Fertigung einer universellen Hüftprothesenpfanne mittels Hochdruckblechumformung

In recent years, the use of hip prostheses has become a routine procedure. The use of total hip replacement has evolved in recent years to a routine procedure. Despite this experience, it always comes back to complications. Especially the migration or loosening of the acetabular component because of the artificial load adaptive bone remodeling is still a current problem. This is due to the changing mechanical situation after the implantation of the prosthesis. Another problem is the high bone loss during implantation, which complicates a revision of the prosthesis. One solution is the use of patient‐specific prostheses. So far, the use of these prostheses is limited due to the time‐consuming and cost‐intensive production. The overall objective of the presented project in this publication is the development and establishment of an innovative concept for the production of patient‐specific hip prosthesis made of titanium plates by sheet metal forming. The idea of this concept involves the development of a two‐stage process. First of all a standardized hip prosthesis is made by high‐pressure sheet metal forming and then individually in the second step. This publication contains the derivation of the standard prosthesis. For this purpose, a design methodology was generated, based on the so‐called agglomerative clustering. In addition, presents the results of the numerical preliminary design of the first stage of the process and the tool developed concept in the course of this paper.     

A parametric method for non-stationary interference suppression in direct sequence spread-spectrum systems

The problem of non-stationary interference suppression in direct sequence spread-spectrum (DS-SS) systems is considered. The phase of interference is approximated by a polynomial within the considered interval. According to the local polynomial Fourier transform (LPFT) principle, the received signal is dechirped by using the obtained phase approximation and the interference is, in turn, suppressed by excising the corrupted low-pass frequency band. For the estimation of polynomial coefficients, we use the product high-order ambiguity function (PHAF), known for its capability to successfully resolve components of a multicomponent polynomial-phase signal (PPS). The proposed method can suppress interferences with both polynomial and non-polynomial phase. In addition, it can suppress both monocomponent and multicomponent interferences. The simulations show that the proposed method outperforms time-frequency (TF) methods, that successfully deal with multicomponent interferences, in terms of the error probability and computational complexity.     

Implicit Meshes for Effective Silhouette Handling

Using silhouettes in uncontrolled environments typically requires handling occlusions as well as changing or cluttered backgrounds, which limits the applicability of most silhouette based methods. For the purpose of 3-D shape modeling, we show that representing generic 3-D surfaces as implicit surfaces lets us effectively address these issues. This desirable behavior is completely independent from the way the surface deformations are parame-trized. To show this, we demonstrate our technique in three very different cases: Modeling the deformations of a piece of paper represented by an ordinary triangulated mesh; reconstruction and tracking a person’s shoulders whose deformations are expressed in terms of Dirichlet Free Form Deformations; reconstructing the shape of a human face parametrized in terms of a Principal Component Analysis model. Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users. This work was supported in part by the Swiss National Science Foundation     

A multimodal hand-based verification system with an aliveness-detection module

This paper presents a multimodal biometrie verification system based on the following hand features: palmprint, four digitprints and four fingerprints. The features are obtained using the Karhunen-Loève transform based approach, and information fusion at the matching-score level was applied. We experimented with different resolutions of the regions of interest, different numbers of features and several normalization and fusion techniques at the matching-score level. To increase the reliability of the system to spoof attacks we included an aliveness-detection module based on thermal images of the hand dor sa. The verification performance when using a system configuration with optimum parameters, i.e., resolution, number of features, normalization and fusion technique, showed an equal error rate (EER) of 0.0020%, which makes the system appropriate for the implementation of high-security biometric systems.     

