Academic literature on the topic 'Range finder'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Range finder.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Range finder"
Gee, Henry. "Range finder." Nature 338, no. 6217 (April 1989): 673. http://dx.doi.org/10.1038/338673a0.
Full textSato, Kosuke. "Silicon Range Finder." Journal of the Robotics Society of Japan 13, no. 3 (1995): 315–18. http://dx.doi.org/10.7210/jrsj.13.315.
Full textDeGeorge, Martin, and Hartwig Ruell. "Microphone range finder." Journal of the Acoustical Society of America 86, no. 6 (December 1989): 2472. http://dx.doi.org/10.1121/1.398420.
Full textGotoh, T., and Y. Kunii. "Evaluation of shadow area segmentation method using Shadow Range Finder Range Finder." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2002 (2002): 18. http://dx.doi.org/10.1299/jsmermd.2002.18_3.
Full textKumar, Charu Pramod. "Ultrasonic Range Finder using 8051." International Journal for Research in Applied Science and Engineering Technology 6, no. 1 (January 31, 2018): 3102–5. http://dx.doi.org/10.22214/ijraset.2018.1429.
Full textEder, Kenneth C., and Christos M. Koukovinis. "Self‐calibrating ultrasonic range finder." Journal of the Acoustical Society of America 84, no. 3 (September 1988): 1128. http://dx.doi.org/10.1121/1.396668.
Full textMOHD RAZALI, Daud, Hiroshi OHROKU, and Kenzo NONAMI. "1A1-B19 Obstacle Avoidance Control by Laser Range Finder for Six-Legged Robot : SLAM by Laser Range Finder for Six-Legged Robot." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2010 (2010): _1A1—B19_1—_1A1—B19_4. http://dx.doi.org/10.1299/jsmermd.2010._1a1-b19_1.
Full textKruapech, Sahapong, and Joewono Widjaja. "Laser range finder using Gaussian beam range equation." Optics & Laser Technology 42, no. 5 (July 2010): 749–54. http://dx.doi.org/10.1016/j.optlastec.2009.11.020.
Full textMikitenko, Volodymyr I., Volodymyr M. Senatorov, and Anatolii Gurnovych. "LAND UNMANNED COMPLEX WITH PASSIVE RANGE MEASUREMENT." Bulletin of Kyiv Polytechnic Institute. Series Instrument Making, no. 62(2) (December 24, 2021): 11–16. http://dx.doi.org/10.20535/1970.62(2).2021.249102.
Full textGelmuda, W., and A. Kos. "Multichannel ultrasonic range finder for blind people navigation." Bulletin of the Polish Academy of Sciences: Technical Sciences 61, no. 3 (September 1, 2013): 633–37. http://dx.doi.org/10.2478/bpasts-2013-0067.
Full textDissertations / Theses on the topic "Range finder"
Chen, Sicheng. "A single-chip real-Time range finder." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/553.
Full textHui, Corinna. "Laser Range Finder Mapping of Floating Vehicle." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54476.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 48).
Using laser range finders as a method of navigation is popular with mobile land robots; however, there has been little research using it with water vehicles. Therefore, this thesis explores the usage and data flow of a laser range finder on a water raft. A unique algorithm for localization and mapping for the sensor is developed and tested both in simulation and in realtime with a vehicle. Both the localization of the vehicle and mapping of its environment are able to achieve precise locations, deviating only a few millimeters of their expected values. With this algorithm, a closed-loop control system is also developed and implemented on the vehicle. The vehicle is able to move to a predefined location and be within a very small range of acceptable values. The control loop is further explored with damping, gain variations, and different trajectories..
by Corinna Hui.
S.B.
Almeida, Jorge Manuel Soares de. "Target tracking using laser range finder with occlusion." Master's thesis, Universidade de Aveiro, 2010. http://hdl.handle.net/10773/2533.
