Relevant bibliographies by topics / Feature processing

Academic literature on the topic 'Feature processing'

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Published: 10 March 2023

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Journal articles on the topic "Feature processing"

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Franceschetti, Giorgio, Antonio Lodice, and Manlio Tesauro. "From image processing to feature processing." Signal Processing 60, no. 1 (July 1997): 51–63. http://dx.doi.org/10.1016/s0165-1684(97)00064-9.

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Hema, Dr A., and R. Saravanakumar. "A Survey on Feature Extraction Technique in Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 448–51. http://dx.doi.org/10.31142/ijtsrd12937.

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Pesti, Jaan A. "Special Feature Issue: Continuous Processing." Organic Process Research & Development 18, no. 11 (November 21, 2014): 1284–85. http://dx.doi.org/10.1021/op500323a.

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Lin, Wei, Yuefei Zhu, and Ruijie Cai. "Processing of Cryptographic Function Identification Based on Multi-feature Progressive Model." Journal of Advances in Computer Networks 3, no. 3 (2015): 180–85. http://dx.doi.org/10.7763/jacn.2015.v3.163.

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Prasad, J. V. D., Babu Sallagundla, and Raghuvira Pratap A. "Multi-Feature Processing Techniques with Information Mining From Remote Sensing Images." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12 (December 20, 2019): 97–106. http://dx.doi.org/10.5373/jardcs/v11i12/20193217.

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Taylor, Steven, and David Badcock. "Processing feature density in preattentive perception." Perception & Psychophysics 44, no. 6 (November 1988): 551–62. http://dx.doi.org/10.3758/bf03207489.

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Visconti di Oleggio Castello, Matteo, Kelsey G. Wheeler, Carlo Cipolli, and M. Ida Gobbini. "Familiarity facilitates feature-based face processing." PLOS ONE 12, no. 6 (June 5, 2017): e0178895. http://dx.doi.org/10.1371/journal.pone.0178895.

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Wayland, Susan, and John E. Taplin. "Feature-processing deficits following brain injury." Brain and Cognition 4, no. 3 (July 1985): 338–55. http://dx.doi.org/10.1016/0278-2626(85)90026-0.

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Wayland, Susan, and John E. Taplin. "Feature-processing deficits following brain injury." Brain and Cognition 4, no. 3 (July 1985): 356–76. http://dx.doi.org/10.1016/0278-2626(85)90027-2.

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Halloran, John W. "Editorial on colloidal processing Centennial Feature." Journal of the American Ceramic Society 100, no. 2 (February 2017): 457. http://dx.doi.org/10.1111/jace.14764.

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Dissertations / Theses on the topic "Feature processing"

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Porter, Nicholas David. "Facial feature processing using artificial neural networks." Thesis, University of Warwick, 1998. http://wrap.warwick.ac.uk/59539/.

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Describing a human face is a natural ability used in eveyday life. To the police, a witness description of a suspect is key evidence in the identification of the suspect. However, the process of examining "mug shots" to find a match to the description is tedious and often unfruitful. If a description could be stored with each photograph and used as a searchable index, this would provide a much more effective means of using "mug shots" for identification purposes. A set of descriptive measures have been defined by Shepherd [73] which seek to describe faces in a manner that may be used for just this purpose. This work investigates methods of automatically determining these descriptive measures from digitised images. Analysis is performed on the images to establish the potential for distinguishing between different categories in these descriptions. This reveals that while some of the classifications are relatively linear, others are very non-linear. Artificial neural networks (ANNs), being often used as non-linear classifiers, are considered as a means of automatically performing the classification of the images. As a comparison, simple linear classifiers are also applied to the same problems.
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Hosie, Judith A. "Feature and configural factors in face processing." Thesis, Cardiff University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293027.

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Dyson, Benjamin J. "Processing and representation in auditory cognition." Thesis, University of York, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270043.

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Pohl, Carsten [Verfasser], and Andrea [Akademischer Betreuer] Kiesel. "Feature processing and feature integration in unconscious processing : A Study with chess novices and experts / Carsten Pohl. Betreuer: Andrea Kiesel." Würzburg : Universitätsbibliothek der Universität Würzburg, 2012. http://d-nb.info/1019487135/34.

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Chen, Xiao Yu. "Feature matching of deformable models /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?MECH%202008%20CHENX.

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Youn, Eun Seog. "Feature selection in support vector machines." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE1000171.

