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Relevant bibliographies by topics / BoxcarSS

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Academic literature on the topic 'BoxcarSS'

Author: Grafiati

Published: 29 July 2024

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Journal articles on the topic "BoxcarSS"

1

Stine, Peter. "Boxcars, 1974." Iowa Review 32, no. 3 (December 2002): 94–98. http://dx.doi.org/10.17077/0021-065x.5604.

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Kate Flaherty. "Boxcars and Books." Prairie Schooner 84, no. 3 (2010): 28–31. http://dx.doi.org/10.1353/psg.2010.0037.

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Romanov, D., A. Filin, R. Compton, and R. Levis. "Phase matching in femtosecond BOXCARS." Optics Letters 32, no. 21 (October 24, 2007): 3161. http://dx.doi.org/10.1364/ol.32.003161.

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Warning, Nathanial. "Rock wren transport in railroad boxcars." Southwestern Naturalist 61, no. 3 (September 2016): 203–9. http://dx.doi.org/10.1894/0038-4909-61.3.203.

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Muller, Squier, De Lange, and Brakenhoff. "CARS microscopy with folded BoxCARS phasematching." Journal of Microscopy 197, no. 2 (February 2000): 150–58. http://dx.doi.org/10.1046/j.1365-2818.2000.00648.x.

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Greene, G. J., G. Cutsogeorge, and M. Ono. "Boxcar photography." Review of Scientific Instruments 60, no. 8 (August 1989): 2690–96. http://dx.doi.org/10.1063/1.1140693.

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Roland, Thomas, Vincent Kemlin, Julien Nillon, Jean-Sébastien Pellé, Olivier Crégutt, Johanna Brazard, Jérémie Léonard, and Stefan Haacke. "BOXCARS-geometry 2DES setup in the 300-340nm range with pulse-to-pulse phase correction at 50kHz." EPJ Web of Conferences 205 (2019): 01009. http://dx.doi.org/10.1051/epjconf/201920501009.

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A 40-nm broad pulse centred at 320nm is produced from an amplified Yb-doped fiber laser operated at 50kHz, and used in a BOXCARS geometry setup for 2DES, with shot-to-shot monitoring of the relative optical phase stability.

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Doerk, T., J. Ehlbeck, P. Jauernik, J. Stańco, J. Uhlenbusch, and T. Wottka. "Narrow-band BoxCARS applied to CO2 laser discharges." Il Nuovo Cimento D 14, no. 10 (October 1992): 1051–63. http://dx.doi.org/10.1007/bf02455367.

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Lestari, Anugrah I., Dony Kushardono, and Athar A. Bayanuddin. "Burned area detection using convolutional neural network based on spatial information of synthetic aperture radar data in Indonesia." GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY 17, no. 2 (July 5, 2024): 36–48. http://dx.doi.org/10.24057/2071-9388-2024-3109.

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Forest and land fires are disasters that often occur in Indonesia which affects neighbouring countries. The burned area can be observed using remote sensing. Synthetic aperture radar (SAR) sensor data is advantageous since it can penetrate clouds and smoke. However, image analysis of SAR data differs from optical data, which is based on properties such as intensity, texture, and polarimetric feature. This research aims to propose a method to detect burned areas from the extracted feature of Sentinel-1 data. The features were classified using the Convolutional Neural Network (CNN) classifier. To find the best input features, several classification schemes were tested, including intensity and polarimetric features by applying the Boxcar speckle filter and the Gray Level Co-occurrence Matrix (GLCM) texture feature without using the Boxcar speckle filter. Additionally, this research investigates the significance of a window size parameter for each scheme. The results show the highest overall accuracy achieved 84% using CNN classification utilizing the GLCM texture features and without conducting the Boxcar speckle filter on the window size of 17×17 pixels when tested on the part region of Pulang Pisau Regency and Kapuas Regency, Central Kalimantan in 2019. The total burned area was 76,098.6 ha. The use of GLCM texture features without conducting the Boxcar speckle filter as input classification performs better than using intensity and polarimetric features that undergo the Boxcar speckle filter. Combining intensity and polarimetric features with performing the Boxcar speckle filter improves better classification performance over utilizing them separately. Furthermore, the selection of window size also contributes to improve the model performance.

