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#### Fachbereich / Organisatorische Einheit

Arctic, Antarctic and alpine biological soil crusts (BSCs) are formed by adhesion of soil particles to exopolysaccharides (EPSs) excreted by cyanobacterial and green algal communities, the pioneers and main primary producers in these habitats. These BSCs provide and inﬂuence many ecosystem services such as soil erodibility, soil formation and nitrogen (N) and carbon (C) cycles. In cold environments degradation rates are low and BSCs continuously increase soil organic C; therefore, these soils are considered to be CO2 sinks. This work provides a novel, nondestructive and highly comparable method to investigate intact BSCs with a focus on cyanobacteria and green algae and their contribution to soil organic C. A new terminology arose,basedonconfocallaserscanningmicroscopy(CLSM) 2-D biomaps, dividing BSCs into a photosynthetic active layer (PAL) made of active photoautotrophic organisms and a photosynthetic inactive layer (PIL) harbouring remnants of cyanobacteria and green algae glued together by their remaining EPSs. By the application of CLSM image analysis (CLSM–IA) to 3-D biomaps, C coming from photosynthetic activeorganismscouldbevisualizedasdepthproﬁleswithC peaks at 0.5 to 2mm depth. Additionally, the CO2 sink character of these cold soil habitats dominated by BSCs could be highlighted, demonstrating that the ﬁrst cubic centimetre of soil consists of between 7 and 17% total organic carbon, identiﬁed by loss on ignition.

In this paper, we demonstrate the power of functional data models for a statistical analysis of stimulus-response experiments which is a quite natural way to look at this kind of data and which makes use of the full information available. In particular, we focus on the detection of a change in the mean of the response in a series of stimulus-response curves where we also take into account dependence in time.

Software defined radios can be implemented on general purpose processors (CPUs), e.g. based on a PC. A processor offers high flexibility: It can not only be used to process the data samples, but also to control receiver functions, display a waterfall or run demodulation software. However, processors can only handle signals of limited bandwidth due to their comparatively low processing speed. For signals of high bandwidth the SDR algorithms have to be implemented as custom designed digital circuits on an FPGA chip. An FPGA provides a very high processing speed, but also lacks flexibility and user interfaces. Recently the FPGA manufacturer Xilinx has
introduced a hybrid system on chip called Zynq, that combines both approaches. It features a dual ARM Cortex-A9 processor and an FPGA, that offer the flexibility of a processor with the processing speed of an FPGA on a single chip. The Zynq is therefore very interesting for use in SDRs. In this paper the
application of the Zynq and its evaluation board (Zedboard) will be discussed. As an example, a direct sampling receiver has been implemented on the Zedboard using a high-speed 16 bit ADC with 250 Msps.

In this paper we consider the problem of decomposing a given integer matrix A into
a positive integer linear combination of consecutive-ones matrices with a bound on the
number of columns per matrix. This problem is of relevance in the realization stage
of intensity modulated radiation therapy (IMRT) using linear accelerators and multileaf
collimators with limited width. Constrained and unconstrained versions of the problem
with the objectives of minimizing beam-on time and decomposition cardinality are considered.
We introduce a new approach which can be used to find the minimum beam-on
time for both constrained and unconstrained versions of the problem. The decomposition
cardinality problem is shown to be NP-hard and an approach is proposed to solve the
lexicographic decomposition problem of minimizing the decomposition cardinality subject
to optimal beam-on time.

Self-adaptation allows software systems to autonomously adjust their behavior during run-time by handling all possible
operating states that violate the requirements of the managed system. This requires an adaptation engine that receives adaptation
requests during the monitoring process of the managed system and responds with an automated and appropriate adaptation
response. During the last decade, several engineering methods have been introduced to enable self-adaptation in software systems.
However, these methods lack addressing (1) run-time uncertainty that hinders the adaptation process and (2) the performance
impacts resulted from the complexity and the large number of the adaptation space. This paper presents CRATER, a framework
that builds an external adaptation engine for self-adaptive software systems. The adaptation engine, which is built on Case-based
Reasoning, handles the aforementioned challenges together. This paper is braced with an experiment illustrating the benefits of
this framework. The experimental results shows the potential of CRATER in terms handling run-time uncertainty and adaptation
remembrance that enhances the performance for large number of adaptation space.

In this paper we construct a numerical solver for the Saint Venant equations. Special attention
is given to the balancing of the source terms, including the bottom slope and variable cross-
sectional profiles. Therefore a special discretization of the pressure law is used, in order to
transfer analytical properties to the numerical method. Based on this approximation a well-
balanced solver is developed, assuring the C-property and depth positivity. The performance
of this method is studied in several test cases focusing on accurate capturing of steady states.

Hardware prototyping is an essential part in the hardware design flow. Furthermore, hardware prototyping usually relies on system-level design and hardware-in-the-loop simulations in order to develop, test and evaluate intellectual property cores. One common task in this process consist on interfacing cores with different port specifications. Data width conversion is used to overcome this issue. This work presents two open source hardware cores compliant with AXI4-Stream bus protocol, where each core performs upsizing/downsizing data width conversion.

Recently convex optimization models were successfully applied
for solving various problems in image analysis and restoration.
In this paper, we are interested in relations between
convex constrained optimization problems
of the form
\({\rm argmin} \{ \Phi(x)\) subject to \(\Psi(x) \le \tau \}\)
and their penalized counterparts
\({\rm argmin} \{\Phi(x) + \lambda \Psi(x)\}\).
We recall general results on the topic by the help of an epigraphical projection.
Then we deal with the special setting \(\Psi := \| L \cdot\|\) with \(L \in \mathbb{R}^{m,n}\)
and \(\Phi := \varphi(H \cdot)\),
where \(H \in \mathbb{R}^{n,n}\) and \(\varphi: \mathbb R^n \rightarrow \mathbb{R} \cup \{+\infty\} \)
meet certain requirements which are often fulfilled in image processing models.
In this case we prove by incorporating the dual problems
that there exists a bijective function
such that
the solutions of the constrained problem coincide with those of the
penalized problem if and only if \(\tau\) and \(\lambda\) are in the graph
of this function.
We illustrate the relation between \(\tau\) and \(\lambda\) for various problems
arising in image processing.
In particular, we point out the relation to the Pareto frontier for joint sparsity problems.
We demonstrate the performance of the
constrained model in restoration tasks of images corrupted by Poisson noise
with the \(I\)-divergence as data fitting term \(\varphi\)
and in inpainting models with the constrained nuclear norm.
Such models can be useful if we have a priori knowledge on the image rather than on the noise level.

Modern society relies on convenience services and mobile communication. Cloud computing is the current trend to make data and applications available at any time on every device. Data centers concentrate computation and storage at central locations, while they claim themselves green due to their optimized maintenance and increased energy efﬁciency. The key enabler for this evolution is the microelectronics industry. The trend to power efﬁcient mobile devices has forced this industry to change its design dogma to: ”keep data locally and reduce data communication whenever possible”. Therefore we ask: is cloud computing repeating the aberrations of its enabling industry?

We consider a variant of the generalized assignment problem (GAP) where the amount of space used in each bin is restricted to be either zero (if the bin is not opened) or above a given lower bound (a minimum quantity). We provide several complexity results for different versions of the problem and give polynomial time exact algorithms and approximation algorithms for restricted cases.
For the most general version of the problem, we show that it does not admit a polynomial time approximation algorithm (unless P=NP), even for the case of a single bin. This motivates to study dual approximation algorithms that compute solutions violating the bin capacities and minimum quantities by a constant factor. When the number of bins is fixed and the minimum quantity of each bin is at least a factor \(\delta>1\) larger than the largest size of an item in the bin, we show how to obtain a polynomial time dual approximation algorithm that computes a solution violating the minimum quantities and bin capacities by at most a factor \(1-\frac{1}{\delta}\) and \(1+\frac{1}{\delta}\), respectively, and whose profit is at least as large as the profit of the best solution that satisfies the minimum quantities and bin capacities strictly.
In particular, for \(\delta=2\), we obtain a polynomial time (1,2)-approximation algorithm.