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MPC-Workshop Februar 2020
(2021)
Printed electronics, due to its manufacturability using printing technology, allows for fabrication on large areas and the usage of flexible substrates and thus enables novel applications. Non-impact printing technology, such as inkjet-printing, permits for flexible, decentralized manufacturing of electronic devices and systems. This further facilitates split-manufacturing in security-critical electrical components, as well as a maximum in design flexibility in terms of free form factors and non-standardized structures with different geometrical sizes, reaching from a few micrometers up to several millimeters.
Based on the technological benefits printed electronics offers, it provides an interesting counterpart to classical silicon-based electronics, which is usually densely integrated on miniaturized, rigid areas. By utilizing both technologies in a complementary manner, novel systems in the form of hybrid systems can be enabled. Whilst hybrid systems, incorporating passive printed components and electrically conductive wiring concepts, are already commercialized, complex printed systems, which also utilize active components remain rare. To enable more complex (hybrid) systems, various building blocks are required. This includes possibilities for lightweight, printed data storage, the capability to provide sustainable, self-powered printed components and especially circuits for secure, unique identification for holistic printed systems, deployed in the internet of things.
The presented thesis focuses on inkjet-printed electronic devices, circuits and hybrid systems. It investigates solutions for current scientific questions in the area of efficient data storage, sustainable electronics and hardware-based security in printed electronics.
For data storage, an inkjet-printed memristor is developed. The device is fully electrically evaluated with a focus on its data storage capabilities. Furthermore, the printed device is of special interest due to its easy manufacturability and integration capabilities. The experimental analysis reveals that the developed memristor is highly suitable as lightweight non-volatile memory device.
In order to enable sustainable electronic systems, an inkjet-printed full-wave rectifier based on near-zero threshold voltage electrolyte-gated transistors is developed and fully electrically characterized. The circuit is capable for small alternating voltage rectification of low-frequency vibration energy harvesters in the sub-volt region. This provides an important building block in enabling sustainable, self-powered electronic systems. The inkjet-printed full-wave rectifier is evaluated by electrical simulation and experimentally.
To tackle hardware-based security for printed electronics, two implementations for inkjet-printed physically unclonable functions are developed and presented. For unique identification, intrinsic variation in active printed devices are exploited. One implementation is based on a crossbar architecture, incorporating integrable electrolyte-gated transistor cells. The second implementation, the so-called differential circuit physically unclonable function, is based on inverter structures, which provide the basis for unique response generation. Both physically unclonable functions are evaluated using an electrical simulation-based approach and experimentally. The differential circuit approach is furthermore fully integrated within a silicon-based electronic platform environment and serves as intrinsic variation source in a hybrid system. The hybrid system physically unclonable function is fully verified regarding performance metrics and is capable to generate highly unique responses for secure identification.
Emerging applications in soft robotics, wearables, smart consumer products or IoT-devices benefit from soft materials, flexible substrates in conjunction with electronic functionality. Due to high production costs and conformity restrictions, rigid silicon technologies do not meet application requirements in these new domains. However, whenever signal processing becomes too comprehensive, silicon technology must be used for the high-performance computing unit. At the same time, designing everything in flexible or printed electronics using conventional digital logic is not feasible yet due to the limitations of printed technologies in terms of performance, power and integration density. We propose to rather use the strengths of neuromorphic computing architectures consisting in their homogeneous topologies, few building blocks and analog signal processing to be mapped to an inkjet-printed hardware architecture. It has remained a challenge to demonstrate non-linear elements besides weighted aggregation. We demonstrate in this work printed hardware building blocks such as inverter-based comprehensive weight representation and resistive crossbars as well as printed transistor-based activation functions. In addition, we present a learning algorithm developed to train the proposed printed NCS architecture based on specific requirements and constraints of the technology.
The present work ties in with the problem of bicycle road assessment that is currently done using expensive special measuring vehicles. Our alternative approach for road condition assessment is to mount a sensor device on a bicycle which sends accelerometer and gyroscope data via WiFi to a classification server. There, a prediction model determines road type and condition based on the sensor data. For the classification task, we compare different machine learning methods with each other, whereby validation accuracies of 99% can be achieved with deep residual networks such as InceptionTime. The main contribution of this work with respect to comparable work is that we achieve excellent accuracies on a realistic dataset classifying road conditions into nine distinct classes that are highly relevant for practice.