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Fomin, O. V., and A. O. Lovska. "Determination of vertical dynamics for a standard Ukrainian boxcar with Y25 bogies." Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 5 (2020): 67–72. http://dx.doi.org/10.33271/nvngu/2021-5/067.

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Purpose. To determine the basic dynamic characteristics of a standard Ukrainian boxcar with the Y25 bogie by means of the mathematic modelling of dynamic loads in the vertical plane and to compare them with the dynamic characteristics obtained for a boxcar with the 18100 bogie. It can be used for substantiation of application of the Y25 bogie as more promising, which can improve the operational efficiency of rail transportation and foster integration of the Ukrainian transport system into the European transportation complex. Methodology. The authors used the mathematical modelling of a boxcar with the Y25 bogie. The research was made in the vertical plane. It was taken into account that an empty car passed over a joint irregularity. The research was made for an 11217boxcar as a predominant type to be used. The authors studied the motion of a car on the 18100 and Y25 bogies. The basic dynamic characteristics were determined for a boxcar with the nominal (design) dimensions of the carrying elements and a boxcar with the actual dimensions (after a long service life) on the basis of the field tests. The differential equations were solved by theRungeKutta method in MathCad software suite. The initial displacements and the speeds were taken equal to zero. The following dynamic characteristics of a boxcar were obtained: acceleration of the body in the center of mass, acceleration of the body in the areas of support on the bogies, forces in the spring suspension of the bogie, and dynamic coefficients of the bogies. Findings. The theoretical research showed that the basic dynamic characteristics of a boxcar with the nominal dimensions were improved by 3851% in comparison to a similar one with the 18100 bogie, and for a boxcar with the actual dimensions the dynamic characteristics were improved by 4350%. Originality. The authors substantiated the application of the Y25 bogie for a standard Ukrainian boxcar with the nominal and actual dimensions of the carrying elements by means of the mathematical modelling of the dynamic loads in the vertical plane. Practical value. Due to lower dynamic loads on the carrying structures of freight cars with the Y25 bogie, it is possible to improve the dynamic characteristics of the structures under the operational modes, to increase the fatigue strength and the operational resource, to decrease the total repair and maintenance costs, to ensure better security and reliability of freight transportation through lower loads on both carrying structures and lashing devices, to increase the speed of freight delivery thanks to better dynamic characteristics of cars, to improve the traffic security, and so on.

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Dissertations / Theses on the topic "BoxcarSS"

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Richardson, Malin, and Unn Sandell. "Motorsimulering av CAN-buss : Boxcar 9-5." Thesis, University West, Department of Technology, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-337.

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Sjölund, Göran, and Stefan Petersson. "Uppbyggnad av ett fordons kompletta elsystem." Thesis, University West, Department of Technology, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-363.

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Khaowong, Samorn. "Comparing different methods of the Escalator Boxcar Train based on the Daphnia model." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-26558.

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Escalator Boxcar Train (EBT) is a numerical analysis used to solve physiologically structured population models (PSPMs) and has been presented in two different variations. This report describes a study using simulation and information from preceding experiments, as well as observations from natural biological systems of Daphnia and humans, in order to attain empirical comparisons between the two EBT methods. The results of the study demonstrate that the algorithms from both methods are equivalent, and that application of EBT in PSPMs does not alter natural biological systems.

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Locci-Lopez, Daniel Eduardo. "Permian Basin Reservoir Quantitative Interpretation Applying the Multi-Scale Boxcar Transform Spectral Decomposition." Thesis, University of Louisiana at Lafayette, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10816133.

