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Metalenses, advanced nanostructured alternatives to conventional lenses, significantly enhance the compactness and functionality of optical systems. Despite progress in monochromatic applications, scaling metalenses to centimeter-sized apertures for
Externí odkaz:
http://arxiv.org/abs/2410.20043
Akademický článek
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For a family of polynomials in two variables, orthogonal with respect to a weight function, we prove under some conditions, equivalence between: the Matrix Pearson equation of the weight, the second order linear partial differential equation, the ort
Externí odkaz:
http://arxiv.org/abs/2407.06995
Autor:
Wium van Zyl
Publikováno v:
Tydskrif vir Letterkunde, Vol 45, Iss 1 (2018)
Externí odkaz:
https://doaj.org/article/9f43efd96b504425862d942a3db47be0
Autor:
Evans, Benjamin David, Trumpp, Raphael, Caccamo, Marco, Jahncke, Felix, Betz, Johannes, Jordaan, Hendrik Willem, Engelbrecht, Herman Arnold
The F1TENTH autonomous driving platform, consisting of 1:10-scale remote-controlled cars, has evolved into a well-established education and research platform. The many publications and real-world competitions span many domains, from classical path pl
Externí odkaz:
http://arxiv.org/abs/2402.18558
Map-based methods for autonomous racing estimate the vehicle's location, which is used to follow a high-level plan. While map-based optimisation methods demonstrate high-performance results, they are limited by requiring a map of the environment. In
Externí odkaz:
http://arxiv.org/abs/2401.17732
Autor:
Jooste, AS, Jordaan, K.
In this paper we consider connection formulae for orthogonal polynomials in the context of Christoffel transformations for the case where a weight function, not necessarily even, is multiplied by an even function $c_{2k}(x),k\in \mathbb{N}_0$. When $
Externí odkaz:
http://arxiv.org/abs/2312.09931
Autor:
Goedhart, S., Cotton, W. D., Camilo, F., Thompson, M. A., Umana, G., Bietenholz, M., Woudt, P. A., Anderson, L. D., Bordiu, C., Buckley, D. A. H., Buemi, C. S., Bufano, F., Cavallaro, F., Chen, H., Chibueze, J. O., Egbo, D., Frank, B. S., Hoare, M. G., Ingallinera, A., Irabor, T., Kraan-Korteweg, R. C., Kurapati, S., Leto, P., Loru, S., Mutale, M., Obonyo, W. O., Plavin, A., Rajohnson, S. H. A., Rigby, A., Riggi, S., Seidu, M., Serra, P., Smart, B. M., Stappers, B. W., Steyn, N., Surnis, M., Trigilio, C., Williams, G. M., Abbott, T. D., Adam, R. M., Asad, K. M. B., Baloyi, T., Bauermeister, E. F., Bennet, T. G. H., Bester, H., Botha, A. G., Brederode, L. R. S., Buchner, S., Burger, J. P., Cheetham, T., Cloete, K., de Villiers, M. S., de Villiers, D. I. L., Toit, L. J. du, Esterhuyse, S. W. P., Fanaroff, B. L., Fourie, D. J., Gamatham, R. R. G., Gatsi, T. G., Geyer, M., Gouws, M., Gumede, S. C., Heywood, I., Hokwana, A., Hoosen, S. W., Horn, D. M., Horrell, L. M. G., Hugo, B. V., Isaacson, A. I., Józsa, G. I. G., Jonas, J. L., Jordaan, J. D. B. L., Joubert, A. F., Julie, R. P. M., Kapp, F. B., Kriek, N., Kriel, H., Krishnan, V. K., Kusel, T. W., Legodi, L. S., Lehmensiek, R., Lord, R. T., Macfarlane, P. S., Magnus, L. G., Magozore, C., Main, J. P. L., Malan, J. A., Manley, J. R., Marais, S. J., Maree, M. D. J., Martens, A., Maruping, P., McAlpine, K., Merry, B. C., Mgodeli, M., Millenaar, R. P., Mokone, O. J., Monama, T. E., New, W. S., Ngcebetsha, B., Ngoasheng, K. J., Nicolson, G. D., Ockards, M. T., Oozeer, N., Passmoor, S. S., Patel, A. A., Peens-Hough, A., Perkins, S. J., Ramaila, A. J. T., Ratcliffe, S. M., Renil, R., Richter, L. L., Salie, S., Sambu, N., Schollar, C. T. G., Schwardt, L. C., Schwartz, R. L., Serylak, M., Siebrits, R., Sirothia, S. K., Slabber, M. J., Smirnov, O. M., Tiplady, A. J., van Balla, T. J., van der Byl, A., Van Tonder, V., Venter, A. J., Venter, M., Welz, M. G., Williams, L. P.
We present the SARAO MeerKAT Galactic Plane Survey (SMGPS), a 1.3 GHz continuum survey of almost half of the Galactic Plane (251\deg $\le l \le$ 358\deg and 2\deg $\le l \le$ 61\deg at $|b| \le 1.5\deg $). SMGPS is the largest, most sensitive and hig
Externí odkaz:
http://arxiv.org/abs/2312.07275
In this paper, we address the issue of increasing the performance of reinforcement learning (RL) solutions for autonomous racing cars when navigating under conditions where practical vehicle modelling errors (commonly known as \emph{model mismatches}
Externí odkaz:
http://arxiv.org/abs/2312.06406
Autor:
Jeffrey Tyssens
Publikováno v:
BMGN: Low Countries Historical Review, Vol 134, Iss 2 (2019)
Externí odkaz:
https://doaj.org/article/66e8bba53d674caa9994b84a51370382