymorphic microsatellite analysis is used to confirm patient identification before radiation therapy or radical surgery, may eliminate patient identification errors among needle biopsies.

Neural Network Design on the SRC-6 Reconfigurable Computer

DTIC Science & Technology

2006-12-01

fingerprint identification. In this field, automatic identification methods are used to save time, especially for the purpose of fingerprint matching in...grid widths and lengths and therefore was useful in producing an accurate canvas with which to create sample training images. The added benefit of...tools available free of charge and readily accessible on the computer, it was simple to design bitmap data files visually on a canvas and then

The Brain's Cutting-Room Floor: Segmentation of Narrative Cinema

PubMed Central

Zacks, Jeffrey M.; Speer, Nicole K.; Swallow, Khena M.; Maley, Corey J.

2010-01-01

Observers segment ongoing activity into meaningful events. Segmentation is a core component of perception that helps determine memory and guide planning. The current study tested the hypotheses that event segmentation is an automatic component of the perception of extended naturalistic activity, and that the identification of event boundaries in such activities results in part from processing changes in the perceived situation. Observers may identify boundaries between events as a result of processing changes in the observed situation. To test this hypothesis and study this potential mechanism, we measured brain activity while participants viewed an extended narrative film. Large transient responses were observed when the activity was segmented, and these responses were mediated by changes in the observed activity, including characters and their interactions, interactions with objects, spatial location, goals, and causes. These results support accounts that propose event segmentation is automatic and depends on processing meaningful changes in the perceived situation; they are the first to show such effects for extended naturalistic human activity. PMID:20953234

Stroop interference associated with efficient reading fluency and prelexical orthographic processing.

PubMed

Mano, Quintino R; Williamson, Brady J; Pae, Hye K; Osmon, David C

2016-01-01

The Stroop Color-Word Test involves a dynamic interplay between reading and executive functioning that elicits intuitions of word reading automaticity. One such intuition is that strong reading skills (i.e., more automatized word reading) play a disruptive role within the test, contributing to Stroop interference. However, evidence has accumulated that challenges this intuition. The present study examined associations among Stroop interference, reading skills (i.e., isolated word identification, grapheme-to-phoneme mapping, phonemic awareness, reading fluency) measured on standardized tests, and orthographic skills measured on experimental computerized tasks. Among university students (N = 152), correlational analyses showed greater Stroop interference to be associated with (a) relatively low scores on all standardized reading tests, and (b) longer response latencies on orthographic tasks. Hierarchical regression demonstrated that reading fluency and prelexical orthographic processing predicted unique and significant variance in Stroop interference beyond baseline rapid naming. Results suggest that strong reading skills, including orthographic processing, play a supportive role in resolving Stroop interference.

Countering MANPADS: study of new concepts and applications: part two

NASA Astrophysics Data System (ADS)

Maltese, Dominique; Vergnolle, Jean-François; Aragones, Julien; Renaudat, Mathieu

2007-04-01

The latest events of ground-to-air Man Portable Air Defense (MANPAD) attacks against aircraft have revealed a new threat both for military and civilian aircraft. Consequently, the implementation of protecting systems (i.e. Directed Infra Red Counter Measure - DIRCM) in order to face IR guided missiles turns out to be now inevitable. In a near future, aircraft will have to possess detection, tracking, identification, targeting and jamming capabilities to face MANPAD threats. Besides, Multiple Missiles attacks become more and more current scenarios to deal with. In this paper, a practical example of DIRCM systems under study at SAGEM DEFENSE & SECURITY Company is presented. The article is the continuation of a previous SPIE one. Self-protection solutions include built-in and automatic locking-on, tracking, identification and laser jamming capabilities, including defeat assessment. Target Designations are provided by a Missile Warning System. Targets scenarios including multiple threats are considered to design systems architectures. In a first step, the article reminds the context, current and future threats (IR seekers of different generations...), and scenarios for system definition. Then, it focuses on potential self-protection systems under study at SAGEM DEFENSE & SECURITY Company. Different strategies including target identification, multi band laser and active imagery have been previously studied in order to design DIRCM System solutions. Thus, results of self-protection scenarios are provided for different MANPAD scenarios to highlight key problems to solve. Data have been obtained from simulation software modeling full DIRCM systems architectures on technical and operational scenarios (parametric studies).

Automatic de-identification of textual documents in the electronic health record: a review of recent research

PubMed Central

2010-01-01

Background In the United States, the Health Insurance Portability and Accountability Act (HIPAA) protects the confidentiality of patient data and requires the informed consent of the patient and approval of the Internal Review Board to use data for research purposes, but these requirements can be waived if data is de-identified. For clinical data to be considered de-identified, the HIPAA "Safe Harbor" technique requires 18 data elements (called PHI: Protected Health Information) to be removed. The de-identification of narrative text documents is often realized manually, and requires significant resources. Well aware of these issues, several authors have investigated automated de-identification of narrative text documents from the electronic health record, and a review of recent research in this domain is presented here. Methods This review focuses on recently published research (after 1995), and includes relevant publications from bibliographic queries in PubMed, conference proceedings, the ACM Digital Library, and interesting publications referenced in already included papers. Results The literature search returned more than 200 publications. The majority focused only on structured data de-identification instead of narrative text, on image de-identification, or described manual de-identification, and were therefore excluded. Finally, 18 publications describing automated text de-identification were selected for detailed analysis of the architecture and methods used, the types of PHI detected and removed, the external resources used, and the types of clinical documents targeted. All text de-identification systems aimed to identify and remove person names, and many included other types of PHI. Most systems used only one or two specific clinical document types, and were mostly based on two different groups of methodologies: pattern matching and machine learning. Many systems combined both approaches for different types of PHI, but the majority relied only on pattern matching, rules, and dictionaries. Conclusions In general, methods based on dictionaries performed better with PHI that is rarely mentioned in clinical text, but are more difficult to generalize. Methods based on machine learning tend to perform better, especially with PHI that is not mentioned in the dictionaries used. Finally, the issues of anonymization, sufficient performance, and "over-scrubbing" are discussed in this publication. PMID:20678228

Automatic de-identification of textual documents in the electronic health record: a review of recent research.

PubMed

Meystre, Stephane M; Friedlin, F Jeffrey; South, Brett R; Shen, Shuying; Samore, Matthew H

2010-08-02

In the United States, the Health Insurance Portability and Accountability Act (HIPAA) protects the confidentiality of patient data and requires the informed consent of the patient and approval of the Internal Review Board to use data for research purposes, but these requirements can be waived if data is de-identified. For clinical data to be considered de-identified, the HIPAA "Safe Harbor" technique requires 18 data elements (called PHI: Protected Health Information) to be removed. The de-identification of narrative text documents is often realized manually, and requires significant resources. Well aware of these issues, several authors have investigated automated de-identification of narrative text documents from the electronic health record, and a review of recent research in this domain is presented here. This review focuses on recently published research (after 1995), and includes relevant publications from bibliographic queries in PubMed, conference proceedings, the ACM Digital Library, and interesting publications referenced in already included papers. The literature search returned more than 200 publications. The majority focused only on structured data de-identification instead of narrative text, on image de-identification, or described manual de-identification, and were therefore excluded. Finally, 18 publications describing automated text de-identification were selected for detailed analysis of the architecture and methods used, the types of PHI detected and removed, the external resources used, and the types of clinical documents targeted. All text de-identification systems aimed to identify and remove person names, and many included other types of PHI. Most systems used only one or two specific clinical document types, and were mostly based on two different groups of methodologies: pattern matching and machine learning. Many systems combined both approaches for different types of PHI, but the majority relied only on pattern matching, rules, and dictionaries. In general, methods based on dictionaries performed better with PHI that is rarely mentioned in clinical text, but are more difficult to generalize. Methods based on machine learning tend to perform better, especially with PHI that is not mentioned in the dictionaries used. Finally, the issues of anonymization, sufficient performance, and "over-scrubbing" are discussed in this publication.

Automatic detection of cardiovascular risk in CT attenuation correction maps in Rb-82 PET/CTs

NASA Astrophysics Data System (ADS)

Išgum, Ivana; de Vos, Bob D.; Wolterink, Jelmer M.; Dey, Damini; Berman, Daniel S.; Rubeaux, Mathieu; Leiner, Tim; Slomka, Piotr J.

2016-03-01

CT attenuation correction (CTAC) images acquired with PET/CT visualize coronary artery calcium (CAC) and enable CAC quantification. CAC scores acquired with CTAC have been suggested as a marker of cardiovascular disease (CVD). In this work, an algorithm previously developed for automatic CAC scoring in dedicated cardiac CT was applied to automatic CAC detection in CTAC. The study included 134 consecutive patients undergoing 82-Rb PET/CT. Low-dose rest CTAC scans were acquired (100 kV, 11 mAs, 1.4mm×1.4mm×3mm voxel size). An experienced observer defined the reference standard with the clinically used intensity level threshold for calcium identification (130 HU). Five scans were removed from analysis due to artifacts. The algorithm extracted potential CAC by intensity-based thresholding and 3D connected component labeling. Each candidate was described by location, size, shape and intensity features. An ensemble of extremely randomized decision trees was used to identify CAC. The data set was randomly divided into training and test sets. Automatically identified CAC was quantified using volume and Agatston scores. In 33 test scans, the system detected on average 469mm3/730mm3 (64%) of CAC with 36mm3 false positive volume per scan. The intraclass correlation coefficient for volume scores was 0.84. Each patient was assigned to one of four CVD risk categories based on the Agatston score (0-10, 11-100, 101-400, <400). The correct CVD category was assigned to 85% of patients (Cohen's linearly weighted κ0.82). Automatic detection of CVD risk based on CAC scoring in rest CTAC images is feasible. This may enable large scale studies evaluating clinical value of CAC scoring in CTAC data.

MASGOMAS PROJECT, New automatic-tool for cluster search on IR photometric surveys

NASA Astrophysics Data System (ADS)

Rübke, K.; Herrero, A.; Borissova, J.; Ramirez-Alegria, S.; García, M.; Marin-Franch, A.

2015-05-01

The Milky Way is expected to contain a large number of young massive (few x 1000 solar masses) stellar clusters, borne in dense cores of gas and dust. Yet, their known number remains small. We have started a programme to search for such clusters, MASGOMAS (MAssive Stars in Galactic Obscured MAssive clusterS). Initially, we selected promising candidates by means of visual inspection of infrared images. In a second phase of the project we have presented a semi-automatic method to search for obscured massive clusters that resulted in the identification of new massive clusters, like MASGOMAS-1 (with more than 10,000 solar masses) and MASGOMAS-4 (a double-cored association of about 3,000 solar masses). We have now developped a new automatic tool for MASGOMAS that allows the identification of a large number of massive cluster candidates from the 2MASS and VVV catalogues. Cluster candidates fulfilling criteria appropriated for massive OB stars are thus selected in an efficient and objective way. We present the results from this tool and the observations of the first selected cluster, and discuss the implications for the Milky Way structure.

Speaker gender identification based on majority vote classifiers

NASA Astrophysics Data System (ADS)

Mezghani, Eya; Charfeddine, Maha; Nicolas, Henri; Ben Amar, Chokri

2017-03-01

Speaker gender identification is considered among the most important tools in several multimedia applications namely in automatic speech recognition, interactive voice response systems and audio browsing systems. Gender identification systems performance is closely linked to the selected feature set and the employed classification model. Typical techniques are based on selecting the best performing classification method or searching optimum tuning of one classifier parameters through experimentation. In this paper, we consider a relevant and rich set of features involving pitch, MFCCs as well as other temporal and frequency-domain descriptors. Five classification models including decision tree, discriminant analysis, nave Bayes, support vector machine and k-nearest neighbor was experimented. The three best perming classifiers among the five ones will contribute by majority voting between their scores. Experimentations were performed on three different datasets spoken in three languages: English, German and Arabic in order to validate language independency of the proposed scheme. Results confirm that the presented system has reached a satisfying accuracy rate and promising classification performance thanks to the discriminating abilities and diversity of the used features combined with mid-level statistics.

49 CFR 599.303 - Agency disposition of dealer application for reimbursement.

Code of Federal Regulations, 2010 CFR

2010-10-01

... correct a non-conforming submission. (d) Electronic rejection. An application is automatically rejected... transaction, or identifies the vehicle identification number of a new or trade-in vehicle that was involved in...

The integrated manual and automatic control of complex flight systems

NASA Technical Reports Server (NTRS)

Schmidt, D. K.

1984-01-01

A unified control synthesis methodology for complex and/or non-conventional flight vehicles are developed. Prediction techniques for the handling characteristics of such vehicles and pilot parameter identification from experimental data are addressed.

Search and Determine Integrated Environment (SADIE)

NASA Astrophysics Data System (ADS)

Sabol, C.; Schumacher, P.; Segerman, A.; Coffey, S.; Hoskins, A.

2012-09-01

A new and integrated high performance computing software applications package called the Search and Determine Integrated Environment (SADIE) is being jointly developed and refined by the Air Force and Naval Research Laboratories (AFRL and NRL) to automatically resolve uncorrelated tracks (UCTs) and build a more complete space object catalog for improved Space Situational Awareness (SSA). The motivation for SADIE is to respond to very challenging needs identified and guidance received from Air Force Space Command (AFSPC) and other senior leaders to develop this technology to support the evolving Joint Space Operations Center (JSpOC) and Alternate Space Control Center (ASC2)-Dahlgren. The JSpOC and JMS SSA mission requirements and threads flow down from the United States Strategic Command (USSTRATCOM). The SADIE suite includes modification and integration of legacy applications and software components that include Search And Determine (SAD), Satellite Identification (SID), and Parallel Catalog (Parcat), as well as other utilities and scripts to enable end-to-end catalog building and maintenance in a parallel processing environment. SADIE is being developed to handle large catalog building challenges in all orbit regimes and includes the automatic processing of radar, fence, and optical data. Real data results are provided for the processing of Air Force Space Surveillance System fence observations and for the processing of Space Surveillance Telescope optical data.

Critical Assessment of Small Molecule Identification 2016: automated methods.

PubMed

Schymanski, Emma L; Ruttkies, Christoph; Krauss, Martin; Brouard, Céline; Kind, Tobias; Dührkop, Kai; Allen, Felicity; Vaniya, Arpana; Verdegem, Dries; Böcker, Sebastian; Rousu, Juho; Shen, Huibin; Tsugawa, Hiroshi; Sajed, Tanvir; Fiehn, Oliver; Ghesquière, Bart; Neumann, Steffen

2017-03-27

The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest ( www.casmi-contest.org ) was held in 2016, with two new categories for automated methods. This article covers the 208 challenges in Categories 2 and 3, without and with metadata, from organization, participation, results and post-contest evaluation of CASMI 2016 through to perspectives for future contests and small molecule annotation/identification. The Input Output Kernel Regression (CSI:IOKR) machine learning approach performed best in "Category 2: Best Automatic Structural Identification-In Silico Fragmentation Only", won by Team Brouard with 41% challenge wins. The winner of "Category 3: Best Automatic Structural Identification-Full Information" was Team Kind (MS-FINDER), with 76% challenge wins. The best methods were able to achieve over 30% Top 1 ranks in Category 2, with all methods ranking the correct candidate in the Top 10 in around 50% of challenges. This success rate rose to 70% Top 1 ranks in Category 3, with candidates in the Top 10 in over 80% of the challenges. The machine learning and chemistry-based approaches are shown to perform in complementary ways. The improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for "known unknowns". As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for "real life" annotations. The true "unknown unknowns" remain to be evaluated in future CASMI contests. Graphical abstract .

Monitoring caustic injuries from emergency department databases using automatic keyword recognition software.

PubMed

Vignally, P; Fondi, G; Taggi, F; Pitidis, A

2011-03-31

In Italy the European Union Injury Database reports the involvement of chemical products in 0.9% of home and leisure accidents. The Emergency Department registry on domestic accidents in Italy and the Poison Control Centres record that 90% of cases of exposure to toxic substances occur in the home. It is not rare for the effects of chemical agents to be observed in hospitals, with a high potential risk of damage - the rate of this cause of hospital admission is double the domestic injury average. The aim of this study was to monitor the effects of injuries caused by caustic agents in Italy using automatic free-text recognition in Emergency Department medical databases. We created a Stata software program to automatically identify caustic or corrosive injury cases using an agent-specific list of keywords. We focused attention on the procedure's sensitivity and specificity. Ten hospitals in six regions of Italy participated in the study. The program identified 112 cases of injury by caustic or corrosive agents. Checking the cases by quality controls (based on manual reading of ED reports), we assessed 99 cases as true positive, i.e. 88.4% of the patients were automatically recognized by the software as being affected by caustic substances (99% CI: 80.6%- 96.2%), that is to say 0.59% (99% CI: 0.45%-0.76%) of the whole sample of home injuries, a value almost three times as high as that expected (p < 0.0001) from European codified information. False positives were 11.6% of the recognized cases (99% CI: 5.1%- 21.5%). Our automatic procedure for caustic agent identification proved to have excellent product recognition capacity with an acceptable level of excess sensitivity. Contrary to our a priori hypothesis, the automatic recognition system provided a level of identification of agents possessing caustic effects that was significantly much greater than was predictable on the basis of the values from current codifications reported in the European Database.

Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors

PubMed Central

Liu, Kai-Chun; Chan, Chia-Tai

2017-01-01

The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality of life and provide adequate medical service for the elderly. Such automatic ADL monitoring requires reliable ADL information on a fine-grained level, especially for the status of interaction between body gestures and the environment in the real-world. In this work, we propose a significant change spotting mechanism for periodic human motion segmentation during cleaning task performance. A novel approach is proposed based on the search for a significant change of gestures, which can manage critical technical issues in activity recognition, such as continuous data segmentation, individual variance, and category ambiguity. Three typical machine learning classification algorithms are utilized for the identification of the significant change candidate, including a Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Naive Bayesian (NB) algorithm. Overall, the proposed approach achieves 96.41% in the F1-score by using the SVM classifier. The results show that the proposed approach can fulfill the requirement of fine-grained human motion segmentation for automatic ADL monitoring. PMID:28106853

Automatic Identification of Critical Data Items in a Database to Mitigate the Effects of Malicious Insiders

NASA Astrophysics Data System (ADS)

White, Jonathan; Panda, Brajendra

A major concern for computer system security is the threat from malicious insiders who target and abuse critical data items in the system. In this paper, we propose a solution to enable automatic identification of critical data items in a database by way of data dependency relationships. This identification of critical data items is necessary because insider threats often target mission critical data in order to accomplish malicious tasks. Unfortunately, currently available systems fail to address this problem in a comprehensive manner. It is more difficult for non-experts to identify these critical data items because of their lack of familiarity and due to the fact that data systems are constantly changing. By identifying the critical data items automatically, security engineers will be better prepared to protect what is critical to the mission of the organization and also have the ability to focus their security efforts on these critical data items. We have developed an algorithm that scans the database logs and forms a directed graph showing which items influence a large number of other items and at what frequency this influence occurs. This graph is traversed to reveal the data items which have a large influence throughout the database system by using a novel metric based formula. These items are critical to the system because if they are maliciously altered or stolen, the malicious alterations will spread throughout the system, delaying recovery and causing a much more malignant effect. As these items have significant influence, they are deemed to be critical and worthy of extra security measures. Our proposal is not intended to replace existing intrusion detection systems, but rather is intended to complement current and future technologies. Our proposal has never been performed before, and our experimental results have shown that it is very effective in revealing critical data items automatically.

Application of image recognition-based automatic hyphae detection in fungal keratitis.

PubMed

Wu, Xuelian; Tao, Yuan; Qiu, Qingchen; Wu, Xinyi

2018-03-01

The purpose of this study is to evaluate the accuracy of two methods in diagnosis of fungal keratitis, whereby one method is automatic hyphae detection based on images recognition and the other method is corneal smear. We evaluate the sensitivity and specificity of the method in diagnosis of fungal keratitis, which is automatic hyphae detection based on image recognition. We analyze the consistency of clinical symptoms and the density of hyphae, and perform quantification using the method of automatic hyphae detection based on image recognition. In our study, 56 cases with fungal keratitis (just single eye) and 23 cases with bacterial keratitis were included. All cases underwent the routine inspection of slit lamp biomicroscopy, corneal smear examination, microorganism culture and the assessment of in vivo confocal microscopy images before starting medical treatment. Then, we recognize the hyphae images of in vivo confocal microscopy by using automatic hyphae detection based on image recognition to evaluate its sensitivity and specificity and compare with the method of corneal smear. The next step is to use the index of density to assess the severity of infection, and then find the correlation with the patients' clinical symptoms and evaluate consistency between them. The accuracy of this technology was superior to corneal smear examination (p < 0.05). The sensitivity of the technology of automatic hyphae detection of image recognition was 89.29%, and the specificity was 95.65%. The area under the ROC curve was 0.946. The correlation coefficient between the grading of the severity in the fungal keratitis by the automatic hyphae detection based on image recognition and the clinical grading is 0.87. The technology of automatic hyphae detection based on image recognition was with high sensitivity and specificity, able to identify fungal keratitis, which is better than the method of corneal smear examination. This technology has the advantages when compared with the conventional artificial identification of confocal microscope corneal images, of being accurate, stable and does not rely on human expertise. It was the most useful to the medical experts who are not familiar with fungal keratitis. The technology of automatic hyphae detection based on image recognition can quantify the hyphae density and grade this property. Being noninvasive, it can provide an evaluation criterion to fungal keratitis in a timely, accurate, objective and quantitative manner.

Automatic classification of 6-month-old infants at familial risk for language-based learning disorder using a support vector machine.

PubMed

Zare, Marzieh; Rezvani, Zahra; Benasich, April A

2016-07-01

This study assesses the ability of a novel, "automatic classification" approach to facilitate identification of infants at highest familial risk for language-learning disorders (LLD) and to provide converging assessments to enable earlier detection of developmental disorders that disrupt language acquisition. Network connectivity measures derived from 62-channel electroencephalogram (EEG) recording were used to identify selected features within two infant groups who differed on LLD risk: infants with a family history of LLD (FH+) and typically-developing infants without such a history (FH-). A support vector machine was deployed; global efficiency and global and local clustering coefficients were computed. A novel minimum spanning tree (MST) approach was also applied. Cross-validation was employed to assess the resultant classification. Infants were classified with about 80% accuracy into FH+ and FH- groups with 89% specificity and precision of 92%. Clustering patterns differed by risk group and MST network analysis suggests that FH+ infants' EEG complexity patterns were significantly different from FH- infants. The automatic classification techniques used here were shown to be both robust and reliable and should provide valuable information when applied to early identification of risk or clinical groups. The ability to identify infants at highest risk for LLD using "automatic classification" strategies is a novel convergent approach that may facilitate earlier diagnosis and remediation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Travtek Evaluation Task C3: Camera Car Study

DOT National Transportation Integrated Search

1998-11-01

A "biometric" technology is an automatic method for the identification, or identity verification, of an individual based on physiological or behavioral characteristics. The primary objective of the study summarized in this tech brief was to make reco...

Identification and on-line monitoring of reduced sulphur species (RSS) by voltammetry in oxic waters.

PubMed

Superville, Pierre-Jean; Pižeta, Ivanka; Omanović, Dario; Billon, Gabriel

2013-08-15

Based on automatic on-line measurements on the Deûle River that showed daily variation of a peak around -0.56V (vs Ag|AgCl 3M), identification of Reduced Sulphur Species (RSS) in oxic waters was performed applying cathodic stripping voltammetry (CSV) with the hanging mercury drop electrode (HMDE). Pseudopolarographic studies accompanied with increasing concentrations of copper revealed the presence of elemental sulphur S(0), thioacetamide (TA) and reduced glutathione (GSH) as the main sulphur compounds in the Deûle River. In order to resolve these three species, a simple procedure was developed and integrated in an automatic on-line monitoring system. During one week monitoring with hourly measurements, GSH and S(0) exhibited daily cycles whereas no consequential pattern was observed for TA. Copyright © 2013 Elsevier B.V. All rights reserved.

Automatic modal identification of cable-supported bridges instrumented with a long-term monitoring system

NASA Astrophysics Data System (ADS)

Ni, Y. Q.; Fan, K. Q.; Zheng, G.; Chan, T. H. T.; Ko, J. M.

2003-08-01

An automatic modal identification program is developed for continuous extraction of modal parameters of three cable-supported bridges in Hong Kong which are instrumented with a long-term monitoring system. The program employs the Complex Modal Indication Function (CMIF) algorithm to identify modal properties from continuous ambient vibration measurements in an on-line manner. By using the LabVIEW graphical programming language, the software realizes the algorithm in Virtual Instrument (VI) style. The applicability and implementation issues of the developed software are demonstrated by using one-year measurement data acquired from 67 channels of accelerometers deployed on the cable-stayed Ting Kau Bridge. With the continuously identified results, normal variability of modal vectors caused by varying environmental and operational conditions is observed. Such observation is very helpful for selection of appropriate measured modal vectors for structural health monitoring applications.

Use of AFIS for linking scenes of crime.

PubMed

Hefetz, Ido; Liptz, Yakir; Vaturi, Shaul; Attias, David

2016-05-01

Forensic intelligence can provide critical information in criminal investigations - the linkage of crime scenes. The Automatic Fingerprint Identification System (AFIS) is an example of a technological improvement that has advanced the entire forensic identification field to strive for new goals and achievements. In one example using AFIS, a series of burglaries into private apartments enabled a fingerprint examiner to search latent prints from different burglary scenes against an unsolved latent print database. Latent finger and palm prints coming from the same source were associated with over than 20 cases. Then, by forensic intelligence and profile analysis the offender's behavior could be anticipated. He was caught, identified, and arrested. It is recommended to perform an AFIS search of LT/UL prints against current crimes automatically as part of laboratory protocol and not by an examiner's discretion. This approach may link different crime scenes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

An intelligent identification algorithm for the monoclonal picking instrument

NASA Astrophysics Data System (ADS)

Yan, Hua; Zhang, Rongfu; Yuan, Xujun; Wang, Qun

2017-11-01

The traditional colony selection is mainly operated by manual mode, which takes on low efficiency and strong subjectivity. Therefore, it is important to develop an automatic monoclonal-picking instrument. The critical stage of the automatic monoclonal-picking and intelligent optimal selection is intelligent identification algorithm. An auto-screening algorithm based on Support Vector Machine (SVM) is proposed in this paper, which uses the supervised learning method, which combined with the colony morphological characteristics to classify the colony accurately. Furthermore, through the basic morphological features of the colony, system can figure out a series of morphological parameters step by step. Through the establishment of maximal margin classifier, and based on the analysis of the growth trend of the colony, the selection of the monoclonal colony was carried out. The experimental results showed that the auto-screening algorithm could screen out the regular colony from the other, which meets the requirement of various parameters.

Automatic identification and normalization of dosage forms in drug monographs

PubMed Central

2012-01-01

Background Each day, millions of health consumers seek drug-related information on the Web. Despite some efforts in linking related resources, drug information is largely scattered in a wide variety of websites of different quality and credibility. Methods As a step toward providing users with integrated access to multiple trustworthy drug resources, we aim to develop a method capable of identifying drug's dosage form information in addition to drug name recognition. We developed rules and patterns for identifying dosage forms from different sections of full-text drug monographs, and subsequently normalized them to standardized RxNorm dosage forms. Results Our method represents a significant improvement compared with a baseline lookup approach, achieving overall macro-averaged Precision of 80%, Recall of 98%, and F-Measure of 85%. Conclusions We successfully developed an automatic approach for drug dosage form identification, which is critical for building links between different drug-related resources. PMID:22336431

Automated bow shock and radiation belt edge identification methods and their application for Cluster, THEMIS/ARTEMIS and Van Allen Probes data

NASA Astrophysics Data System (ADS)

Facsko, Gabor; Sibeck, David; Balogh, Tamas; Kis, Arpad; Wesztergom, Viktor

2017-04-01

The bow shock and the outer rim of the outer radiation belt are detected automatically by our algorithm developed as a part of the Boundary Layer Identification Code Cluster Active Archive project. The radiation belt positions are determined from energized electron measurements working properly onboard all Cluster spacecraft. For bow shock identification we use magnetometer data and, when available, ion plasma instrument data. In addition, electrostatic wave instrument electron density, spacecraft potential measurements and wake indicator auxiliary data are also used so the events can be identified by all Cluster probes in highly redundant way, as the magnetometer and these instruments are still operational in all spacecraft. The capability and performance of the bow shock identification algorithm were tested using known bow shock crossing determined manually from January 29, 2002 to February 3,. The verification enabled 70% of the bow shock crossings to be identified automatically. The method shows high flexibility and it can be applied to observations from various spacecraft. Now these tools have been applied to Time History of Events and Macroscale Interactions during Substorms (THEMIS)/Acceleration, Reconnection, Turbulence, and Electrodynamics of the Moon's Interaction with the Sun (ARTEMIS) magnetic field, plasma and spacecraft potential observations to identify bow shock crossings; and to Van Allen Probes supra-thermal electron observations to identify the edges of the radiation belt. The outcomes of the algorithms are checked manually and the parameters used to search for bow shock identification are refined.

Multi-ball and one-ball geolocation and location verification

NASA Astrophysics Data System (ADS)

Nelson, D. J.; Townsend, J. L.

2017-05-01

We present analysis methods that may be used to geolocate emitters using one or more moving receivers. While some of the methods we present may apply to a broader class of signals, our primary interest is locating and tracking ships from short pulsed transmissions, such as the maritime Automatic Identification System (AIS.) The AIS signal is difficult to process and track since the pulse duration is only 25 milliseconds, and the pulses may only be transmitted every six to ten seconds. Several fundamental problems are addressed, including demodulation of AIS/GMSK signals, verification of the emitter location, accurate frequency and delay estimation and identification of pulse trains from the same emitter. In particular, we present several new correlation methods, including cross-cross correlation that greatly improves correlation accuracy over conventional methods and cross- TDOA and cross-FDOA functions that make it possible to estimate time and frequency delay without the need of computing a two dimensional cross-ambiguity surface. By isolating pulses from the same emitter and accurately tracking the received signal frequency, we are able to accurately estimate the emitter location from the received Doppler characteristics.