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The Short Time Fourier transform and the S-transform are among the most used methods of spectral decomposition to localize spectra in time and frequency. The S-transform utilizes a frequency-dependent Gaussian analysis window that is normalized for energy conservation purposes. The STFT, on the other hand, has a selected fixed time window that does not depend on frequency. In previous literature, it has been demonstrated that the S-transform distorts the Fourier spectra, shifting frequency peaks, and could result in misleading frequency attributes. Therefore, one way of making the S-transform more appropriate for quantitative seismic signal analysis is to ignore the conservation of energy over time requirement. This suggests a hybrid approach between the Short Time Fourier transform and the S-transform for seismic interpretation purposes. In this work, we introduce the Multi-Scale Boxcar transform that has temporal resolution comparable to the S-transform while giving correct Fourier peak frequencies. The Multi-Scale Boxcar transform includes a special analysis window that focusses the analysis on the highest amplitude portion of the Gaussian window, giving a more accurate time-frequency representation of the spectra in comparison with the S-transform. Post-stack seismic data with a strong well logs control was used to demonstrate the differences of the Multi-Scale Boxcar transform and the S-transform. The analysis area in this work is the Pennsylvanian and Lower Permian Horseshoe Atoll Carbonate play in the Midland Basin, a sub-basin in the larger Permian Basin. The Multi-Scale Boxcar transform spectral decomposition method improved the seismic interpretation of the study area, showing better temporal resolution for resolving the layered reservoirs? heterogeneity. The time and depth scale values on the figures are shifted according to the sponsor request, but the relative scale is correct.

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Meier, Steffen Marius [Verfasser], Uwe [Gutachter] Czarnetzki, and Horst [Gutachter] Fichtner. "Plasmadichtebestimmung mittels Dual-Frequency-Multichannel-Boxcar-THz-Time-Domain-Spektroskopie / Steffen Marius Meier ; Gutachter: Uwe Czarnetzki, Horst Fichtner ; Fakultät für Physik und Astronomie." Bochum : Ruhr-Universität Bochum, 2018. http://d-nb.info/1161942327/34.

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Iaconianni, Sara. "Caratterizzazione di dispositivi a giunzione tramite Deep Level Transient Spectroscopy (DLTS)." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14216/.

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I semiconduttori in equilibrio termodinamico sono caratterizzati dalla presenza di stati difettivi, che perturbano la periodicità del reticolo cristallino e di conseguenza alterano la struttura a bande del materiale, introducendo stati energetici disponibili nel gap di energie proibite. Tali stati si dividono in due categorie: shallow levels e deep levels. Gli shallow levels devono il loro nome al fatto che gli stati elettronici ad essi associati hanno energie dell'ordine del meV, si collocano quindi vicino al bordo della banda di valenza o conduzione. I deep levels, al contrario, sono così chiamati poiché gli stati elettronici associati hanno energie vicine al centro dell’energy gap del materiale. I deep levels influenzano alcune importanti proprietà dei materiali (ad esempio il tempo di vita medio τ dei portatori minoritari), inoltre possono essere introdotti volontariamente in un semiconduttore, ad esempio per renderlo semi-isolante. In questo lavoro di tesi è stato implementato l'apparato sperimentale necessario ad effettuare misure di Deep Level Transient Spectroscopy (DLTS), la principale tecnica per caratterizzare i deep levels; è stato inoltre sviluppato un software per controllare gli strumenti ed acquisirne le misure. La DLTS si basa sullo studio delle variazioni della capacità associata alla zona di svuotamento di dispositivi a giunzione dovute ai processi di cattura ed emissione di portatori di carica da parte dei deep levels. Le fasi di cattura sono indotte applicando al campione impulsi di tensione, e si alternano alle fasi emissione: tale alternanza origina una serie di transienti di capacità, che vengono acquisiti (mentre il campione è sottoposto a un riscaldamento costante) e poi opportunamente elaborati al fine di produrre gli spettri di DLTS veri e propri. Utilizzando i metodi descritti nel presente elaborato, gli spettri sono stati infine sfruttati per caratterizzare i deep levels di un campione di Si n-type e di uno di GaAs n-type.

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Monteiro, João André de Matos. "Implementation and evaluation of different scaling methods in single scatter simulation for the siemens brainPET scanner." Master's thesis, 2012. http://hdl.handle.net/10451/7853.

