Cellular neural networks for motion estimation and obstacle detection

Obstacle detection is an important part of Video Processing because it is indispensable for a collision prevention of autonomously navigating moving objects. For example, vehicles driving without human guidance need a robust prediction of potential obstacles, like other vehicles or pedestrians. Most of the common approaches of obstacle detection so far use analytical and statistical methods like motion estimation or generation of maps. In the first part of this contribution a statistical algorithm for obstacle detection in monocular video sequences is presented. The proposed procedure is based on a motion estimation and a planar world model which is appropriate to traffic scenes. The different processing steps of the statistical procedure are a feature extraction, a subsequent displacement vector estimation and a robust estimation of the motion parameters. Since the proposed procedure is composed of several processing steps, the error propagation of the successive steps often leads to inaccurate results. In the second part of this contribution it is demonstrated, that the above mentioned problems can be efficiently overcome by using Cellular Neural Networks (CNN). It will be shown, that a direct obstacle detection algorithm can be easily performed, based only on CNN processing of the input images. Beside the enormous computing power of programmable CNN based devices, the proposed method is also very robust in comparison to the statistical method, because is shows much less sensibility to noisy inputs. Using the proposed approach of obstacle detection in planar worlds, a real time processing of large input images has been made possible

Determination of nitrogen in titanium nitride

Quantitative determination of nitrogen in titanium nitride involves dissolution of TiN in 10M hydrofluoric acid containing an oxidant. Released nitrogen is determined as ammonia. Best oxidizers are ferric chloride, potassium iodate, and potassium dichromate

Robust Simulation of a TaO Memristor Model

This work presents a continuous and differentiable approximation of a Tantalum oxide memristor model which is suited for robust numerical simulations in software. The original model was recently developed at Hewlett Packard labs on the basis of experiments carried out on a memristor manufactured in house. The Hewlett Packard model of the nano-scale device is accurate and may be taken as reference for a deep investigation of the capabilities of the memristor based on Tantalum oxide. However, the model contains discontinuous and piecewise differentiable functions respectively in state equation and Ohm's based law. Numerical integration of the differential algebraic equation set may be significantly facilitated under substitution of these functions with appropriate continuous and differentiable approximations. A detailed investigation of classes of possible continuous and differentiable kernels for the approximation of the discontinuous and piecewise differentiable functions in the original model led to the choice of near optimal candidates. The resulting continuous and differentiable DAE set captures accurately the dynamics of the original model, delivers well-behaved numerical solutions in software, and may be integrated into a commercially-available circuit simulator

Soil water stable isotopes reveal evaporation dynamics at the soil–plant–atmosphere interface of the critical zone

Acknowledgements. We are thankful for the support by Audrey Innes during all laboratory work. We further thank Jonathan Dick for running the isotope analysis of precipitation samples and Annette C. Raffan for her support in the soil texture analysis. We would also like to thank the European Research Council (ERC, project GA 335910 VeWa) for fundingPeer reviewedPublisher PD

The essential value of long-term experimental data for hydrology and water management

We would like to thank the European Research Council ERC for funding the VeWa project and most of Tetzlaff's time (project GA 335910 VeWa). No data were used in producing this manuscript.Peer reviewedPublisher PD