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- Euler method (1)
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- automatic handwriting analysis (1)
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- Segment and strong segment LLL-reduction of lattice bases (2002)
- We present an efficient variant of LLL-reduction of lattice bases in the sense of Lenstra, Lenstra, Lov´asz [LLL82]. We organize LLL-reduction in segments of size k. Local LLL-reduction of segments is done using local coordinates of dimension 2k. Strong segment LLL-reduction yields bases of the same quality as LLL-reduction but the reduction is n-times faster for lattices of dimension n. We extend segment LLL-reduction to iterated subsegments. The resulting reduction algorithm runs in O(n3 log n) arithmetic steps for integer lattices of dimension n with basis vectors of length 2O(n), compared to O(n5) steps for LLL-reduction.

- Enhancing the security of perfect blind DL-signatures (2003)
- We enhance the security of Schnorr blind signatures against the novel one-more-forgery of Schnorr [Sc01] andWagner [W02] which is possible even if the discrete logarithm is hard to compute. We show two limitations of this attack. Firstly, replacing the group G by the s-fold direct product G exp(×s) increases the work of the attack, for a given number of signer interactions, to the s-power while increasing the work of the blind signature protocol merely by a factor s. Secondly, we bound the number of additional signatures per signer interaction that can be forged effectively. That fraction of the additional forged signatures can be made arbitrarily small.

- Handwriting analysis for diagnosis and prognosis of Parkinson’s disease (2006)
- At present, there are no quantitative, objective methods for diagnosing the Parkinson disease. Existing methods of quantitative analysis by myograms suffer by inaccuracy and patient strain; electronic tablet analysis is limited to the visible drawing, not including the writing forces and hand movements. In our paper we show how handwriting analysis can be obtained by a new electronic pen and new features of the recorded signals. This gives good results for diagnostics. Keywords: Parkinson diagnosis, electronic pen, automatic handwriting analysis

- The performance of approximating ordinary differential equations by neural nets (2008)
- The dynamics of many systems are described by ordinary differential equations (ODE). Solving ODEs with standard methods (i.e. numerical integration) needs a high amount of computing time but only a small amount of storage memory. For some applications, e.g. short time weather forecast or real time robot control, long computation times are prohibitive. Is there a method which uses less computing time (but has drawbacks in other aspects, e.g. memory), so that the computation of ODEs gets faster? We will try to discuss this question for the assumption that the alternative computation method is a neural network which was trained on ODE dynamics and compare both methods using the same approximation error. This comparison is done with two different errors. First, we use the standard error that measures the difference between the approximation and the solution of the ODE which is hard to characterize. But in many cases, as for physics engines used in computer games, the shape of the approximation curve is important and not the exact values of the approximation. Therefore, we introduce a subjective error based on the Total Least Square Error (TLSE) which gives more consistent results. For the final performance comparison, we calculate the optimal resource usage for the neural network and evaluate it depending on the resolution of the interpolation points and the inter-point distance. Our conclusion gives a method to evaluate where neural nets are advantageous over numerical ODE integration and where this is not the case. Index Terms—ODE, neural nets, Euler method, approximation complexity, storage optimization.

- Integer point sets minimizing average pairwise L1 distance: What is the optimal shape of a town? (2010)
- An n-town, n[is an element of]N , is a group of n buildings, each occupying a distinct position on a 2-dimensional integer grid. If we measure the distance between two buildings along the axis-parallel street grid, then an n-town has optimal shape if the sum of all pairwise Manhattan distances is minimized. This problem has been studied for cities, i.e., the limiting case of very large n. For cities, it is known that the optimal shape can be described by a differential equation, for which no closed-form solution is known. We show that optimal n-towns can be computed in O(n[superscript 7.5]) time. This is also practically useful, as it allows us to compute optimal solutions up to n=80.