Full textEste trabalho apresenta uma técnica para a detecção e seguimento de múltiplos alvos móveis usando um sensor de distâncias laser em situações de forte oclusão. O processo inicia-se com a aplicação de filtros temporais aos dados em bruto de modo a eliminar o ruído do sensor seguindo-se de uma segmentação em várias fases com o objectivo de contornar o problema da oclusão. Os segmentos obtidos representam objectos presentes no ambiente. Para cada segmento um ponto representativo da sua posição no mundo é calculado, este ponto é definido de modo a ser relativamente invariante à rotação e mudança de forma do objecto. Para fazer o seguimento de alvos uma lista de objectos a seguir é mantida, todos os objectos visíveis são associados a objectos desta lista usando técnicas de procura baseadas na previsão do movimento dos objectos. Uma zona de procura de forma elíptica é definida para cada objecto da lista sendo nesta zona que se dará a associação. A previsão do movimento é feita com base em dois modelos de movimento, um de velocidade constante e um de aceleração constante e com aplicação de filtros de Kalman. O algoritmo foi testado em diversas condições reais e mostrou-se robusto e eficaz no seguimento de pessoas mesmo em situações de extensa oclusão. ABSTRACT: In this work a technique for the detection and tracking of multiple moving targets in situations of strong occlusion using a laser rangefinder is presented. The process starts by the application of temporal filters to the raw data in order to remove noise followed by a multi phase segmentation with the goal of overcoming occlusions. The resulting segments represent objects in the environment. For each segment a representative point is defined; this point is calculated to better represent the object while keeping some invariance to rotation and shape changes. In order to perform the tracking, a list of objects to follow is maintained; all visible objects are associated with objects from this list using search techniques based on the predicted motion of objects. A search zone shaped as an ellipse is defined for each object; it is in this zone that the association is preformed. The motion prediction is based in two motion models, one with constant velocity and the other with constant acceleration and in the application of Kalman filters. The algorithm was tested in diverse real conditions and shown to be robust and effective in the tracking of people even in situations of long occlusions.
Einsele, Tobias. "Localization in indoor environments using a panoramic laser range finder." [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=963995553.
Full textІсенко, А. В. "Електронно-оптичний далекомір." Thesis, Сумський державний університет, 2018. http://essuir.sumdu.edu.ua/handle/123456789/67061.
Full textVega-Brown, Will (William Robert). "The design and implementation of a laser range-finder array for robotics applications." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68862.
Full textCataloged from PDF version of thesis.
We introduce the concept of using a laser range finder array to measure height and tilt for mobile robotics applications. We then present a robust, scalable algorithm for extracting height and tilt measurements from the range finder data. We calibrate the sensors using a precision two-axis system, and evaluate the capabilities of the sensors. Finally, we utilize the sensors and the two-axis system for imaging to illustrate their accuracy.
by Will Vega-Brown.
S.B.
Ferreira, Eduardo Rodolfo Teixeira. "Goal detection using laser range finder for goalkeeper and striker from CAMBADA team." Master's thesis, Universidade de Aveiro, 2015. http://hdl.handle.net/10773/15939.
Full textFor a robot be autonomous and mobile, it requires being attached with a set of sensors that helps it to have a better perception of the surrounding world, to manage to localize itself and the surrounding objects. CAMBADA is the robotic soccer team of the IRIS research group, from IEETA, University of Aveiro, that competes in the Middle-Size League of RoboCup. In competition, in order to win, the main objective of the game it's to score more goals than the conceded, so not conceding goals, and score as much as possible it's desirable, thus, this thesis focus on adapt an agent with a better localization capacity in defensive and offensive moments. It was introduced a laser range finder to the CAMBADA robots, making them capable of detecting their own and the opponent goal, and to detect the opponents in specific game situations. With the new information and adapting the Goalie and Penalty behaviors, the CAMBADA goalkeeper is now able to detect and track its own goal and the CAMBADA striker has a better performance in a penalty situation. The developed work was incorporated within the competition software of the robots, which allows the presentation, in this thesis, of the experimental results obtained with physical robots on the laboratory field.
