Bio-inspired retinal optic flow perception in robotic navigation

This thesis concerns the bio-inspired visual perception of motion with emphasis on locomotion targeting robotic systems. By continuously registering moving visual features in the human retina, a sensation of a visual flow cue is created. An interpretation of visual flow cues forms a low-level motion perception more known as retinal optic flow. Retinal optic flow is often mentioned and credited in human locomotor research but only in theory and simulated environments so far. Reconstructing the retinal optic flow fields using existing methods of estimating optic flow and experimental data from naive test subjects provides further insight into how it interacts with intermittent control behavior and dynamic gazing. The retinal optic flow is successfully demonstrated during a vehicular steering task scenario and further supports the idea that humans may use such perception to aid their ability to correct their steering during navigation.To achieve the reconstruction and estimation of the retinal optic flow, a set of optic flow estimators were fairly and systematically evaluated on the criteria on run-time predictability and reliability, and performance accuracy. A formalized methodology using containerization technology for performing the benchmarking was developed to generate the results. Furthermore, the readiness in road vehicles for the adoption of modern robotic software and related software processes were investigated. This was done with special emphasis on real-time computing and introducing containerization and microservice design paradigm. By doing so, continuous integration, continuous deployment, and continuous experimentation were enabled in order to aid further development and research. With the method of estimating retinal optic flow and its interaction with intermittent control, a more complete vision-based bionic steering control model is to be proposed and tested in a live robotic system

Ensemble representation of uncertainty in Lagrangian satellite rainfall estimates

A new algorithm called Lagrangian Simulation (LSIM) has been developed that enables the interpolation uncertainty present in Lagrangian satellite rainfall algorithms such as the Climate Prediction Center (CPC) morphing technique (CMORPH) to be characterized using an ensemble product. The new algorithm generates ensemble sequences of rainfall fields conditioned on multiplatform multisensor microwave satellite data, demonstrating a conditional simulation approach that overcomes the problem of discontinuous uncertainty fields inherent in this type of product. Each ensemble member is consistent with the information present in the satellite data, while variation between members is indicative of uncertainty in the rainfall retrievals. LSIM is based on the combination of a Markov weather generator, conditioned on both previous and subsequent microwave measurements, and a global optimization procedure that uses simulated annealing to constrain the generated rainfall fields to display appropriate spatial structures. The new algorithm has been validated over a region of the continental United States and has been shown to provide reliable estimates of both point uncertainty distributions and wider spatiotemporal structures

Hardware-Assisted Dependable Systems

Unpredictable hardware faults and software bugs lead to application crashes, incorrect computations, unavailability of internet services, data losses, malfunctioning components, and consequently financial losses or even death of people. In particular, faults in microprocessors (CPUs) and memory corruption bugs are among the major unresolved issues of today. CPU faults may result in benign crashes and, more problematically, in silent data corruptions that can lead to catastrophic consequences, silently propagating from component to component and finally shutting down the whole system. Similarly, memory corruption bugs (memory-safety vulnerabilities) may result in a benign application crash but may also be exploited by a malicious hacker to gain control over the system or leak confidential data. Both these classes of errors are notoriously hard to detect and tolerate. Usual mitigation strategy is to apply ad-hoc local patches: checksums to protect specific computations against hardware faults and bug fixes to protect programs against known vulnerabilities. This strategy is unsatisfactory since it is prone to errors, requires significant manual effort, and protects only against anticipated faults. On the other extreme, Byzantine Fault Tolerance solutions defend against all kinds of hardware and software errors, but are inadequately expensive in terms of resources and performance overhead. In this thesis, we examine and propose five techniques to protect against hardware CPU faults and software memory-corruption bugs. All these techniques are hardware-assisted: they use recent advancements in CPU designs and modern CPU extensions. Three of these techniques target hardware CPU faults and rely on specific CPU features: ∆-encoding efficiently utilizes instruction-level parallelism of modern CPUs, Elzar re-purposes Intel AVX extensions, and HAFT builds on Intel TSX instructions. The rest two target software bugs: SGXBounds detects vulnerabilities inside Intel SGX enclaves, and “MPX Explained” analyzes the recent Intel MPX extension to protect against buffer overflow bugs. Our techniques achieve three goals: transparency, practicality, and efficiency. All our systems are implemented as compiler passes which transparently harden unmodified applications against hardware faults and software bugs. They are practical since they rely on commodity CPUs and require no specialized hardware or operating system support. Finally, they are efficient because they use hardware assistance in the form of CPU extensions to lower performance overhead

