Open Access

Data Science steht derzeit wie kein anderer Begriff für die Auswertung großer Datenmengen mit analytischen Konzepten des Machine Learning oder der künstlichen Intelligenz. Nach der bewussten Wahrnehmung der Big Data und dabei insbesondere der Verfügbarmachung in Unternehmen sind Technologien und Methoden zur Auswertung dort gefordert, wo klassische Business Intelligence an ihre Grenzen stößt. Dieses Buch bietet eine umfassende Einführung in Data Science und deren praktische Relevanz für Unternehmen. Dabei wird auch die Integration von Data Science in ein bereits bestehendes Business-Intelligence-Ökosystem thematisiert. In verschiedenen Beiträgen werden sowohl Aufgabenfelder und Methoden als auch Rollen- und Organisationsmodelle erläutert, die im Zusammenspiel mit Konzepten und Architekturen auf Data Science wirken. Neben den Grundlagen werden unter anderem folgende Themen behandelt: - Data Science und künstliche Intelligenz - Konzeption und Entwicklung von Data-driven Products - Deep Learning - Self-Service im Data-Science-Umfeld - Data Privacy und Fragen zur digitalen Ethik - Customer Churn mit Keras/TensorFlow und H2O - Wirtschaftlichkeitsbetrachtung bei der Auswahl und Entwicklung von Data Science - Predictive Maintenance - Scrum in Data-Science-Projekten Zahlreiche Anwendungsfälle und Praxisbeispiele geben Einblicke in die aktuellen Erfahrungen bei Data-Science-Projekten und erlauben dem Leser einen direkten Transfer in die tägliche Arbeit.

This book provides a comprehensive overview of the use of PET and SPECT in not only classic neurodegenerative disorders but also cerebrovascular disorders, brain tumors, epilepsy, head trauma, coma, sleeping disorders, and inflammatory and infectious diseases of the CNS. The new edition has been revised and updated to reflect recent advances and includes additional chapters, for example on the use of artificial intelligence and machine learning in imaging data analysis, the study of brain connectivity using PET and SPECT images, and the role of PET imaging in modulation of brain functioning by deep brain stimulation. The authors are renowned experts whose dedication to the investigation of neurological disorders through nuclear medicine technology has achieved international recognition. Most chapters are written jointly by a clinical neurologist and a nuclear medicine specialist to ensure a multidisciplinary approach. This state of the art compendium will be invaluable for neurologists and radiologists/nuclear medicine specialists and will also be informative for interested general practitioners and geriatricians. Companion volumes on PET and SPECT in psychiatry and in neurobiological systems complete a trilogy.

The number of use cases for autonomous vehicles is increasing day by day especially in commercial applications. One important application of autonomous vehicles can be found within the parcel delivery section. Here, autonomous cars can massively help to reduce delivery efforts and time by supporting the courier actively. One important component of course is the autonomous vehicle itself. Nevertheless, beside the autonomous vehicle, a flexible and secure communication architecture also is a crucial key component impacting the overall performance of such system since it is required to allow continuous interactions between the vehicle and the other components of the system. The communication system must provide a reliable and secure architecture that is still flexible enough to remain practical and to address several use cases. In this paper, a robust communication architecture for such autonomous fleet-based systems is proposed. The architecture provides a reliable communication between different system entities while keeping those communications secure. The architecture uses different technologies such as Bluetooth Low Energy (BLE), cellular networks and Low Power Wide Area Network (LPWAN) to achieve its goals.

Bei bimodaler Cochlea-Implantat-/Hörgerät-Versorgung kann es aufgrund seitenverschiedener Signalverarbeitung zu einer zeitlich versetzten Stimulation der beiden Modalitäten kommen. Jüngste Studien haben gezeigt, dass durch zeitlichen Abgleich der Modalitäten die Schalllokalisation bei bimodaler Versorgung verbessert werden kann. Um solch einen Abgleich vornehmen zu können, ist die messtechnische Bestimmung der Durchlaufzeit von Hörgeräten erforderlich. Kommerziell verfügbare Hörgerätemessboxen können diese Werte häufig liefern. Die dazu verwendete Signalverarbeitung wird dabei aber oft nicht vollständig offengelegt. In dieser Arbeit wird ein alternativer und nachvollziehbarer Ansatz zum Design eines simplen Messaufbaus basierend auf einem Arduino DUE Mikrocontroller-Board vorgestellt. Hierzu wurde ein Messtisch im 3D-Druck gefertigt, auf welchem Hörgeräte über einen 2-ccm-Kuppler an ein Messmikrofon angeschlossen werden können. Über einen Latenzvergleich mit dem simultan erfassten Signal eines Referenzmikrofons kann die Durchlaufzeit von Hörgeräten bestimmt werden. Frequenzspezifische Durchlaufzeiten werden mittels einer Kreuzkorrelation zwischen Ziel- und Referenzsignal errechnet. Aufnahme, Ausgabe und Speicherung der Signale erfolgt über einen ATMEL SAM3X8E Mikrocontroller, welcher auf dem Arduino DUE-Board verbaut ist. Über eigens entworfene elektronische Schaltungen werden die Mikrofone und der verwendete Lautsprecher angesteuert. Nach Abschluss einer Messung (Messdauer ca. 5 s) werden die Messdaten seriell an einen PC übertragen, auf dem die Datenauswertung mittels MATLAB erfolgt. Erste Validierungen zeigten eine hohe Stabilität der Messergebnisse mit sehr geringen Standardabweichungen im Bereich weniger Mikrosekunden für Pegel zwischen 50 und 75 dB (A). Der Messaufbau wird in laufenden Studien zur Quantifizierung der Durchlaufzeit von Hörgeräten verwendet.

