In today’s world, mobile communication has become one of the most widely used technologies corroborated by growing number of mobile subscriptions and extensive usage of mobile multimedia services. It is a key challenge for the network operators to accommodate such large number of users and high traffic volume. Further, several day-to-day scenarios such as public transportation, public events etc., are now characterized with high mobile data usage. A large number of users avail cellular services in such situations posing high load to the respective base stations. This results in increased number of dropped connections, blocking of new access attempts and blocking of handovers (HO). The users in such system will thus be subjected to poor Quality of Experience (QoE). Beforehand knowledge of the changing data traffic dynamics associated with such practical situations will assist in designing radio resource management schemes aiming to ease the forthcoming congestion situations. The key hypothesis of this thesis is that consideration and utilization of additional context information regarding user, network and his environment is valuable in designing such smart Radio Resource Management(RRM) schemes. Methods are developed to predict the user cell transitions, considering the fact that mobility of the users is not purely random but rather direction oriented. This is particularly used in case of traffic dense moving network or group of users moving jointly in the same vehicle (e.g., bus, train, etc.) to predict the propagation of high load situation among cells well in advance. This enables a proactive triggering of load balancing (LB) in cells anticipating the arrival of high load situation and accommodating the incoming user group or moving network. The evaluated KPIs such as blocked access attempts, dropped connections and blocked HO are reduced. Further, everyday scenario of dynamic crowd formation is considered as another potential case of high load situation. In real world scenarios such as open air festivals, shopping malls, stadiums or public events, several mobile users gather to form a crowd. This poses high load situation to the respective serving base station at the site of crowd formation, thereby leading to congestion. As a consequence, mobile users are subjected to poor QoE due to high dropping and blocking rates. A framework to predict crowd formation in a cell is developed based on coalition of user cell transition prediction, cluster detection and trajectory prediction. This framework is suitably used to prompt context aware load balancing mechanism and activate a small cell at the probable site of crowd formation. Simulations show that proactive LB reduces the dropping of users (23%), blocking of users (10%) and blocked HO (15%). In addition, activation of a Small Cell (SC) at the site of frequent crowd formation leads to further reductions in dropping of users (60%), blocking of users (56%) and blocked HO (59%). Similar to the framework for crowd formation prediction, a concept is developed for predicting vehicular traffic jams. Many vehicular users avail broadband cellular services on a daily basis while traveling. The density of such vehicular users change dynamically in a cell and at certain sites (e.g. signal lights), traffic jams arise frequently leading to a high load situation at respective serving base station. A traffic prediction algorithm is developed from cellular network perspective as a coalition strategy consisting of schemes to predict user cell transition, vehicular cluster/moving network detection, user velocity monitoring etc. The traffic status indication provided by the algorithm is then used to trigger LB and activate/deactivate a small cell suitably. The evaluated KPIs such as blocked access attempts, dropped connections and blocked HO are reduced by approximately 10%, 18% and 18%, respectively due to LB. In addition, switching ON of SC reduces blocked access attempts, dropped connections and blocked HO by circa 42%, 82% and 81%, respectively. Amidst increasing number of connected devices and traffic volume, another key issue for today’s network is to provide uniform service quality despite high mobility. Further, urban scenarios are often characterized by coverage holes which hinder service continuity. A context aware resource allocation scheme is proposed which uses enhanced mobility prediction to facilitate service continuity. Mobility prediction takes into account additional information about the user’s origin and possible destination to predict next road segment. If a coverage hole is anticipated in upcoming road, then additional resources are allocated to respective user and data is buffered suitably. The buffered data is used when the user is in a coverage hole to improve service continuity. Simulation shows improvement in throughput (in coverage hole) by circa 80% and service interruption is reduced by around 90%, for a non-real-time streaming service. Additionally, investigation of context aware procedures is carried out with a focus on user mobility, to find commonalities among different procedures and a general framework is proposed to support mobility context awareness. The new information and interfaces which are required from various entities (e.g., vehicular infrastructure) are discussed as well. Device-to-Device (D2D) communications commonly refer to the technology that enables direct communication between devices, hence relieving the base station from traffic routing. Thus, D2D communication is a feasible solution in crowded situations, where users in proximity requesting to communicate with one another could be granted D2D links for communication, thereby easing the traffic load to serving