Architectural Vulnerability Factor (AVF) Assessment of x86 CPUs using Architectural Correct Execution (ACE) analysis in the Gem5 Simulator

Ο συντελεστής AVF έχει υπολογιστεί για 10 διαφορετικά προγράμματα τόσο για το αρχείο φυσικών ακέραιων καταχωρητών του Gem5 όσο και για τη μνήμη δεδομένων πρώτου επιπέδου. Για κάθε ένα απο αυτά έχουν υπολογιστεί οι χρόνοι εκτέλεσής τους καθώς και τα ευάλωτα διαστήματά τους.In this study, we computed AVF for ten different benchmarks in two different microarchitectural modules, the integer physical integer register file and the L1 Data Cache of Gem5. For each benchmark statistics about its runtime and ACE interval time are reported

Methodologies for Accelerated Analysis of the Reliability and the Energy Efficiency Levels of Modern Microprocessor Architectures

Η εξέλιξη της τεχνολογίας ημιαγωγών, της αρχιτεκτονικής υπολογιστών και της σχεδίασης οδηγεί σε αύξηση της απόδοσης των σύγχρονων μικροεπεξεργαστών, η οποία επίσης συνοδεύεται από αύξηση της ευπάθειας των προϊόντων. Οι σχεδιαστές εφαρμόζουν διάφορες τεχνικές κατά τη διάρκεια της ζωής των ολοκληρωμένων κυκλωμάτων με σκοπό να διασφαλίσουν τα υψηλά επίπεδα αξιοπιστίας των παραγόμενων προϊόντων και να τα προστατέψουν από διάφορες κατηγορίες σφαλμάτων διασφαλίζοντας την ορθή λειτουργία τους. Αυτή η διδακτορική διατριβή προτείνει καινούριες μεθόδους για να διασφαλίσει τα υψηλά επίπεδα αξιοπιστίας και ενεργειακής απόδοσης των σύγχρονων μικροεπεξεργαστών οι οποίες μπορούν να εφαρμοστούν κατά τη διάρκεια του πρώιμου σχεδιαστικού σταδίου, του σταδίου παραγωγής ή του σταδίου της κυκλοφορίας των ολοκληρωμένων κυκλωμάτων στην αγορά. Οι συνεισφορές αυτής της διατριβής μπορούν να ομαδοποιηθούν στις ακόλουθες δύο κατηγορίες σύμφωνα με το στάδιο της ζωής των μικροεπεξεργαστών στο οποίο εφαρμόζονται: • Πρώιμο σχεδιαστικό στάδιο: Η στατιστική εισαγωγή σφαλμάτων σε δομές που είναι μοντελοποιημένες σε προσομοιωτές οι οποίοι στοχεύουν στην μελέτη της απόδοσης είναι μια επιστημονικά καθιερωμένη μέθοδος για την ακριβή μέτρηση της αξιοπιστίας, αλλά υστερεί στον αργό χρόνο εκτέλεσης. Σε αυτή τη διατριβή, αρχικά παρουσιάζουμε ένα νέο πλήρως αυτοματοποιημένο εργαλείο εισαγωγής σφαλμάτων σε μικροαρχιτεκτονικό επίπεδο που στοχεύει στην ακριβή αξιολόγηση της αξιοπιστίας ενός μεγάλου πλήθους μονάδων υλικού σε σχέση με διάφορα μοντέλα σφαλμάτων (παροδικά, διακοπτόμενα, μόνιμα σφάλματα). Στη συνέχεια, χρησιμοποιώντας το ίδιο εργαλείο και στοχεύοντας τα παροδικά σφάλματα, παρουσιάζουμε διάφορες μελέτες σχετιζόμενες με την αξιοπιστία και την απόδοση, οι οποίες μπορούν να βοηθήσουν τις σχεδιαστικές αποφάσεις στα πρώιμα στάδια της ζωής των επεξεργαστών. Τελικά, προτείνουμε δύο μεθοδολογίες για να επιταχύνουμε τα μαζικά πειράματα στατιστικής εισαγωγής σφαλμάτων. Στην πρώτη, επιταχύνουμε τα πειράματα έπειτα από την πραγματική εισαγωγή των σφαλμάτων στις δομές του υλικού. Στη δεύτερη, επιταχύνουμε ακόμη περισσότερο τα πειράματα προτείνοντας τη μεθοδολογία με όνομα MeRLiN, η οποία βασίζεται στη μείωση της αρχικής λίστας σφαλμάτων μέσω της ομαδοποίησής τους σε ισοδύναμες ομάδες έπειτα από κατηγοριοποίηση σύμφωνα με την εντολή που τελικά προσπελαύνει τη δομή που φέρει το σφάλμα. • Παραγωγικό στάδιο και στάδιο κυκλοφορίας στην αγορά: Οι συνεισφορές αυτής της διδακτορικής διατριβής σε αυτά τα στάδια της ζωής των μικροεπεξεργαστών καλύπτουν δύο σημαντικά επιστημονικά