Para que um robô seja autónomo e móvel, necessita estar equipado com vários sensores que o ajudem a ter uma percepção do mundo que o rodeia, de forma a obter a sua própria localização e a detecção de objectos. CAMBADA é a equipa de futebol robótico do grupo de investigação IRIS, da unidade de investigação IEETA, da Universidade de Aveiro que participa na Liga de Robôs Médios da RoboCup. Em competição, para ganhar, o principal objetivo de uma equipa durante um jogo é não sofrer golos e marcar o maior número possível, desta forma, esta tese foca-se em dotar um agente de uma melhor capacidade de localização em situações defensivas e ofensivas de jogo. Foi introduzido um laser range finder aos robôs da equipa CAMBADA, tornando-os aptos a detetar a sua própria baliza e a do adversário, e a detetar oponentes em situações especificas do jogo. Com a nova informação adquirida e adaptando os behaviors Goalie e Penalty, agora o guarda-redes da equipa CAMBADA está apto a detetar e rastrear a sua própria baliza e o avançado da equipa CAMBADA tem uma melhor performance em situações de penalty. O trabalho desenvolvido foi incorporado no software de competição dos robôs, o que permite nesta tese apresentar resultados experimentais de testes efectuados nos robôs em laboratório.
Yang, Christopher S. "Design and calibration of a multi-modal range sensor using passive stereo, structured lighting, and active triangulation laser range finder." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27201.
Full textKilpelä, A. (Ari). "Pulsed time-of-flight laser range finder techniques for fast, high precision measurement applications." Doctoral thesis, University of Oulu, 2004. http://urn.fi/urn:isbn:9514272625.
Full textHERRERA, LUIS ERNESTO YNOQUIO. "MOBILE ROBOT SIMULTANEOUS LOCALIZATION AND MAPPING USING DP-SLAM WITH A SINGLE LASER RANGE FINDER." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=34617@1.
Full textCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
SLAM (Mapeamento e Localização Simultânea) é uma das áreas mais pesquisadas na Robótica móvel. Trata-se do problema, num robô móvel, de construir um mapa sem conhecimento prévio do ambiente e ao mesmo tempo manter a sua localização nele. Embora a tecnologia ofereça sensores cada vez mais precisos, pequenos erros na medição são acumulados comprometendo a precisão na localização, sendo estes evidentes quando o robô retorna a uma posição inicial depois de percorrer um longo caminho. Assim, para melhoria do desempenho do SLAM é necessário representar a sua formulação usando teoria das probabilidades. O SLAM com Filtro Extendido de Kalman (EKF-SLAM) é uma solução básica, e apesar de suas limitações é a técnica mais popular. O Fast SLAM, por outro lado, resolve algumas limitações do EKF-SLAM usando uma instância do filtro de partículas conhecida como Rao-Blackwellized. Outra solução bem sucedida é o DP-SLAM, o qual usa uma representação do mapa em forma de grade de ocupação, com um algoritmo hierárquico que constrói mapas 2D bastante precisos. Todos estes algoritmos usam informação de dois tipos de sensores: odômetros e sensores de distância. O Laser Range Finder (LRF) é um medidor laser de distância por varredura, e pela sua precisão é bastante usado na correção do erro em odômetros. Este trabalho apresenta uma detalhada implementação destas três soluções para o SLAM, focalizado em ambientes fechados e estruturados. Apresenta-se a construção de mapas 2D e 3D em terrenos planos tais como em aplicações típicas de ambientes fechados. A representação dos mapas 2D é feita na forma de grade de ocupação. Por outro lado, a representação dos mapas 3D é feita na forma de nuvem de pontos ao invés de grade, para reduzir o custo computacional. É considerado um robô móvel equipado com apenas um LRF, sem nenhuma informação de odometria. O alinhamento entre varreduras laser é otimizado fazendo o uso de Algoritmos Genéticos. Assim, podem-se construir mapas e ao mesmo tempo localizar o robô sem necessidade de odômetros ou outros sensores. Um simulador em Matlab é implementado para a geração de varreduras virtuais de um LRF em um ambiente 3D (virtual). A metodologia proposta é validada com os dados simulados, assim como com dados experimentais obtidos da literatura, demonstrando a possibilidade de construção de mapas 3D com apenas um sensor LRF.
