Open Access

NAND flash memory is widely used for data storage due to low power consumption, high throughput, short random access latency, and high density. The storage density of the NAND flash memory devices increases from one generation to the next, albeit at the expense of storage reliability. Our objective in this dissertation is to improve the reliability of the NAND flash memory with a low hard implementation cost. We investigate the error characteristic, i.e. the various noises of the NAND flash memory. Based on the error behavior at different life-aging stages, we develop offset calibration techniques that minimize the bit error rate (BER). Furthermore, we introduce data compression to reduce the write amplification effect and support the error correction codes (ECC) unit. In the first scenario, the numerical results show that the data compression can reduce the wear-out by minimizing the amount of data that is written to the flash. In the ECC scenario, the compression gain is used to improve the ECC capability. Based on the first scenario, the write amplification effect can be halved for the considered target flash and data model. By combining the ECC and data compression, the NAND flash memory lifetime improves three fold compared with uncompressed data for the same data model. In order to improve the data reliability of the NAND flash memory, we investigate different ECC schemes based on concatenated codes like product codes, half-product codes, and generalized concatenated codes (GCC). We propose a construction for high-rate GCC for hard-input decoding. ECC based on soft-input decoding can significantly improve the reliability of NAND flash memories. Therefore, we propose a low-complexity soft-input decoding algorithm for high-rate GCC.

This paper presents a new likelihood-based partitioning method of the measurement set for the extended object probability hypothesis density (PHD) filter framework. Recent work has mostly relied on heuristic partitioning methods that cluster the measurement data based on a distance measure between the single measurements. This can lead to poor filter performance if the tracked extended objects are closely spaced. The proposed method called Stochastic Partitioning (StP) is based on sampling methods and was inspired by a former work of Granström et. al. In this work, the StP method is applied to a Gaussian inverse Wishart (GIW) PHD filter and compared to a second filter implementation that uses the heuristic Distance Partitioning (DP) method. The performance is evaluated in Monte Carlo simulations in a scenario where two objects approach each other. It is shown that the sampling based StP method leads to an improved filter performance compared to DP.

While existing resource extraction debates have contributed to a better understanding of national economic and political dilemmas and institutional responses, there are flaws in understanding the specific relevance of the various types of mining schemes for rural households to deal with the various problems they are confronted with. Our paper examines the perceptions of gold mining effects on households in Northern Burkina Faso. The findings of our survey across six districts representing different mining schemes (industrial, artisanal, no mining) highlight the fact that artisanal gold mining can generate job opportunities and cash income for local households; whereas industrial gold mining widely fails to do so. However, the general economic and environmental settings exert a much stronger influence on the household state. Gold mining effects are perceived as being less advantageous in districts where people are suffering from a lack of education, a higher vulnerability to drought and poor market access. Our findings provide empirical support for those who back the enhanced formalization of artisanal and small-scale mining (ASM) and policies that entail more rigorous state monitoring of mining concessions, especially in economic and environmentally disadvantaged contexts. Effectively addressing communal and pro-poor development requires greater attention to the political economy of ASM and corporate mining. It also calls for a greater inclusion of local mining stakeholders and a more effective alignment of international regulatory and advocacy efforts.

A physics lab-setup has been developed for engineering students in their first year at university. The so-called LabTeamCoaching helps to improve general lab skills, such as preparing an experiment, writing a documentation, using graphs and drawing conclusions. By using a flipped classroom approach, students get better involved than in our former physics labs when we applied classical methods. This approach will be described and an overview of our 10 years of experience using this method will be given.

Abstract

We present an alternative approach to grid management in low voltage grids by the use of artificial intelligence. The developed decision support system is based on an artificial neural network (ANN). Due to the fast reaction time of our system, real time grid management will be possible. Remote controllable switches and tap changers in transformer stations are used to actively manage the grid infrastructure. The algorithm can support the distribution system operators to keep the grid in a safe state at any time. Its functionality is demonstrated by a case study using a virtual test grid. The ANN achieves a prediction rate of around 90% for the different grid management strategies. By considering the four most likely solutions proposed by the ANN, the prediction rate increases to 98.8%, with a 0.1 second increase in the running time of the model.

This article gives a summary of the 8th Metallization and Interconnection workshop and attempts to place each contribution in the appropriate context. The field of metallization and interconnection continues to progress at a very fast pace. Several printing techniques can now achieve linewidths below 20 μm. Screen printing is more than ever the dominating metallization technology in the industry, with finger widths of 45 μm in routine mass production and values below 20 μm in the lab. Plating technology is also being improved, particularly through the development of lower cost patterning techniques. Interconnection technology is changing fast, with introduction in mass production of multiwire and shingled cells technologies. New models and characterization techniques are being introduced to study and understand in detail these new interconnection technologies.