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Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia), apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012
A Tomografia por Emissão de Positrões (PET, do Inglês Positron Emission Tomography) trata-se de uma técnica de medicina nuclear a qual é bastante importante no diagnóstico de diversas patologias. Utiliza radiofármacos com um tempo de meia-vida curto, de modo a obter uma imagem de emissão. A taxa de absorção do radiofármaco varia de acordo com o tipo de tecido. Este fenómeno é especialmente importante no diagnóstico de cancro, uma vez que o metabolismo das células tumorais é superior, o que resulta numa maior taxa de absorção do radiofármaco por parte destas células, comparativamente com as células saudáveis. Este fenómeno permite a aquisição de uma imagem funcional. Tal como o nome indica, a PET baseia-se na emissão de positrões pelos radionuclídeos. Cada positrão irá aniquilar-se com um electrão, produzindo dois fotões de 511 keV, os quais são detectados pelo sistema de PET e utilizados para reconstruir uma imagem da distribuição do radiofármaco. A qualidade da imagem é influenciada por vários factores, sendo a dispersão de Compton um dos mais importantes. Este fenómeno provoca uma deflexão na trajectória original dos fotões, resultando na deterioração do contraste da imagem. Actualmente, é possível estimar a quantidade de eventos detectados pelo sistema de PET que sofreram dispersão de Compton e corrigir os dados adquiridos. Vários métodos foram propostos ao longo dos anos para corrigir os dados, sendo bastante utilizados os algoritmos baseados em modelos físicos. Dentro destes, um dos mais importantes é o Single Scatter Simulation (SSS), o qual permite estimar a distribuição de eventos dispersos por interacções de Compton e representar os resultados em sinogramas de dispersão. Contudo, estes sinogramas não se encontram escalados, sendo este passo executado separadamente. Os métodos de escalamento utilizam as contagens detectadas fora do objecto que foi medido (conhecidas como scatter tails) como referência. Um dos problemas relacionados com o escalamento advém do facto destas regiões possuírem contagens muito baixas, o que as torna susceptíveis a ruído de Poisson, dificultando a estimação do factor de escalamento correcto. Este projecto fez uso de dados adquiridos e simulados para o scanner BrainPET da Siemens instalado no Forschungszentrum Jülich. Actualmente, o escalamento dos sinogramas de dispersão é feito utilizando os pacotes de software disponibilizados pelo fabricante, os quais demonstraram resultados razoáveis podendo, no entanto, ser melhorados. O objectivo principal deste trabalho consiste na implementação e avaliação de diversos métodos de escalamento dos sinogramas de dispersão, tanto novos como previamente existentes, tendo em vista melhorar o passo de escalamento do SSS. Isto permitirá uma melhor correcção do efeito da dispersão de Compton nos dados, melhorando a qualidade das imagems de PET. Foram também testados em dados simulados, diversos factores que podem introduzir erros no escalamento dos sinogramas de dispersão, de modo a documentar a sua influência na imagem reconstruída. Os factores testados foram o escalamento incorrecto dos sinogramas de dispersão e o escalamento incorrecto dos sinogramas de eventos aleatórios. Foi também testada a utilização de uma imagem corrigida para atenuação e eventos aleatórios para a estimação dos sinogramas de dispersão em vez de um fantoma gerado matematicamente. No capítulo 2 são introduzidos os conceitos físicos básicos necessários à compreensão do funcionamento dos sistemas de PET. São também expostos os diferentes procedimentos utilizados para adquirir, organizar e corrigir dados, bem como alguns algoritmos de reconstrução. O capítulo 3 foca-se na descrição do scanner BrainPET, nos métodos que foram implementados para escalar os sinogramas de dispersão e nos processos necessários para adquirir os dados utilizados, tanto reais como simulados. Os dados simulados foram adquiridos gerando três conjuntos de sinogramas (sinogramas de eventos verdadeiros não-dispersos, sinogramas de eventos aleatórios e sinogramas de dispersão), somando-os e introduzindo ruído de Poisson. Estes dados foram divididos em cinco grupos, consoante o número de eventos verdadeiros não-dispersados: 1 × 106, 1 × 107, 1 × 108, 1 × 109 e 1 × 1010. Esta divisão permitiu estudar o comportamento de cada método de escalamento a diferentes níveis estatísticos. Os dados reais foram adquiridos utilizando um fantoma cilíndrico com uma concentração uniforme de 18F. Foram implementados três métodos de escalamento. O primeiro, denominado de Standard, é actualmente utilizado no Forschungszentrum Jülich. O segundo, é