Simultaneous Localization and Mapping (SLAM) is one of the most widely researched areas of Robotics. It addresses the mobile robot problem of generating a map without prior knowledge of the environment, while keeping track of its position. Although technology offers increasingly accurate position sensors, even small measurement errors can accumulate and compromise the localization accuracy. This becomes evident when programming a robot to return to its original position after traveling a long distance, based only on its sensor readings. Thus, to improve SLAM s performance it is necessary to represent its formulation using probability theory. The Extended Kalman Filter SLAM (EKF-SLAM) is a basic solution and, despite its shortcomings, it is by far the most popular technique. Fast SLAM, on the other hand, solves some limitations of the EKFSLAM using an instance of the Rao-Blackwellized particle filter. Another successful solution is to use the DP-SLAM approach, which uses a grid representation and a hierarchical algorithm to build accurate 2D maps. All SLAM solutions require two types of sensor information: odometry and range measurement. Laser Range Finders (LRF) are popular range measurement sensors and, because of their accuracy, are well suited for odometry error correction. Furthermore, the odometer may even be eliminated from the system if multiple consecutive LRF scans are matched. This works presents a detailed implementation of these three SLAM solutions, focused on structured indoor environments. The implementation is able to map 2D environments, as well as 3D environments with planar terrain, such as in a typical indoor application. The 2D application is able to automatically generate a stochastic grid map. On the other hand, the 3D problem uses a point cloud representation of the map, instead of a 3D grid, to reduce the SLAM computational effort. The considered mobile robot only uses a single LRF, without any odometry information. A Genetic Algorithm is presented to optimize the matching of LRF scans taken at different instants. Such matching is able not only to map the environment but also localize the robot, without the need for odometers or other sensors. A simulation program is implemented in Matlab to generate virtual LRF readings of a mobile robot in a 3D environment. Both simulated readings and experimental data from the literature are independently used to validate the proposed methodology, automatically generating 3D maps using just a single LRF.
Books on the topic "Range finder"
Faust, Frederick Schiller. The range finder: A western trio. Waterville, Me: Thorndike Press, 2005.
Find full textFaust, Frederick Schiller. The range finder: A western trio. Waterville, Me: Five Star, 2004.
Find full textLevan, N. On-line range prediction system (II). [Los Angeles, Calif: UCLA School of Engineering and Applied Science, 1988.
Find full textLevan, N. On-line range prediction system (II). [Los Angeles, Calif: UCLA School of Engineering and Applied Science, 1988.
Find full textHuang, Fay. Panoramic imaging: Sensor-line cameras and laser range-finders. Chichester, West Sussex, UK: J. Wiley, 2008.
Find full textNandhakumar, N. Determining the 3-D structure and motion of objects using a scanning laser range sensor. Charlottesville, Va: University of Virginia, School of Engineering & Applied Science, Dept. of Electrical Engineering, 1993.
Find full textMurata, Masaaki. Determination of station coordinates, earth rotation and plate motions from LAGEOS laser ranging: 1983-1986. Tokyo: National Aerospace Laboratory, 1988.
Find full textMinott, Peter O. Prelaunch optical characterization of the Laser Geodynamic Satellite (LAGEOS 2). Greenbelt, Md: Goddard Space Flight Center, 1993.
Find full textSchael, Ulrich. Erweiterte Simulation für augensicheres, bildgebendes 3D Laser Radar. Aachen: Shaker, 2004.
