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The proposed approach applies current unsupervised clustering approaches in a different dynamic manner. Instead of taking all the data as input and finding clusters among them, the given approach clusters Holter ECG data (long-term electrocardiography data from a holter monitor) on a given interval which enables a dynamic clustering approach (DCA). Therefore advanced clustering techniques based on the well known Dynamic Time Warping algorithm are used. Having clusters e.g. on a daily basis, clusters can be compared by defining cluster shape properties. Doing this gives a measure for variation in unsupervised cluster shapes and may reveal unknown changes in healthiness. Embedding this approach into wearable devices offers advantages over the current techniques. On the one hand users get feedback if their ECG data characteristic changes unforeseeable over time which makes early detection possible. On the other hand cluster properties like biggest or smallest cluster may help a doctor in making diagnoses or observing several patients. Further, on found clusters known processing techniques like stress detection or arrhythmia classification may be applied.
To evaluate the quality of a person's sleep it is essential to identify the sleep stages and their durations. Currently, the gold standard in terms of sleep analysis is overnight polysomnography (PSG), during which several techniques like EEG (eletroencephalogram), EOG (electrooculogram), EMG (electromyogram), ECG (electrocardiogram), SpO2 (blood oxygen saturation) and for example respiratory airflow and respiratory effort are recorded. These expensive and complex procedures, applied in sleep laboratories, are invasive and unfamiliar for the subjects and it is a reason why it might have an impact on the recorded data. These are the main reasons why low-cost home diagnostic systems are likely to be advantageous. Their aim is to reach a larger population by reducing the number of parameters recorded. Nowadays, many wearable devices promise to measure sleep quality using only the ECG and body-movement signals. This work presents an android application developed in order to proof the accuracy of an algorithm published in the sleep literature. The algorithm uses ECG and body movement recordings to estimate sleep stages. The pre-recorded signals fed into the algorithm have been taken from physionet1 online database. The obtained results have been compared with those of the standard method used in PSG. The mean agreement ratios between the sleep stages REM, Wake, NREM-1, NREM-2 and NREM-3 were 38.1%, 14%, 16%, 75% and 54.3%.
Technology-based ventures provide an important route for successful technology transfer [1], [2]. Their founders are supported in successful technology commercialization by innovation intermediaries [3]. Accordingly, the performance of an innovation system, at least to some extent, depends on the efficiency of these intermediaries in terms of the impact of their scarce resources on the survival and growth of technology-based ventures. To increase their efficiency, intermediaries typically optimize their "intake" by requesting a formal business plan to base their selection on as a hygiene factor [4]-[7]. Thus, some scholars argue that written business plans show significant distortion as being produced only to attract support from innovation intermediaries [6], [8]. Accordingly, they rarely serve for these addressees as a source of information for analyzing the strengths and weaknesses of ventures, in order to derive actionable conclusions and more effectively support ventures [9], [10]. Addressees search for different indicators in business plans for their evaluation [11]. The descriptions of these indicators only evince little empirical proof for the performance of technology-based venture's [8], [12]. This gap is herein addressed, in contrast to the lacking empirical insight, as the most frequently produced artifact of early-stage technology ventures is at the same time a written business plan [10], [13]. This paper addresses this gap by conceptualizing transaction relations described in the written business plan as a means for working around the inevitable inaccuracies and uncertainties that delimit the explanatory abilities [14] of the snapshot model [10] presented by a business plan. Using a qualitative content analysis, we derive from the descriptions of transaction relations in a written business plan valid indicators for the maturity of the venture's value-network in different dimensions [15]. To this extent, this paper presents the findings from a pre-study that was conducted based on a sample of forty business plans from an overall population of 800 business plans in a longitudinal sample from one of Europe's most active innovation systems, the regional State of Baden-Württemberg. Such findings may be used by innovation intermediaries to enhance their efficiency, by enabling these to not only derive individual support strategies for business acceleration but also to analyze the impact of support measures by reliably monitoring maturity progress in venture activities.
In this paper an approach towards databased fault diagnosis of linear electromagnetic actuators is presented. Time and time-frequency-domain methods were applied to extract fault related features from current and voltage measurements. The resulting features were transformed to enhance class separability using either Principal Component Analysis (PCA) or Optimal Transformation. Feature selection and dimensionality reduction was performed employing a modified Fisher-ratio. Fault detection was carried out using a Support-Vector-Machine classifier trained with randomly selected data subsets. Results showed, that not only the used feature sets (time-domain/time-frequency-domain) are crucial for fault detection and classification, but also feature pre-processing. PCA transformed time-domain features allow fault detection and classification without misclassification, relying on current and voltage measurements making two sensors necessary to generate the data. Optimal transformed time-frequency-domain features allow a misclassification free result as well, but as they are calculated from current measurements only, a dedicated voltage sensor is not necessary. Using those features is a promising alternative even for detecting purely supply voltage related faults.
This work investigates soft input decoding for generalized concatenated (GC) codes. The GC codes are constructed from inner nested binary Bose-Chaudhuri-Hocquenghem (BCH)codes and outer Reed-Solomon (RS) codes. In order to enable soft input decoding for the inner BCH block codes, a sequential stack decoding algorithm is used. Ordinary stack decoding of binary block codes requires the complete trellis of the code.
In this work a representation of the block codes based on the trellises of supercodes is proposed in order to reduce the memory requirements for the representation of the BCH codes. Results for the decoding performance of the overall GC code are presented.
Furthermore, an efficient hardware implementation of the GC decoder is proposed.
Small vessels or unmanned surface vehicles only have a limited amount of space and energy available. If these vessels require an active sensing collision avoidance system it is often not possible to mount large sensor systems like X-Band radars. Thus, in this paper an energy efficient automotive radar and a laser range sensor are evaluated for tracking surrounding vessels. For these targets, those type of sensors typically generate more than one detection per scan. Therefore, an extended target tracking problem has to be solved to estimate state end extension of the vessels. In this paper, an extended version of the probabilistic data association filter that uses random matrices is applied. The performance of the tracking system using either radar or laser range data is demonstrated in real experiments.