Some 165 global experts and specialists from industry and academic institutes met at the 8th Metallization & Interconnection Workshop (MIW2019) that took place from 13 to 14 May 2019 in Konstanz, Germany. Participants from 19 countries debated results of 28 oral and 11 poster presentations. All presentations are available on www.metallizationworkshop.info as pdf documents. As in previous editions, lots of room was available for discussions and networking during the two-days program which included panel and market-place discussions as well as social events (reception, workshop dinner). These proceedings contain: a summary of the oral and poster presentations, the results of the survey conducted during the workshop, and peer-reviewed papers based on workshop contributions.

Erfahrungsbericht zum Fortbildungssemester am Bundesamt für Sicherheit in der Informationstechnik (BSI) in Bonn

This document presents an algorithm for a non-obtrusive recognition of Sleep/Wake states using signals derived from ECG, respiration, and body movement captured while lying in a bed. As a core mathematical base of system data analytics, multinomial logistic regression techniques were chosen. Derived parameters of the three signals are used as the input for the proposed method. The overall achieved accuracy rate is 84% for Wake/Sleep stages, with Cohen’s kappa value 0.46. The presented algorithm should support experts in analyzing sleep quality in more detail. The results confirm the potential of this method and disclose several ways for its improvement.

The goal of this paper pretends to show how a bed system with an embedded system with sensor is able to analyze a person’s movement, breathing and recognizing the positions that the subject is lying on the bed during the night without any additional physical contact. The measurements are performed with sensors placed between the mattress and the frame. An Intel Edison board was used as an endpoint that served as a communication node from the mesh network to external service. Two nodes and Intel Edison are attached to the bottom of the bed frame and they are connected to the sensors.

This thesis deals with the object tracking problem of multiple extended objects. For instance, this tracking problem occurs when a car with sensors drives on the road and detects multiple other cars in front of it. When the setup between the senor and the other cars is in a such way that multiple measurements are created by each single car, the cars are called extended objects. This can occur in real world scenarios, mainly with the use of high resolution sensors in near field applications. Such a near field scenario leads a single object to occupy several resolution cells of the sensor so that multiple measurements are generated per scan. The measurements are additionally superimposed by the sensor’s noise. Beside the object generated measurements, there occur false alarms, which are not caused by any object and sometimes in a sensor scan, single objects could be missed so that they not generate any measurements. To handle these scenarios, object tracking filters are needed to process the sensor measurements in order to obtain a stable and accurate estimate of the objects in each sensor scan. In this thesis, the scope is to implement such a tracking filter that handles the extended objects, i.e. the filter estimates their positions and extents. In context of this, the topic of measurement partitioning occurs, which is a pre-processing of the measurement data. With the use of partitioning, the measurements that are likely generated by one object are put into one cluster, also called cell. Then, the obtained cells are processed by the tracking filter for the estimation process. The partitioning of measurement data is a crucial part for the performance of tracking filter because insufficient partitioning leads to bad tracking performance, i.e. inaccurate object estimates. In this thesis, a Gaussian inverse Wishart Probability Hypothesis Density (GIW-PHD) filter was implemented to handle the multiple extended object tracking problem. Within this filter framework, the number of objects are modelled as Random Finite Sets (RFSs) and the objects’ extent as random matrices (RM). The partitioning methods that are used to cluster the measurement data are existing ones as well as a new approach that is based on likelihood sampling methods. The applied classical heuristic methods are Distance Partitioning (DP) and Sub-Partitioning (SP), whereas the proposed likelihood-based approach is called Stochastic Partitioning (StP). The latter was developed in this thesis based on the Stochastic Optimisation approach by Granström et al. An implementation, including the StP method and its integration into the filter framework, is provided within this thesis. The implementations, using the different partitioning methods, were tested on simulated random multi-object scenarios and in a fixed parallel tracking scenario using Monte Carlo methods. Further, a runtime analysis was done to provide an insight into the computational effort using the different partitioning methods. It emphasized, that the StP method outperforms the classical partitioning methods in scenarios, where the objects move spatially close. The filter using StP performs more stable and with more accurate estimates. However, this advantage is associated with a higher computational effort compared to the classical heuristic partitioning methods.