bastante semelhante ao Standard, mas utiliza os dados sem aplicar pré-correcção, designando-se Non Pre-Corrected Data (NPCD) scaling method. O terceiro, aborda o escalamento de uma maneira diferente dos outros dois, denomina-se Maximum Likelihood (ML) scaling method e tem em conta o ruído de Poisson na estimação do factor de escalamento. resultados mostraram que para o método Standard e para o método NPCD não parece existir nenhuma vantagem em usar PI em vez do filtro boxcar actualmente utilizado. Contudo, aquando da utilização do método ML, a PI conferiu uma melhoria de cerca de 1% em relação ao filtro boxcar a todos os níveis estatísticos. Todos os métodos de escalamento demonstraram um comportamento semelhante à introdução de erros nos dados. A influência de cada fonte de erros foi estudada de modo a verificar o seu efeito na imagem reconstruída. Os resultados deverão demonstrar-se úteis como referência futura, caso seja necessário identificar uma fonte de erro nos dados. Apesar dos resultados positivos adquiridos no escalamento, os procedimentos de reconstrução não foram tão bem sucedidos. Estes demonstraram ser tendenciosos caso a estimação dos sinogramas de dispersão não seja feita com o fantoma gerado matematicamente. Este problema pode ser resolvido em dados simulados caso a estimação dos sinogramas de dispersão e reconstrução sejam efectuados várias vezes, mesmo para fracções de dispersão de Compton superiores ou em objectos mais complexos do que um cilindro uniforme. Os dados reais adquiridos com o fantoma cilíndrico foram reconstruídos utilizando o procedimento de rotina, demonstrando os mesmos problemas. Contudo, neste caso a repetição da estimação dos sinogramas de dispersão e reconstrução não resolveram o problema. Ao contrário do que foi observado em dados simulados, a estimação dos sinogramas de dispersão com uma imagem de um cilindro uniforme não resultou numa imagem não tendenciosa. Sendo que nestes dados não foi possível reconstruir uma imagem não tendenciosa, nem pela utilização do fantoma para estimar os sinogramas de dispersão nem pela repetição da estimação e reconstrução. Isto pode ser devido ao facto do SSS não considerar casos de dispersão múltipla, em que um ou ambos os fotões são dispersados mais do que uma vez por interacções de Compton. Os resultados mostrados nas secções 4.1.1 e 4.1.2 sugerem que a utilização do método ML com PI pode ser uma boa alternativa ao método actualmente utilizado. Contudo, devem ser realizados mais estudos, tanto com dados simulados como com dados reais. Actualmente existe um toolkit desenvolvido no Forschungszentrum Jülich denomidado PET REconstruction Software TOolkit (PRESTO). Caso o método ML continue a mostrar melhores resultados que o método utilizado actualmente, este poderá ser implementado em versões futuras do PRESTO, permitindo um melhor escalamento dos sinogramas de dispersão e, consequentemente, contribuir para melhorar a qualidade das imagens de PET.
Compton scattering degrades image contrast in PET, being one of the main phenomena that affects image quality. Several algorithms have been proposed to correct the data. One of the most prominent algorithms is the SSS, which provides no absolute values, but predicts the slope of the scatter distribution. This slope has to be scaled using the scatter tails as reference. However, these have very low counts, thus giving large uncertainties to determine the scatter component. The aim of this project is to implement and evaluate new and existing scatter scaling methods, in order to improve the scaling step of the SSS. Two scatter scaling methods were proposed (the NPCD and the ML methods) and compared with the method currently used in the data from the BrainPET scanner (Standard method). The methods were compared in a single sonogram plane and using multiple sinogram planes in combination with a boxcar filter or Plane Integration (PI). Also, different error sources were investigated in the data to test how they influence the scatter estimation and scaling. The scatter correction procedures were tested with simulated and real data. For a single sinogram plane, both methods performed better than the Standard method. The scaling factors were calculated multiple times using the three methods and the distributions were studied. The standard deviations acquired using the proposed methods have shown a reduction of 4% (NPCD) and 13% (ML), relative to the Standard method. The standard deviation of the ML method can be further reduced by 1% if PI is used instead of the currently used boxcar filter. The reduction in the standard deviations of the distributions indicates that the proposed methods are more accurate. All the scaling methods behaved in the same way with respect to errors in the data.