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Thesis (M.S.)--University of Florida, 2002.
Title from title page of source document. Document formatted into pages; contains x, 50 p.; also contains graphics. Includes vita. Includes bibliographical references.
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Smith, Stephen Mark. "Feature based image sequence understanding." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316951.

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Sommerville, M. G. L. "Viewer-centred geometric feature recognition." Thesis, Heriot-Watt University, 1996. http://hdl.handle.net/10399/691.

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Hocking, Julia. "The anatomical substrates of feature integration during object processing." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/1444274/.

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Objects can be identified from a number of perceptual attributes, including visual, auditory and tactile sensory input. The integration of these perceptual attributes constitutes our semantic knowledge of an object representation. This research uses functional neuroimaging to investigate the brain areas that integrate perceptual features into an object representation, and how these regions are modulated by stimulus- and task-specific features. A series of experiments are reported that utilise different types of perceptual integration, both within and across sensory modalities. These include 1) the integration of visual form with colour, 2) the integration of visual and auditory object features, and 3) the integration of visual and tactile abstract shapes. Across these experiments I have also manipulated additional factors, including the meaning of the perceptual information (meaningful objects versus meaningless shapes), the verbal or non-verbal nature of the perceptual inputs (e.g. spoken words versus environmental sounds) and the congruency of crossmodal inputs. These experiments have identified a network of brain regions both common to, and selective for, different types of object feature integration. For instance, I have identified a common bilateral network involved in the integration and association of crossmodal audiovisual objects and intra-modal auditory or visual object pairs. However, I have also determined that activation in response to the same concepts can be modulated by the type of stimulus input (verbal versus nonverbal), the timing of those inputs (simultaneous versus sequential presentation), and the congruency of stimulus pairs (congruent versus incongruent). Taken together, the results from these experiments demonstrate modulations of neuronal activation by different object attributes at multiple different levels of the object processing hierarchy, from early sensory processing through to stored object representations. Critically, these differential effects have even been observed with the same conceptual stimuli. Together these findings highlight the need for a model of object feature processing that can account for the functional demands that elicit these anatomical differences.
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Zhu, Wenyao. "Time-Series Feature Extraction in Embedded Sensor Processing System." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281820.

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Embedded sensor-based systems mounted with tens or hundreds of sensors can collect enormous time-series data, while the data analysis on those time-series is commonly conducted on the remote server-side. With the development of microprocessors, there have been increasing demands to move the analysis process to the local embedded systems. In this thesis, the objective is to inves- tigate the possibility of the time-series feature extraction methods suitable for the embedded sensor processing systems.As the research problem raised from the objective, we have explored the traditional statistic methods and machine learning approaches on time-series data mining. To narrow down the research scope, the thesis focuses on the similarity search methods together with the clustering algorithms from the time-series feature extraction perspective. In the project, we have chosen and implemented two clustering algorithms, the K-means and the Self-Organizing Map (SOM), combined with two similarity search methods, the Euclidean dis- tance and the Dynamic Time Warping (DTW). The evaluation setup uses four public datasets with labels, and the Rand index (RI) to score the accuracy. We have tested the performance on accuracy and time consumption of the four combinations of the chosen algorithms on the embedded platform.The results show that the SOM with DTW can generally achieve better accuracy with a relatively longer inferring time than the other evaluated meth- ods. Quantitatively, the SOM with DTW can do clustering on one time-series sample of 300 data points for twelve classes in 40 ms using the ESP32 embed- ded microprocessor, with a 4 percentage of accuracy advantage than the fastest K-means with Euclidean distance in RI score. We can conclude that the SOM with DTW algorithm can be used to handle the time-series clustering tasks on the embedded sensor processing systems if the timing requirement is not so stringent.
Inbyggda sensorbaserade system monterade med tiotals eller hundratals senso- rer kan samla in enorma tidsseriedata, medan dataanalysen på dessa tidsserier vanligtvis utförs på en fjärrserver. Med utvecklingen av mikroprocessorer har behovet att flytta analysprocessen till de lokala inbäddade systemen ökat. I detta examensarbete är målet att undersöka vilka tidsserie-extraktionsmetoder som är lämpliga för de inbäddade sensorbehandlingssystemen.Som forskningsproblem för målet har vi undersökt traditionella statistik- metoder och maskininlärningsmetoder för tidsserie-data mining. För att be- gränsa forskningsområdet fokuserar examensarbet på likhetssökningsmetoder tillsammans med klusteralgoritmer från tidsserieens feature extraktionsper- spektiv. I projektet har vi valt och implementerat två klusteralgoritmer, K- means och Self-Organizing Map (SOM), i kombination med två likhetssök- ningsmetoder, det euklidiska avståndet och Dynamic Time Warping (DTW). Resultaten utvärderas med fyra offentliga datasätt med märkt data. Randin- dex (RI) används för att utvärdera noggrannheten. Vi har testat prestandan för noggrannhet och tidsförbrukning för de fyra kombinationerna av de valda al- goritmerna på den inbäddade plattformen.Resultaten visar att SOM med DTW i allmänhet kan uppnå bättre nog- grannhet med en relativt längre inferenstid än de andra utvärderade metoder- na. Kvantitativt kan SOM med DTW uföra klustring på ett tidsserieprov med 300 datapunkter för tolv klasser på 40 ms med en ESP32-inbäddad mikropro- cessor, vilket är en 4-procentig förbättring i noggrannhet i RI-poäng jämfört med det snabbaste K-medel klustringen med Euklidiskt avstånd. Vi drar slut- satsen att SOM med DTW algoritmen kan användas för att hantera tidsserie- klusteruppgifter på de inbäddade sensorbehandlingssystemen om tidsbehovet inte är så strängt.
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Books on the topic "Feature processing"