Find full textMcIntyre, Thomas. The Field & stream hunting optics handbook: An expert's guide to rifle scopes, binoculars, spotting scopes, and range finders. Guilford, Conn: Lyons Press, 2008.
Find full textBook chapters on the topic "Range finder"
Weik, Martin H. "range finder." In Computer Science and Communications Dictionary, 1413. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_15470.
Full textXiao, G. Q., D. B. Patterson, and G. S. Kino. "Optical Range Finder." In Review of Progress in Quantitative Nondestructive Evaluation, 751–57. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1893-4_85.
Full textMartín, J. M., R. Ceres, J. No, and L. Calderón. "Adaptative Ultrasonic Range-Finder for Robotics." In Sensor Devices and Systems for Robotics, 143–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74567-6_11.
Full textDu, Jingjing, Marina Indri, Douwe Dresscher, and Stefano Stramigioli. "Autonomous Exploration Using Kinect and Laser Range Finder." In Advances in Autonomous Robotics, 420–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32527-4_40.
Full textShirai, Y. "Application of Laser Range Finder to Robot Vision." In Sensor Devices and Systems for Robotics, 313–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74567-6_22.
Full textNautiyal, Ram Prakash, Vikas Dua, Ranabir Mandal, and P. K. Sharma. "Optics for Miniaturized Eye-Safe Laser Range Finder." In Springer Proceedings in Physics, 415–18. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9259-1_95.
Full textWen, Chenglu, Ling Qin, Siyuan Lin, and Qingyuan Zhu. "3D Environment Modeling with Hybrid of Laser Range Finder and Range Camera." In Advances in Intelligent and Soft Computing, 397–402. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29390-0_63.
Full textFang, Yong, Cindy Cappelle, and Yassine Ruichek. "Road Detection Using Fisheye Camera and Laser Range Finder." In Lecture Notes in Computer Science, 495–502. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07998-1_57.
Full textMoreno, Francisco-Angel, Grzegorz Cielniak, and Tom Duckett. "Evaluation of Laser Range-Finder Mapping for Agricultural Spraying Vehicles." In Towards Autonomous Robotic Systems, 210–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43645-5_22.
Full textAguirre, Eugenio, Miguel García-Silvente, and Marcelo García-Pérez. "Learning Leg Pattern Using Laser Range Finder in Mobile Robots." In ROBOT 2017: Third Iberian Robotics Conference, 627–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70833-1_51.
Full textConference papers on the topic "Range finder"
Bilynsky, Y., and O. Fedune. "Optoelectronic range finder." In International Conference on Optoelectronic Information Technologies, edited by Sergey V. Svechnikov, Volodymyr P. Kojemiako, and Sergey A. Kostyukevych. SPIE, 2001. http://dx.doi.org/10.1117/12.429781.
Full textLaux, Alan, Linda Mullen, Paul Perez, and Eleonora Zege. "Underwater laser range finder." In SPIE Defense, Security, and Sensing, edited by Weilin W. Hou and Robert Arnone. SPIE, 2012. http://dx.doi.org/10.1117/12.919280.
Full textJohnson, John L., Melissa W. Thie, R. E. Jetton, and Don A. Gregory. "Passive optical target range finder." In SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, edited by David P. Casasent and Andrew G. Tescher. SPIE, 1994. http://dx.doi.org/10.1117/12.177725.
Full textChahl, Javaan Singh. "Alternate optical range finder geometries." In 2014 IEEE Region 10 Symposium. IEEE, 2014. http://dx.doi.org/10.1109/tenconspring.2014.6862999.
Full textSato, Tatsuo. "Multispectral pattern projection range finder." In Electronic Imaging '99, edited by Joseph H. Nurre and Brian D. Corner. SPIE, 1999. http://dx.doi.org/10.1117/12.341069.