Advanced statistical analysis of Raman spectroscopic data for the identification of body fluid traces: semen and blood mixtures.

PubMed

Sikirzhytski, Vitali; Sikirzhytskaya, Aliaksandra; Lednev, Igor K

2012-10-10

Conventional confirmatory biochemical tests used in the forensic analysis of body fluid traces found at a crime scene are destructive and not universal. Recently, we reported on the application of near-infrared (NIR) Raman microspectroscopy for non-destructive confirmatory identification of pure blood, saliva, semen, vaginal fluid and sweat. Here we expand the method to include dry mixtures of semen and blood. A classification algorithm was developed for differentiating pure body fluids and their mixtures. The classification methodology is based on an effective combination of Support Vector Machine (SVM) regression (data selection) and SVM Discriminant Analysis of preprocessed experimental Raman spectra collected using an automatic mapping of the sample. This extensive cross-validation of the obtained results demonstrated that the detection limit of the minor contributor is as low as a few percent. The developed methodology can be further expanded to any binary mixture of complex solutions, including but not limited to mixtures of other body fluids. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

The joint effects of risk status, gender, early literacy and cognitive skills on the presence of dyslexia among a group of high-risk Chinese children.

PubMed

Wong, Simpson W L; McBride-Chang, Catherine; Lam, Catherine; Chan, Becky; Lam, Fanny W F; Doo, Sylvia

2012-02-01

This study sought to examine factors that are predictive of future developmental dyslexia among a group of 5-year-old Chinese children at risk for dyslexia, including 62 children with a sibling who had been previously diagnosed with dyslexia and 52 children who manifested clinical at-risk factors in aspects of language according to testing by paediatricians. The age-5 performances on various literacy and cognitive tasks, gender and group status (familial risk or language delayed) were used to predict developmental dyslexia 2 years later using logistic regression analysis. Results showed that greater risk of dyslexia was related to slower rapid automatized naming, lower scores on morphological awareness, Chinese character recognition and English letter naming, and gender (boys had more risk). Three logistic equations were generated for estimating individual risk of dyslexia. The strongest models were those that included all print-related variables (including speeded number naming, character recognition and letter identification) and gender, with about 70% accuracy or above. Early identification of those Chinese children at risk for dyslexia can facilitate better dyslexia risk management. Copyright © 2012 John Wiley & Sons, Ltd.

Mining Patients' Narratives in Social Media for Pharmacovigilance: Adverse Effects and Misuse of Methylphenidate.

PubMed

Chen, Xiaoyi; Faviez, Carole; Schuck, Stéphane; Lillo-Le-Louët, Agnès; Texier, Nathalie; Dahamna, Badisse; Huot, Charles; Foulquié, Pierre; Pereira, Suzanne; Leroux, Vincent; Karapetiantz, Pierre; Guenegou-Arnoux, Armelle; Katsahian, Sandrine; Bousquet, Cédric; Burgun, Anita

2018-01-01

Background: The Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) have recognized social media as a new data source to strengthen their activities regarding drug safety. Objective: Our objective in the ADR-PRISM project was to provide text mining and visualization tools to explore a corpus of posts extracted from social media. We evaluated this approach on a corpus of 21 million posts from five patient forums, and conducted a qualitative analysis of the data available on methylphenidate in this corpus. Methods: We applied text mining methods based on named entity recognition and relation extraction in the corpus, followed by signal detection using proportional reporting ratio (PRR). We also used topic modeling based on the Correlated Topic Model to obtain the list of the matics in the corpus and classify the messages based on their topics. Results: We automatically identified 3443 posts about methylphenidate published between 2007 and 2016, among which 61 adverse drug reactions (ADR) were automatically detected. Two pharmacovigilance experts evaluated manually the quality of automatic identification, and a f-measure of 0.57 was reached. Patient's reports were mainly neuro-psychiatric effects. Applying PRR, 67% of the ADRs were signals, including most of the neuro-psychiatric symptoms but also palpitations. Topic modeling showed that the most represented topics were related to Childhood and Treatment initiation , but also Side effects . Cases of misuse were also identified in this corpus, including recreational use and abuse. Conclusion: Named entity recognition combined with signal detection and topic modeling have demonstrated their complementarity in mining social media data. An in-depth analysis focused on methylphenidate showed that this approach was able to detect potential signals and to provide better understanding of patients' behaviors regarding drugs, including misuse.

Automatic extraction of road features in urban environments using dense ALS data

NASA Astrophysics Data System (ADS)

Soilán, Mario; Truong-Hong, Linh; Riveiro, Belén; Laefer, Debra

2018-02-01

This paper describes a methodology that automatically extracts semantic information from urban ALS data for urban parameterization and road network definition. First, building façades are segmented from the ground surface by combining knowledge-based information with both voxel and raster data. Next, heuristic rules and unsupervised learning are applied to the ground surface data to distinguish sidewalk and pavement points as a means for curb detection. Then radiometric information was employed for road marking extraction. Using high-density ALS data from Dublin, Ireland, this fully automatic workflow was able to generate a F-score close to 95% for pavement and sidewalk identification with a resolution of 20 cm and better than 80% for road marking detection.

Automatic identification of individual killer whales.

PubMed

Brown, Judith C; Smaragdis, Paris; Nousek-McGregor, Anna

2010-09-01

Following the successful use of HMM and GMM models for classification of a set of 75 calls of northern resident killer whales into call types [Brown, J. C., and Smaragdis, P., J. Acoust. Soc. Am. 125, 221-224 (2009)], the use of these same methods has been explored for the identification of vocalizations from the same call type N2 of four individual killer whales. With an average of 20 vocalizations from each of the individuals the pairwise comparisons have an extremely high success rate of 80 to 100% and the identifications within the entire group yield around 78%.

Automatic Identification of Artifact-Related Independent Components for Artifact Removal in EEG Recordings.

PubMed

Zou, Yuan; Nathan, Viswam; Jafari, Roozbeh

2016-01-01

Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from independent component analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event-related potential (ERP)-related independent components. However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g., identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by nonbiological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature-based clustering algorithm used to identify artifacts which have physiological origins; and 2) the electrode-scalp impedance information employed for identifying nonbiological artifacts. The results on EEG data collected from ten subjects show that our algorithm can effectively detect, separate, and remove both physiological and nonbiological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods.

Automatic Identification of Artifact-related Independent Components for Artifact Removal in EEG Recordings

PubMed Central

Zou, Yuan; Nathan, Viswam; Jafari, Roozbeh

2017-01-01

Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from Independent Component Analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event related potential (ERP)-related independent components (ICs). However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g. identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by non-biological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature based clustering algorithm used to identify artifacts which have physiological origins and 2) the electrode-scalp impedance information employed for identifying non-biological artifacts. The results on EEG data collected from 10 subjects show that our algorithm can effectively detect, separate, and remove both physiological and non-biological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods. PMID:25415992

First tests of a multi-wavelength mini-DIAL system for the automatic detection of greenhouse gases

NASA Astrophysics Data System (ADS)

Parracino, S.; Gelfusa, M.; Lungaroni, M.; Murari, A.; Peluso, E.; Ciparisse, J. F.; Malizia, A.; Rossi, R.; Ventura, P.; Gaudio, P.

2017-10-01

Considering the increase of atmospheric pollution levels in our cities, due to emissions from vehicles and domestic heating, and the growing threat of terrorism, it is necessary to develop instrumentation and gather know-how for the automatic detection and measurement of dangerous substances as quickly and far away as possible. The Multi- Wavelength DIAL, an extension of the conventional DIAL technique, is one of the most powerful remote sensing methods for the identification of multiple substances and seems to be a promising solution compared to existing alternatives. In this paper, first in-field tests of a smart and fully automated Multi-Wavelength mini-DIAL will be presented and discussed in details. The recently developed system, based on a long-wavelength infrared (IR-C) CO2 laser source, has the potential of giving an early warning, whenever something strange is found in the atmosphere, followed by identification and simultaneous concentration measurements of many chemical species, ranging from the most important Greenhouse Gases (GHG) to other harmful Volatile Organic Compounds (VOCs). Preliminary studies, regarding the fingerprint of the investigated substances, have been carried out by cross-referencing database of infrared (IR) spectra, obtained using in-cell measurements, and typical Mixing Ratios in the examined region, extrapolated from the literature. First experiments in atmosphere have been performed into a suburban and moderately-busy area of Rome. Moreover, to optimize the automatic identification of the harmful species to be recognized on the basis of in cell measurements of the absorption coefficient spectra, an advanced multivariate statistical method for classification has been developed and tested.

Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle.

PubMed

Diaz-Varela, R A; Zarco-Tejada, P J; Angileri, V; Loudjani, P

2014-02-15

Agricultural terraces are features that provide a number of ecosystem services. As a result, their maintenance is supported by measures established by the European Common Agricultural Policy (CAP). In the framework of CAP implementation and monitoring, there is a current and future need for the development of robust, repeatable and cost-effective methodologies for the automatic identification and monitoring of these features at farm scale. This is a complex task, particularly when terraces are associated to complex vegetation cover patterns, as happens with permanent crops (e.g. olive trees). In this study we present a novel methodology for automatic and cost-efficient identification of terraces using only imagery from commercial off-the-shelf (COTS) cameras on board unmanned aerial vehicles (UAVs). Using state-of-the-art computer vision techniques, we generated orthoimagery and digital surface models (DSMs) at 11 cm spatial resolution with low user intervention. In a second stage, these data were used to identify terraces using a multi-scale object-oriented classification method. Results show the potential of this method even in highly complex agricultural areas, both regarding DSM reconstruction and image classification. The UAV-derived DSM had a root mean square error (RMSE) lower than 0.5 m when the height of the terraces was assessed against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90% based exclusively on spectral and elevation data derived from the UAV imagery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Developing an Active Traffic Management System for I-70 in Colorado

DOT National Transportation Integrated Search

2012-09-01

The Colorado DOT is at the forefront of developing an Active Traffic Management (ATM) system that not only : considers operation aspects, but also integrates safety measures. In this research, data collected from Automatic : Vehicle Identification (A...

Electrical continuity scanner facilitates identification of wires for soldering to connectors

NASA Technical Reports Server (NTRS)

Boulton, H. C.; Diclemente, R. A.

1966-01-01

Electrical continuity scanner automatically scans 50 wires in 2 seconds to correlate all wires in a circuit with their respective known ends. Modifications made to the basic plan provide circuitry for scanning up to 250 wires.

Automated determination of dust particles trajectories in the coma of comet 67P

NASA Astrophysics Data System (ADS)

Marín-Yaseli de la Parra, J.; Küppers, M.; Perez Lopez, F.; Besse, S.; Moissl, R.

2017-09-01

During more than two years Rosetta spent at comet 67P, it took thousands of images that contain individual dust particles. To arrive at a statistics of the dust properties, automatic image analysis is required. We present a new methodology for fast-dust identification using a star mask reference system for matching a set of images automatically. The main goal is to derive particle size distributions and to determine if traces of the size distribution of primordial pebbles are still present in today's cometary dust [1].

Automatic cytometric device using multiple wavelength excitations

NASA Astrophysics Data System (ADS)

Rongeat, Nelly; Ledroit, Sylvain; Chauvet, Laurence; Cremien, Didier; Urankar, Alexandra; Couderc, Vincent; Nérin, Philippe

2011-05-01

Precise identification of eosinophils, basophils, and specific subpopulations of blood cells (B lymphocytes) in an unconventional automatic hematology analyzer is demonstrated. Our specific apparatus mixes two excitation radiations by means of an acousto-optics tunable filter to properly control fluorescence emission of phycoerythrin cyanin 5 (PC5) conjugated to antibodies (anti-CD20 or anti-CRTH2) and Thiazole Orange. This way our analyzer combining techniques of hematology analysis and flow cytometry based on multiple fluorescence detection, drastically improves the signal to noise ratio and decreases the spectral overlaps impact coming from multiple fluorescence emissions.

Real-time Monitoring of Subsea Gas Pipelines, Offshore Platforms, and Ship Inspection Scores Using an Automatic Identification System

NASA Astrophysics Data System (ADS)

Artana, K. B.; Pitana, T.; Dinariyana, D. P.; Ariana, M.; Kristianto, D.; Pratiwi, E.

2018-06-01

The aim of this research is to develop an algorithm and application that can perform real-time monitoring of the safety operation of offshore platforms and subsea gas pipelines as well as determine the need for ship inspection using data obtained from automatic identification system (AIS). The research also focuses on the integration of shipping database, AIS data, and others to develop a prototype for designing a real-time monitoring system of offshore platforms and pipelines. A simple concept is used in the development of this prototype, which is achieved by using an overlaying map that outlines the coordinates of the offshore platform and subsea gas pipeline with the ship's coordinates (longitude/latitude) as detected by AIS. Using such information, we can then build an early warning system (EWS) relayed through short message service (SMS), email, or other means when the ship enters the restricted and exclusion zone of platforms and pipelines. The ship inspection system is developed by combining several attributes. Then, decision analysis software is employed to prioritize the vessel's four attributes, including ship age, ship type, classification, and flag state. Results show that the EWS can increase the safety level of offshore platforms and pipelines, as well as the efficient use of patrol boats in monitoring the safety of the facilities. Meanwhile, ship inspection enables the port to prioritize the ship to be inspected in accordance with the priority ranking inspection score.

Automatic retinal interest evaluation system (ARIES).

PubMed

Yin, Fengshou; Wong, Damon Wing Kee; Yow, Ai Ping; Lee, Beng Hai; Quan, Ying; Zhang, Zhuo; Gopalakrishnan, Kavitha; Li, Ruoying; Liu, Jiang

2014-01-01

In recent years, there has been increasing interest in the use of automatic computer-based systems for the detection of eye diseases such as glaucoma, age-related macular degeneration and diabetic retinopathy. However, in practice, retinal image quality is a big concern as automatic systems without consideration of degraded image quality will likely generate unreliable results. In this paper, an automatic retinal image quality assessment system (ARIES) is introduced to assess both image quality of the whole image and focal regions of interest. ARIES achieves 99.54% accuracy in distinguishing fundus images from other types of images through a retinal image identification step in a dataset of 35342 images. The system employs high level image quality measures (HIQM) to perform image quality assessment, and achieves areas under curve (AUCs) of 0.958 and 0.987 for whole image and optic disk region respectively in a testing dataset of 370 images. ARIES acts as a form of automatic quality control which ensures good quality images are used for processing, and can also be used to alert operators of poor quality images at the time of acquisition.

Genetic fingerprinting proves cross-correlated automatic photo-identification of individuals as highly efficient in large capture–mark–recapture studies

PubMed Central

Drechsler, Axel; Helling, Tobias; Steinfartz, Sebastian

2015-01-01

Capture–mark–recapture (CMR) approaches are the backbone of many studies in population ecology to gain insight on the life cycle, migration, habitat use, and demography of target species. The reliable and repeatable recognition of an individual throughout its lifetime is the basic requirement of a CMR study. Although invasive techniques are available to mark individuals permanently, noninvasive methods for individual recognition mainly rest on photographic identification of external body markings, which are unique at the individual level. The re-identification of an individual based on comparing shape patterns of photographs by eye is commonly used. Automated processes for photographic re-identification have been recently established, but their performance in large datasets (i.e., > 1000 individuals) has rarely been tested thoroughly. Here, we evaluated the performance of the program AMPHIDENT, an automatic algorithm to identify individuals on the basis of ventral spot patterns in the great crested newt (Triturus cristatus) versus the genotypic fingerprint of individuals based on highly polymorphic microsatellite loci using GENECAP. Between 2008 and 2010, we captured, sampled and photographed adult newts and calculated for 1648 samples/photographs recapture rates for both approaches. Recapture rates differed slightly with 8.34% for GENECAP and 9.83% for AMPHIDENT. With an estimated rate of 2% false rejections (FRR) and 0.00% false acceptances (FAR), AMPHIDENT proved to be a highly reliable algorithm for CMR studies of large datasets. We conclude that the application of automatic recognition software of individual photographs can be a rather powerful and reliable tool in noninvasive CMR studies for a large number of individuals. Because the cross-correlation of standardized shape patterns is generally applicable to any pattern that provides enough information, this algorithm is capable of becoming a single application with broad use in CMR studies for many species. PMID:25628871

Mapping cumulative noise from shipping to inform marine spatial planning.

PubMed

Erbe, Christine; MacGillivray, Alexander; Williams, Rob

2012-11-01

Including ocean noise in marine spatial planning requires predictions of noise levels on large spatiotemporal scales. Based on a simple sound transmission model and ship track data (Automatic Identification System, AIS), cumulative underwater acoustic energy from shipping was mapped throughout 2008 in the west Canadian Exclusive Economic Zone, showing high noise levels in critical habitats for endangered resident killer whales, exceeding limits of "good conservation status" under the EU Marine Strategy Framework Directive. Error analysis proved that rough calculations of noise occurrence and propagation can form a basis for management processes, because spending resources on unnecessary detail is wasteful and delays remedial action.

Applying face identification to detecting hijacking of airplane

NASA Astrophysics Data System (ADS)

Luo, Xuanwen; Cheng, Qiang

2004-09-01

That terrorists hijacked the airplanes and crashed the World Trade Center is disaster to civilization. To avoid the happening of hijack is critical to homeland security. To report the hijacking in time, limit the terrorist to operate the plane if happened and land the plane to the nearest airport could be an efficient way to avoid the misery. Image processing technique in human face recognition or identification could be used for this task. Before the plane take off, the face images of pilots are input into a face identification system installed in the airplane. The camera in front of pilot seat keeps taking the pilot face image during the flight and comparing it with pre-input pilot face images. If a different face is detected, a warning signal is sent to ground automatically. At the same time, the automatic cruise system is started or the plane is controlled by the ground. The terrorists will have no control over the plane. The plane will be landed to a nearest or appropriate airport under the control of the ground or cruise system. This technique could also be used in automobile industry as an image key to avoid car stealth.

A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format.

PubMed

Monteiro, Eriksson; Costa, Carlos; Oliveira, José Luís

2017-05-01

Clinical data sharing between healthcare institutions, and between practitioners is often hindered by privacy protection requirements. This problem is critical in collaborative scenarios where data sharing is fundamental for establishing a workflow among parties. The anonymization of patient information burned in DICOM images requires elaborate processes somewhat more complex than simple de-identification of textual information. Usually, before sharing, there is a need for manual removal of specific areas containing sensitive information in the images. In this paper, we present a pipeline for ultrasound medical image de-identification, provided as a free anonymization REST service for medical image applications, and a Software-as-a-Service to streamline automatic de-identification of medical images, which is freely available for end-users. The proposed approach applies image processing functions and machine-learning models to bring about an automatic system to anonymize medical images. To perform character recognition, we evaluated several machine-learning models, being Convolutional Neural Networks (CNN) selected as the best approach. For accessing the system quality, 500 processed images were manually inspected showing an anonymization rate of 89.2%. The tool can be accessed at https://bioinformatics.ua.pt/dicom/anonymizer and it is available with the most recent version of Google Chrome, Mozilla Firefox and Safari. A Docker image containing the proposed service is also publicly available for the community.

Pitfalls of Establishing DNA Barcoding Systems in Protists: The Cryptophyceae as a Test Case

PubMed Central

Hoef-Emden, Kerstin

2012-01-01

A DNA barcode is a preferrably short and highly variable region of DNA supposed to facilitate a rapid identification of species. In many protistan lineages, a lack of species-specific morphological characters hampers an identification of species by light or electron microscopy, and difficulties to perform mating experiments in laboratory cultures also do not allow for an identification of biological species. Thus, testing candidate barcode markers as well as establishment of accurately working species identification systems are more challenging than in multicellular organisms. In cryptic species complexes the performance of a potential barcode marker can not be monitored using morphological characters as a feedback, but an inappropriate choice of DNA region may result in artifactual species trees for several reasons. Therefore a priori knowledge of the systematics of a group is required. In addition to identification of known species, methods for an automatic delimitation of species with DNA barcodes have been proposed. The Cryptophyceae provide a mixture of systematically well characterized as well as badly characterized groups and are used in this study to test the suitability of some of the methods for protists. As species identification method the performance of blast in searches against badly to well-sampled reference databases has been tested with COI-5P and 5′-partial LSU rDNA (domains A to D of the nuclear LSU rRNA gene). In addition the performance of two different methods for automatic species delimitation, fixed thresholds of genetic divergence and the general mixed Yule-coalescent model (GMYC), have been examined. The study demonstrates some pitfalls of barcoding methods that have to be taken care of. Also a best-practice approach towards establishing a DNA barcode system in protists is proposed. PMID:22970104

Pitfalls of establishing DNA barcoding systems in protists: the cryptophyceae as a test case.

PubMed

Hoef-Emden, Kerstin

2012-01-01

A DNA barcode is a preferrably short and highly variable region of DNA supposed to facilitate a rapid identification of species. In many protistan lineages, a lack of species-specific morphological characters hampers an identification of species by light or electron microscopy, and difficulties to perform mating experiments in laboratory cultures also do not allow for an identification of biological species. Thus, testing candidate barcode markers as well as establishment of accurately working species identification systems are more challenging than in multicellular organisms. In cryptic species complexes the performance of a potential barcode marker can not be monitored using morphological characters as a feedback, but an inappropriate choice of DNA region may result in artifactual species trees for several reasons. Therefore a priori knowledge of the systematics of a group is required. In addition to identification of known species, methods for an automatic delimitation of species with DNA barcodes have been proposed. The Cryptophyceae provide a mixture of systematically well characterized as well as badly characterized groups and are used in this study to test the suitability of some of the methods for protists. As species identification method the performance of blast in searches against badly to well-sampled reference databases has been tested with COI-5P and 5'-partial LSU rDNA (domains A to D of the nuclear LSU rRNA gene). In addition the performance of two different methods for automatic species delimitation, fixed thresholds of genetic divergence and the general mixed Yule-coalescent model (GMYC), have been examined. The study demonstrates some pitfalls of barcoding methods that have to be taken care of. Also a best-practice approach towards establishing a DNA barcode system in protists is proposed.

Crystal identification for a dual-layer-offset LYSO based PET system via Lu-176 background radiation and mean shift algorithm

NASA Astrophysics Data System (ADS)

Wei, Qingyang; Ma, Tianyu; Xu, Tianpeng; Zeng, Ming; Gu, Yu; Dai, Tiantian; Liu, Yaqiang

2018-01-01

Modern positron emission tomography (PET) detectors are made from pixelated scintillation crystal arrays and readout by Anger logic. The interaction position of the gamma-ray should be assigned to a crystal using a crystal position map or look-up table. Crystal identification is a critical procedure for pixelated PET systems. In this paper, we propose a novel crystal identification method for a dual-layer-offset LYSO based animal PET system via Lu-176 background radiation and mean shift algorithm. Single photon event data of the Lu-176 background radiation are acquired in list-mode for 3 h to generate a single photon flood map (SPFM). Coincidence events are obtained from the same data using time information to generate a coincidence flood map (CFM). The CFM is used to identify the peaks of the inner layer using the mean shift algorithm. The response of the inner layer is deducted from the SPFM by subtracting CFM. Then, the peaks of the outer layer are also identified using the mean shift algorithm. The automatically identified peaks are manually inspected by a graphical user interface program. Finally, a crystal position map is generated using a distance criterion based on these peaks. The proposed method is verified on the animal PET system with 48 detector blocks on a laptop with an Intel i7-5500U processor. The total runtime for whole system peak identification is 67.9 s. Results show that the automatic crystal identification has 99.98% and 99.09% accuracy for the peaks of the inner and outer layers of the whole system respectively. In conclusion, the proposed method is suitable for the dual-layer-offset lutetium based PET system to perform crystal identification instead of external radiation sources.

Multilevel Analysis in Analyzing Speech Data

ERIC Educational Resources Information Center

Guddattu, Vasudeva; Krishna, Y.

2011-01-01

The speech produced by human vocal tract is a complex acoustic signal, with diverse applications in phonetics, speech synthesis, automatic speech recognition, speaker identification, communication aids, speech pathology, speech perception, machine translation, hearing research, rehabilitation and assessment of communication disorders and many…

Electronic Toll And Traffic Management Systems, National Cooperative Highway Research Program Synthesis

DOT National Transportation Integrated Search

1993-01-01

ELECTRONIC TOLL COLLECTION OR ETC AND TRAFFIC MANAGEMENT OR ETTM, AUTOMATIC VEHICLE IDENTIFICATION OR AVI : ELECTRONIC TOLL COLLECTION AND TRAFFIC MANAGEMENT (ETTM) SYSTEMS ARE NOT A FUTURISTIC DREAM, THEY ARE OPERATING OR ARE BEING TESTED TODAY I...

Automatic vehicle identification technology applications to toll collection services

DOT National Transportation Integrated Search

1997-01-01

Intelligent transportation systems technologies are being developed and applied through transportation systems in the United States. An example of this type of innovation can be seen on toll roads where a driver is required to deposit a toll in order...

Development and preliminary testing of an automatic turning movements identification system : final report, February 2010.

DOT National Transportation Integrated Search

2010-02-01

It is important for many applications, such as intersection delay estimation and adaptive signal : control, to obtain vehicle turning movement information at signalized intersections. However, : vehicle turning movement information is very time consu...

Robust uncertainty evaluation for system identification on distributed wireless platforms

NASA Astrophysics Data System (ADS)

Crinière, Antoine; Döhler, Michael; Le Cam, Vincent; Mevel, Laurent

2016-04-01

Health monitoring of civil structures by system identification procedures from automatic control is now accepted as a valid approach. These methods provide frequencies and modeshapes from the structure over time. For a continuous monitoring the excitation of a structure is usually ambient, thus unknown and assumed to be noise. Hence, all estimates from the vibration measurements are realizations of random variables with inherent uncertainty due to (unknown) process and measurement noise and finite data length. The underlying algorithms are usually running under Matlab under the assumption of large memory pool and considerable computational power. Even under these premises, computational and memory usage are heavy and not realistic for being embedded in on-site sensor platforms such as the PEGASE platform. Moreover, the current push for distributed wireless systems calls for algorithmic adaptation for lowering data exchanges and maximizing local processing. Finally, the recent breakthrough in system identification allows us to process both frequency information and its related uncertainty together from one and only one data sequence, at the expense of computational and memory explosion that require even more careful attention than before. The current approach will focus on presenting a system identification procedure called multi-setup subspace identification that allows to process both frequencies and their related variances from a set of interconnected wireless systems with all computation running locally within the limited memory pool of each system before being merged on a host supervisor. Careful attention will be given to data exchanges and I/O satisfying OGC standards, as well as minimizing memory footprints and maximizing computational efficiency. Those systems are built in a way of autonomous operations on field and could be later included in a wide distributed architecture such as the Cloud2SM project. The usefulness of these strategies is illustrated on data from a progressive damage action on a prestressed concrete bridge. References [1] E. Carden and P. Fanning. Vibration based condition monitoring: a review. Structural Health Monitoring, 3(4):355-377, 2004. [2] M. Döhler and L. Mevel. Efficient multi-order uncertainty computation for stochastic subspace identification. Mechanical Systems and Signal Processing, 38(2):346-366, 2013. [3] M.Döhler, L. Mevel. Modular subspace-based system identification from multi-setup measurements. IEEE Transactions on Automatic Control, 57(11):2951-2956, 2012. [4] M. Döhler, X.-B. Lam, and L. Mevel. Uncertainty quantification for modal parameters from stochastic subspace identification on multi-setup measurements. MechanicalSystems and Signal Processing, 36(2):562-581, 2013. [5] A Crinière, J Dumoulin, L Mevel, G Andrade-Barosso, M Simonin. The Cloud2SM Project.European Geosciences Union General Assembly (EGU2015), Apr 2015, Vienne, Austria. 2015.

Identification of forensic samples by using an infrared-based automatic DNA sequencer.

PubMed

Ricci, Ugo; Sani, Ilaria; Klintschar, Michael; Cerri, Nicoletta; De Ferrari, Francesco; Giovannucci Uzielli, Maria Luisa

2003-06-01

We have recently introduced a new protocol for analyzing all core loci of the Federal Bureau of Investigation's (FBI) Combined DNA Index System (CODIS) with an infrared (IR) automatic DNA sequencer (LI-COR 4200). The amplicons were labeled with forward oligonucleotide primers, covalently linked to a new infrared fluorescent molecule (IRDye 800). The alleles were displayed as familiar autoradiogram-like images with real-time detection. This protocol was employed for paternity testing, population studies, and identification of degraded forensic samples. We extensively analyzed some simulated forensic samples and mixed stains (blood, semen, saliva, bones, and fixed archival embedded tissues), comparing the results with donor samples. Sensitivity studies were also performed for the four multiplex systems. Our results show the efficiency, reliability, and accuracy of the IR system for the analysis of forensic samples. We also compared the efficiency of the multiplex protocol with ultraviolet (UV) technology. Paternity tests, undegraded DNA samples, and real forensic samples were analyzed with this approach based on IR technology and with UV-based automatic sequencers in combination with commercially-available kits. The comparability of the results with the widespread UV methods suggests that it is possible to exchange data between laboratories using the same core group of markers but different primer sets and detection methods.

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal

PubMed Central

Ramkumar, Barathram; Sabarimalai Manikandan, M.

