The smart car seat: personalized monitoring of vital signs in automotive applications

Embedded wireless sensors are important components of mobile distributed computing networks, and one of the target applications areas is health care. The preservation of mobility for senior citizens is one of the key issues in maintaining an independent lifestyle. Thus health technologies inside a car can contribute both to safety issues (supervision of driver fitness) as well as healthcare issues by monitoring vitals signs imperceptibly. In this paper, three embedded measurement techniques for non-contact monitoring of vital signals have been investigated. Specifically, capacitive electrocardiogram (cECG) monitoring, mechanical movement analysis (ballistocardiogram, BCG) using piezo-foils and inductive impedance monitoring were examined regarding their potential for integration into car seats. All three sensing techniques omit the need for electroconductive contact to the human body, but require defined mechanical boundary conditions (stable distances or, in the case of BCG, frictional connection). The physical principles of operation, the specific boundary conditions regarding automotive integration and the results during wireless operation in a running car are presented. All three sensors were equipped with local intelligence by incorporating a microcontroller. To eliminate the need for additional cabling, a wireless Bluetooth communication module was added and used to transmit data to a measurement PC. Finally, preliminary results obtained during test drives on German city roads and highways are discussed.     

A fully integrated 1‐in. AMOLED display using current feedback based on a five‐mask LTPS CMOS process

Abstract— In the past, a five‐mask LTPS CMOS process requiring only one single ion‐doping step was used. Based on that process, all necessary components for the realization of a fully integrated AMOLED display using a 3T1C current‐feedback pixel circuit has recently been developed. The integrated data driver is based on a newly developed LTPS operational amplifier, which does not require any compensation for Vth or mobility variations. Only one operational amplifier per column is used to perform digital‐to‐analog conversion as well as current control. In order to achieve high‐precision analog behavior, the operational amplifier is embedded in a switched capacitor network. In addition to circuit verification by simulation and analytic analysis, a 1‐in. fully integrated AMOLED demonstrator was successfully built. To the best of the authors' knowledge, this is the first implementation of a fully integrated AMOLED display with current feedback.     

In situ exploration of large dynamic networks

The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization--a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown--right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.     

Interactive High‐Quality Visualization of Higher‐Order Finite Elements

Higher‐order finite element methods have emerged as an important discretization scheme for simulation. They are increasingly used in contemporary numerical solvers, generating a new class of data that must be analyzed by scientists and engineers. Currently available visualization tools for this type of data are either batch oriented or limited to certain cell types and polynomial degrees. Other approaches approximate higher‐order data by resampling resulting in trade‐offs in interactivity and quality. To overcome these limitations, we have developed a distributed visualization system which allows for interactive exploration of non‐conforming unstructured grids, resulting from space‐time discontinuous Galerkin simulations, in which each cell has its own higher‐order polynomial solution. Our system employs GPU‐based raycasting for direct volume rendering of complex grids which feature non‐convex, curvilinear cells with varying polynomial degree. Frequency‐based adaptive sampling accounts for the high variations along rays. For distribution across a GPU cluster, the initial object‐space partitioning is determined by cell characteristics like the polynomial degree and is adapted at runtime by a load balancing mechanism. The performance and utility of our system is evaluated for different aeroacoustic simulations involving the propagation of shock fronts.     

Comparing notions of randomness

It is an open problem in the area of effective (algorithmic) randomness whether Kolmogorov-Loveland randomness coincides with Martin-Löf randomness. Joe Miller and André Nies suggested some variations of Kolmogorov-Loveland randomness to approach this problem and to provide a partial solution. We show that their proposed notion of injective randomness is still weaker than Martin-Löf randomness. Since in this proof some of the ideas we use are clearer, we also show the weaker theorem that permutation randomness is weaker than Martin-Löf randomness.     

Active learning for extended finite state machines

We present a black-box active learning algorithm for inferring extended finite state machines (EFSM)s by dynamic black-box analysis. EFSMs can be used to model both data flow and control behavior of software and hardware components. Different dialects of EFSMs are widely used in tools for model-based software development, verification, and testing. Our algorithm infers a class of EFSMs called register automata. Register automata have a finite control structure, extended with variables (registers), assignments, and guards. Our algorithm is parameterized on a particular theory, i.e., a set of operations and tests on the data domain that can be used in guards.Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We also show that, under these conditions, our framework induces a generalization of the classical Nerode equivalence and canonical automata construction to the symbolic setting. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.     

The Semantic Web: Collective Intelligence on the Web

The World Wide Web has turned hypertext into a success story by enabling world-wide sharing of unstructured information and informal knowledge. The Semantic Web targets the sharing of structured information and formal knowledge pursuing objectives of achieving collective intelligence on the Web. Germane to the structure of the Semantic Web is a layering and standardization of concerns. These concerns are reflected by an architecture of the Semantic Web that we present through a common use case. Semantic Web data for the use case is now found on the Web and is part of a quickly growing set of Semantic Web resources available for formal processing.     

Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis

Without non-linear basis functions many problems can not be solved by linear algorithms. This article proposes a method to automatically construct such basis functions with slow feature analysis (SFA). Non-linear optimization of this unsupervised learning method generates an orthogonal basis on the unknown latent space for a given time series. In contrast to methods like PCA, SFA is thus well suited for techniques that make direct use of the latent space. Real-world time series can be complex, and current SFA algorithms are either not powerful enough or tend to over-fit. We make use of the kernel trick in combination with sparsification to develop a kernelized SFA algorithm which provides a powerful function class for large data sets. Sparsity is achieved by a novel matching pursuit approach that can be applied to other tasks as well. For small data sets, however, the kernel SFA approach leads to over-fitting and numerical instabilities. To enforce a stable solution, we introduce regularization to the SFA objective. We hypothesize that our algorithm generates a feature space that resembles a Fourier basis in the unknown space of latent variables underlying a given real-world time series. We evaluate this hypothesis at the example of a vowel classification task in comparison to sparse kernel PCA. Our results show excellent classification accuracy and demonstrate the superiority of kernel SFA over kernel PCA in encoding latent variables.     

Print your atomic force microscope

Progress in scanning probe microscopy profited from a flourishing multitude of new instrument designs, which lead to novel imaging modes and as a consequence to innovative microscopes. Often these designs were hampered by the restrictions, which conventional milling techniques impose. Modern rapid prototyping techniques, where layer by layer is added to the growing piece either by light driven polymerization or by three-dimensional printing techniques, overcome this constraint, allowing highly concave or even embedded and entangled structures. We have employed such a technique to manufacture an atomic force microscopy (AFM) head, and we compared its performance with a copy milled from aluminum. We tested both AFM heads for single molecule force spectroscopy applications and found little to no difference in the signal-to-noise ratio as well as in the thermal drift. The lower E modulus seems to be compensated by higher damping making this material well suited for low noise and low drift applications. Printing an AFM thus offers unparalleled freedom in the design and the rapid production of application-tailored custom instruments.