Full textRao, M. Kameshwar, and Siu Chung Tam. "Microprocessor-based laser range finder." In Singapore, edited by Soon Fatt Yoon, M. H. Kuok, and Donald E. Silva. SPIE, 1991. http://dx.doi.org/10.1117/12.26089.
Full textRice, Pete, and Joshua Strickon. "Stretchable music with laser range finder." In ACM SIGGRAPH 98 Conference abstracts and applications. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/280953.281325.
Full textChen-Chia Wang, S. Trivedi, Feng Jin, J. Khurgin, D. Temple, U. Hommerid, E. Gad, and A. Corder. "Interferometer-less coherent optical range finder." In CLEO 2001. Technical Digest. Summaries of papers presented at the Conference on Lasers and Electro-Optics. Postconference Technical Digest. IEEE, 2001. http://dx.doi.org/10.1109/cleo.2001.947939.
Full textSaniei, S. Z., G. Mamdoohi, A. F. Abas, M. A. Mahdi, and M. Saraf. "Variable sensitivity laser range finder receiver." In 2011 IEEE 2nd International Conference on Photonics (ICP). IEEE, 2011. http://dx.doi.org/10.1109/icp.2011.6106830.
Full textShemer, Keren, Gil Bashan, Hagai Diamandi, Yosef London, Arik Bergman, Nadav Levanon, and Avi Zadok. "Sequence-Coded Coherent Laser Range Finder." In CLEO: Science and Innovations. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/cleo_si.2019.sm1n.2.
Full textReports on the topic "Range finder"
Todd, T. J., and A. Lennox. Surface contouring with a range finder. Office of Scientific and Technical Information (OSTI), August 1986. http://dx.doi.org/10.2172/6941803.
Full textTodd, Thomas, and Arlene Lennox. Surface Contouring with a Range Finder. Office of Scientific and Technical Information (OSTI), August 1986. http://dx.doi.org/10.2172/1156295.
Full textBambha, Ray P., Kevin L. Schroder, and Thomas A. Reichardt. Eye safe short range standoff aerosol cloud finder. Office of Scientific and Technical Information (OSTI), February 2005. http://dx.doi.org/10.2172/922078.
Full textBeck-Winchatz, Bernhard, and David Jabon. Measuring the Speed of Sound with an Ultrasonic Range Finder David. Ames (Iowa): Iowa State University. Library. Digital Press, January 2012. http://dx.doi.org/10.31274/ahac.8335.
Full textKemmotsu, Keiichi, and Takeo Kanade. Sensor Placement Design for Object Pose Determination with Three Light- Stripe Range Finders. Fort Belvoir, VA: Defense Technical Information Center, May 1994. http://dx.doi.org/10.21236/ada281199.
Full textOgston, Andrea S. Processes Controlling Transfer of Fine-Grained Sediment in Tidal Systems Spanning a Range of Fluvial Influence. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada572944.
Full textKontak, D. J., S. Paradis, Z. Waller, and M. Fayek. Petrographic, fluid inclusion, and secondary ion mass spectrometry stable isotopic (O, S) study of Mississippi Valley-type mineralization in British Columbia and Alberta. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/327994.
Full textAiken, Catherine, Rebecca Kagan, and Michael Page. “Cool Projects” or “Expanding the Efficiency of the Murderous American War Machine?”: AI Professionals’ Views on Working With the Department of Defense. Center for Security and Emerging Technology, November 2020. http://dx.doi.org/10.51593/20190050.
Full textRobson, Jennifer. The Canada Learning Bond, financial capability and tax-filing: Results from an online survey of low and modest income parents. SEED Winnipeg/Carleton University Arthur Kroeger College of Public Affairs, March 2022. http://dx.doi.org/10.22215/clb20220301.
Full textMeadows, Michael. Thesis Review: The Role of SANZ, a Migrant Radio Programme, in Making Sense of Place for South African Migrants in New Zealand. Unitec ePress, November 2016. http://dx.doi.org/10.34074/thes.revw22016.
Full text