Fifth-generation (5G) cellular mobile networks are expected to support mission-critical low latency applications in addition to mobile broadband services, where fourth-generation (4G) cellular networks are unable to support Ultra-Reliable Low Latency Communication (URLLC). However, it might be interesting to understand which latency requirements can be met with both 4G and 5G networks. In this paper, we discuss (1) the components contributing to the latency of cellular networks and (2) evaluate control-plane and user-plane latencies for current-generation narrowband cellular networks and point out the potential improvements to reduce the latency of these networks, (3) present, implement and evaluate latency reduction techniques for latency-critical applications. The two elements we detected, namely the short transmission time interval and the semi-persistent scheduling are very promising as they allow to shorten the delay to processing received information both into the control and data planes. We then analyze the potential of latency reduction techniques for URLLC applications. To this end, we develop these techniques into the long term evolution (LTE) module of ns-3 simulator and then evaluate the performance of the proposed techniques into two different application fields: industrial automation and intelligent transportation systems. Our detailed evaluation results from simulations indicate that LTE can satisfy the low-latency requirements for a large choice of use cases in each field.
In recent years, physically unclonable functions (PUFs) have gained significant attraction in IoT security applications, such as cryptographic key generation and entity authentication. PUFs extract the uncontrollable production characteristics of different devices to generate unique fingerprints for security applications. When generating PUF-based secret keys, the reliability and entropy of the keys are vital factors. This study proposes a novel method for generating PUF-based keys from a set of measurements. Firstly, it formulates the group-based key generation problem as an optimization problem and solves it using integer linear programming (ILP), which guarantees finding the optimum solution. Then, a novel scheme for the extraction of keys from groups is proposed, which we call positioning syndrome coding (PSC). The use of ILP as well as the introduction of PSC facilitates the generation of high-entropy keys with low error correction costs. These new methods have been tested by applying them on the output of a capacitor network PUF. The results confirm the application of ILP and PSC in generating high-quality keys.
Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation
(2021)
This paper presents a systematic approach for accurate short-time cloud coverage prediction based on a machine learning (ML) approach. Based on a newly built omnidirectional ground-based sky camera system, local training and evaluation data sets were created. These were used to train several state-of-the-art deep neural networks for object detection and segmentation. For this purpose, the camera-generated a full hemispherical image every 30 min over two months in daylight conditions with a fish-eye lens. From this data set, a subset of images was selected for training and evaluation according to various criteria. Deep neural networks, based on the two-stage R-CNN architecture, were trained and compared with a U-net segmentation approach implemented by CloudSegNet. All chosen deep networks were then evaluated and compared according to the local situation.
It is considered necessary to implement advanced controllers such as model predictive control (MPC) to utilize the technical flexibility of a building polygeneration system to support the rapidly expanding renewable electricity grid. These can handle multiple inputs and outputs, uncertainties in forecast data, and plant constraints, amongst other features. One of the main issues identified in the literature regarding deploying these controllers is the lack of experimental demonstrations using standard components and communication protocols. In this original work, the economic-MPC-based optimal scheduling of a real-world heat pump-based building energy plant is demonstrated, and its performance is evaluated against two conventional controllers. The demonstration includes the steps to integrate an optimization-based supervisory controller into a typical building automation and control system with off-the-shelf HVAC components and usage of state-of-art algorithms to solve a mixed integer quadratic problem. Technological benefits in terms of fewer constraint violations and a hardware-friendly operation with MPC were identified. Additionally, a strong dependency of the economic benefits on the type of load profile, system design and controller parameters was also identified. Future work for the quantification of these benefits, the application of machine learning algorithms, and the study of forecast deviations is also proposed.
Im Projekt MOBCOM wird ein neues Verfahren zur Zustandsüberwachung von elektrischen Betriebsmitteln in Niederspannungsnetzen und Anlagen entwickelt. Mittels PLC (power line communication) Technologie werden hochfrequente transiente Vorgänge auf dem Stromkanal und dessen Übertragungseigenschaften erfasst und bewertet. Durch Ableiten bestimmter Parameter soll zustandsbedingte Wartung vorhergesagt und so der Ausfall von Betriebsmittel vermieden werden.
Die Digitalisierung kann der Türöffner sein, um effizient die mittelständische Industrie und den Energiemarkt zu verbinden. Das Projekt GaIN hat das Ziel, mit hochaufgelösten Produktions- und Messdaten von zehn mittelständischen Industriebetrieben neuartige Tarife und angepasste Marktplattformen zu entwickeln, die Prognosegüte für Energiebedarf, Nachfrage und Flexibilitätsverfügbarkeit zu erhöhen, die Interaktion vieler flexibler Unternehmen im Verteilnetz und in dem Bilanzkreis zu bewerten und die Auswirkung einer Nutzung der Daten auf die Energiewende anhand einer Systemanalyse zu beurteilen.