Optically pumped magnetometers (OPM) are a very promising alternative to the superconducting quantum interference devices (SQUIDs) used nowadays for Magnetic Field Imaging (MFI), a new method of diagnosis based on the measurement of the magnetic field of the human heart. We present a first measurement combining a multichannel OPMsensor with an existing MFI-system resulting in a fully functional room temperature MFI-system.

In general, this work will deal with measuring complexity. The focus question is towards addressing complexity in an adequate way. This work concentrates on digital circuits and digital hardware. For this field of computer science the complexity for circuits will be calculated.Therefore, a new complexity measure will be introduced, called design entropy. It allows a mathematical calculation of complexity resulting in figures. These allow a direct evaluation and comparison between different systems and realizations. The application and important capabilities of this measurement will be demonstrated on different examples. Paper organizationThis paper is organized as follows. Section 1 identifies a general need for a new and different measurement for complexity. The following section 2 describes the approach and the goals of the design entropy concept. Section 3 analyzes the origins for complexity and can deduce the formulas for the design entropy. Before section 5 presents the formulas of the concept section 4 will clarify some terminology. The final section 6 will apply the formulas on some different examples.

This concept paper proposes a new system-level design methodology for runtime reconfigurable adaptive heterogeneous systems in a real-time environment. Today, among those approaches dealing with runtime reconfiguration and hardware/software co-design, compliance with hard real-time conditions is not guaranteed. Our approach will fill this gap. In contrast to other approaches, we apply methods of real-time analysis to embedded reconfigurable systems. An extended compiler and a runtime resource manager guarantee both synthesis and reconfiguration in a (hard) real-time environment. With this approach, the system can adapt to changes in requirements and operational environments during runtime.

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Looking at computer science, rising complexities, durations and costs for software and hardware development make it necessary to employ a new holistic model to manage those development processes. This paper introduces a new model which is based on abstract states in order to calculate a project's complexity. Based on this complexity it is now possible to estimate key variables such as costs, duration and progress. An abstract definition of states allows users to adjust this model to their project and to their specific requirements. I. INTRODUCTION A. MotivationProject management has become a major issue for modernly organized companies. Most engineering disciplines exert well defined methods and models to manage, control and evaluate projects. But in computer science, the management of hardware and software projects is still very weak. This expresses not only in cost and time overruns but also in a high cancellation rate of such projects. For hardware development it is not enough to simply count transistors on integrated circuits anymore [1].The most common cost model in software development COCOMO (COnstructive COst MOdel) is based on lines of code [2]. Costs and duration of a project are adjusted by a questionnaire which determines its complexity. An empirical study of Chris Kemerer analyzed the accuracy of cost models on 15 large completed business data processing projects and has shown a mean error of 600% for projects planned with COCOMO [3].A better model enables project managers to set a tighter framework to a project. A good project management has to cover three basic purpose fields: a project should a) fulfill its requirements (appropriate), b) in the given time (in time) and c) with the given budget (cost effective) [4]. To achieve those goals, mathematical methods will be presented in the next sections which allow the determination of a project's relevant variables by using states. Furthermore, this model reduces the demand for empirical data. Today, most models base their estimations on empirical data [5]. Gathering this data is expensive and challenging. With fast changes in technology as well as with new abstraction layers, a data comparison from different projects is difficult [6].