base station. D2D links can potentially reuse the radio resources from cellular users (known as D2D underlay) leading to better spectral utilization. However, the mutual interference can hinder system performance. For instance, if D2D links are reusing cellular uplink resources then D2D transmissions cause interference to cellular uplink at base station. Whereas, cellular transmissions cause interference to D2D receivers. To cope up with such issues, location aware resource allocation schemes are proposed for D2D communication. The key aim of such RA scheme is to reuse resources with minimal interference. The RA scheme based on virtual sectoring of a cell leads to approximately 15% more established links and 25% more capacity with respect to a random resource allocation. D2D transmissions cause significant interference to cellular links with which they reuse physical resource blocks, thereby hindering cellular performance. Regulating D2D transmissions to mitigate the aforementioned problem would mean sub-optimal exploitation of D2D communications. As a solution, post-resource allocation power control at cellular users is proposed. Three schemes namely interference aware power control, blind power control and threshold based power control are discussed. Simulation results show reductions in dropping of cellular users due to interference from D2D transmissions, improvement in throughput at base station (uplink) while not hindering the D2D performance.

Ethernet has become an established communication technology in industrial automation. This was possible thanks to the tremendous technological advances and enhancements of Ethernet such as increasing the link-speed, integrating the full-duplex transmission and the use of switches. However these enhancements were still not enough for certain high deterministic industrial applications such as motion control, which requires cycle time below one millisecond and jitter or delay deviation below one microsecond. To meet these high timing requirements, machine and plant manufacturers had to extend the standard Ethernet with real-time capability. As a result, vendor-specific and non-IEEE standard-compliant "Industrial Ethernet" (IE) solutions have emerged. The IEEE Time-Sensitive Networking (TSN) Task Group specifies new IEEE-conformant functionalities and mechanisms to enable the determinism missing from Ethernet. Standard-compliant systems are very attractive to the industry because they guarantee investment security and sustainable solutions. TSN is considered therefore to be an opportunity to increase the performance of established Industrial-Ethernet systems and to move forward to Industry 4.0, which require standard mechanisms. The challenge remains, however, for the Industrial Ethernet organizations to combine their protocols with the TSN standards without running the risk of creating incompatible technologies. TSN specifies 9 standards and enhancements that handle multiple communication aspects. In this thesis, the evaluation of the use of TSN in industrial real-time communication is restricted to four deterministic standards: IEEE802.1AS-Rev, IEEE802.1Qbu IEEE802.3br and IEEE802.1Qbv. The specification of these TSN sub-standards was finished at an early research stage of the thesis and hardware prototypes were available. Integrating TSN into the Industrial-Ethernet protocols is considered a substantial strategical challenge for the industry. The benefits, limits and risks are too complex to estimate without a thorough investigation. The large number of Standard enhancements makes it hard to select the required/appropriate functionalities. In order to cover all real-time classes in the automation [9], four established Industrial-Ethernet protocols have been selected for evaluation and combination with TSN as well as other performance relevant communication features. The objectives of this thesis are to (1) Provide theoretical, simulation and experimental evaluation-methodologies for the timing performance analysis of the deterministic TSN-standards mentioned above. Multiple test-plans are specified to evaluate the performance and compatibility of early version TSN-prototypes from different providers. (2) Investigate multiple approaches and deduce migration strategies to integrate these features into the established Industrial-Ethernet protocols: Sercos III, Profinet IRT, Profinet RT and Ethernet/IP. A scenario of coexistence of time-critical traffic with other traffic in a TSN-network proves that the timing performance for highly deterministic applications, e.g. motion-control, can only be guaranteed by the TSN scheduling algorithm IEEE802.1Qbv. Based on a requirements survey of highly deterministic industrial applications, multiple network scenarios and experiments are presented. The results are summarized into two case studies. The first case study shows that TSN alone is not enough to meet these requirements. The second case study investigates the benefits of additional mechanisms (Gigabit link-speed, minimum cycle time modeling, frame forwarding mechanisms, frame structure, topology migration, etc.) in combination with the TSN features. An implementation prototype of the proposed system and a simulation case study are used for the evaluation of the approach. The prototype is used for the evaluation and validation of the simulation model. Due to given scalability constraints of the prototype (no cut-through functionalities, limited number of TSN-prototypes, etc…), a realistic simulation model, using the network simulation tool OMNEST / OMNeT++, is conducted. The obtained evaluation results show that a minimum cycle time ≤1 ms and a maximum jitter ≤1 μs can be achieved with the presented approaches.

The neural networks have been extensively used for tasks based on image sensors. These models have, in the past decade, consistently performed better than other machine learning methods on tasks of computer vision. It is understood that methods for transfer learning from neural networks trained on large datasets can reduce the total data requirement while training new neural network models. These methods tend not to perform well when the data recording sensor or the recording environment is unique from the existing large datasets. The machine learning literature provides various methods for prior-information inclusion in a learning model. Such methods employ methods like designing biases into the data representation vectors, enforcing priors or physical constraints on the models. Including such information into neural networks for the image frames and image-sequence classification is hard because of the very high dimensional neural network mapping function and little information about the relation between the neural network parameters. In this thesis, we introduce methods for evaluating the statistically learned data representation and combining these information descriptors. We have introduced methods for including information into neural networks. In a series of experiments, we have demonstrated methods for adding the existing model or task information to neural networks. This is done by 1) Adding architectural constraints based on the physical shape information of the input data, 2) including weight priors on neural networks by training them to mimic statistical and physical properties of the data (hand shapes), and 3) by including the knowledge about the classes involved in the classification tasks to modify the neural network outputs. These methods are demonstrated, and their positive influence on the hand shape and hand gesture classification tasks are reported. This thesis also proposes methods for combination of statistical and physical models with parametrized learning models and show improved performances with constant data size. Eventually, these proposals are tied together to develop an in-car hand-shape and hand-gesture classifier based on a Time of Flight sensor.

Betreiber von Niederspannungsnetzen sind im Zuge der Energiewende durch den anhaltenden Ausbau dezentraler Erzeugungsanlagen und dem Aufkommen der Elektromobilität mit steigenden Netzauslastungen konfrontiert. Zukünftig wird ein sicherer Netzbetrieb ohne Leitungsüberlastungen grundsätzlich nur gewährleistet sein, wenn der Netzzustand durch geeignete Systeme ermittelt wird und auf dessen Basis ein intelligentes Netzmanagement mit regelnden Eingriffen erfolgt. Diese Arbeit befasst sich mit der Entwicklung und dem Test eines Verfahrens zur dreiphasigen Zustandsschätzung in vermaschten Niederspannungsnetzen. Als Eingangsdaten dienen dabei Spannungs- und Strommesswerte, welche im Wesentlichen durch Smart Meter an Hausanschlusspunkten messtechnisch erfasst werden. Das Verfahren zielt darauf ab, Grenzwertverletzungen mit einer hohen Wahrscheinlichkeit zu erkennen. Schwerpunkte der Betrachtung sind neben der Systemkonzeptionierung zum einen die Vorverarbeitung der Systemeingangsdaten im Rahmen der Generierung von Ersatzmesswerten sowie der Erkennung von Topologiefehlern und zum anderem die Entwicklung eines Schätzalgorithmus mit linearem Messmodell und der Möglichkeit zur Lokalisierung grob falscher Messdaten.

While the computing industry has shifted from single-core to multi-core processors for performance gain, safety-critical systems (SCSs) still require solutions that enable their transition while guaranteeing safety, requiring no source-code modifications and substantially reducing re-development and re-certification costs, especially for legacy applications that are typically substantial. This dissertation considers the problem of worst-case execution time (WCET) analysis under contentions when deadline-constrained tasks in independent partitioned task set execute on a homogeneous multi-core processor with dynamic time-triggered shared memory bandwidth partitioning in SCSs. Memory bandwidth in multi-core processors is shared across cores and is a significant cause of performance bottleneck and temporal variability of multiple-orders in task’s execution times due to contentions in memory sub-system. Further, the circular dependency is not only between WCET and CPU scheduling of others cores, but also between WCET and memory bandwidth assignments over time to cores. Thus, there is need of solutions that allow tailoring memory bandwidth assignments to workloads over time and computing safe WCET. It is pragmatically infeasible to obtain WCET estimates from static WCET analysis tools for multi-core processors due to the sheer computational complexity involved. We use synchronized periodic memory servers on all cores that regulate each core’s maximum memory bandwidth based on allocated bandwidth over time. First, we present a workload schedulability test for known even-memory-bandwidth-assignment-to-active-cores over time, where the number of active cores represents the cores with non-zero memory bandwidth assignment. Its computational complexity is similar to merge-sort. Second, we demonstrate using a real avionics certified safety-critical application how our method’s use can preserve an existing application’s single-core CPU schedule under contentions on a multi-core processor. It enables incremental certification using composability and requires no-source code modification. Next, we provide a general framework to perform WCET analysis under dynamic memory bandwidth partitioning when changes in memory bandwidth to cores assignment are time-triggered and known. It provides a stall maximization algorithm that has a complexity similar to a concave optimization problem and efficiently implements the WCET analysis. Last, we demonstrate dynamic memory assignments and WCET analysis using our method significantly improves schedulability compared to the stateof-the-art using an Integrated Modular Avionics scenario.

Die Einführung des Internets hat einen stetigen Wandel des täglichen, sowie beruflichen Alltags verursacht. Hierbei ist eine deutliche Verlagerung in den virtuellen Raum (Internet) festzustellen. Zusätzlich hat die Einführung von sozialen Netzwerken, wie beispielsweise Facebook das Verlangen des Nutzers immer „online“ zu sein, deutlich verstärkt. Hinzu kommen die kontinuierlich wachsenden Datenmengen, welche beispielsweise durch Videostreaming (YouTube oder Internet Protocol Television (IPTV)) oder den Austausch von Bildern verursacht werden. Zusätzlich verursachen neue Dienste, welche beispielsweise im Rahmen vom Internet der Dinge und auch Industrie 4.0 eingeführt werden, zusätzliche Datenmengen. Aktuelle Technologien wie Long Term Evolution Advanced (LTE-A) im Funkbereich und Very High Speed Digital Subsciber Line (VDSL) beziehungsweise Glasfaser in kabelgebundenen Netzen, versuchen diesen Anforderungen gerecht zu werden. Angesichts der steigenden Anforderungen an die Mobilität des Nutzers, ist die Verwendung von Funktechnologien unabdingbar. In Verbindung mit dem stetig wachsenden Datenaufkommen und den ansteigenden Datenraten ist ein wachsender Bedarf an Spektrum, also freien, beziehungsweise ungenutzten Frequenzbereichen einhergehend. Für die Identifikation geeigneter Bereiche müssen allerdings eine Vielzahl von Parametern und Einflussfaktoren betrachtet werden. Einer der entscheidenden Parameter ist die entstehende Dämpfung im betrachteten Frequenzbereich, da diese mit steigender Frequenz größer wird und somit die resultierende Abdeckung bei gleichbleibender Sendeleistung sinkt. In aktuellen Funksystemen werden Frequenzen < 6 GHz verwendet, da diese von den Ausbreitungseigenschaften geeignete Eigenschaften aufweisen. Des Weiteren müssen vorhandene Nutzungsrechte, Inhaber des Spektrums, Nutzungsbedingungen und so weiter im Vorfeld abgeklärt werden. In Deutschland wird die Koordination von der Bundesnetzagentur vorgenommen. Aufgrund der Vielfalt der vorhandenen Dienste und Anwendungen