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Books on the topic "BoxcarSS"

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Neubauer, Eric A. FMC boxcars since 1972. [Monrovia? Calif.]: Modern Transport Technical & Historical Society, 1985.

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Lee, Y. S. Boxcar riders. Markham, ON: Scholastic, 2007.

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ill, Dubisch Michael, and Warner Gertrude Chandler 1890-1979, eds. Boxcar children. Edina, MN: Magic Wagon, 2009.

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Field, Barbara. The boxcar children. Seattle, WA: Seattle Children's Theatre, 1999.

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Warner, Gertrude Chandler. The Boxcar Children. New York: Scholastic, 1989.

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Warner, Gertrude Chandler. Benny's boxcar sleepover. Morton Grove, Ill: A. Whitman, 2004.

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Warner, Gertrude Chandler. The Boxcar children. 6th ed. Morton Grove, Ill: A. Whitman, 2002.

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Young, David. Boxcars. Ecco Press, 2000.

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Boxcars. CreateSpace Independent Publishing Platform, 2013.

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Stock, Rhonda Leanne. Blood and Boxcars. Independently Published, 2019.

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Book chapters on the topic "BoxcarSS"

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Owsiak, Andrew P., J. Michael Greig, and Paul F. Diehl. "Making trains from boxcars: studying conflict and conflict management interdependencies." In International Conflict and Conflict Management, 1–22. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003380832-1.

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Grist, Leighton. "Exploitation Cinema and the Youth Market: Boxcar Bertha." In The Films of Martin Scorsese, 1963–77, 43–60. London: Palgrave Macmillan UK, 2000. http://dx.doi.org/10.1057/9780230286146_4.

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De, A., and J. Metz. "Towards a Numerical Analysis of the Escalator Boxcar Train." In Differential Equations with Applications in Biology, Physics, and Enqineering, 91–114. CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742: CRC Press, 2017. http://dx.doi.org/10.1201/9781315141244-8.

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Goetz, Renan, Natali Hritonenko, Angels Xabadia, and Yuri Yatsenko. "Using the Escalator Boxcar Train to Determine the Optimal Management of a Size-Distributed Forest When Carbon Sequestration Is Taken into Account." In Large-Scale Scientific Computing, 334–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78827-0_37.

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Ferrell, Jeff. "Freedom in the Form of a Boxcar." In Drift. University of California Press, 2018. http://dx.doi.org/10.1525/california/9780520295544.003.0006.

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This chapter recounts the trip that the author and his gutter punk traveling companion, Zeke, took aboard a series of freight trains that carried them far into west Texas. The chapter documents the trip’s many drifting experiences: waiting in railyards, hiding from railroad workers, sleeping in the rain, moving from one train or one rail car to another (hotshots, units, boxcars), ultimately arriving in Pecos, Texas—and along the way getting lost in what gutter punks call “the drift.” Interwoven with this narrative are similar accounts from the long history of hoboing and the more recent history of gutter punk train hopping, along with considerations of particular aspects of such travel: dirt, filth, visibility, and “dirty kid” identity; bandana symbolism; beer drinking; and Railroad Workers United. The chapter ends with the author’s discovery of a bit of graffiti that Zeke has written inside the boxcar in which they are travelling, which says “Freedom in the form of a boxcar.”

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"BOXCARS COMING." In Arizona's War Town, 53–76. University of Arizona Press, 2003. http://dx.doi.org/10.2307/j.ctv1kchp7k.9.

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"Boxcar Design." In Encyclopedia of Pain, 288. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28753-4_100225.

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"boxcar, n." In Oxford English Dictionary. 3rd ed. Oxford University Press, 2023. http://dx.doi.org/10.1093/oed/1800940509.