Schwerpunktthema: Die emsige Mehrheit

In the field of autonomously driving vehicles the environment perception containing dynamic objects like other road users is essential. Especially, detecting other vehicles in the road traffic using sensor data is of utmost importance. As the sensor data and the applied system model for the objects of interest are noise corrupted, a filter algorithm must be used to track moving objects. Using LIDAR sensors one object gives rise to more than one measurement per time step and is therefore called extended object. This allows to jointly estimate the objects, position, as well as its orientation, extension and shape. Estimating an arbitrary shaped object comes with a higher computational effort than estimating the shape of an object that can be approximated using a basic geometrical shape like an ellipse or a rectangle. In the case of a vehicle, assuming a rectangular shape is an accurate assumption. A recently developed approach models the contour of a vehicle as periodic B-spline function. This representation is an easy to use tool, as the contour can be specified by some basis points in Cartesian coordinates. Also rotating, scaling and moving the contour is easy to handle using a spline contour. This contour model can be used to develop a measurement model for extended objects, that can be integrated into a tracking filter. Another approach modeling the shape of a vehicle is the so-called bounding box that represents the shape as rectangle. In this thesis the basics of single, multi and extended object tracking, as well as the basics of B-spline functions are addressed. Afterwards, the spline measurement model is established in detail and integrated into an extended Kalman filter to track a single extended object. An implementation of the resulting algorithm is compared with the rectangular shape estimator. The implementation of the rectangular shape estimator is provided. The comparison is done using long-term considerations with Monte Carlo simulations and by analyzing the results of a single run. Therefore, both algorithms are applied to the same measurements. The measurements are generated using an artificial LIDAR sensor in a simulation environment. In a real-world tracking scenario detecting several extended objects and measurements that do not originate from a real object, named clutter measurements, is possible. Also, the sudden appearance and disappearance of an object is possible. A filter framework investigated in recent years that can handle tracking multiple objects in a cluttered environment is a random finite set based approach. The idea of random finite sets and its use in a tracking filter is recapped in this thesis. Afterwards, the spline measurement model is included in a multi extended object tracking framework. An implementation of the resulting filter is investigated in a long-term consideration using Monte Carlo simulations and by analyzing the results of a single run. The multi extended object filter is also applied to artificial LIDAR measurements generated in a simulation environment. The results of comparing the spline based and rectangular based extended object trackers show a more stable performance of the spline extended object tracker. Also, some problems that have to be addressed in future works are discussed. The investigation of the resulting multi extended object tracker shows a successful integration of the spline measurement model in a multi extended object tracker. Also, with these results some problems remain, that have to be solved in future works.

The number of home office workers sitting for many hours is increasing. The sensor chair is tracking users’ sitting behavior which the help of pressure sensors and tries to avoid wrong postures which may cause diseases. The system provides live monitoring of the pressure distribution via web interface, as well as sitting posture prediction in real time. Posture analysis is realized through machine learning algorithm using a decision tree classifier that is compared to a random forest. Data acquisition and aggregation for the learning process happens with a mobile app adding users biometrical data and the taken sitting posture as label. The sensor chair is able to differentiate between an arched back, a neutral posture or a laid back position taken on the chair. The classifier achieves an accuracy of 97.4% on our test set and is comparable to the performance of the random forest with 98.9%.

Part of the publication 'Visionaries and unsung heroes : engineers, design, tomorrow'. It was written to accompany the exhibition "Visionäre und Alltagshelden. Ingenieure - Bauen - Zukunft" at the Oskar-von Miller Forum in Munich.

Autism spectrum disorders (ASD) affect a large number of children both in the Russian Federation and in Germany. Early diagnosis is key for these children, because the sooner parents notice such disorders in a child and the rehabilitation and treatment program starts, the higher the likelihood of his social adaptation. The difficulties in raising such a child lie in the complexity of his learning outside of children's groups and the complexity of his medical care. In this regard, the development of digital applications that facilitate medical care and education of such children at home is important and relevant. The purpose of the project is to improve the availability and quality of healthcare and social adaptation at home of children with ASD through the use of digital technologies.

In this paper, multivariate polynomials in the Bernstein basis over a box (tensorial Bernstein representation) are considered. A new matrix method for the computation of the polynomial coefficients with respect to the Bernstein basis, the so-called Bernstein coefficients, is presented and compared with existing methods. Also matrix methods for the calculation of the Bernstein coefficients over subboxes generated by subdivision of the original box are proposed. All the methods solely use matrix operations such as multiplication, transposition and reshaping; some of them rely on the bidiagonal factorization of the lower triangular Pascal matrix or the factorization of this matrix by a Toeplitz matrix. In the case that the coefficients of the polynomial are due to uncertainties and can be represented in the form of intervals it is shown that the developed methods can be extended to compute the set of the Bernstein coefficients of all members of the polynomial family.