2017-01-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal. PMID:28529758

Multi-stage robust scheme for citrus identification from high resolution airborne images

NASA Astrophysics Data System (ADS)

Amorós-López, Julia; Izquierdo Verdiguier, Emma; Gómez-Chova, Luis; Muñoz-Marí, Jordi; Zoilo Rodríguez-Barreiro, Jorge; Camps-Valls, Gustavo; Calpe-Maravilla, Javier

2008-10-01

Identification of land cover types is one of the most critical activities in remote sensing. Nowadays, managing land resources by using remote sensing techniques is becoming a common procedure to speed up the process while reducing costs. However, data analysis procedures should satisfy the accuracy figures demanded by institutions and governments for further administrative actions. This paper presents a methodological scheme to update the citrus Geographical Information Systems (GIS) of the Comunidad Valenciana autonomous region, Spain). The proposed approach introduces a multi-stage automatic scheme to reduce visual photointerpretation and ground validation tasks. First, an object-oriented feature extraction process is carried out for each cadastral parcel from very high spatial resolution (VHR) images (0.5m) acquired in the visible and near infrared. Next, several automatic classifiers (decision trees, multilayer perceptron, and support vector machines) are trained and combined to improve the final accuracy of the results. The proposed strategy fulfills the high accuracy demanded by policy makers by means of combining automatic classification methods with visual photointerpretation available resources. A level of confidence based on the agreement between classifiers allows us an effective management by fixing the quantity of parcels to be reviewed. The proposed methodology can be applied to similar problems and applications.

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

PubMed

Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M

2017-02-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

Identification of ghost artifact using texture analysis in pediatric spinal cord diffusion tensor images.

PubMed

Alizadeh, Mahdi; Conklin, Chris J; Middleton, Devon M; Shah, Pallav; Saksena, Sona; Krisa, Laura; Finsterbusch, Jürgen; Faro, Scott H; Mulcahey, M J; Mohamed, Feroze B

2018-04-01

Ghost artifacts are a major contributor to degradation of spinal cord diffusion tensor images. A multi-stage post-processing pipeline was designed, implemented and validated to automatically remove ghost artifacts arising from reduced field of view diffusion tensor imaging (DTI) of the pediatric spinal cord. A total of 12 pediatric subjects including 7 healthy subjects (mean age=11.34years) with no evidence of spinal cord injury or pathology and 5 patients (mean age=10.96years) with cervical spinal cord injury were studied. Ghost/true cords, labeled as region of interests (ROIs), in non-diffusion weighted b0 images were segmented automatically using mathematical morphological processing. Initially, 21 texture features were extracted from each segmented ROI including 5 first-order features based on the histogram of the image (mean, variance, skewness, kurtosis and entropy) and 16s-order feature vector elements, incorporating four statistical measures (contrast, correlation, homogeneity and energy) calculated from co-occurrence matrices in directions of 0°, 45°, 90° and 135°. Next, ten features with a high value of mutual information (MI) relative to the pre-defined target class and within the features were selected as final features which were input to a trained classifier (adaptive neuro-fuzzy interface system) to separate the true cord from the ghost cord. The implemented pipeline was successfully able to separate the ghost artifacts from true cord structures. The results obtained from the classifier showed a sensitivity of 91%, specificity of 79%, and accuracy of 84% in separating the true cord from ghost artifacts. The results show that the proposed method is promising for the automatic detection of ghost cords present in DTI images of the spinal cord. This step is crucial towards development of accurate, automatic DTI spinal cord post processing pipelines. Copyright © 2017 Elsevier Inc. All rights reserved.

Automated Steering Control Design by Visual Feedback Approach —System Identification and Control Experiments with a Radio-Controlled Car—

NASA Astrophysics Data System (ADS)

Fujiwara, Yukihiro; Yoshii, Masakazu; Arai, Yasuhito; Adachi, Shuichi

Advanced safety vehicle(ASV)assists drivers’ manipulation to avoid trafic accidents. A variety of researches on automatic driving systems are necessary as an element of ASV. Among them, we focus on visual feedback approach in which the automatic driving system is realized by recognizing road trajectory using image information. The purpose of this paper is to examine the validity of this approach by experiments using a radio-controlled car. First, a practical image processing algorithm to recognize white lines on the road is proposed. Second, a model of the radio-controlled car is built by system identication experiments. Third, an automatic steering control system is designed based on H∞ control theory. Finally, the effectiveness of the designed control system is examined via traveling experiments.

Automatic high-throughput screening of colloidal crystals using machine learning

NASA Astrophysics Data System (ADS)

Spellings, Matthew; Glotzer, Sharon C.

Recent improvements in hardware and software have united to pose an interesting problem for computational scientists studying self-assembly of particles into crystal structures: while studies covering large swathes of parameter space can be dispatched at once using modern supercomputers and parallel architectures, identifying the different regions of a phase diagram is often a serial task completed by hand. While analytic methods exist to distinguish some simple structures, they can be difficult to apply, and automatic identification of more complex structures is still lacking. In this talk we describe one method to create numerical ``fingerprints'' of local order and use them to analyze a study of complex ordered structures. We can use these methods as first steps toward automatic exploration of parameter space and, more broadly, the strategic design of new materials.

Learning Efficient Spatial-Temporal Gait Features with Deep Learning for Human Identification.

PubMed

Liu, Wu; Zhang, Cheng; Ma, Huadong; Li, Shuangqun

2018-02-06

The integration of the latest breakthroughs in bioinformatics technology from one side and artificial intelligence from another side, enables remarkable advances in the fields of intelligent security guard computational biology, healthcare, and so on. Among them, biometrics based automatic human identification is one of the most fundamental and significant research topic. Human gait, which is a biometric features with the unique capability, has gained significant attentions as the remarkable characteristics of remote accessed, robust and security in the biometrics based human identification. However, the existed methods cannot well handle the indistinctive inter-class differences and large intra-class variations of human gait in real-world situation. In this paper, we have developed an efficient spatial-temporal gait features with deep learning for human identification. First of all, we proposed a gait energy image (GEI) based Siamese neural network to automatically extract robust and discriminative spatial gait features for human identification. Furthermore, we exploit the deep 3-dimensional convolutional networks to learn the human gait convolutional 3D (C3D) as the temporal gait features. Finally, the GEI and C3D gait features are embedded into the null space by the Null Foley-Sammon Transform (NFST). In the new space, the spatial-temporal features are sufficiently combined with distance metric learning to drive the similarity metric to be small for pairs of gait from the same person, and large for pairs from different persons. Consequently, the experiments on the world's largest gait database show our framework impressively outperforms state-of-the-art methods.

Two-dimensional PCA-based human gait identification

NASA Astrophysics Data System (ADS)

Chen, Jinyan; Wu, Rongteng

2012-11-01

It is very necessary to recognize person through visual surveillance automatically for public security reason. Human gait based identification focus on recognizing human by his walking video automatically using computer vision and image processing approaches. As a potential biometric measure, human gait identification has attracted more and more researchers. Current human gait identification methods can be divided into two categories: model-based methods and motion-based methods. In this paper a two-Dimensional Principal Component Analysis and temporal-space analysis based human gait identification method is proposed. Using background estimation and image subtraction we can get a binary images sequence from the surveillance video. By comparing the difference of two adjacent images in the gait images sequence, we can get a difference binary images sequence. Every binary difference image indicates the body moving mode during a person walking. We use the following steps to extract the temporal-space features from the difference binary images sequence: Projecting one difference image to Y axis or X axis we can get two vectors. Project every difference image in the difference binary images sequence to Y axis or X axis difference binary images sequence we can get two matrixes. These two matrixes indicate the styles of one walking. Then Two-Dimensional Principal Component Analysis(2DPCA) is used to transform these two matrixes to two vectors while at the same time keep the maximum separability. Finally the similarity of two human gait images is calculated by the Euclidean distance of the two vectors. The performance of our methods is illustrated using the CASIA Gait Database.

Tracking of Cells with a Compact Microscope Imaging System with Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2007-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously

Tracking of cells with a compact microscope imaging system with intelligent controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2007-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to auto-focus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

Operation of a Cartesian Robotic System in a Compact Microscope with Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2006-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

Electromagnetic Compatibility between Marine Automatic Identification and Public Correspondence Systems in the Maritime Mobile VHF Band.

DOT National Transportation Integrated Search

2000-04-01

The Coast Guard funded the National Telecommunications and Information Administration : (NTIA) to perform electromagnetic compatibility (EMC) tests between an ITU-R M. 825-3 : (Characteristics Of a Transponder System Using Digital Selective Calling T...

21 CFR 886.1760 - Ophthalmic refractometer.

Code of Federal Regulations, 2010 CFR

2010-04-01

...) MEDICAL DEVICES OPHTHALMIC DEVICES Diagnostic Devices § 886.1760 Ophthalmic refractometer. (a) Identification. An ophthalmic refractometer is an automatic AC-powered device that consists of a fixation system... of the eye by measuring light reflexes from the retina. (b) Classification. Class I (general controls...

Results from the Crop Identification Technology Assessment for Remote Sensing (CITARS) project

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator); Davis, B. J.; Bizzell, R. M.; Hall, F. G.; Feiveson, A. H.; Malila, W. A.; Rice, D. P.

1976-01-01

The author has identified the following significant results. It was found that several factors had a significant effect on crop identification performance: (1) crop maturity and site characteristics, (2) which of several different single date automatic data processing procedures was used for local recognition, (3) nonlocal recognition, both with and without preprocessing for the extension of recognition signatures, and (4) use of multidate data. It also was found that classification accuracy for field center pixels was not a reliable indicator of proportion estimation performance for whole areas, that bias was present in proportion estimates, and that training data and procedures strongly influenced crop identification performance.

Detection and identification of concealed weapons using matrix pencil

NASA Astrophysics Data System (ADS)

Adve, Raviraj S.; Thayaparan, Thayananthan

2011-06-01

The detection and identification of concealed weapons is an extremely hard problem due to the weak signature of the target buried within the much stronger signal from the human body. This paper furthers the automatic detection and identification of concealed weapons by proposing the use of an effective approach to obtain the resonant frequencies in a measurement. The technique, based on Matrix Pencil, a scheme for model based parameter estimation also provides amplitude information, hence providing a level of confidence in the results. Of specific interest is the fact that Matrix Pencil is based on a singular value decomposition, making the scheme robust against noise.

The power grid AGC frequency bias coefficient online identification method based on wide area information

NASA Astrophysics Data System (ADS)

Wang, Zian; Li, Shiguang; Yu, Ting

2015-12-01

This paper propose online identification method of regional frequency deviation coefficient based on the analysis of interconnected grid AGC adjustment response mechanism of regional frequency deviation coefficient and the generator online real-time operation state by measured data through PMU, analyze the optimization method of regional frequency deviation coefficient in case of the actual operation state of the power system and achieve a more accurate and efficient automatic generation control in power system. Verify the validity of the online identification method of regional frequency deviation coefficient by establishing the long-term frequency control simulation model of two-regional interconnected power system.

Combining Facial Recognition, Automatic License Plate Readers and Closed Circuit Television to Create an Interstate Identification System for Wanted Subjects

DTIC Science & Technology

2015-12-01

IPSFRP search request. The candidate list will contain the agency’s requested number (minimum of2) of candidates or a default number of 20 candidates if...INTERSTATE IDENTIFICATION SYSTEM FOR WANTED SUBJECTS 5. FUNDING NUMBERS 6. AUTHOR(S) Michael J. Thomas 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES...Naval Postgraduate School Monterey, CA 93943-5000 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING /MONITORING AGENCY NAME(S) AND

Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

PubMed

Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

2013-11-01

The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

Neural networks: Alternatives to conventional techniques for automatic docking

NASA Technical Reports Server (NTRS)

Vinz, Bradley L.

1994-01-01

Automatic docking of orbiting spacecraft is a crucial operation involving the identification of vehicle orientation as well as complex approach dynamics. The chaser spacecraft must be able to recognize the target spacecraft within a scene and achieve accurate closing maneuvers. In a video-based system, a target scene must be captured and transformed into a pattern of pixels. Successful recognition lies in the interpretation of this pattern. Due to their powerful pattern recognition capabilities, artificial neural networks offer a potential role in interpretation and automatic docking processes. Neural networks can reduce the computational time required by existing image processing and control software. In addition, neural networks are capable of recognizing and adapting to changes in their dynamic environment, enabling enhanced performance, redundancy, and fault tolerance. Most neural networks are robust to failure, capable of continued operation with a slight degradation in performance after minor failures. This paper discusses the particular automatic docking tasks neural networks can perform as viable alternatives to conventional techniques.

Personal identification based on blood vessels of retinal fundus images

NASA Astrophysics Data System (ADS)

Fukuta, Keisuke; Nakagawa, Toshiaki; Hayashi, Yoshinori; Hatanaka, Yuji; Hara, Takeshi; Fujita, Hiroshi

2008-03-01

Biometric technique has been implemented instead of conventional identification methods such as password in computer, automatic teller machine (ATM), and entrance and exit management system. We propose a personal identification (PI) system using color retinal fundus images which are unique to each individual. The proposed procedure for identification is based on comparison of an input fundus image with reference fundus images in the database. In the first step, registration between the input image and the reference image is performed. The step includes translational and rotational movement. The PI is based on the measure of similarity between blood vessel images generated from the input and reference images. The similarity measure is defined as the cross-correlation coefficient calculated from the pixel values. When the similarity is greater than a predetermined threshold, the input image is identified. This means both the input and the reference images are associated to the same person. Four hundred sixty-two fundus images including forty-one same-person's image pairs were used for the estimation of the proposed technique. The false rejection rate and the false acceptance rate were 9.9×10 -5% and 4.3×10 -5%, respectively. The results indicate that the proposed method has a higher performance than other biometrics except for DNA. To be used for practical application in the public, the device which can take retinal fundus images easily is needed. The proposed method is applied to not only the PI but also the system which warns about misfiling of fundus images in medical facilities.

Need of a consistent and convenient nucleus identification in ENDF files for the automatic construction of the depletion chains

NASA Astrophysics Data System (ADS)

Mosca, Pietro; Mounier, Claude

2016-03-01

The automatic construction of evolution chains recently implemented in GALILEE system is based on the analysis of several ENDF files : the multigroup production cross sections present in the GENDF files processed by NJOY from the ENDF evaluation, the decay file and the fission product yields (FPY) file. In this context, this paper highlights the importance of the nucleus identification to properly interconnect the data mentioned above. The first part of the paper describes the present status of the nucleus identification among the several ENDF files focusing, in particular, on the use of the excited state number and of the isomeric state number. The second part reviews the problems encountered during the automatic construction of the depletion chains using recent ENDF data. The processing of the JEFF-3.1.1, ENDF/B-VII.0 (decay and FPY) and the JEFF-3.2 (production cross section) points out problems about the compliance or not of the nucleus identifiers with the ENDF-6 format and sometimes the inconsistencies among the various ENDF files. In addition, the analysis of EAF-2003 and EAF-2010 shows some incoherence between the ZA product identifier and the reaction identifier MT for the reactions (n, pα) and (n, 2np). As a main result of this work, our suggestion is to change the ENDF format using systematically the isomeric state number to identify the nuclei. This proposal is already compliant to a huge amount ENDF data that are not in agreement with the present ENDF format. This choice is the most convenient because, ultimately, it allows one to give human readable names to the nuclei of the depletion chains.

Processing system of jaws tomograms for pathology identification and surgical guide modeling

NASA Astrophysics Data System (ADS)

Putrik, M. B.; Lavrentyeva, Yu. E.; Ivanov, V. Yu.

2015-11-01

The aim of the study is to create an image processing system, which allows dentists to find pathological resorption and to build surgical guide surface automatically. X-rays images of jaws from cone beam tomography or spiral computed tomography are the initial data for processing. One patient's examination always includes up to 600 images (or tomograms), that's why the development of processing system for fast automation search of pathologies is necessary. X-rays images can be useful not for only illness diagnostic but for treatment planning too. We have studied the case of dental implantation - for successful surgical manipulations surgical guides are used. We have created a processing system that automatically builds jaw and teeth boundaries on the x-ray image. After this step, obtained teeth boundaries used for surgical guide surface modeling and jaw boundaries limit the area for further pathologies search. Criterion for the presence of pathological resorption zones inside the limited area is based on statistical investigation. After described actions, it is possible to manufacture surgical guide using 3D printer and apply it in surgical operation.

Image simulation for automatic license plate recognition

NASA Astrophysics Data System (ADS)

Bala, Raja; Zhao, Yonghui; Burry, Aaron; Kozitsky, Vladimir; Fillion, Claude; Saunders, Craig; Rodríguez-Serrano, José

2012-01-01

Automatic license plate recognition (ALPR) is an important capability for traffic surveillance applications, including toll monitoring and detection of different types of traffic violations. ALPR is a multi-stage process comprising plate localization, character segmentation, optical character recognition (OCR), and identification of originating jurisdiction (i.e. state or province). Training of an ALPR system for a new jurisdiction typically involves gathering vast amounts of license plate images and associated ground truth data, followed by iterative tuning and optimization of the ALPR algorithms. The substantial time and effort required to train and optimize the ALPR system can result in excessive operational cost and overhead. In this paper we propose a framework to create an artificial set of license plate images for accelerated training and optimization of ALPR algorithms. The framework comprises two steps: the synthesis of license plate images according to the design and layout for a jurisdiction of interest; and the modeling of imaging transformations and distortions typically encountered in the image capture process. Distortion parameters are estimated by measurements of real plate images. The simulation methodology is successfully demonstrated for training of OCR.

Accuracy of Automatic Cephalometric Software on Landmark Identification

NASA Astrophysics Data System (ADS)

Anuwongnukroh, N.; Dechkunakorn, S.; Damrongsri, S.; Nilwarat, C.; Pudpong, N.; Radomsutthisarn, W.; Kangern, S.

2017-11-01

This study was to assess the accuracy of an automatic cephalometric analysis software in the identification of cephalometric landmarks. Thirty randomly selected digital lateral cephalograms of patients undergoing orthodontic treatment were used in this study. Thirteen landmarks (S, N, Or, A-point, U1T, U1A, B-point, Gn, Pog, Me, Go, L1T, and L1A) were identified on the digital image by an automatic cephalometric software and on cephalometric tracing by manual method. Superimposition of printed image and manual tracing was done by registration at the soft tissue profiles. The accuracy of landmarks located by the automatic method was compared with that of the manually identified landmarks by measuring the mean differences of distances of each landmark on the Cartesian plane where X and Y coordination axes passed through the center of ear rod. One-Sample T test was used to evaluate the mean differences. Statistically significant mean differences (p<0.05) were found in 5 landmarks (Or, A-point, Me, L1T, and L1A) in horizontal direction and 7 landmarks (Or, A-point, U1T, U1A, B-point, Me, and L1A) in vertical direction. Four landmarks (Or, A-point, Me, and L1A) showed significant (p<0.05) mean differences in both horizontal and vertical directions. Small mean differences (<0.5mm) were found for S, N, B-point, Gn, and Pog in horizontal direction and N, Gn, Me, and L1T in vertical direction. Large mean differences were found for A-point (3.0 < 3.5mm) in horizontal direction and L1A (>4mm) in vertical direction. Only 5 of 13 landmarks (38.46%; S, N, Gn, Pog, and Go) showed no significant mean difference between the automatic and manual landmarking methods. It is concluded that if this automatic cephalometric analysis software is used for orthodontic diagnosis, the orthodontist must correct or modify the position of landmarks in order to increase the accuracy of cephalometric analysis.

Development of a mobile application for amphibian species recognition

NASA Astrophysics Data System (ADS)

Parveen, B.; H, Chew T.; Shamsir, M. S.; Ahmad, N.

2014-02-01

The smartphones mobility and its pervasiveness are beginning to transform practices in biodiversity conservation. The integrated functionalities of a smartphone have created for the public and biodiversity specialists means to identify, gather and record biodiversity data while simultaneously creating knowledge portability in the digital forms of mobile guides. Smartphones enable beginners to recreate the delight of species identification usually reserved for specialist with years of experience. Currently, the advent of Android platform has enabled stakeholders in biodiversity to harness the ubiquity of this platform and create various types of mobile application or "apps" for use in biodiversity research and conservation. However, there is an apparent lack of application devoted to the identification in herpetofauna or amphibian science. Amphibians are a large class of animals with many different species still unidentified under this category. Here we describe the development of an app called Amphibian Recognition Android Application (ARAA) to identify frog amphibian species as well as an accompanying field guide. The app has the amphibian taxonomic key which assists the users in easy and rapid species identification, thus facilitating the process of identification and recording of species occurrences in conservation work. We will also present an overview of the application work flow and how it is designed to meet the needs a conservationist. As this application is still in its beta phase, further research is required to improve the application to include tools such automatic geolocation and geotagging, participative sensing via crowdsourcing and automated identification via image capture. We believe that the introduction of this app will create an impetus to the awareness of nature via species identification.

Testing efficacy of distance and tree-based methods for DNA barcoding of grasses (Poaceae tribe Poeae) in Australia

PubMed Central

Walsh, Neville G.; Cantrill, David J.; Holmes, Gareth D.; Murphy, Daniel J.

2017-01-01

In Australia, Poaceae tribe Poeae are represented by 19 genera and 99 species, including economically and environmentally important native and introduced pasture grasses [e.g. Poa (Tussock-grasses) and Lolium (Ryegrasses)]. We used this tribe, which are well characterised in regards to morphological diversity and evolutionary relationships, to test the efficacy of DNA barcoding methods. A reference library was generated that included 93.9% of species in Australia (408 individuals, x¯ = 3.7 individuals per species). Molecular data were generated for official plant barcoding markers (rbcL, matK) and the nuclear ribosomal internal transcribed spacer (ITS) region. We investigated accuracy of specimen identifications using distance- (nearest neighbour, best-close match, and threshold identification) and tree-based (maximum likelihood, Bayesian inference) methods and applied species discovery methods (automatic barcode gap discovery, Poisson tree processes) based on molecular data to assess congruence with recognised species. Across all methods, success rate for specimen identification of genera was high (87.5–99.5%) and of species was low (25.6–44.6%). Distance- and tree-based methods were equally ineffective in providing accurate identifications for specimens to species rank (26.1–44.6% and 25.6–31.3%, respectively). The ITS marker achieved the highest success rate for specimen identification at both generic and species ranks across the majority of methods. For distance-based analyses the best-close match method provided the greatest accuracy for identification of individuals with a high percentage of “correct” (97.6%) and a low percentage of “incorrect” (0.3%) generic identifications, based on the ITS marker. For tribe Poeae, and likely for other grass lineages, sequence data in the standard DNA barcode markers are not variable enough for accurate identification of specimens to species rank. For recently diverged grass species similar challenges are encountered in the application of genetic and morphological data to species delimitations, with taxonomic signal limited by extensive infra-specific variation and shared polymorphisms among species in both data types. PMID:29084279

Testing efficacy of distance and tree-based methods for DNA barcoding of grasses (Poaceae tribe Poeae) in Australia.

PubMed

Birch, Joanne L; Walsh, Neville G; Cantrill, David J; Holmes, Gareth D; Murphy, Daniel J

2017-01-01

In Australia, Poaceae tribe Poeae are represented by 19 genera and 99 species, including economically and environmentally important native and introduced pasture grasses [e.g. Poa (Tussock-grasses) and Lolium (Ryegrasses)]. We used this tribe, which are well characterised in regards to morphological diversity and evolutionary relationships, to test the efficacy of DNA barcoding methods. A reference library was generated that included 93.9% of species in Australia (408 individuals, [Formula: see text] = 3.7 individuals per species). Molecular data were generated for official plant barcoding markers (rbcL, matK) and the nuclear ribosomal internal transcribed spacer (ITS) region. We investigated accuracy of specimen identifications using distance- (nearest neighbour, best-close match, and threshold identification) and tree-based (maximum likelihood, Bayesian inference) methods and applied species discovery methods (automatic barcode gap discovery, Poisson tree processes) based on molecular data to assess congruence with recognised species. Across all methods, success rate for specimen identification of genera was high (87.5-99.5%) and of species was low (25.6-44.6%). Distance- and tree-based methods were equally ineffective in providing accurate identifications for specimens to species rank (26.1-44.6% and 25.6-31.3%, respectively). The ITS marker achieved the highest success rate for specimen identification at both generic and species ranks across the majority of methods. For distance-based analyses the best-close match method provided the greatest accuracy for identification of individuals with a high percentage of "correct" (97.6%) and a low percentage of "incorrect" (0.3%) generic identifications, based on the ITS marker. For tribe Poeae, and likely for other grass lineages, sequence data in the standard DNA barcode markers are not variable enough for accurate identification of specimens to species rank. For recently diverged grass species similar challenges are encountered in the application of genetic and morphological data to species delimitations, with taxonomic signal limited by extensive infra-specific variation and shared polymorphisms among species in both data types.

Automatic load forecasting. Final report

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

Nelson, D.J.; Vemuri, S.

A method which lends itself to on-line forecasting of hourly electric loads is presented and the results of its use are compared to models developed using the Box-Jenkins method. The method consists of processing the historical hourly loads with a sequential least-squares estimator to identify a finite order autoregressive model which in turn is used to obtain a parsimonious autoregressive-moving average model. A procedure is also defined for incorporating temperature as a variable to improve forecasts where loads are temperature dependent. The method presented has several advantages in comparison to the Box-Jenkins method including much less human intervention and improvedmore » model identification. The method has been tested using three-hourly data from the Lincoln Electric System, Lincoln, Nebraska. In the exhaustive analyses performed on this data base this method produced significantly better results than the Box-Jenkins method. The method also proved to be more robust in that greater confidence could be placed in the accuracy of models based upon the various measures available at the identification stage.« less

Automated identification and predictive tools to help identify high-risk heart failure patients: pilot evaluation.

PubMed

Evans, R Scott; Benuzillo, Jose; Horne, Benjamin D; Lloyd, James F; Bradshaw, Alejandra; Budge, Deborah; Rasmusson, Kismet D; Roberts, Colleen; Buckway, Jason; Geer, Norma; Garrett, Teresa; Lappé, Donald L

2016-09-01

Develop and evaluate an automated identification and predictive risk report for hospitalized heart failure (HF) patients. Dictated free-text reports from the previous 24 h were analyzed each day with natural language processing (NLP), to help improve the early identification of hospitalized patients with HF. A second application that uses an Intermountain Healthcare-developed predictive score to determine each HF patient's risk for 30-day hospital readmission and 30-day mortality was also developed. That information was included in an identification and predictive risk report, which was evaluated at a 354-bed hospital that treats high-risk HF patients. The addition of NLP-identified HF patients increased the identification score's sensitivity from 82.6% to 95.3% and its specificity from 82.7% to 97.5%, and the model's positive predictive value is 97.45%. Daily multidisciplinary discharge planning meetings are now based on the information provided by the HF identification and predictive report, and clinician's review of potential HF admissions takes less time compared to the previously used manual methodology (10 vs 40 min). An evaluation of the use of the HF predictive report identified a significant reduction in 30-day mortality and a significant increase in patient discharges to home care instead of to a specialized nursing facility. Using clinical decision support to help identify HF patients and automatically calculating their 30-day all-cause readmission and 30-day mortality risks, coupled with a multidisciplinary care process pathway, was found to be an effective process to improve HF patient identification, significantly reduce 30-day mortality, and significantly increase patient discharges to home care. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Identification and Comprehension of Symbolic Exit Signs for Small Transport-Category Airplanes

DTIC Science & Technology

2014-02-01

8 Phase Two: Self -illuminated Exit Signs...11 Self -illuminated Exit Sign Comprehension ---------------------------------------------------------------------- 12 Automatic...other sign parameters such as stroke width to height, contrast ratios, and self -illumination, fall within those recommended by 14 CFR 25.812(b)(2

Lower Mississippi River Ports and Waterways Safety System (PAWSS) RF coverage test results

DOT National Transportation Integrated Search

1999-11-01

The Coast Guard plans to operate an Automatic Identification System (AID) Digital Selective Calling (DSC) based transponder system as part of the Ports and Waterways Safety System (PAWSS) in the lower Mississippi River. the AIS uses two duplex channe...

The integrated manual and automatic control of complex flight systems

NASA Technical Reports Server (NTRS)

Schmidt, D. K.

1983-01-01

Development of a unified control synthesis methodology for complex and/or non-conventional flight vehicles, and prediction techniques for the handling characteristics of such vehicles are reported. Identification of pilot dynamics and objectives, using time domain and frequency domain methods is proposed.

33 CFR 401.20 - Automatic Identification System.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Recommendation M.1371-1: 2000, Technical Characteristics For A Universal Shipborne AIS Using Time Division... power receptacle accessible for the pilot's laptop computer; and (5) The Minimum Keyboard Display (MKD... AIS position reports using differential GPS corrections from the U.S. and Canadian Coast Guards...

33 CFR 401.20 - Automatic Identification System.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Recommendation M.1371-1: 2000, Technical Characteristics For A Universal Shipborne AIS Using Time Division... power receptacle accessible for the pilot's laptop computer; and (5) The Minimum Keyboard Display (MKD... AIS position reports using differential GPS corrections from the U.S. and Canadian Coast Guards...

33 CFR 401.20 - Automatic Identification System.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Recommendation M.1371-1: 2000, Technical Characteristics For A Universal Shipborne AIS Using Time Division... power receptacle accessible for the pilot's laptop computer; and (5) The Minimum Keyboard Display (MKD... AIS position reports using differential GPS corrections from the U.S. and Canadian Coast Guards...

33 CFR 401.20 - Automatic Identification System.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Recommendation M.1371-1: 2000, Technical Characteristics For A Universal Shipborne AIS Using Time Division... power receptacle accessible for the pilot's laptop computer; and (5) The Minimum Keyboard Display (MKD... AIS position reports using differential GPS corrections from the U.S. and Canadian Coast Guards...

Automatic Ammunition Identification Technology Project

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

Weil, B.