πεδία. Αρχικά, χρησιμοποιώντας το ολοκληρωμένο κύκλωμα των 48 πυρήνων με ονομασία Intel SCC, προτείνουμε μια τεχνική επιτάχυνσης του εντοπισμού μονίμων σφαλμάτων που εφαρμόζεται κατά τη διάρκεια λειτουργίας αρχιτεκτονικών με πολλούς πυρήνες, η οποία εκμεταλλεύεται το δίκτυο υψηλής ταχύτητας μεταφοράς μηνυμάτων που διατίθεται στα ολοκληρωμένα κυκλώματα αυτού του είδους. Δεύτερον, προτείνουμε μια λεπτομερή στατιστική μεθοδολογία με σκοπό την ακριβή πρόβλεψη σε επίπεδο συστήματος των ασφαλών ορίων λειτουργίας της τάσης των πυρήνων τύπου ARMv8 που βρίσκονται πάνω στη CPU X-Gene 2.The evolution in semiconductor manufacturing technology, computer architecture and design leads to increase in performance of modern microprocessors, which is also accompanied by increase in products’ vulnerability to errors. Designers apply different techniques throughout microprocessors life-time in order to ensure the high reliability requirements of the delivered products that are defined as their ability to avoid service failures that are more frequent and more severe than is acceptable. This thesis proposes novel methods to guarantee the high reliability and energy efficiency requirements of modern microprocessors that can be applied during the early design phase, the manufacturing phase or after the chips release to the market. The contributions of this thesis can be grouped in the two following categories according to the phase of the CPUs lifecycle that are applied at: • Early design phase: Statistical fault injection using microarchitectural structures modeled in performance simulators is a state-of-the-art method to accurately measure the reliability, but suffers from low simulation throughput. In this thesis, we firstly present a novel fully-automated versatile microarchitecture-level fault injection framework (called MaFIN) for accurate characterization of a wide range of hardware components of an x86-64 microarchitecture with respect to various fault models (transient, intermittent, permanent faults). Next, using the same tool and focusing on transient faults, we present several reliability and performance related studies that can assist design decision in the early design phases. Moreover, we propose two methodologies to accelerate the statistical fault injection campaigns. In the first one, we accelerate the fault injection campaigns after the actual injection of the faults in the simulated hardware structures. In the second, we further accelerate the microarchitecture level fault injection campaigns by proposing MeRLiN a fault pre-processing methodology that is based on the pruning of the initial fault list by grouping the faults in equivalent classes according to the instruction access patterns to hardware entries. • Manufacturing phase and release to the market: The contributions of this thesis in these phases of microprocessors life-cycle cover two important aspects. Firstly, using the 48-core Intel’s SCC architecture, we propose a technique to accelerate online error detection of permanent faults for many-core architectures by exploiting their high-speed message passing on-chip network. Secondly, we propose a comprehensive statistical analysis methodology to accurately predict at the system level the safe voltage operation margins of the ARMv8 cores of the X- Gene 2 chip when it operates in scaled voltage conditions