ist es leicht ersichtlich, dass der Frequenzbereich < 6 GHz stark ausgelastet ist. Neben den kontinuierlich ausgelasteten Diensten wie zum Beispiel Long Term Evolution (LTE) oder Digital Video Broadcast (DVB), gibt es spektrale Bereiche, die nur eine geringe zeitliche Auslastung aufweisen. Markant hierfür sind Frequenzbereiche, welche beispielsweise ausschließlich für militärische Nutzung reserviert sind. Bei genauerer Betrachtung fällt auf, dass sich dies nicht ausschließlich auf den zeitlichen Bereich beschränkt, vielmehr ergibt sich eine Kombination aus zeitlicher und räumlicher Beschränkung, da die Nutzung meist auf einen räumlichen Bereich eingrenzbar ist. Eine weitere Einschränkung resultiert aus der derzeit starren Vergabe von Frequenzbereichen. Die Zuteilung basiert auf langwierigen Antragsverfahren und macht somit eine kurzfristige variable Zuteilung unmöglich. Um diesem Problem gerecht zu werden, erfolgt im Rahmen dieser Arbeit die Entwicklung eines generischen Spektrum-Management-Systems (SMSs) zur dynamischen Zuteilung vorhandener Ressourcen. Eine Anforderung an das System ist die Unterstützung von bereits bekannten Spektrum Sharing Verfahren, wie beispielsweise Licensed Shared Access (LSA) beziehungsweise Authorized Shared Access (ASA) oder Spectrum Load Smoothing (SLS). Hierfür wird eine Analyse der derzeit bekannten Sharing Verfahren vorgenommen und diese bezüglich ihrer Anwendbarkeit charakterisiert. DesWeiteren werden die Frequenzbereiche unterhalb 6 GHz hinsichtlich ihrer Verwendbarkeiten und regulatorischen Anforderungen betrachtet. Zusätzlich wird ein erweiterter Anforderungskatalog an das Spektrum-Management-System (SMS) entwickelt, welcher als Grundlage für das Systemdesign verwendet wird. Essentiell ist hierbei, dass alle (potentiellen) Nutzer beziehungsweise Inhaber eines spektralen Bereiches die Funktionalität eines derartigen Systems verwenden können. Hieraus ergibt sich bereits die Anforderung der Skalierbarkeit des Systems. Zur Entwicklung einer geeigneten Systemarchitektur werden bereits vorhandene Lösungsansätze zur Verwaltung und Speicherung von Daten hinsichtlich ihrer Anwendbarkeit verglichen und bewertet. Des Weiteren erfolgt die Einbeziehung der geografischen Position. Um dies adäquat gewährleisten zu können, werden hierarchische Strukturen in Netzwerken untersucht und auf ihre Verwendbarkeit geprüft. Das Ziel dieser Arbeit ist die Entwicklung eines Spektrum-Management- Systems (SMSs) durch Adaption bereits vorhandener Technologien und Verfahren, sowie der Berücksichtigung aller definierten Anforderungen. Es hat sich gezeigt, dass die Verwendung einer zentralisierten Broker- Lösung nicht geeignet ist, da die Verzögerungszeit einen exponentiellförmigen Verlauf bezüglich der Anzahl der Anfragen aufweist und somit nicht skaliert. Dies kann mittels einer Distributed Hash Table (DHT)- basierten Erweiterung überwunden werden ohne dabei die Funktionalität der Broker-Lösung einzuschränken. Für die Einbringung der Geoinformation hat sich die hierarchische Struktur, vergleichbar zum Domain Naming Service (DNS) als geeignet erwiesen. Als Parameter für die Evaluierung hat sich die resultierende Zugriffszeit, das heißt die Zeit welche das System benötigt um Anfragen zu bearbeiten, sowie die resultierende Anzahl der versorgbaren Nutzer herausgestellt. Für die Simulation wird ein urbanes Areal mit fünf Gebäuden betrachtet. In der Mitte befindet sich ein sechsstöckiges Firmengebäude, welches in jedem Stockwerk mit einem Wireless Local Area Network Access Point (WLAN-AP) ausgestattet ist. Umliegend befinden sich vier Privathäuser, welche jeweils mit einem WLAN-AP ausgestattet sind. Das komplette Areal wird von drei Mobilfunkbetreibern mit je einer Basisstation (BS) versorgt. Als Ausgangspunkt für die Evaluierung erfolgt der Betrieb ohne SMS. Aus den Ergebnissen wird deutlich, dass eine Überlastung der Long Term Evolution Basisstationen (LTE-BSen) vorliegt (im Speziellen bei Betreiber A und B). Im zweiten Durchlauf wird das Szenario mit einem SMS betrachtet. Zusätzlich kommen in diesem Fall noch Mikro Basisstationen (Mikro-BSen) zum Einsatz, welche von der Spezifikation vergleichbar zu einem Wireless Local Area Network (WLAN) sind. Hier zeigt sich ein deutlich ausgewogeneres Systemverhalten. Alle BSen und Access Points (APs) befinden sich deutlich unterhalb der Volllastgrenze. Die Untersuchungen im Rahmen dieser Arbeit belegen, dass ein heterogenes, zeitweise überlastetes Funksystem, vollständig harmonisiert werden kann. Des Weiteren ermöglicht der Einsatz eines SMSs die effiziente Verwendung von temporär ungenutzten Frequenzbereichen (sogenannte White- und Gray-spaces).

