Bewirtschaftungsmaßnahmen zur Reduzierung der Nitratbelastung im Einzugsgebiet der Quellfassung Schleinitz

Grundwasser - Im Untersuchungsgebiet Schleinitz führte eine Grünlandumnutzung einer ehemaligen Ackerfläche über einen Zeitraum von annähernd 6 Jahren zu einer...     

Highly linear wide‐swing continuous tuning of CMOS transconductors

A technique to improve the input and output range of CMOS transconductors with resistive current division for continuous tuning is presented. Using it, a tunable transconductor is proposed which features high linearity over a wide input range and simplicity. Measurement results of the transconductor, fabricated in a 0.5 µm CMOS process, show an IM3 of ?66 dB for a ±1.65 V supply and two input tones centered at 1 MHz of 1 V_pp each, and only 0.7 mW of power consumption. This represents an improvement of 13 dB versus the same transconductor using conventional current division. Copyright © 2013 John Wiley & Sons, Ltd.     

Power‐efficient analog design based on the class AB super source follower

A class AB version of the conventional super source follower (SSF) is described. The circuit greatly increases slew rate (SR) and current efficiency, maintaining the low distortion and low output resistance of the SSF. Class AB operation is achieved without extra power dissipation or supply requirements, and without bandwidth or noise degradation. The circuit can advantageously replace the SSF in a wide variety of analog systems, opening a new research line in analog design. To illustrate the widespread application of this cell, a class AB differential unity‐gain buffer, a class AB differential current mirror and two class AB differential transconductors are designed, fabricated in a 0.5µm CMOS technology and tested. Measurement results using a dual supply of ±1.65V show that the proposed class AB version of the SSF improves SR by a factor 21.5 and increases bandwidth by 10%, keeping noise level, input range, power consumption, and supply requirements unaltered. The fabricated class AB current mirror features a THD at 100 kHz of ? 62dB for signal currents 20 times larger than the bias current. The fabricated transconductors feature an IM3 at 1 MHz of ? 56.6dB for output currents more than 13 times larger than the bias currents. Copyright © 2011 John Wiley & Sons, Ltd.     

Lyapunov Design of Multivariable MRAC via Generalized Passivation

A Lyapunov design of multiple input, multiple output (MIMO) model‐reference adaptive control (MRAC) systems is proposed using new generalized passivity concepts based on WSPR and WASPR properties. The new design avoids control overparameterization and leads to the simplest unnormalized gradient algorithm by means of a passivating multiplier. To this end, a necessary and sufficient condition on the high frequency gain matrix to achieve the WASPR property is established. This condition is referred to as Positive Diagonal Jordan (PDJ) form. Based on the robustness WSPR and WASPR properties, a systematic determination of a robust passivating matrix multiplier is then presented. The advantages of the new design are illustrated by numerical simulation including a robotics adaptive visual servoing problem and a 4‐input‐4‐output dynamic system. © 2014 Chinese Automatic Control Society and Wiley Publishing Asia Pty Ltd     

A variational multiscale method for incompressible turbulent flows: Bubble functions and fine scale fields

This paper presents a residual-based turbulence model for the incompressible Navier–Stokes equations. The method is derived employing the variational multiscale (VMS) framework. A multiscale decomposition of the continuous solution and a priori unique decomposition of the admissible spaces of functions lead to two coupled nonlinear problems termed as the coarse-scale and the fine-scale sub-problems. The fine-scale velocity field is assumed to be nonlinear and time-dependent and is modeled via the bubble functions approach applied directly to the fine-scale sub-problem. A significant contribution in this paper is a systematic and consistent derivation of the fine-scale variational operator, commonly termed as the stabilization tensor that possesses the right order in the advective and diffusive limits, and variationally projects the fine-scale solution onto the coarse-scale space. A direct treatment of the fine-scale problem via bubble functions offers several fine-scale approximation options with varying degrees of mathematical sophistication that are investigated via benchmark problems. Numerical accuracy of the proposed method is shown on a forced-isotropic turbulence problem, statistically stationary turbulent channel flow problems at Re_T = 395 and 590, and non-equilibrium turbulent flow around a cylinder at Re = 3,900.     

Mining data from intensive care patients

In this paper we describe the application of data mining methods for predicting the evolution of patients in an intensive care unit. We discuss the importance of such methods for health care and other application domains of engineering. We argue that this problem is an important but challenging one for the current state of the art data mining methods and explain what improvements on current methods would be useful. We present a promising study on a preliminary data set that demonstrates some of the possibilities in this area.     

Evaluating opportunistic networks in disaster scenarios

Forwarding data in scenarios where devices have sporadic connectivity is a challenge. An example scenario is a disaster area, where forwarding information generated in the incident location, like victims' medical data, to a coordination point is critical for quick, accurate and coordinated intervention. New applications are being developed based on mobile devices and wireless opportunistic networks as a solution to destroyed or overused communication networks. But the performance of opportunistic routing methods applied to emergency scenarios is unknown today. In this paper, we compare and contrast the efficiency of the most significant opportunistic routing protocols through simulations in realistic disaster scenarios in order to show how the different characteristics of an emergency scenario impact in the behaviour of each one of them.     

A polynomial time computable metric between point sets

Measuring the similarity or distance between sets of points in a metric space is an important problem in machine learning and has also applications in other disciplines e.g. in computational geometry, philosophy of science, methods for updating or changing theories, . Recently Eiter and Mannila have proposed a new measure which is computable in polynomial time. However, it is not a distance function in the mathematical sense because it does not satisfy the trian gle inequality. We introduce a new measure which is a metric while being computable in polynomial time. We also present a variant which computes a normalised metric and a variant which can associate different weights with the points in the set. Received: 18 October 1999 / 8 January 2001