Supervised clustering of label ranking data using label preference information

This paper studies supervised clustering in the context of label ranking data. The goal is to partition the feature space into K clusters, such that they are compact in both the feature and label ranking space. This type of clustering has many potential applications. For example, in target marketing we might want to come up with K different offers or marketing strategies for our target audience. Thus, we aim at clustering the customers’ feature space into K clusters by leveraging the revealed or stated, potentially incomplete customer preferences over products, such that the preferences of customers within one cluster are more similar to each other than to those of customers in other clusters. We establish several baseline algorithms and propose two principled algorithms for supervised clustering. In the first baseline, the clusters are created in an unsupervised manner, followed by assigning a representative label ranking to each cluster. In the second baseline, the label ranking space is clustered first, followed by partitioning the feature space based on the central rankings. In the third baseline, clustering is applied on a new feature space consisting of both features and label rankings, followed by mapping back to the original feature and ranking space. The RankTree principled approach is based on a Ranking Tree algorithm previously proposed for label ranking prediction. Our modification starts with K random label rankings and iteratively splits the feature space to minimize the ranking loss, followed by re-calculation of the K rankings based on cluster assignments. The MM-PL approach is a multi-prototype supervised clustering algorithm based on the Plackett-Luce (PL) probabilistic ranking model. It represents each cluster with a union of Voronoi cells that are defined by a set of prototypes, and assign each cluster with a set of PL label scores that determine the cluster central ranking. Cluster membership and ranking prediction for a new instance are determined by cluster membership of its nearest prototype. The unknown cluster PL parameters and prototype positions are learned by minimizing the ranking loss, based on two variants of the expectation-maximization algorithm. Evaluation of the proposed algorithms was conducted on synthetic and real-life label ranking data by considering several measures of cluster goodness: (1) cluster compactness in feature space, (2) cluster compactness in label ranking space and (3) label ranking prediction loss. Experimental results demonstrate that the proposed MM-PL and RankTree models are superior to the baseline models. Further, MM-PL is has shown to be much better than other algorithms at handling situations with significant fraction of missing label preferences.     

INFERENCE PROCEDURES FOR FUZZY KNOWLEDGE REPRESENTATION SCHEME

This article presents a formal model of the knowledge representation scheme based on the fuzzy Petri net (FPN) theory. The model is represented as a 13-tuple consisting of the components of the FPN, two functions that give semantic meanings to the scheme and a set of contradictions. For the scheme, called the knowledge representation scheme based on the fuzzy Petri nets theory (KRFPN) the fuzzy inheritance and fuzzy recognition-inference procedures based on the dynamical properties of the FPN, are described in detail. The upper-time complexity of both the proposed inference algorithms is O(nm), where n is the number of places (concepts) and m is the number of transitions (relations) in the scheme. Illustrative examples of the fuzzy inheritance and the fuzzy recognition algorithms for the knowledge base, designed by the KRFPN, are given.     

A new method for computing fuzzy functional dependencies in relational database systems

In this paper, we present a new method for computing fuzzy functional dependencies between attributes in fuzzy relational database systems. The method is based on the use of fuzzy implications. A literature analysis has shown that there is no algorithm that would enable the identification of attribute relationships in fuzzy relational schemas. This fact was the motive for development a new methodology in the analysis of fuzzy functional dependencies over a given set of attributes. Solving this, not so new problem, is not only research challenge having theoretical importance, but it also has practical significance. Possible applications of the proposed methodology include GIS, data mining, information retrieval, reducing data redundancy in fuzzy relations through implementation of logical database model, estimation of missing values etc.     

The Use of Object-Oriented Modeling for Water Resources Planning in Egypt

Water problems are omnipresent and are already becoming a limiting factor in the development of many countries. Currently the balance between the available and required water in Egypt is fragile. Any movement away from the balancing point means either less ambitious economic development or depletion of the resources and degradation of the environment. The continuing revolution in computer hardware and software is expected to provide more insight into the water problems and to alleviate some of the future water crises. In this paper we have investigated potential benefits which can be accrued from the application of object-oriented modeling in water resources.     

Complex spreading sequences with a wide range of correlationproperties

This paper presents a new family of complex valued pseudo-random sequences for use in coded multiple access communication systems. The family offers a very wide range of values for both auto-correlation (AC) and cross-correlation (CC) functions, allowing great flexibility in the selection of characteristics of sequence sets. Based on the measure described in this paper for the mean-square aperiodic AC and CC values of a set of sequences, the correlation properties of sets of these sequences are compared to well-known sequence sets, and it is shown that sets from the new family of sequences have superior qualities. Tables of parameters for various sequence sets are presented to enable the construction and comparison of sets from this new family