Improving System Reliability for Cyber-Physical Systems

Cyber-physical systems (CPS) are systems featuring a tight combination of, and coordination between, the system's computational and physical elements. Cyber-physical systems include systems ranging from critical infrastructure such as a power grid and transportation system to health and biomedical devices. System reliability, i.e., the ability of a system to perform its intended function under a given set of environmental and operational conditions for a given period of time, is a fundamental requirement of cyber-physical systems. An unreliable system often leads to disruption of service, financial cost and even loss of human life. An important and prevalent type of cyber-physical system meets the following criteria: processing large amounts of data; employing software as a system component; running online continuously; having operator-in-the-loop because of human judgment and an accountability requirement for safety critical systems. This thesis aims to improve system reliability for this type of cyber-physical system. To improve system reliability for this type of cyber-physical system, I present a system evaluation approach entitled automated online evaluation (AOE), which is a data-centric runtime monitoring and reliability evaluation approach that works in parallel with the cyber-physical system to conduct automated evaluation along the workflow of the system continuously using computational intelligence and self-tuning techniques and provide operator-in-the-loop feedback on reliability improvement. For example, abnormal input and output data at or between the multiple stages of the system can be detected and flagged through data quality analysis. As a result, alerts can be sent to the operator-in-the-loop. The operator can then take actions and make changes to the system based on the alerts in order to achieve minimal system downtime and increased system reliability. One technique used by the approach is data quality analysis using computational intelligence, which applies computational intelligence in evaluating data quality in an automated and efficient way in order to make sure the running system perform reliably as expected. Another technique used by the approach is self-tuning which automatically self-manages and self-configures the evaluation system to ensure that it adapts itself based on the changes in the system and feedback from the operator. To implement the proposed approach, I further present a system architecture called autonomic reliability improvement system (ARIS). This thesis investigates three hypotheses. First, I claim that the automated online evaluation empowered by data quality analysis using computational intelligence can effectively improve system reliability for cyber-physical systems in the domain of interest as indicated above. In order to prove this hypothesis, a prototype system needs to be developed and deployed in various cyber-physical systems while certain reliability metrics are required to measure the system reliability improvement quantitatively. Second, I claim that the self-tuning can effectively self-manage and self-configure the evaluation system based on the changes in the system and feedback from the operator-in-the-loop to improve system reliability. Third, I claim that the approach is efficient. It should not have a large impact on the overall system performance and introduce only minimal extra overhead to the cyberphysical system. Some performance metrics should be used to measure the efficiency and added overhead quantitatively. Additionally, in order to conduct efficient and cost-effective automated online evaluation for data-intensive CPS, which requires large volumes of data and devotes much of its processing time to I/O and data manipulation, this thesis presents COBRA, a cloud-based reliability assurance framework. COBRA provides automated multi-stage runtime reliability evaluation along the CPS workflow using data relocation services, a cloud data store, data quality analysis and process scheduling with self-tuning to achieve scalability, elasticity and efficiency. Finally, in order to provide a generic way to compare and benchmark system reliability for CPS and to extend the approach described above, this thesis presents FARE, a reliability benchmark framework that employs a CPS reliability model, a set of methods and metrics on evaluation environment selection, failure analysis, and reliability estimation. The main contributions of this thesis include validation of the above hypotheses and empirical studies of ARIS automated online evaluation system, COBRA cloud-based reliability assurance framework for data-intensive CPS, and FARE framework for benchmarking reliability of cyber-physical systems. This work has advanced the state of the art in the CPS reliability research, expanded the body of knowledge in this field, and provided some useful studies for further research

OPTIMAL MAINTENANCE PROGRAM OF A WASTE-TO-ENERGY PLANT : Case Study: wasteWOIMA®

Waste-to-energy (WtE) plant is a complex system which requires different maintenances to be reliable and available in its full functionalities. Maintenance has a crucial impact on the performance, availability and reliability of the WTE plant. The inadequacies of WTE plant lifetime maintenance may increase the production costs, and more it negatively affects the competitiveness, makes the downtime longer and the Mean-Time-Failure is bigger. The thesis focuses on the maintenance of WtE plant and it reviews the existing literature about Waste-to-Energy maintenance program and then find the best combination that better suits a Waste-to-Energy plant. This thesis has into two parts: the first part identifies the critical factors that enable high availability of Waste-to-Energy plant and the second addresses the identification of the right criteria for spare part selection. Both parts are aimed at enhancing the availability of Waste-to-Energy plant. A survey was sent to Waste-to-Energy professionals to collect data and compare that data to the findings in literature. DEMATEL method is chosen over the other methods for the pragmatic methodology used to construct and analyze the structural model involving causal relationship between multiple factors. It also integrates different expert knowledge that helps to investigate internal relationship and significance degrees of all the chosen factors. One advantage is that it can present a derived relationship through a cause-effect diagram. Critical factors through a visual structural model can be found, as well as the interdependent relationship amount factors are identified and evaluated while using DEMTEL. Key findings of the study revealed that human, economic, equipment and tools, management and environment factors have important impact of the effectiveness of the maintenance and the availability of the WtE plant, whatever the maintenance strategies from preventive to corrective maintenance through the condition maintenance. Quality, Lead time, Price and severity of spare part failure are keys criteria to consider while selecting spare part for WtE plant. The main limitation is that, the sample was a bit small since only few responds to the survey. Limitation of the thesis is related to the amount of the data collected. The findings cannot be generalized as it is affected by the limitation. The survey encounters probably the lack of cooperation from respondents as the study was not directly requested and done from their companies. It would be interesting to do further research of the topic by using data from different plants operated by the case company, to make the research more objective. This will help the case company knowing real issues their plants face. It could be interesting to do further research by focusing for instance on different locations and population because different climatic and environment factors may influence the failure rate of the plants items; dust, humidity, cultural factors