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Sharifi-Razavi, Athena, Amir Moghadam Ahmadi, and Ashkan Mowla. "Boxcar ventricle." In Pictorial Atlas of Neuroradiological Signs, 120–21. Elsevier, 2024. http://dx.doi.org/10.1016/b978-0-443-23528-3.00058-3.

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"THE BOXCARS OF CONSOLIDATED RAIL FREIGHT." In Green Squall, 9–10. Yale University Press, 2017. http://dx.doi.org/10.12987/9780300129649-007.

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Conference papers on the topic "BoxcarSS"

1

Danesh-Yazdi, Amir, Oleg Goushcha, Niell Elvin, and Yiannis Andreopoulos. "Train of Frozen Boxcars Model for Fluidic Harvesters." In 47th AIAA Fluid Dynamics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-3811.

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2

Funk, David J., Byron A. Palmer, and Richard Oldenborg. "CARS as a spectroscopic probe of a methyl chloride-air flat flame." In Laser Applications to Chemical Analysis. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/laca.1992.tub2.

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3

Sezer, Osman G., and Yucel Altunbasak. "Adaptive boxcar/wavelet transform." In IS&T/SPIE Electronic Imaging, edited by Majid Rabbani and Robert L. Stevenson. SPIE, 2009. http://dx.doi.org/10.1117/12.806166.

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4

Zhao, Shuo-Yan, Yan Shao, and Pei-Lin Zhang. "Temperature and concentration measurements of NO gas by folded BOXCARS." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.tucc6.

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Abstract:

Experimental CARS spectra of the Q-branch (v = 0-v = 1) of NO gas at different temperatures (300–1400 K) has been performed with a folded BOXCARS technique that can provide high spatial resolution for diagnostic purposes. A theoretical calculation taking into account the laser linewidth and lineshape is presented. By comparing the theoretical spectra with the experimental ones, the point temperatures measured by the CARS method are determined. The measurement accuracy of the method is discussed. CARS measurements have also been performed for various NO gas pressure (0.04–1 atm) in order to study the pressure dependence of the CARS spectral shape. A concentration measurement method using the intensity of virbration–rotational line is discussed. It is found that the concentration dependence of line intensity is in agreement with the theoretical result for high J values.

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5

Sochor, Jakub, Adam Herout, and Jiri Havel. "BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.328.

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6

Danesh-Yazdi, Amir, Oleg Goushcha, Niell Elvin, and Yiannis Andreopoulos. "Fluidic harvester under train of frozen boxcars (TFB) loading: a parametric study." In Active and Passive Smart Structures and Integrated Systems XII, edited by Alper Erturk. SPIE, 2018. http://dx.doi.org/10.1117/12.2299963.

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7

Yeow, Kin-Woon, and Matthias Becker. "JIS: Pest Population Prognosis with Escalator Boxcar Train." In 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2018. http://dx.doi.org/10.1109/ieem.2018.8607724.

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8

Zhong, Dinghua, Kun Song, and Kao Wang. "Design of a Bidirectional Belt Conveyor for Boxcar." In 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/mmebc-16.2016.223.

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9

Samolis, Panagis D., Xuedong Zhu, and Michelle Y. Sander. "Boxcar gating for time-resolved mid-infrared photothermal imaging of axon-bundle water boundaries." In CLEO: Applications and Technology. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_at.2023.atu4q.4.

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Li, Zhengzhi, Jianzhong Wu, Yongan Tang, and Zhiwei Tian. "Laser-induced fluorescence spectra of tea and bamboo leaves." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1993. http://dx.doi.org/10.1364/oam.1993.mr.6.

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Reports on the topic "BoxcarSS"

1

Greene, G. J., G. Cutsogeorge, and M. Ono. Boxcar photography. Office of Scientific and Technical Information (OSTI), July 1987. http://dx.doi.org/10.2172/6169434.

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2

Seigler, R. S. Norfolk Southern boxcar blocking/bracing plan for the mixed waste disposal initiative project. Environmental Restoration Program. Office of Scientific and Technical Information (OSTI), January 1994. http://dx.doi.org/10.2172/10132019.

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3

Forklift operator is crushed by a roll of paper when it falls out of a railroad boxcar in California. U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, October 1997. http://dx.doi.org/10.26616/nioshsface97ca007.

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