1993-01-01

The Automatic Ammunition Identification Technology (AAIT) Project is an activity of the Robotics Process Systems Division at the Oak Ridge National Laboratory (ORNL) for the US Army's Project Manager-Ammunition Logistics (PM-AMMOLOG) at the Picatinny Arsenal in Picatinny, New Jersey. The project objective is to evaluate new two-dimensional bar code symbologies for potential use in ammunition logistics systems and automated reloading equipment. These new symbologies are a significant improvement over typical linear bar codes since machine-readable alphanumeric messages up to 2000 characters long are achievable. These compressed data symbologies are expected to significantly improve logistics and inventory management tasks and permitmore » automated feeding and handling of ammunition to weapon systems. The results will be increased throughout capability, better inventory control, reduction of human error, lower operation and support costs, and a more timely re-supply of various weapon systems. This paper will describe the capabilities of existing compressed data symbologies and the symbol testing activities being conducted at ORNL for the AAIT Project.« less

Automatic Ammunition Identification Technology Project. Ammunition Logistics Program

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

Weil, B.

1993-03-01

The Automatic Ammunition Identification Technology (AAIT) Project is an activity of the Robotics & Process Systems Division at the Oak Ridge National Laboratory (ORNL) for the US Army`s Project Manager-Ammunition Logistics (PM-AMMOLOG) at the Picatinny Arsenal in Picatinny, New Jersey. The project objective is to evaluate new two-dimensional bar code symbologies for potential use in ammunition logistics systems and automated reloading equipment. These new symbologies are a significant improvement over typical linear bar codes since machine-readable alphanumeric messages up to 2000 characters long are achievable. These compressed data symbologies are expected to significantly improve logistics and inventory management tasks andmore » permit automated feeding and handling of ammunition to weapon systems. The results will be increased throughout capability, better inventory control, reduction of human error, lower operation and support costs, and a more timely re-supply of various weapon systems. This paper will describe the capabilities of existing compressed data symbologies and the symbol testing activities being conducted at ORNL for the AAIT Project.« less

The ALICE-HMPID Detector Control System: Its evolution towards an expert and adaptive system

NASA Astrophysics Data System (ADS)

De Cataldo, G.; Franco, A.; Pastore, C.; Sgura, I.; Volpe, G.

2011-05-01

The High Momentum Particle IDentification (HMPID) detector is a proximity focusing Ring Imaging Cherenkov (RICH) for charged hadron identification. The HMPID is based on liquid C 6F 14 as the radiator medium and on a 10 m 2 CsI coated, pad segmented photocathode of MWPCs for UV Cherenkov photon detection. To ensure full remote control, the HMPID is equipped with a detector control system (DCS) responding to industrial standards for robustness and reliability. It has been implemented using PVSS as Slow Control And Data Acquisition (SCADA) environment, Programmable Logic Controller as control devices and Finite State Machines for modular and automatic command execution. In the perspective of reducing human presence at the experiment site, this paper focuses on DCS evolution towards an expert and adaptive control system, providing, respectively, automatic error recovery and stable detector performance. HAL9000, the first prototype of the HMPID expert system, is then presented. Finally an analysis of the possible application of the adaptive features is provided.

Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation

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

Jin, Ye; Ma, Xiaosong; Liu, Qing Gary

2015-01-01

Parallel application benchmarks are indispensable for evaluating/optimizing HPC software and hardware. However, it is very challenging and costly to obtain high-fidelity benchmarks reflecting the scale and complexity of state-of-the-art parallel applications. Hand-extracted synthetic benchmarks are time-and labor-intensive to create. Real applications themselves, while offering most accurate performance evaluation, are expensive to compile, port, reconfigure, and often plainly inaccessible due to security or ownership concerns. This work contributes APPRIME, a novel tool for trace-based automatic parallel benchmark generation. Taking as input standard communication-I/O traces of an application's execution, it couples accurate automatic phase identification with statistical regeneration of event parameters tomore » create compact, portable, and to some degree reconfigurable parallel application benchmarks. Experiments with four NAS Parallel Benchmarks (NPB) and three real scientific simulation codes confirm the fidelity of APPRIME benchmarks. They retain the original applications' performance characteristics, in particular the relative performance across platforms.« less

Analysis and automatic identification of sleep stages using higher order spectra.

PubMed

Acharya, U Rajendra; Chua, Eric Chern-Pin; Chua, Kuang Chua; Min, Lim Choo; Tamura, Toshiyo

2010-12-01

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.

Assessing sound exposure from shipping in coastal waters using a single hydrophone and Automatic Identification System (AIS) data.

PubMed

Merchant, Nathan D; Witt, Matthew J; Blondel, Philippe; Godley, Brendan J; Smith, George H

2012-07-01

Underwater noise from shipping is a growing presence throughout the world's oceans, and may be subjecting marine fauna to chronic noise exposure with potentially severe long-term consequences. The coincidence of dense shipping activity and sensitive marine ecosystems in coastal environments is of particular concern, and noise assessment methodologies which describe the high temporal variability of sound exposure in these areas are needed. We present a method of characterising sound exposure from shipping using continuous passive acoustic monitoring combined with Automatic Identification System (AIS) shipping data. The method is applied to data recorded in Falmouth Bay, UK. Absolute and relative levels of intermittent ship noise contributions to the 24-h sound exposure level are determined using an adaptive threshold, and the spatial distribution of potential ship sources is then analysed using AIS data. This technique can be used to prioritize shipping noise mitigation strategies in coastal marine environments. Copyright © 2012 Elsevier Ltd. All rights reserved.

Completing fishing monitoring with spaceborne Vessel Detection System (VDS) and Automatic Identification System (AIS) to assess illegal fishing in Indonesia.

PubMed

Longépé, Nicolas; Hajduch, Guillaume; Ardianto, Romy; Joux, Romain de; Nhunfat, Béatrice; Marzuki, Marza I; Fablet, Ronan; Hermawan, Indra; Germain, Olivier; Subki, Berny A; Farhan, Riza; Muttaqin, Ahmad Deni; Gaspar, Philippe

2017-10-26

The Indonesian fisheries management system is now equipped with the state-of-the-art technologies to deter and combat Illegal, Unreported and Unregulated (IUU) fishing. Since October 2014, non-cooperative fishing vessels can be detected from spaceborne Vessel Detection System (VDS) based on high resolution radar imagery, which directly benefits to coordinated patrol vessels in operation context. This study attempts to monitor the amount of illegal fishing in the Arafura Sea based on this new source of information. It is analyzed together with Vessel Monitoring System (VMS) and satellite-based Automatic Identification System (Sat-AIS) data, taking into account their own particularities. From October 2014 to March 2015, i.e. just after the establishment of a new moratorium by the Indonesian authorities, the estimated share of fishing vessels not carrying VMS, thus being illegal, ranges from 42 to 47%. One year later in January 2016, this proportion decreases and ranges from 32 to 42%. Copyright © 2017 Elsevier Ltd. All rights reserved.

Benefit Analyses of Technologies for Automatic Identification to Be Implemented in the Healthcare Sector

NASA Astrophysics Data System (ADS)

Krey, Mike; Schlatter, Ueli

The tasks and objectives of automatic identification (Auto-ID) are to provide information on goods and products. It has already been established for years in the areas of logistics and trading and can no longer be ignored by the German healthcare sector. Some German hospitals have already discovered the capabilities of Auto-ID. Improvements in quality, safety and reductions in risk, cost and time are aspects and areas where improvements are achievable. Privacy protection, legal restraints, and the personal rights of patients and staff members are just a few aspects which make the heath care sector a sensible field for the implementation of Auto-ID. Auto-ID in this context contains the different technologies, methods and products for the registration, provision and storage of relevant data. With the help of a quantifiable and science-based evaluation, an answer is sought as to which Auto-ID has the highest capability to be implemented in healthcare business.

Automatic corn-soybean classification using Landsat MSS data. I - Near-harvest crop proportion estimation. II - Early season crop proportion estimation

NASA Technical Reports Server (NTRS)

Badhwar, G. D.

1984-01-01

The techniques used initially for the identification of cultivated crops from Landsat imagery depended greatly on the iterpretation of film products by a human analyst. This approach was not very effective and objective. Since 1978, new methods for crop identification are being developed. Badhwar et al. (1982) showed that multitemporal-multispectral data could be reduced to a simple feature space of alpha and beta and that these features would separate corn and soybean very well. However, there are disadvantages related to the use of alpha and beta parameters. The present investigation is concerned with a suitable method for extracting the required features. Attention is given to a profile model for crop discrimination, corn-soybean separation using profile parameters, and an automatic labeling (target recognition) method. The developed technique is extended to obtain a procedure which makes it possible to estimate the crop proportion of corn and soybean from Landsat data early in the growing season.

Automatic analysis and quantification of fluorescently labeled synapses in microscope images

NASA Astrophysics Data System (ADS)

Yona, Shai; Katsman, Alex; Orenbuch, Ayelet; Gitler, Daniel; Yitzhaky, Yitzhak

2011-09-01

The purpose of this work is to classify and quantify synapses and their properties in the cultures of a mouse's hippocampus, from images acquired by a fluorescent microscope. Quantification features include the number of synapses, their intensity and their size characteristics. The images obtained by the microscope contain hundreds to several thousands of synapses with various elliptic-like shape features and intensities. These images also include other features such as glia cells and other biological objects beyond the focus plane; those features reduce the visibility of the synapses and interrupt the segmentation process. The proposed method comprises several steps, including background subtraction, identification of suspected centers of synapses as local maxima of small neighborhoods, evaluation of the tendency of objects to be synapses according to intensity properties at their larger neighborhoods, classification of detected synapses into categories as bulks or single synapses and finally, delimiting the borders of each synapse.

Automatic detection and notification of "wrong patient-wrong location'' errors in the operating room.

PubMed

Sandberg, Warren S; Häkkinen, Matti; Egan, Marie; Curran, Paige K; Fairbrother, Pamela; Choquette, Ken; Daily, Bethany; Sarkka, Jukka-Pekka; Rattner, David

2005-09-01

When procedures and processes to assure patient location based on human performance do not work as expected, patients are brought incrementally closer to a possible "wrong patient-wrong procedure'' error. We developed a system for automated patient location monitoring and management. Real-time data from an active infrared/radio frequency identification tracking system provides patient location data that are robust and can be compared with an "expected process'' model to automatically flag wrong-location events as soon as they occur. The system also generates messages that are automatically sent to process managers via the hospital paging system, thus creating an active alerting function to annunciate errors. We deployed the system to detect and annunciate "patient-in-wrong-OR'' events. The system detected all "wrong-operating room (OR)'' events, and all "wrong-OR'' locations were correctly assigned within 0.50+/-0.28 minutes (mean+/-SD). This corresponded to the measured latency of the tracking system. All wrong-OR events were correctly annunciated via the paging function. This experiment demonstrates that current technology can automatically collect sufficient data to remotely monitor patient flow through a hospital, provide decision support based on predefined rules, and automatically notify stakeholders of errors.

Visible and NIR spectral band combination to produce high security ID tags for automatic identification

NASA Astrophysics Data System (ADS)

Pérez-Cabré, Elisabet; Millán, María S.; Javidi, Bahram

2006-09-01

Verification of a piece of information and/or authentication of a given object or person are common operations carried out by automatic security systems that can be applied, for instance, to control the entrance to restricted areas, access to public buildings, identification of cardholders, etc. Vulnerability of such security systems may depend on the ease of counterfeiting the information used as a piece of identification for verification and authentication. To protect data against tampering, the signature that identifies an object is usually encrypted to avoid an easy recognition at human sight and an easy reproduction using conventional devices for imaging or scanning. To make counterfeiting even more difficult, we propose to combine data from visible and near infrared (NIR) spectral bands. By doing this, neither the visible content nor the NIR data by theirselves are sufficient to allow the signature recognition and thus, the identification of a given object. Only the appropriate combination of both signals permits a satisfactory authentication. In addition, the resulting signature is encrypted following a fully-phase encryption technique and the obtained complex-amplitude distribution is encoded on an ID tag. Spatial multiplexing of the encrypted signature allows us to build a distortion-invariant ID tag, so that remote authentication can be achieved even if the tag is captured under rotation or at different distances. We also explore the possibility of using partial information of the encrypted signature to simplify the ID tag design.

PRIDE: new developments and new datasets.

PubMed

Jones, Philip; Côté, Richard G; Cho, Sang Yun; Klie, Sebastian; Martens, Lennart; Quinn, Antony F; Thorneycroft, David; Hermjakob, Henning

2008-01-01

The PRIDE (http://www.ebi.ac.uk/pride) database of protein and peptide identifications was previously described in the NAR Database Special Edition in 2006. Since this publication, the volume of public data in the PRIDE relational database has increased by more than an order of magnitude. Several significant public datasets have been added, including identifications and processed mass spectra generated by the HUPO Brain Proteome Project and the HUPO Liver Proteome Project. The PRIDE software development team has made several significant changes and additions to the user interface and tool set associated with PRIDE. The focus of these changes has been to facilitate the submission process and to improve the mechanisms by which PRIDE can be queried. The PRIDE team has developed a Microsoft Excel workbook that allows the required data to be collated in a series of relatively simple spreadsheets, with automatic generation of PRIDE XML at the end of the process. The ability to query PRIDE has been augmented by the addition of a BioMart interface allowing complex queries to be constructed. Collaboration with groups outside the EBI has been fruitful in extending PRIDE, including an approach to encode iTRAQ quantitative data in PRIDE XML.

Two-level structural sparsity regularization for identifying lattices and defects in noisy images

DOE PAGES

Li, Xin; Belianinov, Alex; Dyck, Ondrej E.; ...

2018-03-09

Here, this paper presents a regularized regression model with a two-level structural sparsity penalty applied to locate individual atoms in a noisy scanning transmission electron microscopy image (STEM). In crystals, the locations of atoms is symmetric, condensed into a few lattice groups. Therefore, by identifying the underlying lattice in a given image, individual atoms can be accurately located. We propose to formulate the identification of the lattice groups as a sparse group selection problem. Furthermore, real atomic scale images contain defects and vacancies, so atomic identification based solely on a lattice group may result in false positives and false negatives.more » To minimize error, model includes an individual sparsity regularization in addition to the group sparsity for a within-group selection, which results in a regression model with a two-level sparsity regularization. We propose a modification of the group orthogonal matching pursuit (gOMP) algorithm with a thresholding step to solve the atom finding problem. The convergence and statistical analyses of the proposed algorithm are presented. The proposed algorithm is also evaluated through numerical experiments with simulated images. The applicability of the algorithm on determination of atom structures and identification of imaging distortions and atomic defects was demonstrated using three real STEM images. In conclusion, we believe this is an important step toward automatic phase identification and assignment with the advent of genomic databases for materials.« less

Two-level structural sparsity regularization for identifying lattices and defects in noisy images

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

Li, Xin; Belianinov, Alex; Dyck, Ondrej E.

Here, this paper presents a regularized regression model with a two-level structural sparsity penalty applied to locate individual atoms in a noisy scanning transmission electron microscopy image (STEM). In crystals, the locations of atoms is symmetric, condensed into a few lattice groups. Therefore, by identifying the underlying lattice in a given image, individual atoms can be accurately located. We propose to formulate the identification of the lattice groups as a sparse group selection problem. Furthermore, real atomic scale images contain defects and vacancies, so atomic identification based solely on a lattice group may result in false positives and false negatives.more » To minimize error, model includes an individual sparsity regularization in addition to the group sparsity for a within-group selection, which results in a regression model with a two-level sparsity regularization. We propose a modification of the group orthogonal matching pursuit (gOMP) algorithm with a thresholding step to solve the atom finding problem. The convergence and statistical analyses of the proposed algorithm are presented. The proposed algorithm is also evaluated through numerical experiments with simulated images. The applicability of the algorithm on determination of atom structures and identification of imaging distortions and atomic defects was demonstrated using three real STEM images. In conclusion, we believe this is an important step toward automatic phase identification and assignment with the advent of genomic databases for materials.« less

Automatic Temporal Tracking of Supra-Glacial Lakes

NASA Astrophysics Data System (ADS)

Liang, Y.; Lv, Q.; Gallaher, D. W.; Fanning, D.

2010-12-01

During the recent years, supra-glacial lakes in Greenland have attracted extensive global attention as they potentially play an important role in glacier movement, sea level rise, and climate change. Previous works focused on classification methods and individual cloud-free satellite images, which have limited capabilities in terms of tracking changes of lakes over time. The challenges of tracking supra-glacial lakes automatically include (1) massive amount of satellite images with diverse qualities and frequent cloud coverage, and (2) diversity and dynamics of large number of supra-glacial lakes on the Greenland ice sheet. In this study, we develop an innovative method to automatically track supra-glacial lakes temporally using the Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data. The method works for both cloudy and cloud-free data and is unsupervised, i.e., no manual identification is required. After selecting the highest-quality image within each time interval, our method automatically detects supra-glacial lakes in individual images, using adaptive thresholding to handle diverse image qualities. We then track lakes across time series of images as lakes appear, change in size, and disappear. Using multi-year MODIS data during melting season, we demonstrate that this new method can detect and track supra-glacial lakes in both space and time with 95% accuracy. Attached figure shows an example of the current result. Detailed analysis of the temporal variation of detected lakes will be presented. (a) One of our experimental data. The Investigated region is centered at Jakobshavn Isbrae glacier in west Greenland. (b) Enlarged view of part of ice sheet. It is partially cloudy and with supra-glacial lakes on it. Lakes are shown as dark spots. (c) Current result. Red spots are detected lakes.

Sentry: An Automated Close Approach Monitoring System for Near-Earth Objects

NASA Astrophysics Data System (ADS)

Chamberlin, A. B.; Chesley, S. R.; Chodas, P. W.; Giorgini, J. D.; Keesey, M. S.; Wimberly, R. N.; Yeomans, D. K.

2001-11-01

In response to international concern about potential asteroid impacts on Earth, NASA's Near-Earth Object (NEO) Program Office has implemented a new system called ``Sentry'' to automatically update the orbits of all NEOs on a daily basis and compute Earth close approaches up to 100 years into the future. Results are published on our web site (http://neo.jpl.nasa.gov/) and updated orbits and ephemerides made available via the JPL Horizons ephemeris service (http://ssd.jpl.nasa.gov/horizons.html). Sentry collects new and revised astrometric observations from the Minor Planet Center (MPC) via their electronic circulars (MPECs) in near real time as well as radar and optical astrometry sent directly from observers. NEO discoveries and identifications are detected in MPECs and processed appropriately. In addition to these daily updates, Sentry synchronizes with each monthly batch of MPC astrometry and automatically updates all NEO observation files. Daily and monthly processing of NEO astrometry is managed using a queuing system which allows for manual intervention of selected NEOs without interfering with the automatic system. At the heart of Sentry is a fully automatic orbit determination program which handles outlier rejection and ensures convergence in the new solution. Updated orbital elements and their covariances are published via Horizons and our NEO web site, typically within 24 hours. A new version of Horizons, in development, will allow computation of ephemeris uncertainties using covariance data. The positions of NEOs with updated orbits are numerically integrated up to 100 years into the future and each close approach to any perturbing body in our dynamic model (all planets, Moon, Ceres, Pallas, Vesta) is recorded. Significant approaches are flagged for extended analysis including Monte Carlo studies. Results, such as minimum encounter distances and future Earth impact probabilities, are published on our NEO web site.

Segmentation And Quantification Of Black Holes In Multiple Sclerosis

PubMed Central

Datta, Sushmita; Sajja, Balasrinivasa Rao; He, Renjie; Wolinsky, Jerry S.; Gupta, Rakesh K.; Narayana, Ponnada A.

2006-01-01

A technique that involves minimal operator intervention was developed and implemented for identification and quantification of black holes on T1- weighted magnetic resonance images (T1 images) in multiple sclerosis (MS). Black holes were segmented on T1 images based on grayscale morphological operations. False classification of black holes was minimized by masking the segmented images with images obtained from the orthogonalization of T2-weighted and T1 images. Enhancing lesion voxels on postcontrast images were automatically identified and eliminated from being included in the black hole volume. Fuzzy connectivity was used for the delineation of black holes. The performance of this algorithm was quantitatively evaluated on 14 MS patients. PMID:16126416

Automatic Identification of Alpine Mass Movements by a Combination of Seismic and Infrasound Sensors

PubMed Central

Hübl, Johannes; McArdell, Brian W.; Walter, Fabian

2018-01-01

The automatic detection and identification of alpine mass movements such as debris flows, debris floods, or landslides have been of increasing importance for devising mitigation measures in densely populated and intensively used alpine regions. Since these mass movements emit characteristic seismic and acoustic waves in the low-frequency range (<30 Hz), several approaches have already been developed for detection and warning systems based on these signals. However, a combination of the two methods, for improving detection probability and reducing false alarms, is still applied rarely. This paper presents an update and extension of a previously published approach for a detection and identification system based on a combination of seismic and infrasound sensors. Furthermore, this work evaluates the possible early warning times at several test sites and aims to analyze the seismic and infrasound spectral signature produced by different sediment-related mass movements to identify the process type and estimate the magnitude of the event. Thus, this study presents an initial method for estimating the peak discharge and total volume of debris flows based on infrasound data. Tests on several catchments show that this system can detect and identify mass movements in real time directly at the sensor site with high accuracy and a low false alarm ratio. PMID:29789449

FragIdent--automatic identification and characterisation of cDNA-fragments.

PubMed

Seelow, Dominik; Goehler, Heike; Hoffmann, Katrin

2009-03-02

Many genetic studies and functional assays are based on cDNA fragments. After the generation of cDNA fragments from an mRNA sample, their content is at first unknown and must be assigned by sequencing reactions or hybridisation experiments. Even in characterised libraries, a considerable number of clones are wrongly annotated. Furthermore, mix-ups can happen in the laboratory. It is therefore essential to the relevance of experimental results to confirm or determine the identity of the employed cDNA fragments. However, the manual approach for the characterisation of these fragments using BLAST web interfaces is not suited for larger number of sequences and so far, no user-friendly software is publicly available. Here we present the development of FragIdent, an application for the automatic identification of open reading frames (ORFs) within cDNA-fragments. The software performs BLAST analyses to identify the genes represented by the sequences and suggests primers to complete the sequencing of the whole insert. Gene-specific information as well as the protein domains encoded by the cDNA fragment are retrieved from Internet-based databases and included in the output. The application features an intuitive graphical interface and is designed for researchers without any bioinformatics skills. It is suited for projects comprising up to several hundred different clones. We used FragIdent to identify 84 cDNA clones from a yeast two-hybrid experiment. Furthermore, we identified 131 protein domains within our analysed clones. The source code is freely available from our homepage at http://compbio.charite.de/genetik/FragIdent/.

Laser-aided material identification for the waste sorting process

NASA Astrophysics Data System (ADS)

Haferkamp, Heinz; Burmester, Ingo; Engel, Kai

1994-03-01

The LZH has carried out investigations in the field of rapid laser-supported material- identification systems for automatic material-sorting systems. The aim of this research is the fast identification of different sorts of plastics coming from recycled rubbish or electronic waste. Within a few milliseconds a spot on the sample which has to be identified is heated with a CO2 laser. The different and specific chemical and physical material properties of the examined sample cause a different temperature distribution on the surface which is measured with an IR thermographic system. This `thermal impulse response' has to be analyzed by means of a computer system. The results of previous investigations have shown that material identification of different sorts of plastics can possibly be done at a frequency of 30 Hz. Due to economic efficiency, a high velocity identification process is necessary to sort huge waste currents.

Design and performance study of an orthopaedic surgery robotized module for automatic bone drilling.

PubMed

Boiadjiev, George; Kastelov, Rumen; Boiadjiev, Tony; Kotev, Vladimir; Delchev, Kamen; Zagurski, Kazimir; Vitkov, Vladimir

2013-12-01

Many orthopaedic operations involve drilling and tapping before the insertion of screws into a bone. This drilling is usually performed manually, thus introducing many problems. These include attaining a specific drilling accuracy, preventing blood vessels from breaking, and minimizing drill oscillations that would widen the hole. Bone overheating is the most important problem. To avoid such problems and reduce the subjective factor, automated drilling is recommended. Because numerous parameters influence the drilling process, this study examined some experimental methods. These concerned the experimental identification of technical drilling parameters, including the bone resistance force and temperature in the drilling process. During the drilling process, the following parameters were monitored: time, linear velocity, angular velocity, resistance force, penetration depth, and temperature. Specific drilling effects were revealed during the experiments. The accuracy was improved at the starting point of the drilling, and the error for the entire process was less than 0.2 mm. The temperature deviations were kept within tolerable limits. The results of various experiments with different drilling velocities, drill bit diameters, and penetration depths are presented in tables, as well as the curves of the resistance force and temperature with respect to time. Real-time digital indications of the progress of the drilling process are shown. Automatic bone drilling could entirely solve the problems that usually arise during manual drilling. An experimental setup was designed to identify bone drilling parameters such as the resistance force arising from variable bone density, appropriate mechanical drilling torque, linear speed of the drill, and electromechanical characteristics of the motors, drives, and corresponding controllers. Automatic drilling guarantees greater safety for the patient. Moreover, the robot presented is user-friendly because it is simple to set robot tasks, and process data are collected in real time. Copyright © 2013 John Wiley & Sons, Ltd.

Automatic Identification of Messages Related to Adverse Drug Reactions from Online User Reviews using Feature-based Classification.

PubMed

Liu, Jingfang; Zhang, Pengzhu; Lu, Yingjie

2014-11-01

User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs) and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews. We conducted experiments on online user reviews using different feature set and different classification technique. Firstly, the messages from three communities, allergy community, schizophrenia community and pain management community, were collected, the 3000 messages were annotated. Secondly, the N-gram-based features set and medical domain-specific features set were generated. Thirdly, three classification techniques, SVM, C4.5 and Naïve Bayes, were used to perform classification tasks separately. Finally, we evaluated the performance of different method using different feature set and different classification technique by comparing the metrics including accuracy and F-measure. In terms of accuracy, the accuracy of SVM classifier was higher than 0.8, the accuracy of C4.5 classifier or Naïve Bayes classifier was lower than 0.8; meanwhile, the combination feature sets including n-gram-based feature set and domain-specific feature set consistently outperformed single feature set. In terms of F-measure, the highest F-measure is 0.895 which was achieved by using combination feature sets and a SVM classifier. In all, we can get the best classification performance by using combination feature sets and SVM classifier. By using combination feature sets and SVM classifier, we can get an effective method to identify messages related to ADRs automatically from online user reviews.

Automatic localization of IASLC-defined mediastinal lymph node stations on CT images using fuzzy models

NASA Astrophysics Data System (ADS)

Matsumoto, Monica M. S.; Beig, Niha G.; Udupa, Jayaram K.; Archer, Steven; Torigian, Drew A.

2014-03-01

Lung cancer is associated with the highest cancer mortality rates among men and women in the United States. The accurate and precise identification of the lymph node stations on computed tomography (CT) images is important for staging disease and potentially for prognosticating outcome in patients with lung cancer, as well as for pretreatment planning and response assessment purposes. To facilitate a standard means of referring to lymph nodes, the International Association for the Study of Lung Cancer (IASLC) has recently proposed a definition of the different lymph node stations and zones in the thorax. However, nodal station identification is typically performed manually by visual assessment in clinical radiology. This approach leaves room for error due to the subjective and potentially ambiguous nature of visual interpretation, and is labor intensive. We present a method of automatically recognizing the mediastinal IASLC-defined lymph node stations by modifying a hierarchical fuzzy modeling approach previously developed for body-wide automatic anatomy recognition (AAR) in medical imagery. Our AAR-lymph node (AAR-LN) system follows the AAR methodology and consists of two steps. In the first step, the various lymph node stations are manually delineated on a set of CT images following the IASLC definitions. These delineations are then used to build a fuzzy hierarchical model of the nodal stations which are considered as 3D objects. In the second step, the stations are automatically located on any given CT image of the thorax by using the hierarchical fuzzy model and object recognition algorithms. Based on 23 data sets used for model building, 22 independent data sets for testing, and 10 lymph node stations, a mean localization accuracy of within 1-6 voxels has been achieved by the AAR-LN system.

Automatic classification of the sub-techniques (gears) used in cross-country ski skating employing a mobile phone.

PubMed

Stöggl, Thomas; Holst, Anders; Jonasson, Arndt; Andersson, Erik; Wunsch, Tobias; Norström, Christer; Holmberg, Hans-Christer

2014-10-31

The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

The Liquid Argon Software Toolkit (LArSoft): Goals, Status and Plan

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

Pordes, Rush; Snider, Erica

LArSoft is a toolkit that provides a software infrastructure and algorithms for the simulation, reconstruction and analysis of events in Liquid Argon Time Projection Chambers (LArTPCs). It is used by the ArgoNeuT, LArIAT, MicroBooNE, DUNE (including 35ton prototype and ProtoDUNE) and SBND experiments. The LArSoft collaboration provides an environment for the development, use, and sharing of code across experiments. The ultimate goal is to develop fully automatic processes for reconstruction and analysis of LArTPC events. The toolkit is based on the art framework and has a well-defined architecture to interface to other packages, including to GEANT4 and GENIE simulation softwaremore » and the Pandora software development kit for pattern recognition. It is designed to facilitate and support the evolution of algorithms including their transition to new computing platforms. The development of the toolkit is driven by the scientific stakeholders involved. The core infrastructure includes standard definitions of types and constants, means to input experiment geometries as well as meta and event- data in several formats, and relevant general utilities. Examples of algorithms experiments have contributed to date are: photon-propagation; particle identification; hit finding, track finding and fitting; electromagnetic shower identification and reconstruction. We report on the status of the toolkit and plans for future work.« less

Metaphor Identification in Large Texts Corpora

PubMed Central

Neuman, Yair; Assaf, Dan; Cohen, Yohai; Last, Mark; Argamon, Shlomo; Howard, Newton; Frieder, Ophir

2013-01-01

Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms’ performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus. PMID:23658625

Research on gait-based human identification

NASA Astrophysics Data System (ADS)

Li, Youguo

Gait recognition refers to automatic identification of individual based on his/her style of walking. This paper proposes a gait recognition method based on Continuous Hidden Markov Model with Mixture of Gaussians(G-CHMM). First, we initialize a Gaussian mix model for training image sequence with K-means algorithm, then train the HMM parameters using a Baum-Welch algorithm. These gait feature sequences can be trained and obtain a Continuous HMM for every person, therefore, the 7 key frames and the obtained HMM can represent each person's gait sequence. Finally, the recognition is achieved by Front algorithm. The experiments made on CASIA gait databases obtain comparatively high correction identification ratio and comparatively strong robustness for variety of bodily angle.

