Christoph Böddeker

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This report describes the computation of gradients by algorithmic differentiation for statistically optimum beamforming operations. Especially the derivation of complex-valued functions is a key component of this approach. Therefore the real-valued algorithmic differentiation is extended via the complex-valued chain rule. In addition to the basic mathematic(More)
We present an algorithm for clustering complex-valued unit length vectors on the unit hypersphere, which we call complex spherical k-mode clustering, as it can be viewed as a generalization of the spherical k-means algorithm to normalized complex-valued vectors. We show how the proposed algorithm can be derived from the Expectation Maximization algorithm(More)
The perceptional of the motion of objects is a key problem for a mobile robot to perform tasks in a dynamic environment. Thus, we present a real-time approach for tracking multiple moving objects. The proposed algorithm initially detects moving regions and a dense optical flow technique is exclusively applied to those regions between two consecutive frames.(More)
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