Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Timm, Fabian"'
Autor:
Mörstedt, Timm Fabian, Teixeira, Wallace Santos, Viitanen, Arto, Kivijärvi, Heidi, Tiiri, Maaria, Rasola, Miika, Gunyho, Andras Marton, Kundu, Suman, Lattier, Louis, Vadimov, Vasilii, Catelani, Gianluigi, Sevriuk, Vasilii, Heinsoo, Johannes, Räbinä, Jukka, Ankerhold, Joachim, Möttönen, Mikko
We experimentally demonstrate the fast generation of thermal states of a transmon using a single-junction quantum-circuit refrigerator (QCR) as an in-situ-tunable environment. Through single-shot readout, we monitor the transmon up to its third-excit
Externí odkaz:
http://arxiv.org/abs/2402.09594
Autor:
Mörstedt, Timm Fabian, Viitanen, Arto, Vadimov, Vasilii, Sevriuk, Vasilii, Partanen, Matti, Hyyppä, Eric, Catelani, Gianluigi, Silveri, Matti, Tan, Kuan Yen, Möttönen, Mikko
Publikováno v:
Annalen der Physik 534, 2100543 (2022)
We review the recent progress in direct active cooling of the quantum-electric degrees freedom in engineered circuits, or quantum-circuit refrigeration. In 2017, the invention of a quantum-circuit refrigerator (QCR) based on photon-assisted tunneling
Externí odkaz:
http://arxiv.org/abs/2111.11234
Autor:
Feng, Di, Wang, Zining, Zhou, Yiyang, Rosenbaum, Lars, Timm, Fabian, Dietmayer, Klaus, Tomizuka, Masayoshi, Zhan, Wei
The availability of many real-world driving datasets is a key reason behind the recent progress of object detection algorithms in autonomous driving. However, there exist ambiguity or even failures in object labels due to error-prone annotation proce
Externí odkaz:
http://arxiv.org/abs/2012.12195
This paper presents an novel object type classification method for automotive applications which uses deep learning with radar reflections. The method provides object class information such as pedestrian, cyclist, car, or non-obstacle. The method is
Externí odkaz:
http://arxiv.org/abs/2010.09273
Deep neural networks (DNNs) are usually over-parameterized to increase the likelihood of getting adequate initial weights by random initialization. Consequently, trained DNNs have many redundancies which can be pruned from the model to reduce complex
Externí odkaz:
http://arxiv.org/abs/2009.08169
Reliable uncertainty estimation is crucial for robust object detection in autonomous driving. However, previous works on probabilistic object detection either learn predictive probability for bounding box regression in an un-supervised manner, or use
Externí odkaz:
http://arxiv.org/abs/2008.04168
Lane detection is an essential part of the perception sub-architecture of any automated driving (AD) or advanced driver assistance system (ADAS). When focusing on low-cost, large scale products for automated driving, model-driven approaches for the d
Externí odkaz:
http://arxiv.org/abs/2004.07639
Autor:
Wang, Zining, Feng, Di, Zhou, Yiyang, Rosenbaum, Lars, Timm, Fabian, Dietmayer, Klaus, Tomizuka, Masayoshi, Zhan, Wei
The availability of real-world datasets is the prerequisite for developing object detection methods for autonomous driving. While ambiguity exists in object labels due to error-prone annotation process or sensor observation noises, current object det
Externí odkaz:
http://arxiv.org/abs/2003.03644
Publikováno v:
Neurocomputing 2020
Deep neural networks (DNNs) have been proven to outperform classical methods on several machine learning benchmarks. However, they have high computational complexity and require powerful processing units. Especially when deployed on embedded systems,
Externí odkaz:
http://arxiv.org/abs/2002.08204
This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection. We explicitly model uncertainties in the classification and regression tasks, and leverage uncerta
Externí odkaz:
http://arxiv.org/abs/2002.00216