High-speed holographic correlation system for video identification on the internet

NASA Astrophysics Data System (ADS)

Watanabe, Eriko; Ikeda, Kanami; Kodate, Kashiko

2013-12-01

Automatic video identification is important for indexing, search purposes, and removing illegal material on the Internet. By combining a high-speed correlation engine and web-scanning technology, we developed the Fast Recognition Correlation system (FReCs), a video identification system for the Internet. FReCs is an application thatsearches through a number of websites with user-generated content (UGC) and detects video content that violates copyright law. In this paper, we describe the FReCs configuration and an approach to investigating UGC websites using FReCs. The paper also illustrates the combination of FReCs with an optical correlation system, which is capable of easily replacing a digital authorization sever in FReCs with optical correlation.

Coupling System Design Optimization : A Survey and Assessment of Automatic Coupling Concepts for Rail Freight Cars : Volume 2. Text and Appendices.

DOT National Transportation Integrated Search

1978-05-01

The purpose of this study is to provide an independent identification, classification, and analysis of significant freight car coupling system concepts offering potential for improved safety and operating costs over the present system. The basic meth...

47 CFR 64.1602 - Restrictions on use and sale of telephone subscriber information provided pursuant to automatic...

Code of Federal Regulations, 2010 CFR

2010-10-01

....1602 Section 64.1602 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON CARRIER SERVICES (CONTINUED) MISCELLANEOUS RULES RELATING TO COMMON CARRIERS Calling Party Telephone Number... Identification or charge number services on interstate calls to any person shall provide such services under a...

Coupling System Design Optimization : A Survey and Assessment of Automatic Coupling Concepts for Rail Freight Cars : Volume 1. Executive Summary.

DOT National Transportation Integrated Search

1978-05-01

The purpose of this study is to provide an independent identification, classification, and analysis of significant freight car coupling systems concepts offering potential for improved safety and operating costs over the present system. The basic met...

Wireless tracking of cotton modules Part II: automatic machine identification and system testing

USDA-ARS?s Scientific Manuscript database

Mapping the harvest location of cotton modules is essential to practical understanding and utilization of spatial-variability information in fiber quality. A wireless module-tracking system was recently developed, but automation of the system is required before it will find practical use on the far...

Automatic Identification System (AIS) Transmit Testing in Louisville Phase 2

DTIC Science & Technology

2014-08-01

project. Two of the captains were Capt. David Williams and Capt. Spencer Kennedy. After leaving SCI, the team members went to Crounse Inc. and met...team members had a phone conference with Herbert Taylor (VP Operations, Kongsberg Maritime Simulation Inc.) to discuss the integration of AIS data in

Automatic Content Recommendation and Aggregation According to SCORM

ERIC Educational Resources Information Center

Neves, Daniel Eugênio; Brandão, Wladmir Cardoso; Ishitani, Lucila

2017-01-01

Although widely used, the SCORM metadata model for content aggregation is difficult to be used by educators, content developers and instructional designers. Particularly, the identification of contents related with each other, in large repositories, and their aggregation using metadata as defined in SCORM, has been demanding efforts of computer…

75 FR 43487 - Proposed Information Collection; Comment Request; Vessel Monitoring System Requirements in...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-26

... Monitoring System Requirements in the Western Pacific Pelagic Longline Fishery), OMB Control No. 0648-0519... requirement from OMB Control No. 0648-0584 (Permitting, Vessel Identification and Vessel Monitoring System... one collection (OMB Control No. 0648-0441). II. Method of Collection Automatic. III. Data OMB Control...

33 CFR 169.235 - What exemptions are there from reporting?

Code of Federal Regulations, 2010 CFR

2010-07-01

... SECURITY (CONTINUED) PORTS AND WATERWAYS SAFETY SHIP REPORTING SYSTEMS Transmission of Long Range... this subpart if it is— (a) Fitted with an operating automatic identification system (AIS), under 33 CFR 164.46, and operates only within 20 nautical miles of the United States baseline, (b) A warship, naval...

Measuring border delay and crossing times at the US-Mexico border : final report on automated crossing and wait time measurement.

DOT National Transportation Integrated Search

2012-08-01

A pilot test implemented a radio frequency identification (RFID) system to automatically measure travel times of US-bound commercial vehicles at a selected Port of Entry (POE) on the USMexico border under long-term, real-world conditions. The init...

33 CFR 164.43 - Automatic Identification System Shipborne Equipment-Prince William Sound.

Code of Federal Regulations, 2011 CFR

2011-07-01

...GPS) receiver; (2) Marine band Non-Directional Beacon receiver capable of receiving dGPS error... frequency; and (4) Control unit. (b) An AISSE must have the following capabilities: (1) Use dGPS to sense... Recommended Standards for Differential NAVSTAR GPS Service in determining the required information; (3...

Classroom Tech

ERIC Educational Resources Information Center

Instructor, 2006

2006-01-01

This article features the latest classroom technologies namely the FLY Pentop, WriteToLearn, and a new iris scan identification system. The FLY Pentop is a computerized pen from Leapster that "magically" understands what kids write and draw on special FLY paper. WriteToLearn is an automatic grading software from Pearson Knowledge Technologies and…

English Complex Verb Constructions: Identification and Inference

ERIC Educational Resources Information Center

Tu, Yuancheng

2012-01-01

The fundamental problem faced by automatic text understanding in Natural Language Processing (NLP) is to identify semantically related pieces of text and integrate them together to compute the meaning of the whole text. However, the principle of compositionality runs into trouble very quickly when real language is examined with its frequent…

Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

NASA Technical Reports Server (NTRS)

Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

1995-01-01

This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system.

Identification of granite varieties from colour spectrum data.

PubMed

Araújo, María; Martínez, Javier; Ordóñez, Celestino; Vilán, José Antonio

2010-01-01

The granite processing sector of the northwest of Spain handles many varieties of granite with specific technical and aesthetic properties that command different prices in the natural stone market. Hence, correct granite identification and classification from the outset of processing to the end-product stage optimizes the management and control of stocks of granite slabs and tiles and facilitates the operation of traceability systems. We describe a methodology for automatically identifying granite varieties by processing spectral information captured by a spectrophotometer at various stages of processing using functional machine learning techniques.

Identification of Granite Varieties from Colour Spectrum Data

PubMed Central

Araújo, María; Martínez, Javier; Ordóñez, Celestino; Vilán, José Antonio

2010-01-01

The granite processing sector of the northwest of Spain handles many varieties of granite with specific technical and aesthetic properties that command different prices in the natural stone market. Hence, correct granite identification and classification from the outset of processing to the end-product stage optimizes the management and control of stocks of granite slabs and tiles and facilitates the operation of traceability systems. We describe a methodology for automatically identifying granite varieties by processing spectral information captured by a spectrophotometer at various stages of processing using functional machine learning techniques. PMID:22163673

In-flight wobble identification for Galileo

NASA Technical Reports Server (NTRS)

Lai, J. Y.; Wong, E. C.

1984-01-01

To achieve in-flight wobble compensation for Galileo, wobble identification is implemented using star scanner data or automatic gain control (AGC) signal as measurement in all-spin mode. The star scanner provides spacecraft attitude in inertial space while the AGC signal provides the spacecraft pointing relative to earth. A linear observation model is defined for each sensor which is being applied to a Kalman Estimator. It can be shown from simulation that better result can be achieved using a combined set of data than any one sensor alone due to correlation reduction among error sources.

A manual and an automatic TERS based virus discrimination

NASA Astrophysics Data System (ADS)

Olschewski, Konstanze; Kämmer, Evelyn; Stöckel, Stephan; Bocklitz, Thomas; Deckert-Gaudig, Tanja; Zell, Roland; Cialla-May, Dana; Weber, Karina; Deckert, Volker; Popp, Jürgen

2015-02-01

Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%.Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr07033j

Automatic extraction of disease-specific features from Doppler images

NASA Astrophysics Data System (ADS)

Negahdar, Mohammadreza; Moradi, Mehdi; Parajuli, Nripesh; Syeda-Mahmood, Tanveer

2017-03-01

Flow Doppler imaging is widely used by clinicians to detect diseases of the valves. In particular, continuous wave (CW) Doppler mode scan is routinely done during echocardiography and shows Doppler signal traces over multiple heart cycles. Traditionally, echocardiographers have manually traced such velocity envelopes to extract measurements such as decay time and pressure gradient which are then matched to normal and abnormal values based on clinical guidelines. In this paper, we present a fully automatic approach to deriving these measurements for aortic stenosis retrospectively from echocardiography videos. Comparison of our method with measurements made by echocardiographers shows large agreement as well as identification of new cases missed by echocardiographers.

A review on exudates detection methods for diabetic retinopathy.

PubMed

Joshi, Shilpa; Karule, P T

2018-01-01

The presence of exudates on the retina is the most characteristic symptom of diabetic retinopathy. As exudates are among early clinical signs of DR, their detection would be an essential asset to the mass screening task and serve as an important step towards automatic grading and monitoring of the disease. Reliable identification and classification of exudates are of inherent interest in an automated diabetic retinopathy screening system. Here we review the numerous early studies that used for automatic exudates detection with the aim of providing decision support in addition to reducing the workload of an ophthalmologist. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

MICRA: an automatic pipeline for fast characterization of microbial genomes from high-throughput sequencing data.

PubMed

Caboche, Ségolène; Even, Gaël; Loywick, Alexandre; Audebert, Christophe; Hot, David

2017-12-19

The increase in available sequence data has advanced the field of microbiology; however, making sense of these data without bioinformatics skills is still problematic. We describe MICRA, an automatic pipeline, available as a web interface, for microbial identification and characterization through reads analysis. MICRA uses iterative mapping against reference genomes to identify genes and variations. Additional modules allow prediction of antibiotic susceptibility and resistance and comparing the results of several samples. MICRA is fast, producing few false-positive annotations and variant calls compared to current methods, making it a tool of great interest for fully exploiting sequencing data.

Automatic interpretation of oblique ionograms

NASA Astrophysics Data System (ADS)

Ippolito, Alessandro; Scotto, Carlo; Francis, Matthew; Settimi, Alessandro; Cesaroni, Claudio

2015-03-01

We present an algorithm for the identification of trace characteristics of oblique ionograms allowing determination of the Maximum Usable Frequency (MUF) for communication between the transmitter and receiver. The algorithm automatically detects and rejects poor quality ionograms. We performed an exploratory test of the algorithm using data from a campaign of oblique soundings between Rome, Italy (41.90 N, 12.48 E) and Chania, Greece (35.51 N, 24.01 E) and also between Kalkarindji, Australia (17.43 S, 130.81 E) and Culgoora, Australia (30.30 S, 149.55 E). The success of these tests demonstrates the applicability of the method to ionograms recorded by different ionosondes in various helio and geophysical conditions.

SAVLOC, computer program for automatic control and analysis of X-ray fluorescence experiments

NASA Technical Reports Server (NTRS)

Leonard, R. F.

1977-01-01

A program for a PDP-15 computer is presented which provides for control and analysis of trace element determinations by using X-ray fluorescence. The program simultaneously handles data accumulation for one sample and analysis of data from previous samples. Data accumulation consists of sample changing, timing, and data storage. Analysis requires the locating of peaks in X-ray spectra, determination of intensities of peaks, identification of origins of peaks, and determination of a real density of the element responsible for each peak. The program may be run in either a manual (supervised) mode or an automatic (unsupervised) mode.

Automatic Target Recognition Based on Cross-Plot

PubMed Central

Wong, Kelvin Kian Loong; Abbott, Derek

2011-01-01

Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository. PMID:21980508

Automatic identification of bacterial types using statistical imaging methods

NASA Astrophysics Data System (ADS)

Trattner, Sigal; Greenspan, Hayit; Tepper, Gapi; Abboud, Shimon

2003-05-01

The objective of the current study is to develop an automatic tool to identify bacterial types using computer-vision and statistical modeling techniques. Bacteriophage (phage)-typing methods are used to identify and extract representative profiles of bacterial types, such as the Staphylococcus Aureus. Current systems rely on the subjective reading of plaque profiles by human expert. This process is time-consuming and prone to errors, especially as technology is enabling the increase in the number of phages used for typing. The statistical methodology presented in this work, provides for an automated, objective and robust analysis of visual data, along with the ability to cope with increasing data volumes.

Provider risk factors for medication administration error alerts: analyses of a large-scale closed-loop medication administration system using RFID and barcode.

PubMed

Hwang, Yeonsoo; Yoon, Dukyong; Ahn, Eun Kyoung; Hwang, Hee; Park, Rae Woong

2016-12-01

To determine the risk factors and rate of medication administration error (MAE) alerts by analyzing large-scale medication administration data and related error logs automatically recorded in a closed-loop medication administration system using radio-frequency identification and barcodes. The subject hospital adopted a closed-loop medication administration system. All medication administrations in the general wards were automatically recorded in real-time using radio-frequency identification, barcodes, and hand-held point-of-care devices. MAE alert logs recorded during a full 1 year of 2012. We evaluated risk factors for MAE alerts including administration time, order type, medication route, the number of medication doses administered, and factors associated with nurse practices by logistic regression analysis. A total of 2 874 539 medication dose records from 30 232 patients (882.6 patient-years) were included in 2012. We identified 35 082 MAE alerts (1.22% of total medication doses). The MAE alerts were significantly related to administration at non-standard time [odds ratio (OR) 1.559, 95% confidence interval (CI) 1.515-1.604], emergency order (OR 1.527, 95%CI 1.464-1.594), and the number of medication doses administered (OR 0.993, 95%CI 0.992-0.993). Medication route, nurse's employment duration, and working schedule were also significantly related. The MAE alert rate was 1.22% over the 1-year observation period in the hospital examined in this study. The MAE alerts were significantly related to administration time, order type, medication route, the number of medication doses administered, nurse's employment duration, and working schedule. The real-time closed-loop medication administration system contributed to improving patient safety by preventing potential MAEs. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Spatial-spectral preprocessing for endmember extraction on GPU's

NASA Astrophysics Data System (ADS)

Jimenez, Luis I.; Plaza, Javier; Plaza, Antonio; Li, Jun

2016-10-01

Spectral unmixing is focused in the identification of spectrally pure signatures, called endmembers, and their corresponding abundances in each pixel of a hyperspectral image. Mainly focused on the spectral information contained in the hyperspectral images, endmember extraction techniques have recently included spatial information to achieve more accurate results. Several algorithms have been developed for automatic or semi-automatic identification of endmembers using spatial and spectral information, including the spectral-spatial endmember extraction (SSEE) where, within a preprocessing step in the technique, both sources of information are extracted from the hyperspectral image and equally used for this purpose. Previous works have implemented the SSEE technique in four main steps: 1) local eigenvectors calculation in each sub-region in which the original hyperspectral image is divided; 2) computation of the maxima and minima projection of all eigenvectors over the entire hyperspectral image in order to obtain a candidates pixels set; 3) expansion and averaging of the signatures of the candidate set; 4) ranking based on the spectral angle distance (SAD). The result of this method is a list of candidate signatures from which the endmembers can be extracted using various spectral-based techniques, such as orthogonal subspace projection (OSP), vertex component analysis (VCA) or N-FINDR. Considering the large volume of data and the complexity of the calculations, there is a need for efficient implementations. Latest- generation hardware accelerators such as commodity graphics processing units (GPUs) offer a good chance for improving the computational performance in this context. In this paper, we develop two different implementations of the SSEE algorithm using GPUs. Both are based on the eigenvectors computation within each sub-region of the first step, one using the singular value decomposition (SVD) and another one using principal component analysis (PCA). Based on our experiments with hyperspectral data sets, high computational performance is observed in both cases.

Automatic identification of IASLC-defined mediastinal lymph node stations on CT scans using multi-atlas organ segmentation

NASA Astrophysics Data System (ADS)

Hoffman, Joanne; Liu, Jiamin; Turkbey, Evrim; Kim, Lauren; Summers, Ronald M.

2015-03-01

Station-labeling of mediastinal lymph nodes is typically performed to identify the location of enlarged nodes for cancer staging. Stations are usually assigned in clinical radiology practice manually by qualitative visual assessment on CT scans, which is time consuming and highly variable. In this paper, we developed a method that automatically recognizes the lymph node stations in thoracic CT scans based on the anatomical organs in the mediastinum. First, the trachea, lungs, and spines are automatically segmented to locate the mediastinum region. Then, eight more anatomical organs are simultaneously identified by multi-atlas segmentation. Finally, with the segmentation of those anatomical organs, we convert the text definitions of the International Association for the Study of Lung Cancer (IASLC) lymph node map into patient-specific color-coded CT image maps. Thus, a lymph node station is automatically assigned to each lymph node. We applied this system to CT scans of 86 patients with 336 mediastinal lymph nodes measuring equal or greater than 10 mm. 84.8% of mediastinal lymph nodes were correctly mapped to their stations.

An automatic method for segmentation of fission tracks in epidote crystal photomicrographs

NASA Astrophysics Data System (ADS)

de Siqueira, Alexandre Fioravante; Nakasuga, Wagner Massayuki; Pagamisse, Aylton; Tello Saenz, Carlos Alberto; Job, Aldo Eloizo

2014-08-01

Manual identification of fission tracks has practical problems, such as variation due to observe-observation efficiency. An automatic processing method that could identify fission tracks in a photomicrograph could solve this problem and improve the speed of track counting. However, separation of nontrivial images is one of the most difficult tasks in image processing. Several commercial and free softwares are available, but these softwares are meant to be used in specific images. In this paper, an automatic method based on starlet wavelets is presented in order to separate fission tracks in mineral photomicrographs. Automatization is obtained by the Matthews correlation coefficient, and results are evaluated by precision, recall and accuracy. This technique is an improvement of a method aimed at segmentation of scanning electron microscopy images. This method is applied in photomicrographs of epidote phenocrystals, in which accuracy higher than 89% was obtained in fission track segmentation, even for difficult images. Algorithms corresponding to the proposed method are available for download. Using the method presented here, a user could easily determine fission tracks in photomicrographs of mineral samples.

Automatic and Direct Identification of Blink Components from Scalp EEG

PubMed Central

Kong, Wanzeng; Zhou, Zhanpeng; Hu, Sanqing; Zhang, Jianhai; Babiloni, Fabio; Dai, Guojun

2013-01-01

Eye blink is an important and inevitable artifact during scalp electroencephalogram (EEG) recording. The main problem in EEG signal processing is how to identify eye blink components automatically with independent component analysis (ICA). Taking into account the fact that the eye blink as an external source has a higher sum of correlation with frontal EEG channels than all other sources due to both its location and significant amplitude, in this paper, we proposed a method based on correlation index and the feature of power distribution to automatically detect eye blink components. Furthermore, we prove mathematically that the correlation between independent components and scalp EEG channels can be translating directly from the mixing matrix of ICA. This helps to simplify calculations and understand the implications of the correlation. The proposed method doesn't need to select a template or thresholds in advance, and it works without simultaneously recording an electrooculography (EOG) reference. The experimental results demonstrate that the proposed method can automatically recognize eye blink components with a high accuracy on entire datasets from 15 subjects. PMID:23959240

P19-S Managing Proteomics Data from Data Generation and Data Warehousing to Central Data Repository and Journal Reviewing Processes

PubMed Central

Thiele, H.; Glandorf, J.; Koerting, G.; Reidegeld, K.; Blüggel, M.; Meyer, H.; Stephan, C.

2007-01-01

In today’s proteomics research, various techniques and instrumentation bioinformatics tools are necessary to manage the large amount of heterogeneous data with an automatic quality control to produce reliable and comparable results. Therefore a data-processing pipeline is mandatory for data validation and comparison in a data-warehousing system. The proteome bioinformatics platform ProteinScape has been proven to cover these needs. The reprocessing of HUPO BPP participants’ MS data was done within ProteinScape. The reprocessed information was transferred into the global data repository PRIDE. ProteinScape as a data-warehousing system covers two main aspects: archiving relevant data of the proteomics workflow and information extraction functionality (protein identification, quantification and generation of biological knowledge). As a strategy for automatic data validation, different protein search engines are integrated. Result analysis is performed using a decoy database search strategy, which allows the measurement of the false-positive identification rate. Peptide identifications across different workflows, different MS techniques, and different search engines are merged to obtain a quality-controlled protein list. The proteomics identifications database (PRIDE), as a public data repository, is an archiving system where data are finally stored and no longer changed by further processing steps. Data submission to PRIDE is open to proteomics laboratories generating protein and peptide identifications. An export tool has been developed for transferring all relevant HUPO BPP data from ProteinScape into PRIDE using the PRIDE.xml format. The EU-funded ProDac project will coordinate the development of software tools covering international standards for the representation of proteomics data. The implementation of data submission pipelines and systematic data collection in public standards–compliant repositories will cover all aspects, from the generation of MS data in each laboratory to the conversion of all the annotating information and identifications to a standardized format. Such datasets can be used in the course of publishing in scientific journals.

Preclinical Biokinetic Modelling of Tc-99m Radiophamaceuticals Obtained from Semi-Automatic Image Processing.

PubMed

Cornejo-Aragón, Luz G; Santos-Cuevas, Clara L; Ocampo-García, Blanca E; Chairez-Oria, Isaac; Diaz-Nieto, Lorenza; García-Quiroz, Janice

2017-01-01

The aim of this study was to develop a semi automatic image processing algorithm (AIPA) based on the simultaneous information provided by X-ray and radioisotopic images to determine the biokinetic models of Tc-99m radiopharmaceuticals from quantification of image radiation activity in murine models. These radioisotopic images were obtained by a CCD (charge couple device) camera coupled to an ultrathin phosphorous screen in a preclinical multimodal imaging system (Xtreme, Bruker). The AIPA consisted of different image processing methods for background, scattering and attenuation correction on the activity quantification. A set of parametric identification algorithms was used to obtain the biokinetic models that characterize the interaction between different tissues and the radiopharmaceuticals considered in the study. The set of biokinetic models corresponded to the Tc-99m biodistribution observed in different ex vivo studies. This fact confirmed the contribution of the semi-automatic image processing technique developed in this study.

Automated feature detection and identification in digital point-ordered signals

DOEpatents

Oppenlander, Jane E.; Loomis, Kent C.; Brudnoy, David M.; Levy, Arthur J.

1998-01-01

A computer-based automated method to detect and identify features in digital point-ordered signals. The method is used for processing of non-destructive test signals, such as eddy current signals obtained from calibration standards. The signals are first automatically processed to remove noise and to determine a baseline. Next, features are detected in the signals using mathematical morphology filters. Finally, verification of the features is made using an expert system of pattern recognition methods and geometric criteria. The method has the advantage that standard features can be, located without prior knowledge of the number or sequence of the features. Further advantages are that standard features can be differentiated from irrelevant signal features such as noise, and detected features are automatically verified by parameters extracted from the signals. The method proceeds fully automatically without initial operator set-up and without subjective operator feature judgement.

Automatic extraction of blocks from 3D point clouds of fractured rock

NASA Astrophysics Data System (ADS)

Chen, Na; Kemeny, John; Jiang, Qinghui; Pan, Zhiwen

2017-12-01

This paper presents a new method for extracting blocks and calculating block size automatically from rock surface 3D point clouds. Block size is an important rock mass characteristic and forms the basis for several rock mass classification schemes. The proposed method consists of four steps: 1) the automatic extraction of discontinuities using an improved Ransac Shape Detection method, 2) the calculation of discontinuity intersections based on plane geometry, 3) the extraction of block candidates based on three discontinuities intersecting one another to form corners, and 4) the identification of "true" blocks using an improved Floodfill algorithm. The calculated block sizes were compared with manual measurements in two case studies, one with fabricated cardboard blocks and the other from an actual rock mass outcrop. The results demonstrate that the proposed method is accurate and overcomes the inaccuracies, safety hazards, and biases of traditional techniques.

Design and development of an automatic data acquisition system for a balance study using a smartcard system.

PubMed

Ambrozy, C; Kolar, N A; Rattay, F

2010-01-01

For measurement value logging of board angle values during balance training, it is necessary to develop a measurement system. This study will provide data for a balance study using the smartcard. The data acquisition comes automatically. An individually training plan for each proband is necessary. To store the proband identification a smartcard with an I2C data bus protocol and an E2PROM memory system is used. For reading the smartcard data a smartcard reader is connected via universal serial bus (USB) to a notebook. The data acquisition and smartcard read programme is designed with Microsoft® Visual C#. A training plan file contains the individual training plan for each proband. The data of the test persons are saved in a proband directory. Each event is automatically saved as a log-file for the exact documentation. This system makes study development easy and time-saving.

Attendance fingerprint identification system using arduino and single board computer

NASA Astrophysics Data System (ADS)

Muchtar, M. A.; Seniman; Arisandi, D.; Hasanah, S.

2018-03-01

Fingerprint is one of the most unique parts of the human body that distinguishes one person from others and is easily accessed. This uniqueness is supported by technology that can automatically identify or recognize a person called fingerprint sensor. Yet, the existing Fingerprint Sensor can only do fingerprint identification on one machine. For the mentioned reason, we need a method to be able to recognize each user in a different fingerprint sensor. The purpose of this research is to build fingerprint sensor system for fingerprint data management to be centralized so identification can be done in each Fingerprint Sensor. The result of this research shows that by using Arduino and Raspberry Pi, data processing can be centralized so that fingerprint identification can be done in each fingerprint sensor with 98.5 % success rate of centralized server recording.

Phase coherence adaptive processor for automatic signal detection and identification

NASA Astrophysics Data System (ADS)

Wagstaff, Ronald A.

2006-05-01

A continuously adapting acoustic signal processor with an automatic detection/decision aid is presented. Its purpose is to preserve the signals of tactical interest, and filter out other signals and noise. It utilizes single sensor or beamformed spectral data and transforms the signal and noise phase angles into "aligned phase angles" (APA). The APA increase the phase temporal coherence of signals and leave the noise incoherent. Coherence thresholds are set, which are representative of the type of source "threat vehicle" and the geographic area or volume in which it is operating. These thresholds separate signals, based on the "quality" of their APA coherence. An example is presented in which signals from a submerged source in the ocean are preserved, while clutter signals from ships and noise are entirely eliminated. Furthermore, the "signals of interest" were identified by the processor's automatic detection aid. Similar performance is expected for air and ground vehicles. The processor's equations are formulated in such a manner that they can be tuned to eliminate noise and exploit signal, based on the "quality" of their APA temporal coherence. The mathematical formulation for this processor is presented, including the method by which the processor continuously self-adapts. Results show nearly complete elimination of noise, with only the selected category of signals remaining, and accompanying enhancements in spectral and spatial resolution. In most cases, the concept of signal-to-noise ratio looses significance, and "adaptive automated /decision aid" is more relevant.

Automated Microbiological Detection/Identification System

PubMed Central

Aldridge, C.; Jones, P. W.; Gibson, S.; Lanham, J.; Meyer, M.; Vannest, R.; Charles, R.

1977-01-01

An automated, computerized system, the AutoMicrobic System, has been developed for the detection, enumeration, and identification of bacteria and yeasts in clinical specimens. The biological basis for the system resides in lyophilized, highly selective and specific media enclosed in wells of a disposable plastic cuvette; introduction of a suitable specimen rehydrates and inoculates the media in the wells. An automated optical system monitors, and the computer interprets, changes in the media, with enumeration and identification results automatically obtained in 13 h. Sixteen different selective media were developed and tested with a variety of seeded (simulated) and clinical specimens. The AutoMicrobic System has been extensively tested with urine specimens, using a urine test kit (Identi-Pak) that contains selective media for Escherichia coli, Proteus species, Pseudomonas aeruginosa, Klebsiella-Enterobacter species, Serratia species, Citrobacter freundii, group D enterococci, Staphylococcus aureus, and yeasts (Candida species and Torulopsis glabrata). The system has been tested with 3,370 seeded urine specimens and 1,486 clinical urines. Agreement with simultaneous conventional (manual) cultures, at levels of 70,000 colony-forming units per ml (or more), was 92% or better for seeded specimens; clinical specimens yielded results of 93% or better for all organisms except P. aeruginosa, where agreement was 86%. System expansion in progress includes antibiotic susceptibility testing and compatibility with most types of clinical specimens. Images PMID:334798

ATR architecture for multisensor fusion

NASA Astrophysics Data System (ADS)

Hamilton, Mark K.; Kipp, Teresa A.

1996-06-01

The work of the U.S. Army Research Laboratory (ARL) in the area of algorithms for the identification of static military targets in single-frame electro-optical (EO) imagery has demonstrated great potential in platform-based automatic target identification (ATI). In this case, the term identification is used to mean being able to tell the difference between two military vehicles -- e.g., the M60 from the T72. ARL's work includes not only single-sensor forward-looking infrared (FLIR) ATI algorithms, but also multi-sensor ATI algorithms. We briefly discuss ARL's hybrid model-based/data-learning strategy for ATI, which represents a significant step forward in ATI algorithm design. For example, in the case of single sensor FLIR it allows the human algorithm designer to build directly into the algorithm knowledge that can be adequately modeled at this time, such as the target geometry which directly translates into the target silhouette in the FLIR realm. In addition, it allows structure that is not currently well understood (i.e., adequately modeled) to be incorporated through automated data-learning algorithms, which in a FLIR directly translates into an internal thermal target structure signature. This paper shows the direct applicability of this strategy to both the single-sensor FLIR as well as the multi-sensor FLIR and laser radar.

Text de-identification for privacy protection: a study of its impact on clinical text information content.

PubMed

Meystre, Stéphane M; Ferrández, Óscar; Friedlin, F Jeffrey; South, Brett R; Shen, Shuying; Samore, Matthew H

2014-08-01

As more and more electronic clinical information is becoming easier to access for secondary uses such as clinical research, approaches that enable faster and more collaborative research while protecting patient privacy and confidentiality are becoming more important. Clinical text de-identification offers such advantages but is typically a tedious manual process. Automated Natural Language Processing (NLP) methods can alleviate this process, but their impact on subsequent uses of the automatically de-identified clinical narratives has only barely been investigated. In the context of a larger project to develop and investigate automated text de-identification for Veterans Health Administration (VHA) clinical notes, we studied the impact of automated text de-identification on clinical information in a stepwise manner. Our approach started with a high-level assessment of clinical notes informativeness and formatting, and ended with a detailed study of the overlap of select clinical information types and Protected Health Information (PHI). To investigate the informativeness (i.e., document type information, select clinical data types, and interpretation or conclusion) of VHA clinical notes, we used five different existing text de-identification systems. The informativeness was only minimally altered by these systems while formatting was only modified by one system. To examine the impact of de-identification on clinical information extraction, we compared counts of SNOMED-CT concepts found by an open source information extraction application in the original (i.e., not de-identified) version of a corpus of VHA clinical notes, and in the same corpus after de-identification. Only about 1.2-3% less SNOMED-CT concepts were found in de-identified versions of our corpus, and many of these concepts were PHI that was erroneously identified as clinical information. To study this impact in more details and assess how generalizable our findings were, we examined the overlap between select clinical information annotated in the 2010 i2b2 NLP challenge corpus and automatic PHI annotations from our best-of-breed VHA clinical text de-identification system (nicknamed 'BoB'). Overall, only 0.81% of the clinical information exactly overlapped with PHI, and 1.78% partly overlapped. We conclude that automated text de-identification's impact on clinical information is small, but not negligible, and that improved clinical acronyms and eponyms disambiguation could significantly reduce this impact. Copyright © 2014 Elsevier Inc. All rights reserved.

SWAT Check: A screening tool to assist users in the identification of potential model application problems

USDA-ARS?s Scientific Manuscript database

The Soil and Water Assessment Tool (SWAT) is a basin scale hydrologic model developed by the US Department of Agriculture-Agricultural Research Service. SWAT's broad applicability, user friendly model interfaces, and automatic calibration software have led to a rapid increase in the number of new u...

Dismount Threat Recognition through Automatic Pose Identification

DTIC Science & Technology

2012-03-01

10 2.2.2 Enabling Technologies . . . . . . . . . . . . . . 11 2.2.3 Associative Memory Neural Networks . . . . . . 12 III. Methodology...20 3.2.3 Creating Separability . . . . . . . . . . . . . . . 23 3.3 Training the Associative Memory Neural Network... Effects of Parameter and Method Choices . . . . . . . . 30 4.3.1 Decimel versus Bipolar . . . . . . . . . . . . . . 30 4.3.2 Bipolar and Binary Values

47 CFR 80.231 - Technical Requirements for Class B Automatic Identification System (AIS) equipment.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 12th Street, SW., Washington, DC (Reference Information Center), call 1-888-225-5322 or at the National... de Varembe, CH-1211 Geneva 20, Switzerland, or from the American National Standards Institute (ANSI... qualified person in the business of installing marine communications equipment on board vessels. In no event...

Automatic Identification System (AIS) Collection and Reach-back System: System Description

DTIC Science & Technology

2014-08-20

installation. 8.1.1 Power The HPS requires three NEMA 5 outlets, one each for the RPC, BPC, and KG-175D TACLANE-micro COMSEC device. The HPS draws less than...Military Sealift Command NEMA National Electrical Manufacturers Association NMEA National Marine Electronics Association NRL Naval Research Laboratory OTH-G

Research Study of River Information Services on the US Inland Waterway Network

DTIC Science & Technology

2012-12-01

management department, team leader and AIS expert • Mario Sattler, development of traffic management department, reporting expert • Christoph Plasil...Coast Guard (USCG) Nationwide Automatic Identification System (NAIS) and the lessons learned from AIS implementation on European waterways the concept...11 7.3.1 Enlarging of the AIS network

Automatic Identification of Nutritious Contexts for Learning Vocabulary Words

ERIC Educational Resources Information Center

Mostow, Jack; Gates, Donna; Ellison, Ross; Goutam, Rahul

2015-01-01

Vocabulary knowledge is crucial to literacy development and academic success. Previous research has shown learning the meaning of a word requires encountering it in diverse informative contexts. In this work, we try to identify "nutritious" contexts for a word--contexts that help students build a rich mental representation of the word's…

Interactive Media to Support Language Acquisition for Deaf Students

ERIC Educational Resources Information Center

Parton, Becky Sue; Hancock, Robert; Crain-Dorough, Mindy; Oescher, Jeff

2009-01-01

Tangible computing combines digital feedback with physical interactions - an important link for young children. Through the use of Radio Frequency Identification (RFID) technology, a real-world object (i.e. a chair) or a symbolic toy (i.e. a stuffed bear) can be tagged so that students can activate multimedia learning modules automatically. The…

ON THE QUANTITATIVE EVALUATION OF THE TERMINOLOGY OF A VOCABULARY.

ERIC Educational Resources Information Center

KRAVETS, L.G.

THE CREATION OF AN INDUSTRIAL SYSTEM OF MACHINE TRANSLATION WITH AUTOMATIC INDEXING OF THE TRANSLATED MATERIALS PRESUMES THE DEVELOPMENT OF DICTIONARIES WHICH PROVIDE FOR THE IDENTIFICATION OF KEY WORDS AND WORD COMBINATIONS, FOLLOWED BY THEIR TRANSLATION INTO THE DESCRIPTORS OF THE SEARCH LANGUAGE. THREE SIGNS WHICH SHOW THAT A GIVEN WORD IS A…

USCG AIS Broadcast

Science.gov Websites

these cooler months. Did you know your body can cool 25 times faster in water than in air? That water Traffic Service began broadcasting Automatic Identification System (AIS) test messages to select test participants in the area via standard AIS channels. These broadcasts-originating from MMSI 003660471-are less

Active learning approach for detection of hard exudates, cotton wool spots, and drusen in retinal images

NASA Astrophysics Data System (ADS)

Sánchez, Clara I.; Niemeijer, Meindert; Kockelkorn, Thessa; Abràmoff, Michael D.; van Ginneken, Bram

2009-02-01

Computer-aided Diagnosis (CAD) systems for the automatic identification of abnormalities in retinal images are gaining importance in diabetic retinopathy screening programs. A huge amount of retinal images are collected during these programs and they provide a starting point for the design of machine learning algorithms. However, manual annotations of retinal images are scarce and expensive to obtain. This paper proposes a dynamic CAD system based on active learning for the automatic identification of hard exudates, cotton wool spots and drusen in retinal images. An uncertainty sampling method is applied to select samples that need to be labeled by an expert from an unlabeled set of 4000 retinal images. It reduces the number of training samples needed to obtain an optimum accuracy by dynamically selecting the most informative samples. Results show that the proposed method increases the classification accuracy compared to alternative techniques, achieving an area under the ROC curve of 0.87, 0.82 and 0.78 for the detection of hard exudates, cotton wool spots and drusen, respectively.

Tooth labeling in cone-beam CT using deep convolutional neural network for forensic identification

NASA Astrophysics Data System (ADS)

Miki, Yuma; Muramatsu, Chisako; Hayashi, Tatsuro; Zhou, Xiangrong; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi

2017-03-01

In large disasters, dental record plays an important role in forensic identification. However, filing dental charts for corpses is not an easy task for general dentists. Moreover, it is laborious and time-consuming work in cases of large scale disasters. We have been investigating a tooth labeling method on dental cone-beam CT images for the purpose of automatic filing of dental charts. In our method, individual tooth in CT images are detected and classified into seven tooth types using deep convolutional neural network. We employed the fully convolutional network using AlexNet architecture for detecting each tooth and applied our previous method using regular AlexNet for classifying the detected teeth into 7 tooth types. From 52 CT volumes obtained by two imaging systems, five images each were randomly selected as test data, and the remaining 42 cases were used as training data. The result showed the tooth detection accuracy of 77.4% with the average false detection of 5.8 per image. The result indicates the potential utility of the proposed method for automatic recording of dental information.

Hybrid EEG--Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal.

PubMed

Mannan, Malik M Naeem; Kim, Shinjung; Jeong, Myung Yung; Kamran, M Ahmad

2016-02-19

Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data.

A novel thermal face recognition approach using face pattern words

NASA Astrophysics Data System (ADS)

Zheng, Yufeng

2010-04-01

A reliable thermal face recognition system can enhance the national security applications such as prevention against terrorism, surveillance, monitoring and tracking, especially at nighttime. The system can be applied at airports, customs or high-alert facilities (e.g., nuclear power plant) for 24 hours a day. In this paper, we propose a novel face recognition approach utilizing thermal (long wave infrared) face images that can automatically identify a subject at both daytime and nighttime. With a properly acquired thermal image (as a query image) in monitoring zone, the following processes will be employed: normalization and denoising, face detection, face alignment, face masking, Gabor wavelet transform, face pattern words (FPWs) creation, face identification by similarity measure (Hamming distance). If eyeglasses are present on a subject's face, an eyeglasses mask will be automatically extracted from the querying face image, and then masked with all comparing FPWs (no more transforms). A high identification rate (97.44% with Top-1 match) has been achieved upon our preliminary face dataset (of 39 subjects) from the proposed approach regardless operating time and glasses-wearing condition.e

Terahertz spectroscopic investigation of human gastric normal and tumor tissues

NASA Astrophysics Data System (ADS)

Hou, Dibo; Li, Xian; Cai, Jinhui; Ma, Yehao; Kang, Xusheng; Huang, Pingjie; Zhang, Guangxin

2014-09-01

Human dehydrated normal and cancerous gastric tissues were measured using transmission time-domain terahertz spectroscopy. Based on the obtained terahertz absorption spectra, the contrasts between the two kinds of tissue were investigated and techniques for automatic identification of cancerous tissue were studied. Distinctive differences were demonstrated in both the shape and amplitude of the absorption spectra between normal and tumor tissue. Additionally, some spectral features in the range of 0.2~0.5 THz and 1~1.5 THz were revealed for all cancerous gastric tissues. To systematically achieve the identification of gastric cancer, principal component analysis combined with t-test was used to extract valuable information indicating the best distinction between the two types. Two clustering approaches, K-means and support vector machine (SVM), were then performed to classify the processed terahertz data into normal and cancerous groups. SVM presented a satisfactory result with less false classification cases. The results of this study implicate the potential of the terahertz technique to detect gastric cancer. The applied data analysis methodology provides a suggestion for automatic discrimination of terahertz spectra in other applications.

30 CFR 27.23 - Automatic warning device.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Automatic warning device. 27.23 Section 27.23... Automatic warning device. (a) An automatic warning device shall be suitably constructed for incorporation in... automatic warning device shall include an alarm signal (audible or colored light), which shall be made to...

30 CFR 27.23 - Automatic warning device.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Automatic warning device. 27.23 Section 27.23... Automatic warning device. (a) An automatic warning device shall be suitably constructed for incorporation in... automatic warning device shall include an alarm signal (audible or colored light), which shall be made to...

30 CFR 27.23 - Automatic warning device.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Automatic warning device. 27.23 Section 27.23... Automatic warning device. (a) An automatic warning device shall be suitably constructed for incorporation in... automatic warning device shall include an alarm signal (audible or colored light), which shall be made to...

30 CFR 27.23 - Automatic warning device.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Automatic warning device. 27.23 Section 27.23... Automatic warning device. (a) An automatic warning device shall be suitably constructed for incorporation in... automatic warning device shall include an alarm signal (audible or colored light), which shall be made to...

Data-driven discovery of new Dirac semimetal materials

NASA Astrophysics Data System (ADS)

Yan, Qimin; Chen, Ru; Neaton, Jeffrey

In recent years, a significant amount of materials property data from high-throughput computations based on density functional theory (DFT) and the application of database technologies have enabled the rise of data-driven materials discovery. In this work, we initiate the extension of the data-driven materials discovery framework to the realm of topological semimetal materials and to accelerate the discovery of novel Dirac semimetals. We implement current available and develop new workflows to data-mine the Materials Project database for novel Dirac semimetals with desirable band structures and symmetry protected topological properties. This data-driven effort relies on the successful development of several automatic data generation and analysis tools, including a workflow for the automatic identification of topological invariants and pattern recognition techniques to find specific features in a massive number of computed band structures. Utilizing this approach, we successfully identified more than 15 novel Dirac point and Dirac nodal line systems that have not been theoretically predicted or experimentally identified. This work is supported by the Materials Project Predictive Modeling Center through the U.S. Department of Energy, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, under Contract No. DE-AC02-05CH11231.

Crystal Identification in Dual-Layer-Offset DOI-PET Detectors Using Stratified Peak Tracking Based on SVD and Mean-Shift Algorithm

NASA Astrophysics Data System (ADS)

Wei, Qingyang; Dai, Tiantian; Ma, Tianyu; Liu, Yaqiang; Gu, Yu

2016-10-01

An Anger-logic based pixelated PET detector block requires a crystal position map (CPM) to assign the position of each detected event to a most probable crystal index. Accurate assignments are crucial to PET imaging performance. In this paper, we present a novel automatic approach to generate the CPMs for dual-layer offset (DLO) PET detectors using a stratified peak tracking method. In which, the top and bottom layers are distinguished by their intensity difference and the peaks of the top and bottom layers are tracked based on a singular value decomposition (SVD) and mean-shift algorithm in succession. The CPM is created by classifying each pixel to its nearest peak and assigning the pixel with the crystal index of that peak. A Matlab-based graphical user interface program was developed including the automatic algorithm and a manual interaction procedure. The algorithm was tested for three DLO PET detector blocks. Results show that the proposed method exhibits good performance as well as robustness for all the three blocks. Compared to the existing methods, our approach can directly distinguish the layer and crystal indices using the information of intensity and offset grid pattern.

Processing system of jaws tomograms for pathology identification and surgical guide modeling

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

Putrik, M. B., E-mail: pmb-88@mail.ru; Ivanov, V. Yu.; Lavrentyeva, Yu. E.

The aim of the study is to create an image processing system, which allows dentists to find pathological resorption and to build surgical guide surface automatically. X-rays images of jaws from cone beam tomography or spiral computed tomography are the initial data for processing. One patient’s examination always includes up to 600 images (or tomograms), that’s why the development of processing system for fast automation search of pathologies is necessary. X-rays images can be useful not for only illness diagnostic but for treatment planning too. We have studied the case of dental implantation – for successful surgical manipulations surgical guidesmore » are used. We have created a processing system that automatically builds jaw and teeth boundaries on the x-ray image. After this step, obtained teeth boundaries used for surgical guide surface modeling and jaw boundaries limit the area for further pathologies search. Criterion for the presence of pathological resorption zones inside the limited area is based on statistical investigation. After described actions, it is possible to manufacture surgical guide using 3D printer and apply it in surgical operation.« less

Automated detection of diabetic retinopathy on digital fundus images.

PubMed

Sinthanayothin, C; Boyce, J F; Williamson, T H; Cook, H L; Mensah, E; Lal, S; Usher, D

2002-02-01

The aim was to develop an automated screening system to analyse digital colour retinal images for important features of non-proliferative diabetic retinopathy (NPDR). High performance pre-processing of the colour images was performed. Previously described automated image analysis systems were used to detect major landmarks of the retinal image (optic disc, blood vessels and fovea). Recursive region growing segmentation algorithms combined with the use of a new technique, termed a 'Moat Operator', were used to automatically detect features of NPDR. These features included haemorrhages and microaneurysms (HMA), which were treated as one group, and hard exudates as another group. Sensitivity and specificity data were calculated by comparison with an experienced fundoscopist. The algorithm for exudate recognition was applied to 30 retinal images of which 21 contained exudates and nine were without pathology. The sensitivity and specificity for exudate detection were 88.5% and 99.7%, respectively, when compared with the ophthalmologist. HMA were present in 14 retinal images. The algorithm achieved a sensitivity of 77.5% and specificity of 88.7% for detection of HMA. Fully automated computer algorithms were able to detect hard exudates and HMA. This paper presents encouraging results in automatic identification of important features of NPDR.

ADS's Dexter Data Extraction Applet

NASA Astrophysics Data System (ADS)

Demleitner, M.; Accomazzi, A.; Eichhorn, G.; Grant, C. S.; Kurtz, M. J.; Murray, S. S.

The NASA Astrophysics Data System (ADS) now holds 1.3 million scanned pages, containing numerous plots and figures for which the original data sets are lost or inaccessible. The availability of scans of the figures can significantly ease the regeneration of the data sets. For this purpose, the ADS has developed Dexter, a Java applet that supports the user in this process. Dexter's basic functionality is to let the user manually digitize a plot by marking points and defining the coordinate transformation from the logical to the physical coordinate system. Advanced features include automatic identification of axes, tracing lines and finding points matching a template. This contribution both describes the operation of Dexter from a user's point of view and discusses some of the architectural issues we faced during implementation.

Automatic identification of bird targets with radar via patterns produced by wing flapping.

PubMed

Zaugg, Serge; Saporta, Gilbert; van Loon, Emiel; Schmaljohann, Heiko; Liechti, Felix

2008-09-06

Bird identification with radar is important for bird migration research, environmental impact assessments (e.g. wind farms), aircraft security and radar meteorology. In a study on bird migration, radar signals from birds, insects and ground clutter were recorded. Signals from birds show a typical pattern due to wing flapping. The data were labelled by experts into the four classes BIRD, INSECT, CLUTTER and UFO (unidentifiable signals). We present a classification algorithm aimed at automatic recognition of bird targets. Variables related to signal intensity and wing flapping pattern were extracted (via continuous wavelet transform). We used support vector classifiers to build predictive models. We estimated classification performance via cross validation on four datasets. When data from the same dataset were used for training and testing the classifier, the classification performance was extremely to moderately high. When data from one dataset were used for training and the three remaining datasets were used as test sets, the performance was lower but still extremely to moderately high. This shows that the method generalizes well across different locations or times. Our method provides a substantial gain of time when birds must be identified in large collections of radar signals and it represents the first substantial step in developing a real time bird identification radar system. We provide some guidelines and ideas for future research.

A new technology for automatic identification and sorting of plastics for recycling.

PubMed

Ahmad, S R

2004-10-01

A new technology for automatic sorting of plastics, based upon optical identification of fluorescence signatures of dyes, incorporated in such materials in trace concentrations prior to product manufacturing, is described. Three commercial tracers were selected primarily on the basis of their good absorbency in the 310-370 nm spectral band and their identifiable narrow-band fluorescence signatures in the visible band of the spectrum when present in binary combinations. This absorption band was selected because of the availability of strong emission lines in this band from a commercial Hg-arc lamp and high fluorescence quantum yields of the tracers at this excitation wavelength band. The plastics chosen for tracing and identification are HDPE, LDPE, PP, EVA, PVC and PET and the tracers were compatible and chemically non-reactive with the host matrices and did not affect the transparency of the plastics. The design of a monochromatic and collimated excitation source, the sensor system are described and their performances in identifying and sorting plastics doped with tracers at a few parts per million concentration levels are evaluated. In an industrial sorting system, the sensor was able to sort 300 mm long plastic bottles at a conveyor belt speed of 3.5 m.sec(-1) with a sorting purity of -95%. The limitation was imposed due to mechanical singulation irregularities at high speed and the limited processing speed of the computer used.

Performance of wavelet analysis and neural networks for pathological voices identification

NASA Astrophysics Data System (ADS)

Salhi, Lotfi; Talbi, Mourad; Abid, Sabeur; Cherif, Adnane

2011-09-01

Within the medical environment, diverse techniques exist to assess the state of the voice of the patient. The inspection technique is inconvenient for a number of reasons, such as its high cost, the duration of the inspection, and above all, the fact that it is an invasive technique. This study focuses on a robust, rapid and accurate system for automatic identification of pathological voices. This system employs non-invasive, non-expensive and fully automated method based on hybrid approach: wavelet transform analysis and neural network classifier. First, we present the results obtained in our previous study while using classic feature parameters. These results allow visual identification of pathological voices. Second, quantified parameters drifting from the wavelet analysis are proposed to characterise the speech sample. On the other hand, a system of multilayer neural networks (MNNs) has been developed which carries out the automatic detection of pathological voices. The developed method was evaluated using voice database composed of recorded voice samples (continuous speech) from normophonic or dysphonic speakers. The dysphonic speakers were patients of a National Hospital 'RABTA' of Tunis Tunisia and a University Hospital in Brussels, Belgium. Experimental results indicate a success rate ranging between 75% and 98.61% for discrimination of normal and pathological voices using the proposed parameters and neural network classifier. We also compared the average classification rate based on the MNN, Gaussian mixture model and support vector machines.

Infrared Cephalic-Vein to Assist Blood Extraction Tasks: Automatic Projection and Recognition

NASA Astrophysics Data System (ADS)

Lagüela, S.; Gesto, M.; Riveiro, B.; González-Aguilera, D.

2017-05-01

Thermal infrared band is not commonly used in photogrammetric and computer vision algorithms, mainly due to the low spatial resolution of this type of imagery. However, this band captures sub-superficial information, increasing the capabilities of visible bands regarding applications. This fact is especially important in biomedicine and biometrics, allowing the geometric characterization of interior organs and pathologies with photogrammetric principles, as well as the automatic identification and labelling using computer vision algorithms. This paper presents advances of close-range photogrammetry and computer vision applied to thermal infrared imagery, with the final application of Augmented Reality in order to widen its application in the biomedical field. In this case, the thermal infrared image of the arm is acquired and simultaneously projected on the arm, together with the identification label of the cephalic-vein. This way, blood analysts are assisted in finding the vein for blood extraction, especially in those cases where the identification by the human eye is a complex task. Vein recognition is performed based on the Gaussian temperature distribution in the area of the vein, while the calibration between projector and thermographic camera is developed through feature extraction and pattern recognition. The method is validated through its application to a set of volunteers, with different ages and genres, in such way that different conditions of body temperature and vein depth are covered for the applicability and reproducibility of the method.

A support vector machine approach to the automatic identification of fluorescence spectra emitted by biological agents

NASA Astrophysics Data System (ADS)

Gelfusa, M.; Murari, A.; Lungaroni, M.; Malizia, A.; Parracino, S.; Peluso, E.; Cenciarelli, O.; Carestia, M.; Pizzoferrato, R.; Vega, J.; Gaudio, P.

2016-10-01

Two of the major new concerns of modern societies are biosecurity and biosafety. Several biological agents (BAs) such as toxins, bacteria, viruses, fungi and parasites are able to cause damage to living systems either humans, animals or plants. Optical techniques, in particular LIght Detection And Ranging (LIDAR), based on the transmission of laser pulses and analysis of the return signals, can be successfully applied to monitoring the release of biological agents into the atmosphere. It is well known that most of biological agents tend to emit specific fluorescence spectra, which in principle allow their detection and identification, if excited by light of the appropriate wavelength. For these reasons, the detection of the UVLight Induced Fluorescence (UV-LIF) emitted by BAs is particularly promising. On the other hand, the stand-off detection of BAs poses a series of challenging issues; one of the most severe is the automatic discrimination between various agents which emit very similar fluorescence spectra. In this paper, a new data analysis method, based on a combination of advanced filtering techniques and Support Vector Machines, is described. The proposed approach covers all the aspects of the data analysis process, from filtering and denoising to automatic recognition of the agents. A systematic series of numerical tests has been performed to assess the potential and limits of the proposed methodology. The first investigations of experimental data have already given very encouraging results.

Signal-Noise Identification of Magnetotelluric Signals Using Fractal-Entropy and Clustering Algorithm for Targeted De-Noising

NASA Astrophysics Data System (ADS)

Li, Jin; Zhang, Xian; Gong, Jinzhe; Tang, Jingtian; Ren, Zhengyong; Li, Guang; Deng, Yanli; Cai, Jin

A new technique is proposed for signal-noise identification and targeted de-noising of Magnetotelluric (MT) signals. This method is based on fractal-entropy and clustering algorithm, which automatically identifies signal sections corrupted by common interference (square, triangle and pulse waves), enabling targeted de-noising and preventing the loss of useful information in filtering. To implement the technique, four characteristic parameters — fractal box dimension (FBD), higuchi fractal dimension (HFD), fuzzy entropy (FuEn) and approximate entropy (ApEn) — are extracted from MT time-series. The fuzzy c-means (FCM) clustering technique is used to analyze the characteristic parameters and automatically distinguish signals with strong interference from the rest. The wavelet threshold (WT) de-noising method is used only to suppress the identified strong interference in selected signal sections. The technique is validated through signal samples with known interference, before being applied to a set of field measured MT/Audio Magnetotelluric (AMT) data. Compared with the conventional de-noising strategy that blindly applies the filter to the overall dataset, the proposed method can automatically identify and purposefully suppress the intermittent interference in the MT/AMT signal. The resulted apparent resistivity-phase curve is more continuous and smooth, and the slow-change trend in the low-frequency range is more precisely reserved. Moreover, the characteristic of the target-filtered MT/AMT signal is close to the essential characteristic of the natural field, and the result more accurately reflects the inherent electrical structure information of the measured site.

Segmentation of financial seals and its implementation on a DSP-based system

NASA Astrophysics Data System (ADS)

He, Jin; Liu, Tiegen; Guo, Jingjing; Zhang, Hao

2009-11-01

Automatic seal imprint identification is an important part of modern financial security. Accurate segmentation is the basis of correct identification. In this paper, a DSP (digital signal processor) based identification system was designed, and an adaptive algorithm was proposed to extract binary seal images from financial instruments. As the kernel of the identification system, a DSP chip of TMS320DM642 was used to implement image processing, controlling and coordinating works of each system module. The proposed algorithm consisted of three stages, including extraction of grayscale seal image, denoising and binarization. A grayscale seal image was extracted by color transform from a financial instrument image. Adaptive morphological operations were used to highlight details of the extracted grayscale seal image and smooth the background. After median filter for noise elimination, the filtered seal image was binarized by Otsu's method. The algorithm was developed based on the DSP development environment CCS and real-time operation system DSP/BIOS. To simplify the implementation of the proposed algorithm, the calibration of white balance and the coarse positioning of the seal imprint were implemented by TMS320DM642 controlling image acquisition. IMGLIB of TMS320DM642 was used for the efficiency improvement. The experiment result showed that financial seal imprints, even with intricate and dense strokes can be correctly segmented by the proposed algorithm. Adhesion and incompleteness distortions in the segmentation results were reduced, even when the original seal imprint had a poor quality.

Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining.

PubMed

Hettne, Kristina M; Williams, Antony J; van Mulligen, Erik M; Kleinjans, Jos; Tkachenko, Valery; Kors, Jan A

2010-03-23

Previously, we developed a combined dictionary dubbed Chemlist for the identification of small molecules and drugs in text based on a number of publicly available databases and tested it on an annotated corpus. To achieve an acceptable recall and precision we used a number of automatic and semi-automatic processing steps together with disambiguation rules. However, it remained to be investigated which impact an extensive manual curation of a multi-source chemical dictionary would have on chemical term identification in text. ChemSpider is a chemical database that has undergone extensive manual curation aimed at establishing valid chemical name-to-structure relationships. We acquired the component of ChemSpider containing only manually curated names and synonyms. Rule-based term filtering, semi-automatic manual curation, and disambiguation rules were applied. We tested the dictionary from ChemSpider on an annotated corpus and compared the results with those for the Chemlist dictionary. The ChemSpider dictionary of ca. 80 k names was only a 1/3 to a 1/4 the size of Chemlist at around 300 k. The ChemSpider dictionary had a precision of 0.43 and a recall of 0.19 before the application of filtering and disambiguation and a precision of 0.87 and a recall of 0.19 after filtering and disambiguation. The Chemlist dictionary had a precision of 0.20 and a recall of 0.47 before the application of filtering and disambiguation and a precision of 0.67 and a recall of 0.40 after filtering and disambiguation. We conclude the following: (1) The ChemSpider dictionary achieved the best precision but the Chemlist dictionary had a higher recall and the best F-score; (2) Rule-based filtering and disambiguation is necessary to achieve a high precision for both the automatically generated and the manually curated dictionary. ChemSpider is available as a web service at http://www.chemspider.com/ and the Chemlist dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web at http://www.biosemantics.org/chemlist.

Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining

PubMed Central

2010-01-01

Background Previously, we developed a combined dictionary dubbed Chemlist for the identification of small molecules and drugs in text based on a number of publicly available databases and tested it on an annotated corpus. To achieve an acceptable recall and precision we used a number of automatic and semi-automatic processing steps together with disambiguation rules. However, it remained to be investigated which impact an extensive manual curation of a multi-source chemical dictionary would have on chemical term identification in text. ChemSpider is a chemical database that has undergone extensive manual curation aimed at establishing valid chemical name-to-structure relationships. Results We acquired the component of ChemSpider containing only manually curated names and synonyms. Rule-based term filtering, semi-automatic manual curation, and disambiguation rules were applied. We tested the dictionary from ChemSpider on an annotated corpus and compared the results with those for the Chemlist dictionary. The ChemSpider dictionary of ca. 80 k names was only a 1/3 to a 1/4 the size of Chemlist at around 300 k. The ChemSpider dictionary had a precision of 0.43 and a recall of 0.19 before the application of filtering and disambiguation and a precision of 0.87 and a recall of 0.19 after filtering and disambiguation. The Chemlist dictionary had a precision of 0.20 and a recall of 0.47 before the application of filtering and disambiguation and a precision of 0.67 and a recall of 0.40 after filtering and disambiguation. Conclusions We conclude the following: (1) The ChemSpider dictionary achieved the best precision but the Chemlist dictionary had a higher recall and the best F-score; (2) Rule-based filtering and disambiguation is necessary to achieve a high precision for both the automatically generated and the manually curated dictionary. ChemSpider is available as a web service at http://www.chemspider.com/ and the Chemlist dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web at http://www.biosemantics.org/chemlist. PMID:20331846

Automated fault-management in a simulated spaceflight micro-world

NASA Technical Reports Server (NTRS)

Lorenz, Bernd; Di Nocera, Francesco; Rottger, Stefan; Parasuraman, Raja

2002-01-01

BACKGROUND: As human spaceflight missions extend in duration and distance from Earth, a self-sufficient crew will bear far greater onboard responsibility and authority for mission success. This will increase the need for automated fault management (FM). Human factors issues in the use of such systems include maintenance of cognitive skill, situational awareness (SA), trust in automation, and workload. This study examine the human performance consequences of operator use of intelligent FM support in interaction with an autonomous, space-related, atmospheric control system. METHODS: An expert system representing a model-base reasoning agent supported operators at a low level of automation (LOA) by a computerized fault finding guide, at a medium LOA by an automated diagnosis and recovery advisory, and at a high LOA by automate diagnosis and recovery implementation, subject to operator approval or veto. Ten percent of the experimental trials involved complete failure of FM support. RESULTS: Benefits of automation were reflected in more accurate diagnoses, shorter fault identification time, and reduced subjective operator workload. Unexpectedly, fault identification times deteriorated more at the medium than at the high LOA during automation failure. Analyses of information sampling behavior showed that offloading operators from recovery implementation during reliable automation enabled operators at high LOA to engage in fault assessment activities CONCLUSIONS: The potential threat to SA imposed by high-level automation, in which decision advisories are automatically generated, need not inevitably be counteracted by choosing a lower LOA. Instead, freeing operator cognitive resources by automatic implementation of recover plans at a higher LOA can promote better fault comprehension, so long as the automation interface is designed to support efficient information sampling.

Finding complex biological relationships in recent PubMed articles using Bio-LDA.

PubMed

Wang, Huijun; Ding, Ying; Tang, Jie; Dong, Xiao; He, Bing; Qiu, Judy; Wild, David J

2011-03-23

The overwhelming amount of available scholarly literature in the life sciences poses significant challenges to scientists wishing to keep up with important developments related to their research, but also provides a useful resource for the discovery of recent information concerning genes, diseases, compounds and the interactions between them. In this paper, we describe an algorithm called Bio-LDA that uses extracted biological terminology to automatically identify latent topics, and provides a variety of measures to uncover putative relations among topics and bio-terms. Relationships identified using those approaches are combined with existing data in life science datasets to provide additional insight. Three case studies demonstrate the utility of the Bio-LDA model, including association predication, association search and connectivity map generation. This combined approach offers new opportunities for knowledge discovery in many areas of biology including target identification, lead hopping and drug repurposing.

Advances in multi-sensor data fusion: algorithms and applications.

PubMed

Dong, Jiang; Zhuang, Dafang; Huang, Yaohuan; Fu, Jingying

2009-01-01

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme.

Finding Complex Biological Relationships in Recent PubMed Articles Using Bio-LDA

PubMed Central

Wang, Huijun; Ding, Ying; Tang, Jie; Dong, Xiao; He, Bing; Qiu, Judy; Wild, David J.

2011-01-01

The overwhelming amount of available scholarly literature in the life sciences poses significant challenges to scientists wishing to keep up with important developments related to their research, but also provides a useful resource for the discovery of recent information concerning genes, diseases, compounds and the interactions between them. In this paper, we describe an algorithm called Bio-LDA that uses extracted biological terminology to automatically identify latent topics, and provides a variety of measures to uncover putative relations among topics and bio-terms. Relationships identified using those approaches are combined with existing data in life science datasets to provide additional insight. Three case studies demonstrate the utility of the Bio-LDA model, including association predication, association search and connectivity map generation. This combined approach offers new opportunities for knowledge discovery in many areas of biology including target identification, lead hopping and drug repurposing. PMID:21448266

Japan's 2014 General Election: Political Bots, Right-Wing Internet Activism, and Prime Minister Shinzō Abe's Hidden Nationalist Agenda.

PubMed

Schäfer, Fabian; Evert, Stefan; Heinrich, Philipp

2017-12-01

In this article, we present results on the identification and behavioral analysis of social bots in a sample of 542,584 Tweets, collected before and after Japan's 2014 general election. Typical forms of bot activity include massive Retweeting and repeated posting of (nearly) the same message, sometimes used in combination. We focus on the second method and present (1) a case study on several patterns of bot activity, (2) methodological considerations on the automatic identification of such patterns and the prerequisite near-duplicate detection, and (3) we give qualitative insights into the purposes behind the usage of social/political bots. We argue that it was in the latency of the semi-public sphere of social media-and not in the visible or manifest public sphere (official campaign platform, mass media)-where Shinzō Abe's hidden nationalist agenda interlocked and overlapped with the one propagated by organizations such as Nippon Kaigi and Internet right-wingers (netto uyo) during the election campaign, the latter potentially forming an enormous online support army of Abe's agenda.

Japan's 2014 General Election: Political Bots, Right-Wing Internet Activism, and Prime Minister Shinzō Abe's Hidden Nationalist Agenda

PubMed Central

Schäfer, Fabian; Evert, Stefan; Heinrich, Philipp

2017-01-01

Abstract In this article, we present results on the identification and behavioral analysis of social bots in a sample of 542,584 Tweets, collected before and after Japan's 2014 general election. Typical forms of bot activity include massive Retweeting and repeated posting of (nearly) the same message, sometimes used in combination. We focus on the second method and present (1) a case study on several patterns of bot activity, (2) methodological considerations on the automatic identification of such patterns and the prerequisite near-duplicate detection, and (3) we give qualitative insights into the purposes behind the usage of social/political bots. We argue that it was in the latency of the semi-public sphere of social media—and not in the visible or manifest public sphere (official campaign platform, mass media)—where Shinzō Abe's hidden nationalist agenda interlocked and overlapped with the one propagated by organizations such as Nippon Kaigi and Internet right-wingers (netto uyo) during the election campaign, the latter potentially forming an enormous online support army of Abe's agenda. PMID:29182493

The PREP pipeline: standardized preprocessing for large-scale EEG analysis.

PubMed

Bigdely-Shamlo, Nima; Mullen, Tim; Kothe, Christian; Su, Kyung-Min; Robbins, Kay A

2015-01-01

The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.

Writer identification on historical Glagolitic documents

NASA Astrophysics Data System (ADS)

Fiel, Stefan; Hollaus, Fabian; Gau, Melanie; Sablatnig, Robert

2013-12-01

This work aims at automatically identifying scribes of historical Slavonic manuscripts. The quality of the ancient documents is partially degraded by faded-out ink or varying background. The writer identification method used is based on image features, which are described with Scale Invariant Feature Transform (SIFT) features. A visual vocabulary is used for the description of handwriting characteristics, whereby the features are clustered using a Gaussian Mixture Model and employing the Fisher kernel. The writer identification approach is originally designed for grayscale images of modern handwritings. But contrary to modern documents, the historical manuscripts are partially corrupted by background clutter and water stains. As a result, SIFT features are also found on the background. Since the method shows also good results on binarized images of modern handwritings, the approach was additionally applied on binarized images of the ancient writings. Experiments show that this preprocessing step leads to a significant performance increase: The identification rate on binarized images is 98.9%, compared to an identification rate of 87.6% gained on grayscale images.

Automated colour identification in melanocytic lesions.

PubMed

Sabbaghi, S; Aldeen, M; Garnavi, R; Varigos, G; Doliantis, C; Nicolopoulos, J

2015-08-01

Colour information plays an important role in classifying skin lesion. However, colour identification by dermatologists can be very subjective, leading to cases of misdiagnosis. Therefore, a computer-assisted system for quantitative colour identification is highly desirable for dermatologists to use. Although numerous colour detection systems have been developed, few studies have focused on imitating the human visual perception of colours in melanoma application. In this paper we propose a new methodology based on QuadTree decomposition technique for automatic colour identification in dermoscopy images. Our approach mimics the human perception of lesion colours. The proposed method is trained on a set of 47 images from NIH dataset and applied to a test set of 190 skin lesions obtained from PH2 dataset. The results of our proposed method are compared with a recently reported colour identification method using the same dataset. The effectiveness of our method in detecting colours in dermoscopy images is vindicated by obtaining approximately 93% accuracy when the CIELab1 colour space is used.

Input-output identification of controlled discrete manufacturing systems

NASA Astrophysics Data System (ADS)

Estrada-Vargas, Ana Paula; López-Mellado, Ernesto; Lesage, Jean-Jacques

2014-03-01

The automated construction of discrete event models from observations of external system's behaviour is addressed. This problem, often referred to as system identification, allows obtaining models of ill-known (or even unknown) systems. In this article, an identification method for discrete event systems (DESs) controlled by a programmable logic controller is presented. The method allows processing a large quantity of observed long sequences of input/output signals generated by the controller and yields an interpreted Petri net model describing the closed-loop behaviour of the automated DESs. The proposed technique allows the identification of actual complex systems because it is sufficiently efficient and well adapted to cope with both the technological characteristics of industrial controllers and data collection requirements. Based on polynomial-time algorithms, the method is implemented as an efficient software tool which constructs and draws the model automatically; an overview of this tool is given through a case study dealing with an automated manufacturing system.

Electronic labelling in recycling of manufactured articles.

PubMed

Olejnik, Lech; Krammer, Alfred

2002-12-01

The concept of a recycling system aiming at the recovery of resources from manufactured articles is proposed. The system integrates electronic labels for product identification and internet for global data exchange. A prototype for the recycling of electric motors has been developed, which implements a condition-based recycling decision system to automatically select the environmentally and economically appropriate recycling strategy, thereby opening a potential market for second-hand motors and creating a profitable recycling process itself. The project has been designed to evaluate the feasibility of electronic identification applied on a large number of motors and to validate the system in real field conditions.

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

NASA Astrophysics Data System (ADS)

Sedano, Fernando; Kempeneers, Pieter; Strobl, Peter; Kucera, Jan; Vogt, Peter; Seebach, Lucia; San-Miguel-Ayanz, Jesús

2011-09-01

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.

K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited

NASA Astrophysics Data System (ADS)

Wang, Dong

2016-03-01

Gears are the most commonly used components in mechanical transmission systems. Their failures may cause transmission system breakdown and result in economic loss. Identification of different gear crack levels is important to prevent any unexpected gear failure because gear cracks lead to gear tooth breakage. Signal processing based methods mainly require expertize to explain gear fault signatures which is usually not easy to be achieved by ordinary users. In order to automatically identify different gear crack levels, intelligent gear crack identification methods should be developed. The previous case studies experimentally proved that K-nearest neighbors based methods exhibit high prediction accuracies for identification of 3 different gear crack levels under different motor speeds and loads. In this short communication, to further enhance prediction accuracies of existing K-nearest neighbors based methods and extend identification of 3 different gear crack levels to identification of 5 different gear crack levels, redundant statistical features are constructed by using Daubechies 44 (db44) binary wavelet packet transform at different wavelet decomposition levels, prior to the use of a K-nearest neighbors method. The dimensionality of redundant statistical features is 620, which provides richer gear fault signatures. Since many of these statistical features are redundant and highly correlated with each other, dimensionality reduction of redundant statistical features is conducted to obtain new significant statistical features. At last, the K-nearest neighbors method is used to identify 5 different gear crack levels under different motor speeds and loads. A case study including 3 experiments is investigated to demonstrate that the developed method provides higher prediction accuracies than the existing K-nearest neighbors based methods for recognizing different gear crack levels under different motor speeds and loads. Based on the new significant statistical features, some other popular statistical models including linear discriminant analysis, quadratic discriminant analysis, classification and regression tree and naive Bayes classifier, are compared with the developed method. The results show that the developed method has the highest prediction accuracies among these statistical models. Additionally, selection of the number of new significant features and parameter selection of K-nearest neighbors are thoroughly investigated.

Who identifies with suicidal film characters? Determinants of identification with suicidal protagonists of drama films.

PubMed

Till, Benedikt; Herberth, Arno; Sonneck, Gernot; Vitouch, Peter; Niederkrotenthaler, Thomas

2013-06-01

Identification with a media character is an influential factor for the effects of a media product on the recipient, but still very little is known about this cognitive process. This study investigated to what extent identification of a recipient with the suicidal protagonist of a film drama is influenced by the similarity between them in terms of sex, age, and education as well as by the viewer's empathy and suicidality. Sixty adults were assigned randomly to one of two film groups. Both groups watched a drama that concluded with the tragic suicide of the protagonist. Identification, empathy, suicidality, as well as socio-demographic data were measured by questionnaires that were applied before and after the movie screening. Results indicated that identification was not associated with socio-demographic similarity or the viewer's suicidality. However, the greater the subjects' empathy was, the more they identified with the protagonist in one of the two films. This investigation provides evidence that challenges the common assumption that identification with a film character is automatically generated when viewer and protagonist are similar in terms of sex, age, education or attitude.

Automatic Text Structuring and Summarization.

ERIC Educational Resources Information Center

Salton, Gerard; And Others

1997-01-01

Discussion of the use of information retrieval techniques for automatic generation of semantic hypertext links focuses on automatic text summarization. Topics include World Wide Web links, text segmentation, and evaluation of text summarization by comparing automatically generated abstracts with manually prepared abstracts. (Author/LRW)

75 FR 67362 - Notice of Public Information Collection(s) Being Submitted for Review and Approval to the Office...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-02

... Commission also deferred the date for initial distribution of Automatic Location Identification (ALI)-capable... activating new ALI-capable handsets; deferred the date by which a carrier must achieve full penetration of ALI-capable handsets by one year; modified the manner in which the Commission defined full penetration...

Cognitive Conflict in a Syllable Identification Task Causes Transient Activation of Speech Perception Area

ERIC Educational Resources Information Center

Saetrevik, Bjorn; Specht, Karsten

2012-01-01

It has previously been shown that task performance and frontal cortical activation increase after cognitive conflict. This has been argued to support a model of attention where the level of conflict automatically adjusts the amount of cognitive control applied. Conceivably, conflict could also modulate lower-level processing pathways, which would…

Query Classification and Study of University Students' Search Trends

ERIC Educational Resources Information Center

Maabreh, Majdi A.; Al-Kabi, Mohammed N.; Alsmadi, Izzat M.

2012-01-01

Purpose: This study is an attempt to develop an automatic identification method for Arabic web queries and divide them into several query types using data mining. In addition, it seeks to evaluate the impact of the academic environment on using the internet. Design/methodology/approach: The web log files were collected from one of the higher…

78 FR 44265 - Unified Agenda of Federal Regulatory and Deregulatory Actions

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-23

... . RIN: 1625-AB27 184. Marine Vapor Control Systems Legal Authority: 33 U.S.C. 1225; 42 U.S.C. 7511b(f)(2... facility and vessel vapor control systems (VCSs). The proposed changes would make VCS requirements more... Requirements for 1625-AA99 Notices of Arrival and Departure, and Automatic Identification System. 182...

Software Methodology Catalog. Second Edition. Revision

DTIC Science & Technology

1989-03-01

structured design involve characterization of the data flow through graphical representation, identification of the various transform elements, assembling...and graphical diagrams to facilitate communication within the team. The diagrams are consistent with the design language and can be automatically...organization, box structure graphics provide a visual means of client communication. These box structures are used during analysis and design to review

A modular approach to detection and identification of defects in rough lumber

Treesearch

Sang Mook Lee; A. Lynn Abbott; Daniel L. Schmoldt

2001-01-01

This paper describes a prototype scanning system that can automatically identify several important defects on rough hardwood lumber. The scanning system utilizes 3 laser sources and an embedded-processor camera to capture and analyze profile and gray-scale images. The modular approach combines the detection of wane (the curved sides of a board, possibly containing...

The Relationship Between Reading Fluency and Vocabulary in Fifth Grade Turkish Students

ERIC Educational Resources Information Center

Yildirim, Kasim; Rasinski, Timothy; Ates, Seyit; Fitzgerald, Shawn; Zimmerman, Belinda; Yildiz, Mustafa

2014-01-01

Reading fluency has traditionally been recognized as a competency associated with word recognition and comprehension. As readers become more automatic in word identification they are able to devote less attention and cognitive resources to word decoding and more to text comprehension. The act of reading itself has been associated with growth in…

Three-dimensional reconstruction from serial sections in PC-Windows platform by using 3D_Viewer.

PubMed

Xu, Yi-Hua; Lahvis, Garet; Edwards, Harlene; Pitot, Henry C

2004-11-01

Three-dimensional (3D) reconstruction from serial sections allows identification of objects of interest in 3D and clarifies the relationship among these objects. 3D_Viewer, developed in our laboratory for this purpose, has four major functions: image alignment, movie frame production, movie viewing, and shift-overlay image generation. Color images captured from serial sections were aligned; then the contours of objects of interest were highlighted in a semi-automatic manner. These 2D images were then automatically stacked at different viewing angles, and their composite images on a projected plane were recorded by an image transform-shift-overlay technique. These composition images are used in the object-rotation movie show. The design considerations of the program and the procedures used for 3D reconstruction from serial sections are described. This program, with a digital image-capture system, a semi-automatic contours highlight method, and an automatic image transform-shift-overlay technique, greatly speeds up the reconstruction process. Since images generated by 3D_Viewer are in a general graphic format, data sharing with others is easy. 3D_Viewer is written in MS Visual Basic 6, obtainable from our laboratory on request.

Terminologies for text-mining; an experiment in the lipoprotein metabolism domain

PubMed Central

Alexopoulou, Dimitra; Wächter, Thomas; Pickersgill, Laura; Eyre, Cecilia; Schroeder, Michael

2008-01-01

Background The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them. Results We share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition (ATR) methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods. Conclusions Given high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking ATR results as input and following the guidelines we described. Availability The TFIDF term recognition is available as Web Service, described at PMID:18460175

Automatic detection of patient identification and positioning errors in radiation therapy treatment using 3-dimensional setup images.

PubMed

Jani, Shyam S; Low, Daniel A; Lamb, James M

2015-01-01

To develop an automated system that detects patient identification and positioning errors between 3-dimensional computed tomography (CT) and kilovoltage CT planning images. Planning kilovoltage CT images were collected for head and neck (H&N), pelvis, and spine treatments with corresponding 3-dimensional cone beam CT and megavoltage CT setup images from TrueBeam and TomoTherapy units, respectively. Patient identification errors were simulated by registering setup and planning images from different patients. For positioning errors, setup and planning images were misaligned by 1 to 5 cm in the 6 anatomical directions for H&N and pelvis patients. Spinal misalignments were simulated by misaligning to adjacent vertebral bodies. Image pairs were assessed using commonly used image similarity metrics as well as custom-designed metrics. Linear discriminant analysis classification models were trained and tested on the imaging datasets, and misclassification error (MCE), sensitivity, and specificity parameters were estimated using 10-fold cross-validation. For patient identification, our workflow produced MCE estimates of 0.66%, 1.67%, and 0% for H&N, pelvis, and spine TomoTherapy images, respectively. Sensitivity and specificity ranged from 97.5% to 100%. MCEs of 3.5%, 2.3%, and 2.1% were obtained for TrueBeam images of the above sites, respectively, with sensitivity and specificity estimates between 95.4% and 97.7%. MCEs for 1-cm H&N/pelvis misalignments were 1.3%/5.1% and 9.1%/8.6% for TomoTherapy and TrueBeam images, respectively. Two-centimeter MCE estimates were 0.4%/1.6% and 3.1/3.2%, respectively. MCEs for vertebral body misalignments were 4.8% and 3.6% for TomoTherapy and TrueBeam images, respectively. Patient identification and gross misalignment errors can be robustly and automatically detected using 3-dimensional setup images of different energies across 3 commonly treated anatomical sites. Copyright © 2015 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Automatic Classification of the Sub-Techniques (Gears) Used in Cross-Country Ski Skating Employing a Mobile Phone

PubMed Central

Stöggl, Thomas; Holst, Anders; Jonasson, Arndt; Andersson, Erik; Wunsch, Tobias; Norström, Christer; Holmberg, Hans-Christer

2014-01-01

The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear. PMID:25365459

[Sudden cardiac death out of the hospital and early defibrillation].

PubMed

Marín-Huerta, E; Peinado, R; Asso, A; Loma, A; Villacastín, J P; Muñiz, J; Brugada, J

2000-06-01

Since most sudden cardiac death victims show neither symptoms before the event nor other signs or risk factors that would have identified them as a high risk population before their cardiac arrest, emergency out-of-hospital medical services must be improved in order to obtain a higher survival in these patients. Early defibrillation is an essential part of the chain of survival that also includes the early identification of the victim, activation of the emergency medical system, immediate arrival of trained personnel who can perform basic cardiopulmonary resuscitation and early initiation of advanced cardiac life support that would raise the survival rate for sudden cardiac arrest victims. Many studies have demonstrated the enormous importance of early defibrillation in patients with a cardiac arrest due to ventricular fibrillation. The most important predictor of survival in these individuals is the time that elapses until electric defibrillation, the longer the time to defbrillation the lower the number of patients who are eventually discharged. Multiple studies have demonstrated that automatic external defibrillation will reduce the time elapsed to defibrillation and thus improve survival. For these reason, public access defibrillation to allow the use of automatic external defibrillators by minimally trained members of the lay public, has received increasing interest on the part of a groving number of companies, cities or countries. The automatic external defibrillaton, as performed by a lay person is being investigated. The liberalization of its application, if is demonstrated to be effective, will need to be accompanied by legal measures to endorse it and appropriate health education, probably during secondary education.

What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks.

PubMed

Bruland, Philipp; Doods, Justin; Storck, Michael; Dugas, Martin

2017-01-01

Data dictionaries provide structural meta-information about data definitions in health information technology (HIT) systems. In this regard, reusing healthcare data for secondary purposes offers several advantages (e.g. reduce documentation times or increased data quality). Prerequisites for data reuse are its quality, availability and identical meaning of data. In diverse projects, research data warehouses serve as core components between heterogeneous clinical databases and various research applications. Given the complexity (high number of data elements) and dynamics (regular updates) of electronic health record (EHR) data structures, we propose a clinical metadata warehouse (CMDW) based on a metadata registry standard. Metadata of two large hospitals were automatically inserted into two CMDWs containing 16,230 forms and 310,519 data elements. Automatic updates of metadata are possible as well as semantic annotations. A CMDW allows metadata discovery, data quality assessment and similarity analyses. Common data models for distributed research networks can be established based on similarity analyses.

Novel 3-D free-form surface profilometry for reverse engineering

NASA Astrophysics Data System (ADS)

Chen, Liang-Chia; Huang, Zhi-Xue

2005-01-01

This article proposes an innovative 3-D surface contouring approach for automatic and accurate free-form surface reconstruction using a sensor integration concept. The study addresses a critical problem in accurate measurement of free-form surfaces by developing an automatic reconstruction approach. Unacceptable measuring accuracy issues are mainly due to the errors arising from the use of inadequate measuring strategies, ending up with inaccurate digitised data and costly post-data processing in Reverse Engineering (RE). This article is thus aimed to develop automatic digitising strategies for ensuring surface reconstruction efficiency, as well as accuracy. The developed approach consists of two main stages, namely the rapid shape identification (RSI) and the automated laser scanning (ALS) for completing 3-D surface profilometry. This developed approach effectively utilises the advantages of on-line geometric information to evaluate the degree of satisfaction of user-defined digitising accuracy under a triangular topological patch. An industrial case study was used to attest the feasibility of the approach.

Automatic script identification from images using cluster-based templates

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

Hochberg, J.; Kerns, L.; Kelly, P.

We have developed a technique for automatically identifying the script used to generate a document that is stored electronically in bit image form. Our approach differs from previous work in that the distinctions among scripts are discovered by an automatic learning procedure, without any handson analysis. We first develop a set of representative symbols (templates) for each script in our database (Cyrillic, Roman, etc.). We do this by identifying all textual symbols in a set of training documents, scaling each symbol to a fixed size, clustering similar symbols, pruning minor clusters, and finding each cluster`s centroid. To identify a newmore » document`s script, we identify and scale a subset of symbols from the document and compare them to the templates for each script. We choose the script whose templates provide the best match. Our current system distinguishes among the Armenian, Burmese, Chinese, Cyrillic, Ethiopic, Greek, Hebrew, Japanese, Korean, Roman, and Thai scripts with over 90% accuracy.« less

Multisource oil spill detection

NASA Astrophysics Data System (ADS)

Salberg, Arnt B.; Larsen, Siri O.; Zortea, Maciel

2013-10-01

In this paper we discuss how multisource data (wind, ocean-current, optical, bathymetric, automatic identification systems (AIS)) may be used to improve oil spill detection in SAR images, with emphasis on the use of automatic oil spill detection algorithms. We focus particularly on AIS, optical, and bathymetric data. For the AIS data we propose an algorithm for integrating AIS ship tracks into automatic oil spill detection in order to improve the confidence estimate of a potential oil spill. We demonstrate the use of ancillary data on a set of SAR images. Regarding the use of optical data, we did not observe a clear correspondence between high chlorophyll values (estimated from products derived from optical data) and observed slicks in the SAR image. Bathymetric data was shown to be a good data source for removing false detections caused by e.g. sand banks on low tide. For the AIS data we observed that a polluter could be identified for some dark slicks, however, a precise oil drift model is needed in order to identify the polluter with high certainty.

Automatic identification of the number of food items in a meal using clustering techniques based on the monitoring of swallowing and chewing.

PubMed

Lopez-Meyer, Paulo; Schuckers, Stephanie; Makeyev, Oleksandr; Fontana, Juan M; Sazonov, Edward

2012-09-01

The number of distinct foods consumed in a meal is of significant clinical concern in the study of obesity and other eating disorders. This paper proposes the use of information contained in chewing and swallowing sequences for meal segmentation by food types. Data collected from experiments of 17 volunteers were analyzed using two different clustering techniques. First, an unsupervised clustering technique, Affinity Propagation (AP), was used to automatically identify the number of segments within a meal. Second, performance of the unsupervised AP method was compared to a supervised learning approach based on Agglomerative Hierarchical Clustering (AHC). While the AP method was able to obtain 90% accuracy in predicting the number of food items, the AHC achieved an accuracy >95%. Experimental results suggest that the proposed models of automatic meal segmentation may be utilized as part of an integral application for objective Monitoring of Ingestive Behavior in free living conditions.

Automatic inoculating apparatus. [includes movable carraige, drive motor, and swabbing motor

NASA Technical Reports Server (NTRS)

Wilkins, J. R.; Mills, S. M. (Inventor)

1974-01-01

An automatic inoculating apparatus for agar trays is described and using a simple inoculating element, such as a cotton swab or inoculating loop. The apparatus includes a movable carriage for supporting the tray to be inoculated, a drive motor for moving the tray along a trackway, and a swabbing motor for automatically swabbing the tray during the movement. An actuator motor controls lowering of the inoculating element onto the tray and lifting of the inoculating element. An electrical control system, including limit microswitches, enables automatic control of the actuator motor and return of the carriage to the initial position after inoculating is completed.

[The mediating role of anger in the relationship between automatic thoughts and physical aggression in adolescents].

PubMed

Yavuzer, Yasemin; Karataş, Zeynep

2013-01-01

This study aimed to examine the mediating role of anger in the relationship between automatic thoughts and physical aggression in adolescents. The study included 224 adolescents in the 9th grade of 3 different high schools in central Burdur during the 2011-2012 academic year. Participants completed the Aggression Questionnaire and Automatic Thoughts Scale in their classrooms during counseling sessions. Data were analyzed using simple and multiple linear regression analysis. There were positive correlations between the adolescents' automatic thoughts, and physical aggression, and anger. According to regression analysis, automatic thoughts effectively predicted the level of physical aggression (b= 0.233, P < 0.001)) and anger (b= 0.325, P < 0.001). Analysis of the mediating role of anger showed that anger fully mediated the relationship between automatic thoughts and physical aggression (Sobel z = 5.646, P < 0.001). Anger fully mediated the relationship between automatic thoughts and physical aggression. Providing adolescents with anger management skills training is very important for the prevention of physical aggression. Such training programs should include components related to the development of an awareness of dysfunctional and anger-triggering automatic thoughts, and how to change them. As the study group included adolescents from Burdur, the findings can only be generalized to groups with similar characteristics.

Differences between autogenous and reactive obsessions in terms of metacognitions and automatic thoughts.

PubMed

Keleş Altun, İlkay; Uysal, Emel; Özkorumak Karagüzel, Evrim

2017-01-01

Obsessive compulsive disorder (OCD) is characterized by obsessions and compulsions. Obsessions have been classified as autogenous obsessions and reactive obsessions on the basis of the cognitive theory of Lee and Kwon. The aim of this study was to investigate the differences between autogenous groups (AG) and reactive groups (RG) in terms of metacognition and automatic thoughts, for the purpose of investigating the differences of cognitive appraisals. One hundred and thirty-three patients diagnosed with OCD were included in the study as the patient group. A control group was formed of 133 age, gender and education-matched healthy individuals. The OCD group patients were separated into subgroups according to the primary obsessions. The sociodemographic data, and the Yale-Brown Obsessive Compulsive Scale, Metacognition Questionnaire-30 (MCQ-30), Automatic Thoughts Questionnaire (ATQ), Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI) scores of the AG, RG, and control groups were compared. The MCQ-30 (total) and the subscales of MCQ-30 and ATQ scale points were seen to be significantly higher in the AG than in the RG and significantly higher in the RG than in the control group. In the reactive obsession group, the predictive variables of the ATQ points were determined to be MCQ-30 (total), BDI and BAI. In the autogenous obsession group, the predictive variables of the ATQ points were determined to be BDI and BAI. In the current study, differences were determined between the AG and the RG in respect of metacognitions and automatic thoughts. In light of these results, the recommended grouping can be considered useful in the identification of OCD sub-types. There is a need for further studies to identify more homogenous sub-types of OCD. Future multi-centered studies of sub-typing with larger samples using more specific instruments to sub-type and dimensional evaluation will be useful for detailed evaluation and better understanding of the subject.