Electrolyte-gated thin-film transistors (EGTs) with indium oxide channel, and expected lifetime of three months, enable low-voltage operation (~1 V) in the field of printed electronics (PEs). The channel width of our printed EGTs is varied between 200 and 1000 μm, whereas a channel length between 10 and 100 μm is used. Due to the lack of uniform performance p-type metal oxide semiconductors, n-type EGTs and passive elements are used to design circuits. For logic gates, transistor-resistor logic has been employed so far, but depletion and enhancement-mode EGTs in a transistor-transistor logic boost the circuit performance in terms of delay and signal swing. In this article, the threshold voltage of the EGT, which determines the operation mode, is tuned through sizing of the EGTs channel geometry. The feasibility of both transistor operation modes is demonstrated for logic gates and ring oscillators. An inverter operating at a supply voltage of 1 V shows a maximum gain of 9.6 and a propagation delay time of 0.7 ms, which represents an improvement of ~ 2x for the gain and oscillation frequency, in comparison with the resistor-transistor logic design. Moreover, the power consumption is reduced by 6x.

In the domain of printed electronics (PE), field-effect transistors (FETs) with an oxide semiconductor channel are very promising. In particular, the use of high gate-capacitance of the composite solid polymer electrolytes (CSPEs) as a gate-insulator ensures extremely low voltage requirements. Besides high gate capacitance, such CSPEs are proven to be easily printable, stable in air over wide temperature ranges, and possess high ion conductivity. These CSPEs can be sensitive to moisture, especially for high surface-to-volume ratio printed thin films. In this paper, we provide a comprehensive experimental study on the effect of humidity on CSPE-gated single transistors. At the circuit level, the performance of ring oscillators (ROs) has been compared for various humidity conditions. The experimental results of the electrolyte-gated FETs (EGFETs) demonstrate rather comparable currents between 30%-90% humidity levels. However, the shifted transistor parameters lead to a significant performance change of the RO frequency behavior. The study in this paper shows the need of an impermeable encapsulation for the CSPE-gated FETs to ensure identical performance at all humidity conditions.

Current training methods for deep neural networks boil down to very high dimensional and non-convex optimization problems which are usually solved by a wide range of stochastic gradient descent methods. While these approaches tend to work in practice, there are still many gaps in the theoretical understanding of key aspects like convergence and generalization guarantees, which are induced by the properties of the optimization surface (loss landscape). In order to gain deeper insights, a number of recent publications proposed methods to visualize and analyze the otimization surfaces. However, the computational cost of these methods are very high, making it hardly possible to use them on larger networks. In this paper, we present the GradVis Toolbox, an open source library for efficient and scalable visualization and analysis of deep neural network loss landscapes in Tesorflow and PyTorch. Introducing more efficient mathematical formulations and a novel parallelization scheme, GradVis allows to plot 2d and 3d projections of optimization surfaces and trajectories, as well as high resolution second order gradient information for large networks.

Harmonic-afflicted effects of permanent magnet synchronous machines with high power density are hardly faced by traditional current PI controllers, due to limited controller bandwidth. As a consequence, currents and lastly torque ripples appear. In this paper, a new deadbeat current controller architecture has been presented, which is capable to encounter the effects of these harmonics. This new control algorithm, here named “Hybrid-Deadbeat-Controller”, combines the stability and the low steady-state errors offered by common PI regulators with the high dynamic offered by the deadbeat control. Therefore, a novel control algorithm is proposed, capable of either compensating the current harmonics in order to get smoother currents or to control a varying reference value to achieve a smoother torque. The information needed to calculate the optimal reference currents is based on an online parameter estimation feeding an optimization algorithm to achieve an optimal torque output and will be investigated in future research. In order to ensure the stability of the controller over the whole area of operation even under the influence of effects changing the system’s parameter, this work as well focusses on the robustness of the “hybrid” dead beat controller.