Field-effect transistor (FET) sensors and in particular their nanoscale variant of silicon nanowire transistors are very promising technology platforms for label-free biosensor applications. These devices directly detect the intrinsic electrical charge of biomolecules at the sensor’s liquid-solid interface. The maturity of micro fabrication techniques enables very large FET sensor arrays for massive multiplex detection. However, the direct detection of charged molecules in liquids faces a significant limitation due to a charge screening effect in physiological solutions, which inhibits the realization of point-of-care applications. As an alternative, impedance spectroscopy with FET devices has the potential to enable measurements in physiological samples. Even though promising studies were published in the field, impedimetric detection with silicon FET devices is not well understood. The first goal of this thesis was to understand the device performances and to relate the effects seen in biosensing experiments to device and biomolecule types. A model approach should help to understand the capability and limitations of the impedimetric measurement method with FET biosensors. In addition, to obtain experimental results, a high precision readout device was needed. Consequently, the second goal was to build up multi-channel, highly accurate amplifier systems that would also enable future multi-parameter handheld devices. A PSPICE FET model for potentiometric and impedimetric detection was adapted to the experiments and further expanded to investigate the sensing mechanism, the working principle, and effects of side parameters for the biosensor experiments. For potentiometric experiments, the pH sensitivity of the sensors was also included in this modelling approach. For impedimetric experiments, solutions of different conductivity were used to validate the suggested theories and assumptions. The impedance spectra showed two pronounced frequency domains: a low-pass characteristic at lower frequencies and a resonance effect at higher frequencies. The former can be interpreted as a contribution of the source and double layer capacitances. The latter can be interpreted as a combined effect of the drain capacitance with the operational amplifier in the transimpedance circuit. Two readout systems, one as a laboratory system and one as a point-of-care demonstrator, were developed and used for several chemical and biosensing experiments. The PSPICE model applied to the sensors and circuits were utilized to optimize the systems and to explain the sensor responses. The systems as well as the developed modelling approach were a significant step towards portable instruments with combined transducer principles in future healthcare applications.