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S, Aguado Alberto, ed. Feature extraction and image processing. Oxford: Newnes, 2002.

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Hu, Li, and Zhiguo Zhang, eds. EEG Signal Processing and Feature Extraction. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2.

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Porter, Nicholas David. Facial feature processing using artificial neural networks. [s.l.]: typescript, 1998.

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Nixon, Mark. Feature Extraction & Image Processing for Computer Vision. 3rd ed. Burlington: Elsevier Science, 2012.

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Trost, Harald. Feature formalisms and linguistic ambiguity. New York: Ellis Horwood, 1993.

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Tuytelaars, Tinne. Local invariant feature detectors: A survey. Hanover, MA: Now Publishers, 2008.

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Anders, Søgaard, and Haugereid Petter, eds. Typed feature structure grammars. Frankfurt am Main: Lang, 2009.

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Anders, Søgaard, and Haugereid Petter, eds. Typed feature structure grammars. Frankfurt am Main: Lang, 2009.

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Rand, Robert S. Texture analysis and cartographic feature extraction. Fort Belvoir, Va: U.S. Army Corps of Engineers, Engineer Topographic Laboratories, 1985.

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United States. Forest Service. Engineering Staff., ed. Cartographic feature files: A synopsis for the user. Washington, DC: U.S. Dept. of Agriculture, Forest Service, Engineering Staff, 1996.

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Book chapters on the topic "Feature processing"

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Xu, Long, Weisi Lin, and C. C. Jay Kuo. "Image Features and Feature Processing." In SpringerBriefs in Electrical and Computer Engineering, 37–65. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-468-9_3.

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Awcock, G. J., and R. Thomas. "Feature Extraction." In Applied Image Processing, 148–75. London: Macmillan Education UK, 1995. http://dx.doi.org/10.1007/978-1-349-13049-8_6.

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Owens, F. J. "Feature Extraction." In Signal Processing of Speech, 70–87. London: Macmillan Education UK, 1993. http://dx.doi.org/10.1007/978-1-349-22599-6_4.

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Sen, Soumya, Anjan Dutta, and Nilanjan Dey. "Feature Extraction." In Audio Processing and Speech Recognition, 45–66. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6098-5_3.

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Tschumperlé, David, Christophe Tilmant, and Vincent Barra. "Feature Extraction." In Digital Image Processing with C++, 121–50. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003323693-6.

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Marciuska, Sarunas, Cigdem Gencel, Xiaofeng Wang, and Pekka Abrahamsson. "Feature Usage Diagram for Feature Reduction." In Lecture Notes in Business Information Processing, 223–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38314-4_16.

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Richter, Michael M., Sheuli Paul, Veton Këpuska, and Marius Silaghi. "Feature Extraction." In Signal Processing and Machine Learning with Applications, 221–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-45372-9_11.

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Wan, Cen. "Feature Selection Paradigms." In Advanced Information and Knowledge Processing, 17–23. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97919-9_3.

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Jankowski, Norbert, and Krzysztof Usowicz. "Analysis of Feature Weighting Methods Based on Feature Ranking Methods for Classification." In Neural Information Processing, 238–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24958-7_28.