The Use of Automatic Indexing for Authority Control.

ERIC Educational Resources Information Center

Dillon, Martin; And Others

1981-01-01

Uses an experimental system for authority control on a collection of bibliographic records to demonstrate the resemblance between thesaurus-based automatic indexing and automatic authority control. Details of the automatic indexing system are given, results discussed, and the benefits of the resemblance examined. Included are a rules appendix and…

75 FR 80886 - Ninth Meeting-RTCA Special Committee 220: Automatic Flight Guidance and Control

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-23

... 220: Automatic Flight Guidance and Control AGENCY: Federal Aviation Administration (FAA), DOT. ACTION: Notice of RTCA Special Committee 220: Automatic Flight Guidance and Control meeting. SUMMARY: The FAA is... for a Special Committee 220: Automatic Flight Guidance and Control meeting. The agenda will include...

Identification, display, and use of symmetry elements in atomic and electronic structure models.

PubMed

Khosrovani, N; Kung, P W; Freeman, C M; Gorman, A M; Kölmel, C M; Levine, S M; Newsam, J M

1999-01-01

Crystallographic symmetry plays an important role in structure determination from diffraction or scattering data, in spectroscopy and in simulations. It is convenient and insightful to integrate the display and use of such symmetry data with data analysis and modeling methods. We outline the integration of a suite of crystallographic algorithms, closely coupled with interactive graphical displays. These include techniques for identifying the unit cell of a solid, for automatically determining space and point group symmetries, for generalized displays of symmetry elements overlaid on structural models, and for construction, editing, and transformation of models subject to symmetry constraints. In addition, electron densities derived from periodic density functional calculations can be symmetrized and displayed with the corresponding symmetry elements. Applications of these various capabilities in crystallographic research are illustrated by topical examples.

Benefit of the Use of GCxGC/MS Profiles for 1D GC/MS Data Treatment Illustrated by the Analysis of Pyrolysis Products from East Asian Handmade Papers

NASA Astrophysics Data System (ADS)

Han, Bin; Lob, Silvia; Sablier, Michel

2018-06-01

In this study, we report the use of pyrolysis-GCxGC/MS profiles for an optimized treatment of data issued from pyrolysis-GC/MS combined with the automatic deconvolution software Automated Mass Spectral Deconvolution and Identification System (AMDIS). The method was illustrated by the characterization of marker compounds of East Asian handmade papers through the examination of pyrolysis-GCxGC/MS data to get information which was used for manually identifying low concentrated and co-eluting compounds in 1D GC/MS data. The results showed that the merits of a higher separation power for co-eluting compounds and a better sensitivity for low concentration compounds offered by a GCxGC system can be used effectively for AMDIS 1D GC/MS data treatment: (i) the compound distribution in pyrolysis-GCxGC/MS profiles can be used as "peak finder" for manual check of low concentration and co-eluting compound identification in 1D GC/MS data, and (ii) pyrolysis-GCxGC/MS profiles can provide better quality mass spectra with observed higher match factors in the AMDIS automatic match process. The combination of 2D profile with AMDIS was shown to contribute efficiently to a better characterization of compound profiles in the chromatograms obtained by 1D analysis in focusing on the mass spectral identification. [Figure not available: see fulltext.

Automatic Identification System modular receiver for academic purposes

NASA Astrophysics Data System (ADS)

Cabrera, F.; Molina, N.; Tichavska, M.; Araña, V.

2016-07-01

The Automatic Identification System (AIS) standard is encompassed within the Global Maritime Distress and Safety System (GMDSS), in force since 1999. The GMDSS is a set of procedures, equipment, and communication protocols designed with the aim of increasing the safety of sea crossings, facilitating navigation, and the rescue of vessels in danger. The use of this system not only is increasingly attractive to security issues but also potentially creates intelligence products throughout the added-value information that this network can transmit from ships on real time (identification, position, course, speed, dimensions, flag, among others). Within the marine electronics market, commercial receivers implement this standard and allow users to access vessel-broadcasted information if in the range of coverage. In addition to satellite services, users may request actionable information from private or public AIS terrestrial networks where real-time feed or historical data can be accessed from its nodes. This paper describes the configuration of an AIS receiver based on a modular design. This modular design facilitates the evaluation of specific modules and also a better understanding of the standard and the possibility of changing hardware modules to improve the performance of the prototype. Thus, the aim of this paper is to describe the system's specifications, its main hardware components, and to present educational didactics on the setup and use of a modular and terrestrial AIS receiver. The latter is for academic purposes and in undergraduate studies such as electrical engineering, telecommunications, and maritime studies.

Automatic address validation and health record review to identify homeless Social Security disability applicants.

PubMed

Erickson, Jennifer; Abbott, Kenneth; Susienka, Lucinda

2018-06-01

Homeless patients face a variety of obstacles in pursuit of basic social services. Acknowledging this, the Social Security Administration directs employees to prioritize homeless patients and handle their disability claims with special care. However, under existing manual processes for identification of homelessness, many homeless patients never receive the special service to which they are entitled. In this paper, we explore address validation and automatic annotation of electronic health records to improve identification of homeless patients. We developed a sample of claims containing medical records at the moment of arrival in a single office. Using address validation software, we reconciled patient addresses with public directories of homeless shelters, veterans' hospitals and clinics, and correctional facilities. Other tools annotated electronic health records. We trained random forests to identify homeless patients and validated each model with 10-fold cross validation. For our finished model, the area under the receiver operating characteristic curve was 0.942. The random forest improved sensitivity from 0.067 to 0.879 but decreased positive predictive value to 0.382. Presumed false positive classifications bore many characteristics of homelessness. Organizations could use these methods to prompt early collection of information necessary to avoid labor-intensive attempts to reestablish contact with homeless individuals. Annually, such methods could benefit tens of thousands of patients who are homeless, destitute, and in urgent need of assistance. We were able to identify many more homeless patients through a combination of automatic address validation and natural language processing of unstructured electronic health records. Copyright © 2018. Published by Elsevier Inc.

[Terahertz Spectroscopic Identification with Deep Belief Network].

PubMed

Ma, Shuai; Shen, Tao; Wang, Rui-qi; Lai, Hua; Yu, Zheng-tao

2015-12-01

Feature extraction and classification are the key issues of terahertz spectroscopy identification. Because many materials have no apparent absorption peaks in the terahertz band, it is difficult to extract theirs terahertz spectroscopy feature and identify. To this end, a novel of identify terahertz spectroscopy approach with Deep Belief Network (DBN) was studied in this paper, which combines the advantages of DBN and K-Nearest Neighbors (KNN) classifier. Firstly, cubic spline interpolation and S-G filter were used to normalize the eight kinds of substances (ATP, Acetylcholine Bromide, Bifenthrin, Buprofezin, Carbazole, Bleomycin, Buckminster and Cylotriphosphazene) terahertz transmission spectra in the range of 0.9-6 THz. Secondly, the DBN model was built by two restricted Boltzmann machine (RBM) and then trained layer by layer using unsupervised approach. Instead of using handmade features, the DBN was employed to learn suitable features automatically with raw input data. Finally, a KNN classifier was applied to identify the terahertz spectrum. Experimental results show that using the feature learned by DBN can identify the terahertz spectrum of different substances with the recognition rate of over 90%, which demonstrates that the proposed method can automatically extract the effective features of terahertz spectrum. Furthermore, this KNN classifier was compared with others (BP neural network, SOM neural network and RBF neural network). Comparisons showed that the recognition rate of KNN classifier is better than the other three classifiers. Using the approach that automatic extract terahertz spectrum features by DBN can greatly reduce the workload of feature extraction. This proposed method shows a promising future in the application of identifying the mass terahertz spectroscopy.

Identification of triacylglycerol using automated annotation of high resolution multistage mass spectral trees.

PubMed

Wang, Xiupin; Peng, Qingzhi; Li, Peiwu; Zhang, Qi; Ding, Xiaoxia; Zhang, Wen; Zhang, Liangxiao

2016-10-12

High complexity of identification for non-target triacylglycerols (TAGs) is a major challenge in lipidomics analysis. To identify non-target TAGs, a powerful tool named accurate MS(n) spectrometry generating so-called ion trees is used. In this paper, we presented a technique for efficient structural elucidation of TAGs on MS(n) spectral trees produced by LTQ Orbitrap MS(n), which was implemented as an open source software package, or TIT. The TIT software was used to support automatic annotation of non-target TAGs on MS(n) ion trees from a self-built fragment ion database. This database includes 19108 simulate TAG molecules from a random combination of fatty acids and corresponding 500582 self-built multistage fragment ions (MS ≤ 3). Our software can identify TAGs using a "stage-by-stage elimination" strategy. By utilizing the MS(1) accurate mass and referenced RKMD, the TIT software can discriminate unique elemental composition candidates. The regiospecific isomers of fatty acyl chains will be distinguished using MS(2) and MS(3) fragment spectra. We applied the algorithm to the selection of 45 TAG standards and demonstrated that the molecular ions could be 100% correctly assigned. Therefore, the TIT software could be applied to TAG identification in complex biological samples such as mouse plasma extracts. Copyright © 2016 Elsevier B.V. All rights reserved.

Automatic cell identification and visualization using digital holographic microscopy with head mounted augmented reality devices.

PubMed

O'Connor, Timothy; Rawat, Siddharth; Markman, Adam; Javidi, Bahram

2018-03-01

We propose a compact imaging system that integrates an augmented reality head mounted device with digital holographic microscopy for automated cell identification and visualization. A shearing interferometer is used to produce holograms of biological cells, which are recorded using customized smart glasses containing an external camera. After image acquisition, segmentation is performed to isolate regions of interest containing biological cells in the field-of-view, followed by digital reconstruction of the cells, which is used to generate a three-dimensional (3D) pseudocolor optical path length profile. Morphological features are extracted from the cell's optical path length map, including mean optical path length, coefficient of variation, optical volume, projected area, projected area to optical volume ratio, cell skewness, and cell kurtosis. Classification is performed using the random forest classifier, support vector machines, and K-nearest neighbor, and the results are compared. Finally, the augmented reality device displays the cell's pseudocolor 3D rendering of its optical path length profile, extracted features, and the identified cell's type or class. The proposed system could allow a healthcare worker to quickly visualize cells using augmented reality smart glasses and extract the relevant information for rapid diagnosis. To the best of our knowledge, this is the first report on the integration of digital holographic microscopy with augmented reality devices for automated cell identification and visualization.

A novel monodisperse SiO2@C-dot for the rapid and facile identification of latent fingermarks using self-quenching resistant solid-state fluorescence.

PubMed

Peng, Di; Liu, Xiang; Huang, Mengjun; Wang, Dan; Liu, Renlong

2018-04-24

Solid powder fluorescence shows great potential for application in medicine, biology, and engineering, especially in the identification of latent fingermarks in forensic science. However, conventional developing methods suffer from some drawbacks, such as low contrast, low sensitivity, low selectivity, and high toxicity. To conquer these challenges, novel SiO2@C-dot microspheres were prepared via a facile one-pot hydrothermal method by using citric acid as a carbon source and aminosilane as a nitrogen source. Interestingly, the results showed that the resultant powders possess good monodispersity, high fluorescence emission, and resistance to self-quenching. Additionally, the mechanism for the solid-state fluorescence of SiO2@C-dot compounds was also investigated. More importantly, the fingermarks on various surfaces, including transparent glasses, ceramic tiles, transparent plastics, aluminum alloys, plastic cards, painted woods, artificial leathers, and Chinese paper money, developed by the powders have indicated well-defined papillary ridges under a 365 nm UV lamp. The novel strategy of using monodisperse SiO2@C-dot microspheres as a fluorescent label for developing latent fingermarks showed greater advantages compared to conventional methods, which was also demonstrated using the automatic fingerprint identification system. It is simple, rapid, low-cost, nontoxic, and effective, and is expected to be a promising alternative for the development of latent fingerprints in forensic science.

Low-cost real-time automatic wheel classification system

NASA Astrophysics Data System (ADS)

Shabestari, Behrouz N.; Miller, John W. V.; Wedding, Victoria

1992-11-01

This paper describes the design and implementation of a low-cost machine vision system for identifying various types of automotive wheels which are manufactured in several styles and sizes. In this application, a variety of wheels travel on a conveyor in random order through a number of processing steps. One of these processes requires the identification of the wheel type which was performed manually by an operator. A vision system was designed to provide the required identification. The system consisted of an annular illumination source, a CCD TV camera, frame grabber, and 386-compatible computer. Statistical pattern recognition techniques were used to provide robust classification as well as a simple means for adding new wheel designs to the system. Maintenance of the system can be performed by plant personnel with minimal training. The basic steps for identification include image acquisition, segmentation of the regions of interest, extraction of selected features, and classification. The vision system has been installed in a plant and has proven to be extremely effective. The system properly identifies the wheels correctly up to 30 wheels per minute regardless of rotational orientation in the camera's field of view. Correct classification can even be achieved if a portion of the wheel is blocked off from the camera. Significant cost savings have been achieved by a reduction in scrap associated with incorrect manual classification as well as a reduction of labor in a tedious task.

State Identification for Planetary Rovers: Learning and Recognition

NASA Technical Reports Server (NTRS)

Aycard, Olivier; Washington, Richard

1999-01-01

A planetary rover must be able to identify states where it should stop or change its plan. With limited and infrequent communication from ground, the rover must recognize states accurately. However, the sensor data is inherently noisy, so identifying the temporal patterns of data that correspond to interesting or important states becomes a complex problem. In this paper, we present an approach to state identification using second-order Hidden Markov Models. Models are trained automatically on a set of labeled training data; the rover uses those models to identify its state from the observed data. The approach is demonstrated on data from a planetary rover platform.

Automatic pattern identification of rock moisture based on the Staff-RF model

NASA Astrophysics Data System (ADS)

Zheng, Wei; Tao, Kai; Jiang, Wei

2018-04-01

Studies on the moisture and damage state of rocks generally focus on the qualitative description and mechanical information of rocks. This method is not applicable to the real-time safety monitoring of rock mass. In this study, a musical staff computing model is used to quantify the acoustic emission signals of rocks with different moisture patterns. Then, the random forest (RF) method is adopted to form the staff-RF model for the real-time pattern identification of rock moisture. The entire process requires only the computing information of the AE signal and does not require the mechanical conditions of rocks.

On the recognition of emotional vocal expressions: motivations for a holistic approach.

PubMed

Esposito, Anna; Esposito, Antonietta M

2012-10-01

Human beings seem to be able to recognize emotions from speech very well and information communication technology aims to implement machines and agents that can do the same. However, to be able to automatically recognize affective states from speech signals, it is necessary to solve two main technological problems. The former concerns the identification of effective and efficient processing algorithms capable of capturing emotional acoustic features from speech sentences. The latter focuses on finding computational models able to classify, with an approximation as good as human listeners, a given set of emotional states. This paper will survey these topics and provide some insights for a holistic approach to the automatic analysis, recognition and synthesis of affective states.

Image-based automatic recognition of larvae

NASA Astrophysics Data System (ADS)

Sang, Ru; Yu, Guiying; Fan, Weijun; Guo, Tiantai

2010-08-01

As the main objects, imagoes have been researched in quarantine pest recognition in these days. However, pests in their larval stage are latent, and the larvae spread abroad much easily with the circulation of agricultural and forest products. It is presented in this paper that, as the new research objects, larvae are recognized by means of machine vision, image processing and pattern recognition. More visional information is reserved and the recognition rate is improved as color image segmentation is applied to images of larvae. Along with the characteristics of affine invariance, perspective invariance and brightness invariance, scale invariant feature transform (SIFT) is adopted for the feature extraction. The neural network algorithm is utilized for pattern recognition, and the automatic identification of larvae images is successfully achieved with satisfactory results.

Species distribution modeling based on the automated identification of citizen observations.

PubMed

Botella, Christophe; Joly, Alexis; Bonnet, Pierre; Monestiez, Pascal; Munoz, François

2018-02-01

A species distribution model computed with automatically identified plant observations was developed and evaluated to contribute to future ecological studies. We used deep learning techniques to automatically identify opportunistic plant observations made by citizens through a popular mobile application. We compared species distribution modeling of invasive alien plants based on these data to inventories made by experts. The trained models have a reasonable predictive effectiveness for some species, but they are biased by the massive presence of cultivated specimens. The method proposed here allows for fine-grained and regular monitoring of some species of interest based on opportunistic observations. More in-depth investigation of the typology of the observations and the sampling bias should help improve the approach in the future.

Tidal Analysis and Arrival Process Mining Using Automatic Identification System (AIS) Data

DTIC Science & Technology

2017-01-01

elevation at the time of vessel movement and calculating the tidal dependence (TD) parameter to 23 U.S. port areas for the years 2012– 2014. Tidal prediction...predictions, obtained from the National Oceanographic and Atmospheric Administration, are used to rank relative tidal dependence for arriving cargo and...sector traffic percentages and tidal dependence metric ............................. 11 Arrival process mining

Evaluating a Bilingual Text-Mining System with a Taxonomy of Key Words and Hierarchical Visualization for Understanding Learner-Generated Text

ERIC Educational Resources Information Center

Kong, Siu Cheung; Li, Ping; Song, Yanjie

2018-01-01

This study evaluated a bilingual text-mining system, which incorporated a bilingual taxonomy of key words and provided hierarchical visualization, for understanding learner-generated text in the learning management systems through automatic identification and counting of matching key words. A class of 27 in-service teachers studied a course…

Automatic Identification of Topic Tags from Texts Based on Expansion-Extraction Approach

ERIC Educational Resources Information Center

Yang, Seungwon

2013-01-01

Identifying topics of a textual document is useful for many purposes. We can organize the documents by topics in digital libraries. Then, we could browse and search for the documents with specific topics. By examining the topics of a document, we can quickly understand what the document is about. To augment the traditional manual way of topic…

Introduction to IdentiFINDERs

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

Rees, Brian G.

These are slides from a presentation. The identiFINDER provides information on radiation levels. It can automatically identify isotopes in its library. It can save spectra for transfer to a computer, and has a 4-8 hour battery life. The following is covered: an overview, operating modes, getting started, finder mode, search, identification mode, dose & rate, warning & alarm, options (ultra LGH), options (identifinder2), and general procedure.

Automatic detection and identification of brown stink bug, Euschistus servus, and southern green stink bug, Nezara viridula, (Heteroptera: Pentatomidae) using intraspecific substrate-borne vibrational signals

USDA-ARS?s Scientific Manuscript database

Stink bugs cost the southeastern cotton industry millions of dollars each year in crop losses and control costs. These losses are reduced by strategic pesticide applications; however, current methods of monitoring these pests for making management decisions are time-consuming and costly. Therefore, ...

Wheat cultivation: Identifying and estimating area by means of LANDSAT data

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Mendonca, F. J.; Cottrell, D. A.; Tardin, A. T.; Lee, D. C. L.; Shimabukuro, Y. E.; Moreira, M. A.; Delima, A. M.; Maia, F. C. S.

1981-01-01

Automatic classification of LANDSAT data supported by aerial photography for identification and estimation of wheat growing areas was evaluated. Data covering three regions in the State of Rio Grande do Sul, Brazil were analyzed. The average correct classification of IMAGE-100 data was 51.02% and 63.30%, respectively, for the periods of July and of September/October, 1979.

A Comprehensive, Automatically Updated Fungal ITS Sequence Dataset for Reference-Based Chimera Control in Environmental Sequencing Efforts.

PubMed

Nilsson, R Henrik; Tedersoo, Leho; Ryberg, Martin; Kristiansson, Erik; Hartmann, Martin; Unterseher, Martin; Porter, Teresita M; Bengtsson-Palme, Johan; Walker, Donald M; de Sousa, Filipe; Gamper, Hannes Andres; Larsson, Ellen; Larsson, Karl-Henrik; Kõljalg, Urmas; Edgar, Robert C; Abarenkov, Kessy

2015-01-01

The nuclear ribosomal internal transcribed spacer (ITS) region is the most commonly chosen genetic marker for the molecular identification of fungi in environmental sequencing and molecular ecology studies. Several analytical issues complicate such efforts, one of which is the formation of chimeric-artificially joined-DNA sequences during PCR amplification or sequence assembly. Several software tools are currently available for chimera detection, but rely to various degrees on the presence of a chimera-free reference dataset for optimal performance. However, no such dataset is available for use with the fungal ITS region. This study introduces a comprehensive, automatically updated reference dataset for fungal ITS sequences based on the UNITE database for the molecular identification of fungi. This dataset supports chimera detection throughout the fungal kingdom and for full-length ITS sequences as well as partial (ITS1 or ITS2 only) datasets. The performance of the dataset on a large set of artificial chimeras was above 99.5%, and we subsequently used the dataset to remove nearly 1,000 compromised fungal ITS sequences from public circulation. The dataset is available at http://unite.ut.ee/repository.php and is subject to web-based third-party curation.

Semi-automatic 10/20 Identification Method for MRI-Free Probe Placement in Transcranial Brain Mapping Techniques.

PubMed

Xiao, Xiang; Zhu, Hao; Liu, Wei-Jie; Yu, Xiao-Ting; Duan, Lian; Li, Zheng; Zhu, Chao-Zhe

2017-01-01

The International 10/20 system is an important head-surface-based positioning system for transcranial brain mapping techniques, e.g., fNIRS and TMS. As guidance for probe placement, the 10/20 system permits both proper ROI coverage and spatial consistency among multiple subjects and experiments in a MRI-free context. However, the traditional manual approach to the identification of 10/20 landmarks faces problems in reliability and time cost. In this study, we propose a semi-automatic method to address these problems. First, a novel head surface reconstruction algorithm reconstructs head geometry from a set of points uniformly and sparsely sampled on the subject's head. Second, virtual 10/20 landmarks are determined on the reconstructed head surface in computational space. Finally, a visually-guided real-time navigation system guides the experimenter to each of the identified 10/20 landmarks on the physical head of the subject. Compared with the traditional manual approach, our proposed method provides a significant improvement both in reliability and time cost and thus could contribute to improving both the effectiveness and efficiency of 10/20-guided MRI-free probe placement.

Hybrid EEG—Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal

PubMed Central

Mannan, Malik M. Naeem; Kim, Shinjung; Jeong, Myung Yung; Kamran, M. Ahmad

2016-01-01

Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data. PMID:26907276

Integration of low level and ontology derived features for automatic weapon recognition and identification

NASA Astrophysics Data System (ADS)

Sirakov, Nikolay M.; Suh, Sang; Attardo, Salvatore

2011-06-01

This paper presents a further step of a research toward the development of a quick and accurate weapons identification methodology and system. A basic stage of this methodology is the automatic acquisition and updating of weapons ontology as a source of deriving high level weapons information. The present paper outlines the main ideas used to approach the goal. In the next stage, a clustering approach is suggested on the base of hierarchy of concepts. An inherent slot of every node of the proposed ontology is a low level features vector (LLFV), which facilitates the search through the ontology. Part of the LLFV is the information about the object's parts. To partition an object a new approach is presented capable of defining the objects concavities used to mark the end points of weapon parts, considered as convexities. Further an existing matching approach is optimized to determine whether an ontological object matches the objects from an input image. Objects from derived ontological clusters will be considered for the matching process. Image resizing is studied and applied to decrease the runtime of the matching approach and investigate its rotational and scaling invariance. Set of experiments are preformed to validate the theoretical concepts.

Knowledge management in secondary pharmaceutical manufacturing by mining of data historians-A proof-of-concept study.

PubMed

Meneghetti, Natascia; Facco, Pierantonio; Bezzo, Fabrizio; Himawan, Chrismono; Zomer, Simeone; Barolo, Massimiliano

2016-05-30

In this proof-of-concept study, a methodology is proposed to systematically analyze large data historians of secondary pharmaceutical manufacturing systems using data mining techniques. The objective is to develop an approach enabling to automatically retrieve operation-relevant information that can assist the management in the periodic review of a manufactory system. The proposed methodology allows one to automatically perform three tasks: the identification of single batches within the entire data-sequence of the historical dataset, the identification of distinct operating phases within each batch, and the characterization of a batch with respect to an assigned multivariate set of operating characteristics. The approach is tested on a six-month dataset of a commercial-scale granulation/drying system, where several millions of data entries are recorded. The quality of results and the generality of the approach indicate that there is a strong potential for extending the method to even larger historical datasets and to different operations, thus making it an advanced PAT tool that can assist the implementation of continual improvement paradigms within a quality-by-design framework. Copyright © 2016 Elsevier B.V. All rights reserved.

Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

NASA Astrophysics Data System (ADS)

Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan

2017-10-01

This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image.

PubMed

Singh, Anushikha; Dutta, Malay Kishore; ParthaSarathi, M; Uher, Vaclav; Burget, Radim

2016-02-01

Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A Comprehensive, Automatically Updated Fungal ITS Sequence Dataset for Reference-Based Chimera Control in Environmental Sequencing Efforts

PubMed Central

Nilsson, R. Henrik; Tedersoo, Leho; Ryberg, Martin; Kristiansson, Erik; Hartmann, Martin; Unterseher, Martin; Porter, Teresita M.; Bengtsson-Palme, Johan; Walker, Donald M.; de Sousa, Filipe; Gamper, Hannes Andres; Larsson, Ellen; Larsson, Karl-Henrik; Kõljalg, Urmas; Edgar, Robert C.; Abarenkov, Kessy

2015-01-01

The nuclear ribosomal internal transcribed spacer (ITS) region is the most commonly chosen genetic marker for the molecular identification of fungi in environmental sequencing and molecular ecology studies. Several analytical issues complicate such efforts, one of which is the formation of chimeric—artificially joined—DNA sequences during PCR amplification or sequence assembly. Several software tools are currently available for chimera detection, but rely to various degrees on the presence of a chimera-free reference dataset for optimal performance. However, no such dataset is available for use with the fungal ITS region. This study introduces a comprehensive, automatically updated reference dataset for fungal ITS sequences based on the UNITE database for the molecular identification of fungi. This dataset supports chimera detection throughout the fungal kingdom and for full-length ITS sequences as well as partial (ITS1 or ITS2 only) datasets. The performance of the dataset on a large set of artificial chimeras was above 99.5%, and we subsequently used the dataset to remove nearly 1,000 compromised fungal ITS sequences from public circulation. The dataset is available at http://unite.ut.ee/repository.php and is subject to web-based third-party curation. PMID:25786896

Training lay-people to use automatic external defibrillators: are all of their needs being met?

PubMed

Harrison-Paul, Russell; Timmons, Stephen; van Schalkwyk, Wilna Dirkse

2006-10-01

We explored the experiences of lay people who have been trained to use automatic external defibrillators. The research questions were: (1) How can training courses help prepare people for dealing with real life situations? (2) Who is ultimately responsible for providing critical incident debriefing and how should this be organised? (3) What is the best process for providing feedback to those who have used an AED? Fifty-three semi-structured, qualitative interviews were conducted, some with those who had been trained and others with trainers. Locations included airports, railway stations, private companies and first responder schemes. Geographically, we covered Nottinghamshire, Lincolnshire, Yorkshire, Staffordshire, Essex and the West Midlands in the UK. Our analysis of the data indicates that most people believe scenarios based within their place of work were most useful in preparing for 'real life'. Many people had not received critical incident debriefing after using an AED. There were a variety of systems in place to provide support after an incident, many of which were informal. Training scenarios should be conducted outside the classroom. There should be more focus on critical incident debriefing during training and a clear identification of who should provide support after an incident. Other issues which were of interest included: (1) people's views on do not attempt resuscitation (DNAR); (2) perceived boundaries of responsibility when using an AED; (3) when is someone no longer 'qualified' to use an AED?

Chemical etching for automatic processing of integrated circuits

NASA Technical Reports Server (NTRS)

Kennedy, B. W.

1981-01-01

Chemical etching for automatic processing of integrated circuits is discussed. The wafer carrier and loading from a receiving air track into automatic furnaces and unloading onto a sending air track are included.

ED09-0290-32

NASA Image and Video Library

2009-10-01

The F-16D Automatic Collision Avoidance Technology aircraft tests of the Automatic Ground Collision Avoidance System, or Auto-GCAS, included flights in areas of potentially hazardous terrain, including canyons and mountains.

Use of seatbelts in cars with automatic belts.

PubMed Central

Williams, A F; Wells, J K; Lund, A K; Teed, N J

1992-01-01

Use of seatbelts in late model cars with automatic or manual belt systems was observed in suburban Washington, DC, Chicago, Los Angeles, and Philadelphia. In cars with automatic two-point belt systems, the use of shoulder belts by drivers was substantially higher than in the same model cars with manual three-point belts. This finding was true in varying degrees whatever the type of automatic belt, including cars with detachable nonmotorized belts, cars with detachable motorized belts, and especially cars with nondetachable motorized belts. Most of these automatic shoulder belts systems include manual lap belts. Use of lap belts was lower in cars with automatic two-point belt systems than in the same model cars with manual three-point belts; precisely how much lower could not be reliably estimated in this survey. Use of shoulder and lap belts was slightly higher in General Motors cars with detachable automatic three-point belts compared with the same model cars with manual three-point belts; in Hondas there was no difference in the rates of use of manual three-point belts and the rates of use of automatic three-point belts. PMID:1561301