”In contemporary electronics 80% of a chip may perform digital functions but the 20% of analog functions may take 80% of the development time.” [1]. Aggravating this, the demands on analog design is increasing with rapid technology scaling. Most designs have moved away from analog to digital domains, where possible, however, interacting with the environment will always require analog to digital data conversion. Adding to this problem, the number of sensors used in consumer and industry related products are rapidly increasing. Designers of ADCs are dealing with this problem in several ways, the most important is the migration towards digital designs and time domain techniques. Time to Digital Converters (TDC) are becoming increasingly popular for robust signal processing. Biological neurons make use of spikes, which carry spike timing information and will not be affected by the problems related to technology scaling. Neuromorphic ADCs still remain exotic with few implementations in sub-micron technologies Table 2.7. Even among these few designs, the strengths of biological neurons are rarely exploited. From a previous work [2], LUCOS, a high dynamic range image sensor, the efficiency of spike processing has been validated. The ideas from this work can be generalized to make a highly effective sensor signal conditioning system, which carries the promise to be robust to technology scaling. The goal of this work is to create a novel spiking neural ADC as a novel form of a Multi-Sensor Signal Conditioning and Conversion system, which • Will be able to interface with or be a part of a System on Chip with traditional analog or advanced digital components. • Will have a graceful degradation. • Will be robust to noise and jitter related problems. • Will be able to learn and adapt to static errors and dynamic errors. • Will be capable of self-repair, self-monitoring and self-calibration Sensory systems in humans and other animals analyze the environment using several techniques. These techniques have been evolved and perfected to help the animal sur- vive. Different animals specialize in different sense organs, however, the peripheral neural network architectures remain similar among various animal species with few ex- ceptions. While there are many biological sensing techniques present, most popularly used engineering techniques are based on intensity detection, frequency detection, and edge detection. These techniques are used with traditional analog processing (e.g., colorvi sensors using filters), and with biological techniques (e.g. LUCOS chip [2]). The local- ization capability of animals has never been fully utilized. One of the most important capabilities for animals, vertebrates or invertebrates, is the capability for localization. The object of localization can be predator, prey, sources of water, or food. Since these are basic necessities for survival, they evolve much faster due to the survival of the fittest. In fact, localization capabilities, even if the sensors are different, have convergently evolved to have same processing methods (coincidence detection) in their peripheral neurons (for e.g., forked tongue of a snake, antennae of a cockroach, acoustic localization in fishes and mammals). This convergent evolution increases the validity of the technique. In this work, localization concepts based on acoustic localization and tropotaxis are investigated and employed for creation of novel ADCs. Unlike intensity and frequency detection, which are not linear (for e.g. eyes saturate in bright light, loose color perception in low light), localization is inherently linear. This is mainly because the accurate localization of predator or prey can be the difference between life and death for an animal. Figure 1 visually explains the ADC concept proposed in this work. This has two parts. (1) Sensor to Spike(time) Conversion (SSC), (2) Spike(time) to Digital Conversion(SDC). Both of the structures have been designed with models of biological neurons. The combination of these two structures is called SSDC. To efficiently implement the proposed concept, a comparison of several biological neural models is made and two models are shortlisted. Various synapse structures are also studied. From this study, Leaky Integrate and Fire neuron (LIF) is chosen since it fulfills all the requirements of the proposed structure. The analog neuron and synapse designs from Indiveri et. al. [3], [4] were taken, and simulations were conducted using cadence and the behavioral equivalence with biological counterpart was checked. The LIF neuron had features, that were not required for the proposed approach. A simple LIF neuron stripped of these features and was designed to be as fast as allowed by the technology. The SDC was designed with the neural building blocks and the delays were designed using buffer chains. This SDC converts incoming Time Interval Code (TIC) to sparse place coding using coincidence detection. Coincidence detection is a property of spiking neurons, which is a time domain equivalent of a Gaussian Kernel. The SDC is designed to have an online reconfigurable Gaussian kernel width, weight, threshold, and refractory period. The advantage of sparse place codes, which contain rank order coding wasvii Figure 1: ADC as a localization problem (right), Jeffress model of sound localization visualized (left). The values t 1 and t 2 indicate the time taken from the source to s1 and s2 respectively. described in our work [5]. A time based winner take all circuit with memory was created based on a previous work [6] for reading out of sparse place codes asynchronously. The SSC was also initially designed with the same building blocks. Additionally, a differential synapse was designed for better SSC. The sensor element considered wasviii a Wheatstone full bridge AMR sensor AFF755 from Sensitec GmbH. A reconfigurable version of the synapse was also designed for a more generic sensor interface. The first prototype chip SSDCα was designed with 257 modules of coincidence detectors realizing the SDC and the SSC. Since the spike times are the most important information, the spikes can be treated as digital pulses. This provides the capability for digital communication between analog modules. This creates a lot of freedom for use of digital processing between the discussed analog modules. This advantage is fully exploited in the design of SSDCα. Three SSC modules are multiplexed to the SDC. These SSC modules also provide outputs from the chip simultaneously. A rising edge detecting fixed pulse width generation circuit is used to create pulses that are best suited for efficient performance of the SDC. The delay lines are made reconfigurable to increase robustness and modify the span of the SDC. The readout technique used in the first prototype is a relatively slow but safe shift register. It is used to analyze the characteristics of the core work. This will be replaced by faster alternatives discussed in the work. The area of the chip is 8.5 mm 2 . It has a sampling rate from DC to 150 kHz. It has a resolution from 8-bit to 13-bit. It has 28,200 transistors on the chip. It has been designed in 350 nm CMOS technology from ams. The chip has been manufactured and tested with a sampling rate of 10 kHz and a theoretical resolution of 8 bits. However, due to the limitations of our Time-Interval-Generator, we are able to confirm for only 4 bits of resolution. The key novel contributions of this work are • Neuromorphic implementation of AD conversion as a localization problem based on sound localization and tropotaxis concepts found in nature. • Coincidence detection with sparse place coding to enhance resolution. • Graceful degradation without redundant elements, inherent robustness to noise, which helps in scaling of technologies • Amenable to local adaptation and self-x features. Conceptual goals have all been fulfilled, with the exception of adaptation. The feasibility for local adaptation has been shown with promising results and further investigation is required for future work. This thesis work acts as a baseline, paving the way for R&D in a new direction. The chip design has used 350 nm ams hitkit as a vehicle to prove the functionality of the core concept. The concept can be easily ported to present aggressively-scaled-technologies and future technologies.

In current practices of system-on-chip (SoC) design a trend can be observed to integrate more and more low-level software components into the system hardware at different levels of granularity. The implementation of important control functions and communication structures is frequently shifted from the SoC’s hardware into its firmware. As a result, the tight coupling of hardware and software at a low level of granularity raises substantial verification challenges since the conventional practice of verifying hardware and software independently is no longer sufficient. This calls for new methods for verification based on a joint analysis of hardware and software. This thesis proposes hardware-dependent models of low-level software for performing formal verification. The proposed models are conceived to represent the software integrated with its hardware environment according to the current SoC design practices. Two hardware/software integration scenarios are addressed in this thesis, namely, speed-independent communication of the processor with its hardware periphery and cycle-accurate integration of firmware into an SoC module. For speed-independent hardware/software integration an approach for equivalence checking of hardware-dependent software is proposed and an evaluated. For the case of cycle-accurate hardware/software integration, a model for hardware/software co-verification has been developed and experimentally evaluated by applying it to property checking.

Safety-related Systems (SRS) protect from the unacceptable risk resulting from failures of technical systems. The average probability of dangerous failure on demand (PFD) of these SRS in low demand mode is limited by standards. Probabilistic models are applied to determine the average PFD and verify the specified limits. In this thesis an effective framework for probabilistic modeling of complex SRS is provided. This framework enables to compute the average, instantaneous, and maximum PFD. In SRS, preventive maintenance (PM) is essential to achieve an average PFD in compliance with specified limits. PM intends to reveal dangerous undetected failures and provides repair if necessary. The introduced framework pays special attention to the precise and detailed modeling of PM. Multiple so far neglected degrees of freedom of the PM are considered, such as two types of elementwise PM at arbitrarily variable times. As shown by analyses, these degrees of freedom have a significant impact on the average, instantaneous, and maximum PFD. The PM is optimized to improve the average or maximum PFD or both. A well-known heuristic nonlinear optimization method (Nelder-Mead method) is applied to minimize the average or maximum PFD or a weighted trade-off. A significant improvement of the objectives and an improved protection are achieved. These improvements are achieved via the available degrees of freedom of the PM and without additional effort. Moreover, a set of rules is presented to decide for a given SRS if significant improvements will be achieved by optimization of the PM. These rules are based on the well-known characteristics of the SRS, e.g. redundancy or no redundancy, complete or incomplete coverage of PM. The presented rules aim to support the decision whether the optimization is advantageous for a given SRS and if it should be applied or not.