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Adhikary, Jyoti Ranjan, and M. Narasimha Murty. "Feature Selection for Unsupervised Learning." In Neural Information Processing, 382–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34487-9_47.

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Conference papers on the topic "Feature processing"

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Kobbelt, Leif, and Mario Botsch. "Feature sensitive mesh processing." In the 18th spring conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/984952.984956.

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Wang, Chensheng, Joris S. M. Vergeest, Pieter J. Stappers, and Willem F. Bronsvoort. "Freeform Feature Retrieval by Signal Processing." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57061.

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Feature retrieval is of great importance in shape modelling, in terms of supporting design reuse by obtaining reusable geometric entities. However, conventional techniques for feature retrieval are generally limited to the extraction of feature lines, curve segments, or surfaces, and the feature distortion imposed by feature interaction remains unconsidered. This paper investigates approaches for freeform feature retrieval by means of signal processing techniques. By treating features or regions of interest as surface signals, we employ digital filters to separate the feature signal from that of the domain surface, retrieving the “pure” feature from an existing shape model. Strategies for different model types are elaborated, for instance, the exact feature retrieval method designed for shape models with explicit data structure, such as B-Rep, or other accessible representations; and the signal filtering method for models with structured or unstructured data sets, such as that in mesh or point cloud models. Specifically, in the signal filtering method feature retrieval is implemented by the convolving operator in the frequency domain. By transforming the problem of shape decomposition from geometric extraction in the spatial domain to computation in the frequency domain, the proposed methods not only brings in significant computational efficiency, but also reduces the complexity of problem solving for feature retrieval. Provided examples show that the proposed approaches can achieve satisfactory results for simple geometries, whereas for sophisticated shapes guidelines for the design of dedicated filters are elaborated.
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Zeng, Zhiqiang. "Gabor feature-based complete fisher discriminant framework for facial feature extraction." In Signal Processing (WCSP 2009). IEEE, 2009. http://dx.doi.org/10.1109/wcsp.2009.5371732.

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Indrawan, G., B. Sitohang, and S. Akbar. "Parallel processing for Fingerprint feature extraction." In 2011 International Conference on Electrical Engineering and Informatics (ICEEI). IEEE, 2011. http://dx.doi.org/10.1109/iceei.2011.6021606.

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Chouaib, Hassan, Nicole Vincent, Florence Cloppet, and Salvator Tabbone. "Generic Feature Selection and Document Processing." In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 2009. http://dx.doi.org/10.1109/icdar.2009.200.

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Xia, Deshen, Hua Li, and Yong Qiu. "SVD spectral feature of image processing." In Photonics East '96, edited by David P. Casasent. SPIE, 1996. http://dx.doi.org/10.1117/12.256312.

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Tang, Dejun, Weishi Zhang, Xiaolu Qu, and Dujuan Wang. "A feature fusion method for feature extraction." In Fourth International Conference on Digital Image Processing (ICDIP 2012), edited by Mohamed Othman, Sukumar Senthilkumar, and Xie Yi. SPIE, 2012. http://dx.doi.org/10.1117/12.946076.

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Lecoq, Simon, Jean Le Bellego, Angel Gonzalez, Benoit Larras, and Antoine Frappe. "Low-complexity feature extraction unit for “Wake-on-Feature” speech processing." In 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 2018. http://dx.doi.org/10.1109/icecs.2018.8617967.

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Li, Yang, and Yuichi Tanaka. "Structural Features In Feature Space For Structure-Aware Graph Convolution." In 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. http://dx.doi.org/10.1109/icip42928.2021.9506377.

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Li, Jirong. "Feature Selection Based on Correlation between Fuzzy Features and Optimal Fuzzy-Valued Feature Subset Selection." In 2008 Fourth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2008. http://dx.doi.org/10.1109/iih-msp.2008.292.

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Reports on the topic "Feature processing"

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Jagler, Karl B. Wavelet Signal Processing for Transient Feature Extraction. Fort Belvoir, VA: Defense Technical Information Center, March 1992. http://dx.doi.org/10.21236/ada250519.

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Osher, Stanley, and Leonid Rudin. Feature-Oriented Signal Processing Under Nonlinear Partial Differential Equations. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada259951.

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Blundell, S. Micro-terrain and canopy feature extraction by breakline and differencing analysis of gridded elevation models : identifying terrain model discontinuities with application to off-road mobility modeling. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40185.

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Elevation models derived from high-resolution airborne lidar scanners provide an added dimension for identification and extraction of micro-terrain features characterized by topographic discontinuities or breaklines. Gridded digital surface models created from first-return lidar pulses are often combined with lidar-derived bare-earth models to extract vegetation features by model differencing. However, vegetative canopy can also be extracted from the digital surface model alone through breakline analysis by taking advantage of the fine-scale changes in slope that are detectable in high-resolution elevation models of canopy. The identification and mapping of canopy cover and micro-terrain features in areas of sparse vegetation is demonstrated with an elevation model for a region of western Montana, using algorithms for breaklines, elevation differencing, slope, terrain ruggedness, and breakline gradient direction. These algorithms were created at the U.S. Army Engineer Research Center – Geospatial Research Laboratory (ERDC-GRL) and can be accessed through an in-house tool constructed in the ENVI/IDL environment. After breakline processing, products from these algorithms are brought into a Geographic Information System as analytical layers and applied to a mobility routing model, demonstrating the effect of breaklines as obstacles in the calculation of optimal, off-road routes. Elevation model breakline analysis can serve as significant added value to micro-terrain feature and canopy mapping, obstacle identification, and route planning.
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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.
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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.
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Treisman, Anne. Visual Information Processing in the Perception of Features and Objects. Fort Belvoir, VA: Defense Technical Information Center, January 1988. http://dx.doi.org/10.21236/ada192026.

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Blundell, S. Tutorial : the DEM Breakline and Differencing Analysis Tool—step-by-step workflows and procedures for effective gridded DEM analysis. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/46085.

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The DEM Breakline and Differencing Analysis Tool is the result of a multi-year research effort in the analysis of digital elevation models (DEMs) and the extraction of features associated with breaklines identified on the DEM by numerical analysis. Developed in the ENVI/IDL image processing application, the tool is designed to serve as an aid to research in the investigation of DEMs by taking advantage of local variation in the height. A set of specific workflow exercises is described as applied to a diverse set of four sample DEMs. These workflows instruct the user in applying the tool to extract and analyze features associated with terrain, vegetative canopy, and built structures. Optimal processing parameter choices, subject to user modification, are provided along with sufficient explanation to train the user in elevation model analysis through the creation of customized output overlays.
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Blundell, S. User guide : the DEM Breakline and Differencing Analysis Tool—gridded elevation model analysis with a convenient graphical user interface. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45040.

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Gridded elevation models of the earth’s surface derived from airborne lidar data or other sources can provide qualitative and quantitative information about the terrain and its surface features through analysis of the local spatial variation in elevation. The DEM Breakline and Differencing Analysis Tool was developed to extract and display micro-terrain features and vegetative cover based on the numerical modeling of elevation discontinuities or breaklines (breaks-in-slope), slope, terrain ruggedness, local surface optima, and the local elevation difference between first surface and bare earth input models. Using numerical algorithms developed in-house at the U.S. Army Engineer Research and Development Center, Geospatial Research Laboratory, various parameters are calculated for each cell in the model matrix in an initial processing phase. The results are combined and thresholded by the user in different ways for display and analysis. A graphical user interface provides control of input models, processing, and display as color-mapped overlays. Output displays can be saved as images, and the overlay data can be saved as raster layers for input into geographic information systems for further analysis.
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Mazorchuk, Mariia S., Tetyana S. Vakulenko, Anna O. Bychko, Olena H. Kuzminska, and Oleksandr V. Prokhorov. Cloud technologies and learning analytics: web application for PISA results analysis and visualization. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4451.

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This article analyzes the ways to apply Learning Analytics, Cloud Technologies, and Big Data in the field of education on the international level. This paper provides examples of international analytical researches and cloud technologies used to process the results of those researches. It considers the PISA research methodology and related tools, including the IDB Analyzer application, free R intsvy environment for processing statistical data, and cloud-based web application PISA Data Explorer. The paper justifies the necessity of creating a stand-alone web application that supports Ukrainian localization and provides Ukrainian researchers with rapid access to well-structured PISA data. In particular, such an application should provide for data across the factorial features and indicators applied at the country level and demonstrate the Ukrainian indicators compared to the other countries’ results. This paper includes a description of the application core functionalities, architecture, and technologies used for development. The proposed solution leverages the shiny package available with R environment that allows implementing both the UI and server sides of the application. The technical implementation is a proven solution that allows for simplifying the access to PISA data for Ukrainian researchers and helping them utilize the calculation results on the key features without having to apply tools for processing statistical data.
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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.
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