Cumincad : CUMINCAD References : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

PDF papers
References

Hits 1 to 20 of 12291

Reformat results as: short short into frame detailed detailed into frame

Details	Citation	Select
	Archer, B. (1996) Activity Data Method: A Method Of Recording User Requirements , Great Britain: Hmso

	Archibald, M. M., Ambagtsheer, R. C., Casey, M. G., & Lawless, M. (2019) Using zoom videoconferencing for qualitative data collection: perceptions and experiences of researchers and participants , International journal of qualitative methods, 18, 1609406919874596

	Ardic, S. l., Kirdar, G., Lima, A.B. (2020) An exploratory urban analysis via big data approach: Eindhoven case , L.C. Werner & D. Koering (Eds.), Proceedings of the 38th International Online Conference on Education and Research Computer Aided Architectural Design Europe (pp. 309-318). Technische Universität Berlin. http://papers.cumincad.org/data/works/att/ecaade2020_089.pdf

	Ardiç ÿI S, Kirdar G and Lima A B (2020) An exploratory urban analysis via big data approach: eindhoven case , LC Werner and D Koering (eds) Proceedings of the 38th international online conference on education and research in computer aided architectural design in Europe Technische Universitat Berlin, 2020, pp309 ¨ –318

	Arens, Edward (1986) A Method for Designing Naturally Cooled Buildings Using Bin Climate Data , ASHRAE Transactions 773-792, no. 2. Envelope Systems: 14

	Arentze T.A. e Timmermans H.J.P. (2000) Data Needs, Data Collection and Data Quality Requirement of Activity-Based Transport Demand Models , Paper presented at Workshop on Modeler’s Needs

	Arentze, T., F. Hofman, N. Kalfs and H. J. P.Timmermans (1997) Data needs, data collection and data quality requirements of activity-based transport demand models , International Conference on Transport Survey Quality and Innovation, Grainau, Germany

	Arianna Rech, Ruxandra Chiujdea, Claudia Colmo, Gabriella Rossi, Paul Nicholas, Martin Tamke, Mette Ramsgaard Thomsen, and Daugaard Anders Egede (2021) Predicting Response: Waste-Based Biopolymer Slurry Recipe for 3d-Printing (CelluloseFloc_v01) [Data set] , Zenodo

	Ariza I, Mirjan A, Gandia A, et al. (2018) In place detailing , ACADIA, pp. 312–321, http://papers.cumincad.org/data/works/att/acadia18_312.pdf

	Arman Najari, Diego Pajarito, and Areti Markopoulou (2020) Data Modeling of Cities, a Machine Learning Application , SimAUD ‘20: Proceedings of the 11th Annual Symposium on Simulation for Architecture and Urban Design. 1–8

	Armeni, I, Sax, S, Zamir, AR and Savarese, S (2017) Joint 2d-3d-semantic data for indoor scene understanding , arXiv preprint arXiv:1702.01105

	Armstrong, M., Densham, P. and Rushton, G. (1986) Architecture for a microcomputer based spatial decision support system , Proceedings of the Second International Symposium on Spatial Data Handling, Williamsville, NY, USA, pp. 120-131

	Arribas-Bel D. (2014) Accidental, open and everywhere: emerging data sources for the understanding of cities , Applied Geography ; 49: 45–53

	Arribas-Bel, D (2014) Accidental, open and everywhere: Emerging data sources for the understanding of cities , Applied Geography, 49, pp. 45-53

	Arribas-Bel, D (2014) Accidental, open and everywhere: Emerging data sources for the understanding of cities. , Applied Geography, 49, pp. 45-53

	Artale A., Franconi E., Guarino N., Pazzi L. (1996) Part-Whole Relations in Object-Centered Systems: An overview , Data & Knowledge Engineering (DKE) journal 20 pp. 347-383 – North-Holland, Elsevier, 1996

	Artopoulos, G. (2022) Creating a Time Machine of Future Pasts: Data Integration and Interoperability for Cross-disciplinary Research on Urban Heritage Clusters , SUMAC '22 Proceedings of the 4th ACM International workshop on Structuring and Understanding of Multimedia Heritage Contents, Lisbon, 10 October 2022, pp. 3-4

	As, I., Pal, S. & Basu, P. (2018) Artificial intelligence in architecture: Generating conceptual design via deep learning , International Journal of Architectural Computing, 16(4), 306–327. https://doi.org/10.1177/1478077118800982Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017). Geometric deep learning: Going beyond Euclidean data. IEEE Signal Processing Magazine, 34(4), 18–42. https://doi.org/10.1109/MSP.2017.2693418

	Aschwanden, G., D.P.A. (2016) Sneighbourhood detection with analytical tools, Identifying neighbourhoods from geospatial and transportation data though graph evaluation and learning algorithms , 21st International Conference of the Association for Computer-Aided Architectural Design Research in Asia CAADRIA, 13–22 10

	Aschwanden, G., Wullschleger, T., Müller, H., Schmitt, G. (2010) Agent based Emission Evaluation of Traffic in Dynamic City Models , Proceedings of the 28th eCAADe Conference, 15-18 September 2010, Zurich, Switzerland, pp. 717-726. Available at: http://papers.cumincad.org/data/works/att/ecaade2010_229.content.pdf (Accessed 30 March 2022)

For more results click below: