A small perturbation based optimization approach for the frequency placement of high aspect ratio wings

Design denotes the transformation of an identified need to its physical embodiment in a traditionally iterative approach of trial and error. Conceptual design plays a prominent role but an almost infinite number of possible solutions at the outset of design necessitates fast evaluations. The traditional practice of empirical databases loses adequacy for novel concepts and an ever increasing system complexity and resource scarsity mandate new approaches to adequately capture system characteristics. Contemporary concerns in atmospheric science and homeland security created an operational need for unconventional configurations. Unmanned long endurance flight at high altitudes offers a unique showcase for the exploration of new design spaces and the incidental deficit of conceptual modeling and simulation capabilities. The present research effort evolves around the development of an efficient and accurate optimization algorithm for high aspect ratio wings subject to natural frequency constraints. Foundational corner stones are beam dimensional reduction and modal perturbation redesign. Local and global analyses inherent to the former suggest corresponding levels of local and global optimization. The present approach departs from this suggestion. It introduces local level surrogate models to capacitate a methodology that consists of multi level analyses feeding into a single level optimization. The innovative heart of the new algorithm originates in small perturbation theory. A sequence of small perturbation solutions allows the optimizer to make incremental movements within the design space. It enables a directed search that is free of costly gradients. System matrices are decomposed based on a Timoshenko stiffness effect separation. The formulation of respective linear changes falls back on surrogate models that approximate cross sectional properties. Corresponding functional responses are readily available. Their direct use by the small perturbation based optimizer ensures constitutive laws and eliminates a previously necessary optimization at the local level. The great economy of the developed algorithm makes it suitable for the conceptual phase of aircraft design.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Bauchau, Olivier; Committee Member: Schrage, Daniel; Committee Member: Volovoi, Vitali; Committee Member: Yu, Wenbi

Temporal analysis and scheduling of hard real-time radios running on a multi-processor

On a multi-radio baseband system, multiple independent transceivers must share the resources of a multi-processor, while meeting each its own hard real-time requirements. Not all possible combinations of transceivers are known at compile time, so a solution must be found that either allows for independent timing analysis or relies on runtime timing analysis. This thesis proposes a design flow and software architecture that meets these challenges, while enabling features such as independent transceiver compilation and dynamic loading, and taking into account other challenges such as ease of programming, efficiency, and ease of validation. We take data flow as the basic model of computation, as it fits the application domain, and several static variants (such as Single-Rate, Multi-Rate and Cyclo-Static) have been shown to possess strong analytical properties. Traditional temporal analysis of data flow can provide minimum throughput guarantees for a self-timed implementation of data flow. Since transceivers may need to guarantee strictly periodic execution and meet latency requirements, we extend the analysis techniques to show that we can enforce strict periodicity for an actor in the graph; we also provide maximum latency analysis techniques for periodic, sporadic and bursty sources. We propose a scheduling strategy and an automatic scheduling flow that enable the simultaneous execution of multiple transceivers with hard-realtime requirements, described as Single-Rate Data Flow (SRDF) graphs. Each transceiver has its own execution rate and starts and stops independently from other transceivers, at times unknown at compile time, on a multiprocessor. We show how to combine scheduling and mapping decisions with the input application data flow graph to generate a worst-case temporal analysis graph. We propose algorithms to find a mapping per transceiver in the form of clusters of statically-ordered actors, and a budget for either a Time Division Multiplex (TDM) or Non-Preemptive Non-Blocking Round Robin (NPNBRR) scheduler per cluster per transceiver. The budget is computed such that if the platform can provide it, then the desired minimum throughput and maximum latency of the transceiver are guaranteed, while minimizing the required processing resources. We illustrate the use of these techniques to map a combination of WLAN and TDS-CDMA receivers onto a prototype Software-Defined Radio platform. The functionality of transceivers for standards with very dynamic behavior – such as WLAN – cannot be conveniently modeled as an SRDF graph, since SRDF is not capable of expressing variations of actor firing rules depending on the values of input data. Because of this, we propose a restricted, customized data flow model of computation, Mode-Controlled Data Flow (MCDF), that can capture the data-value dependent behavior of a transceiver, while allowing rigorous temporal analysis, and tight resource budgeting. We develop a number of analysis techniques to characterize the temporal behavior of MCDF graphs, in terms of maximum latencies and throughput. We also provide an extension to MCDF of our scheduling strategy for SRDF. The capabilities of MCDF are then illustrated with a WLAN 802.11a receiver model. Having computed budgets for each transceiver, we propose a way to use these budgets for run-time resource mapping and admissibility analysis. During run-time, at transceiver start time, the budget for each cluster of statically-ordered actors is allocated by a resource manager to platform resources. The resource manager enforces strict admission control, to restrict transceivers from interfering with each other’s worst-case temporal behaviors. We propose algorithms adapted from Vector Bin-Packing to enable the mapping at start time of transceivers to the multi-processor architecture, considering also the case where the processors are connected by a network on chip with resource reservation guarantees, in which case we also find routing and resource allocation on the network-on-chip. In our experiments, our resource allocation algorithms can keep 95% of the system resources occupied, while suffering from an allocation failure rate of less than 5%. An implementation of the framework was carried out on a prototype board. We present performance and memory utilization figures for this implementation, as they provide insights into the costs of adopting our approach. It turns out that the scheduling and synchronization overhead for an unoptimized implementation with no hardware support for synchronization of the framework is 16.3% of the cycle budget for a WLAN receiver on an EVP processor at 320 MHz. However, this overhead is less than 1% for mobile standards such as TDS-CDMA or LTE, which have lower rates, and thus larger cycle budgets. Considering that clock speeds will increase and that the synchronization primitives can be optimized to exploit the addressing modes available in the EVP, these results are very promising

Timing in Technischen Sicherheitsanforderungen für Systementwürfe mit heterogenen Kritikalitätsanforderungen

Traditionally, timing requirements as (technical) safety requirements have been avoided through clever functional designs. New vehicle automation concepts and other applications, however, make this harder or even impossible and challenge design automation for cyber-physical systems to provide a solution. This thesis takes upon this challenge by introducing cross-layer dependency analysis to relate timing dependencies in the bounded execution time (BET) model to the functional model of the artifact. In doing so, the analysis is able to reveal where timing dependencies may violate freedom from interference requirements on the functional layer and other intermediate model layers. For design automation this leaves the challenge how such dependencies are avoided or at least be bounded such that the design is feasible: The results are synthesis strategies for implementation requirements and a system-level placement strategy for run-time measures to avoid potentially catastrophic consequences of timing dependencies which are not eliminated from the design. Their applicability is shown in experiments and case studies. However, all the proposed run-time measures as well as very strict implementation requirements become ever more expensive in terms of design effort for contemporary embedded systems, due to the system's complexity. Hence, the second part of this thesis reflects on the design aspect rather than the analysis aspect of embedded systems and proposes a timing predictable design paradigm based on System-Level Logical Execution Time (SL-LET). Leveraging a timing-design model in SL-LET the proposed methods from the first part can now be applied to improve the quality of a design -- timing error handling can now be separated from the run-time methods and from the implementation requirements intended to guarantee them. The thesis therefore introduces timing diversity as a timing-predictable execution theme that handles timing errors without having to deal with them in the implemented application. An automotive 3D-perception case study demonstrates the applicability of timing diversity to ensure predictable end-to-end timing while masking certain types of timing errors.Traditionell wurden Timing-Anforderungen als (technische) Sicherheitsanforderungen durch geschickte funktionale Entwürfe vermieden. Neue Fahrzeugautomatisierungskonzepte und Anwendungen machen dies jedoch schwieriger oder gar unmöglich; Aufgrund der Problemkomplexität erfordert dies eine Entwurfsautomatisierung für cyber-physische Systeme heraus. Diese Arbeit nimmt sich dieser Herausforderung an, indem sie eine schichtenübergreifende Abhängigkeitsanalyse einführt, um zeitliche Abhängigkeiten im Modell der beschränkten Ausführungszeit (BET) mit dem funktionalen Modell des Artefakts in Beziehung zu setzen. Auf diese Weise ist die Analyse in der Lage, aufzuzeigen, wo Timing-Abhängigkeiten die Anforderungen an die Störungsfreiheit auf der funktionalen Schicht und anderen dazwischenliegenden Modellschichten verletzen können. Für die Entwurfsautomatisierung ergibt sich daraus die Herausforderung, wie solche Abhängigkeiten vermieden oder zumindest so eingegrenzt werden können, dass der Entwurf machbar ist: Das Ergebnis sind Synthesestrategien für Implementierungsanforderungen und eine Platzierungsstrategie auf Systemebene für Laufzeitmaßnahmen zur Vermeidung potentiell katastrophaler Folgen von Timing-Abhängigkeiten, die nicht aus dem Entwurf eliminiert werden. Ihre Anwendbarkeit wird in Experimenten und Fallstudien gezeigt. Allerdings werden alle vorgeschlagenen Laufzeitmaßnahmen sowie sehr strenge Implementierungsanforderungen für moderne eingebettete Systeme aufgrund der Komplexität des Systems immer teurer im Entwurfsaufwand. Daher befasst sich der zweite Teil dieser Arbeit eher mit dem Entwurfsaspekt als mit dem Analyseaspekt von eingebetteten Systemen und schlägt ein Entwurfsparadigma für vorhersagbares Timing vor, das auf der System-Level Logical Execution Time (SL-LET) basiert. Basierend auf einem Timing-Entwurfsmodell in SL-LET können die vorgeschlagenen Methoden aus dem ersten Teil nun angewandt werden, um die Qualität eines Entwurfs zu verbessern -- die Behandlung von Timing-Fehlern kann nun von den Laufzeitmethoden und von den Implementierungsanforderungen, die diese garantieren sollen, getrennt werden. In dieser Arbeit wird daher Timing Diversity als ein Thema der Timing-Vorhersage in der Ausführung eingeführt, das Timing-Fehler behandelt, ohne dass sie in der implementierten Anwendung behandelt werden müssen. Anhand einer Fallstudie aus dem Automobilbereich (3D-Umfeldwahrnehmung) wird die Anwendbarkeit von Timing-Diversität demonstriert, um ein vorhersagbares Ende-zu-Ende-Timing zu gewährleisten und gleichzeitig in der Lage zu sein, bestimmte Arten von Timing-Fehlern zu maskieren

A Study on the Improvement of Data Collection in Data Centers and Its Analysis on Deep Learning-based Applications

Big data are usually stored in data center networks for processing and analysis through various cloud applications. Such applications are a collection of data-intensive jobs which often involve many parallel flows and are network bound in the distributed environment. The recent networking abstraction, coflow, for data parallel programming paradigm to express the communication requirements has opened new opportunities to network scheduling for such applications. Therefore, I propose coflow based network scheduling algorithm, Coflourish, to enhance the job completion time for such data-parallel applications, in the presence of the increased background traffic to mimic the cloud environment infrastructure. It outperforms Varys, the state-of-the-art coflow scheduling technique, by 75.5% under various workload conditions. However, such technique often requires customized operating systems, customized computing frameworks or external proprietary software-defined networking (SDN) switches. Consequently, in order to achieve the minimal application completion time, through coflow scheduling, coflow routing, and per-rate per-flow scheduling paradigm with minimum customization to the hosts and switches, I propose another scheduling technique, MinCOF which exploits the OpenFlow SDN. MinCOF provides faster deployability and no proprietary system requirements. It also decreases the average coflow completion time by 12.94% compared to the latest OpenFlow-based coflow scheduling and routing framework. Although the challenges related to analysis and processing of big data can be handled effectively through addressing the network issues. Sometimes, there are also challenges to analyze data effectively due to the limited data size. To further analyze such collected data, I use various deep learning approaches. Specifically, I design a framework to collect Twitter data during natural disaster events and then deploy deep learning model to detect the fake news spreading during such crisis situations. The wide-spread of fake news during disaster events disrupts the rescue missions and recovery activities, costing human lives and delayed response. My deep learning model classifies such fake events with 91.47% accuracy and F1 score of 90.89 to help the emergency managers during crisis. Therefore, this study focuses on providing network solutions to decrease the application completion time in the cloud environment, in addition to analyze the data collected using the deployed network framework to further use it to solve the real-world problems using the various deep learning approaches

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Efficient Security Protocols for Constrained Devices

During the last decades, more and more devices have been connected to the Internet.Today, there are more devices connected to the Internet than humans.An increasingly more common type of devices are cyber-physical devices.A device that interacts with its environment is called a cyber-physical device.Sensors that measure their environment and actuators that alter the physical environment are both cyber-physical devices.Devices connected to the Internet risk being compromised by threat actors such as hackers.Cyber-physical devices have become a preferred target for threat actors since the consequence of an intrusion disrupting or destroying a cyber-physical system can be severe.Cyber attacks against power and energy infrastructure have caused significant disruptions in recent years.Many cyber-physical devices are categorized as constrained devices.A constrained device is characterized by one or more of the following limitations: limited memory, a less powerful CPU, or a limited communication interface.Many constrained devices are also powered by a battery or energy harvesting, which limits the available energy budget.Devices must be efficient to make the most of the limited resources.Mitigating cyber attacks is a complex task, requiring technical and organizational measures.Constrained cyber-physical devices require efficient security mechanisms to avoid overloading the systems limited resources.In this thesis, we present research on efficient security protocols for constrained cyber-physical devices.We have implemented and evaluated two state-of-the-art protocols, OSCORE and Group OSCORE.These protocols allow end-to-end protection of CoAP messages in the presence of untrusted proxies.Next, we have performed a formal protocol verification of WirelessHART, a protocol for communications in an industrial control systems setting.In our work, we present a novel attack against the protocol.We have developed a novel architecture for industrial control systems utilizing the Digital Twin concept.Using a state synchronization protocol, we propagate state changes between the digital and physical twins.The Digital Twin can then monitor and manage devices.We have also designed a protocol for secure ownership transfer of constrained wireless devices. Our protocol allows the owner of a wireless sensor network to transfer control of the devices to a new owner.With a formal protocol verification, we can guarantee the security of both the old and new owners.Lastly, we have developed an efficient Private Stream Aggregation (PSA) protocol.PSA allows devices to send encrypted measurements to an aggregator.The aggregator can combine the encrypted measurements and calculate the decrypted sum of the measurements.No party will learn the measurement except the device that generated it

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications