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Although the Hospice Foundation in Constance knew they had a personnel
problem, they were unsure how to begin to fix it. In addition to difficulties in
finding and keeping employees, the Hospice Foundation’s employees were
often on sick leave, adding pressure on remaining staff. Twelve communication
design students in the masters program at the University of Applied
Sciences in Constance (HTWG Konstanz) conducted a study aimed at
identifying the causes for these problems and, more generally, understanding
how the employees work and feel. Even though the methods in this
study are well known, it presents an important prototype for designers and
design researchers because of its success in finding useful insights. It also
serves as a pre-design project briefing for both management and designers.
It demonstrates the usefulness of qualitative methods in providing a deeper
understanding of a complex situation and its usefulness as a strategic tool
and for defining a project’s focus and scope. Ideally, it also provides insights
into health care for the elderly.
Deep neural networks have become a veritable alternative to classic speaker recognition and clustering methods in recent years. However, while the speech signal clearly is a time series, and despite the body of literature on the benefits of prosodic (suprasegmental) features, identifying voices has usually not been approached with sequence learning methods. Only recently has a recurrent neural network (RNN) been successfully applied to this task, while the use of convolutional neural networks (CNNs) (that are not able to capture arbitrary time dependencies, unlike RNNs) still prevails. In this paper, we show the effectiveness of RNNs for speaker recognition by improving state of the art speaker clustering performance and robustness on the classic TIMIT benchmark. We provide arguments why RNNs are superior by experimentally showing a “sweet spot” of the segment length for successfully capturing prosodic information that has been theoretically predicted in previous work.
Generalised concatenated (GC) codes are well suited for error correction in flash memories for high-reliability data storage. The GC codes are constructed from inner extended binary Bose–Chaudhuri–Hocquenghem (BCH) codes and outer Reed–Solomon codes. The extended BCH codes enable high-rate GC codes and low-complexity soft input decoding. This work proposes a decoder architecture for high-rate GC codes. For such codes, outer error and erasure decoding are mandatory. A pipelined decoder architecture is proposed that achieves a high data throughput with hard input decoding. In addition, a low-complexity soft input decoder is proposed. This soft decoding approach combines a bit-flipping strategy with algebraic decoding. The decoder components for the hard input decoding can be utilised which reduces the overhead for the soft input decoding. Nevertheless, the soft input decoding achieves a significant coding gain compared with hard input decoding.
Influence of Temperature on the Corrosion behaviour of Stainless Steels under Tribological Stress
(2018)
Posterpräsentation
Today’s markets are characterized by fast and radical changes, posing an essential challenge to established companies. Startups, yet, seem to be more capable in developing radical innovations to succeed in those volatile markets. Thus, established companies started to experiment with various approaches to implement startup-like structures in their organization. Internal corporate accelerators (ICAs) are a novel form of corporate venturing, aiming to foster bottom-up innovations through intrapreneurship. However, ICAs still lack empirical investigations. This work contributes to a deeper understanding of the interface between the ICA and the core organization and the respective support activities (resource access and support services) that create an innovation-supportive work environment for the intrapreneurial team. The results of this qualitative study, comprising 12 interviews with ICA teams out of two German high-tech companies, show that the resources provided by ICAs differ from the support activities of external accelerators. Further, the study shows that some resources show both supportive as well as obstructive potential for the intrapreneurial teams within the ICA.
Corporate venturing has gained much attention due
to challenges and changes that occur because of discontinuous
innovations – which seem to be promoted by digitalization. In this
context, open innovation has become a promising tool for
established companies to strengthen their innovation capabilities.
While the external opening of the innovation process has gained
much attention, the internal opening lacks on investigations.
Especially new organizational forms, such as Internal Corporate
Accelerators, have not been investigated sufficiently. This study,
which is based on 13 interviews from two German tech-companies,
contributes to a better understanding of this new form of corporate
venturing and the resulting effects on the organizational renewal.
Long-term sleep monitoring can be done primarily in the home environment. Good patient acceptance requires low user and installation barriers. The selection of parameters in this approach is significantly limited compared to a PSG session. The aim is a qualified selection of parameters, which on the one hand allow a sufficiently good classification of sleep phases and on the other hand can be detected by non-invasive methods.
Autonomous moving systems require very detailed information about their environment and potential colliding objects. Thus, the systems are equipped with high resolution sensors. These sensors have the property to generate more than one detection per object per time step. This results in an additional complexity for the target tracking algorithm, since standard tracking filters assume that an object generates at most one detection per object. This requires new methods for data association and system state filtering.
As new data association methods, in this thesis two different extensions of the Joint Integrated Probabilistic Data Association (JIPDA) filter to assign more than one detection to tracks are proposed.
The first method that is introduced, is a generalization of the JIPDA to assign a variable number of measurements to each track based on some predefined statistical models, which will be called Multi Detection - Joint Integrated Probabilistic Data Association (MD-JIPDA).
Since this scheme suffers from exponential increase of association hypotheses, also a new approximation scheme is presented. The second method is an extension for the special case, when the number and locations of measurements are a priori known. In preparation of this method, a new notation and computation scheme for the standard Joint Integrated Data Association is outlined, which also enables the derivation of a new fast approximation scheme called balanced permanent-JIPDA.
For state filtering, also two different concepts are applied: the Random Matrix Framework and the Measurement Generating Points. For the Random Matrix framework, first an alternative prediction method is proposed to account for kinematic state changes in the extension state prediction as well. Secondly, various update methods are investigated to account for the polar to Cartesian noise transformation problem. The filtering concepts are connected with the new MD-JIPDA and their characteristics analyzed with various Monte Carlo simulations.
In case an object can be modeled by a finite number of fixed Measurement Generating Points (MGP), also a proposition to track these object via a JIPDA filter is made. In this context, a fast Track-to-Track fusion algorithm is proposed as well and compared against the MGP-JIPDA.
The proposed algorithms are evaluated in two applications where scanning is done using radar sensors only. The first application is a typical automotive scenario, where a passenger car is equipped with six radar sensors to cover its complete environment.
In this application, the location of the measurements on an object can be considered stationary and that is has a rectangular shape. Thus, the MGP based algorithms are applied here. The filters are evaluated by tracking especially vehicles on nearside lanes.
The second application covers the tracking of vessels on inland waters. Here, two different kind of Radar systems are applied, but for both sensors a uniform distribution of the measurements over the target's extent can be assumed. Further, the assumption that the targets have elliptical shape holds, and so the Random Matrix Framework in combination with the MD-JIPDA is evaluated.
Exemplary test scenarios also illustrate the performance of this tracking algorithm.