REAL-TIME ERROR DETECTION AND CORRECTION FOR ROBUST OPERATION OF AUTONOMOUS SYSTEMS USING ENCODED STATE CHECKS

The objective of the proposed research is to develop methodologies, support algorithms and software-hardware infrastructure for detection, diagnosis, and correction of failures for actuators, sensors and control software in linear and nonlinear state variable systems with the help of multiple checks employed in the system. This objective is motivated by the proliferation of autonomous sense-and-control real-time systems, such as intelligent robots and self-driven cars which must maintain a minimum level of performance in the presence of electro-mechanical degradation of system-level components in the field as well as external attacks in the form of transient errors. A key focus is on rapid recovery from the effects of such anomalies and impairments with minimal impact on system performance while maintaining low implementation overhead as opposed to traditional schemes for recovery that rely on duplication or triplication. On-line detection, diagnosis and correction techniques are investigated and rely on analysis of system under test response signatures to real-time stimulus. For on-line error detection and diagnosis, linear and nonlinear state space encodings of the system under test are used and specific properties of the codes, as well as machine learning model based approaches were used are analyzed in real-time. Recovery is initiated by copying check model values to correct error for sensor and control software malfunction, and by redesigning the controller parameter on-the-fly for actuators to restore system performance. Future challenges that need to be addressed include viability studies of the proposed techniques on mobile autonomous system in distributed setting as well as application to systems with soft as well as hard real-time performance constraints.Ph.D

Concurrent error detection in 2-D separable linear transform

As process technology continues to scale to smaller geometries and reduces the supply voltage, reliability of the resulting semiconductor becomes a greater concern. The effect of deep submicron noise, soft errors, variation, and aging degradation pose challenges on the functional correctness of VLSI systems and places roadblocks on reductions in scale. On the other side, as computing moves toward mobile, the energy efficiency of digital systems becomes one of the most important design metrics. However, reliability and energy efficiency are contradicting design requirements. Adding a voltage guard band is the most common method to mitigate the reliability impacts in such instances. Low power design technique like voltage over-scaling (VOS) even reduces the power by scaling the supply voltage just before data-dependant timing errors start to appear. Concurrent error detection is the solution to tackle reliability and energy-efficiency in a unified manner. Fault tolerance can be deployed at different design hierarchies. Given its low overhead, algorithm level error detection is an attractive approach. In this work, a generic weighted checksum code based error detection algorithm targeted generic 2-D separable linear transform is proposed. This technique encodes the input array at the 2-D linear trans- formation level, and algorithms are designed to operate on encoded data and produce encoded output data. The proposed error detection technique is a system-level method and therefore can be used in existing hardware or software 2-D linear transformation architectures with low overhead. The mathematic proof of the algorithm is provided within the scope of this dissertation. The checksum weighting vector for several common transforms are derived as examples, error detection cost and algorithm effectiveness are analyzed. In traditional fault tolerance study, the error is often evaluated at the boolean level. Many DSP applications, like 2-D linear transformation used in the multimedia compression system, do not require exactly correct results, but rather that the quality of the output is within the acceptable range. A generic quality aware error detection in the 2-D separable linear transform is proposed by extending the above property and defining the errors at the functional level. As an example, the quality-aware error detection technique is deployed on a low-power wavelet lifting transform architecture in JPEG2000. A low-cost Signal to Noise Ratio (SNR) aware detection logic based on proposed scheme is integrated into the discrete wavelet lifting transform architecture. This detection logic checks whether the image quality degradation caused by voltage over-scaling induced timing errors is acceptable and determines the optimal voltage set point in operating conditions at run time. This novel quality-based error detection approach is significantly different from traditional error detection schemes which look for exact data equivalence. A simulation result for one design shows that the supply voltage can be scaled down to 75% of the nominal voltage in typical process corner without significant image quality degradation, which translates to 9.15mW power consumption (44% power saving).Electrical and Computer Engineerin

ULTRA-FAST AND MEMORY-EFFICIENT LOOKUPS FOR CLOUD, NETWORKED SYSTEMS, AND MASSIVE DATA MANAGEMENT

Systems that process big data (e.g., high-traffic networks and large-scale storage) prefer data structures and algorithms with small memory and fast processing speed. Efficient and fast algorithms play an essential role in system design, despite the improvement of hardware. This dissertation is organized around a novel algorithm called Othello Hashing. Othello Hashing supports ultra-fast and memory-efficient key-value lookup, and it fits the requirements of the core algorithms of many large-scale systems and big data applications. Using Othello hashing, combined with domain expertise in cloud, computer networks, big data, and bioinformatics, I developed the following applications that resolve several major challenges in the area. Concise: Forwarding Information Base. A Forwarding Information Base is a data structure used by the data plane of a forwarding device to determine the proper forwarding actions for packets. The polymorphic property of Othello Hashing the separation of its query and control functionalities, which is a perfect match to the programmable networks such as Software Defined Networks. Using Othello Hashing, we built a fast and scalable FIB named \textit{Concise}. Extensive evaluation results on three different platforms show that Concise outperforms other FIB designs. SDLB: Cloud Load Balancer. In a cloud network, the layer-4 load balancer servers is a device that acts as a reverse proxy and distributes network or application traffic across a number of servers. We built a software load balancer with Othello Hashing techniques named SDLB. SDLB is able to accomplish two functionalities of the SDLB using one Othello query: to find the designated server for packets of ongoing sessions and to distribute new or session-free packets. MetaOthello: Taxonomic Classification of Metagenomic Sequences. Metagenomic read classification is a critical step in the identification and quantification of microbial species sampled by high-throughput sequencing. Due to the growing popularity of metagenomic data in both basic science and clinical applications, as well as the increasing volume of data being generated, efficient and accurate algorithms are in high demand. We built a system to support efficient classification of taxonomic sequences using its k-mer signatures. SeqOthello: RNA-seq Sequence Search Engine. Advances in the study of functional genomics produced a vast supply of RNA-seq datasets. However, how to quickly query and extract information from sequencing resources remains a challenging problem and has been the bottleneck for the broader dissemination of sequencing efforts. The challenge resides in both the sheer volume of the data and its nature of unstructured representation. Using the Othello Hashing techniques, we built the SeqOthello sequence search engine. SeqOthello is a reference-free, alignment-free, and parameter-free sequence search system that supports arbitrary sequence query against large collections of RNA-seq experiments, which enables large-scale integrative studies using sequence-level data

High Performance Data Acquisition and Analysis Routines for the Nab Experiment

Probes of the Standard Model of particle physics are pushing further and further into the so-called “precision frontier”. In order to reach the precision goals of these experiments, a combination of elegant experimental design and robust data acquisition and analysis is required. Two experiments that embody this philosophy are the Nab and Calcium-45 experiments. These experiments are probing the understanding of the weak interaction by examining the beta decay of the free neutron and Calcium-45 respectively. They both aim to measure correlation parameters in the neutron beta decay alphabet, a and b. The parameter a, the electron-neutrino correlation coefficient, is sensitive to λ, the ratio of the axial-vector and vector coupling strengths in the decay of the free neutron. This parameter λ, in tandem with a precision measurement of the neutron lifetime τ , provides a measurement of the matrix element Vud from the CKM quark mixing matrix. The CKM matrix, as a rotation matrix, must be unitary. Probes of Vud and Vus in recent years have revealed tension in this unitarity at the 2.2σ level. The measurement of a via decay of free cold neutrons serves as an additional method of extraction for Vud that is sensitive to a different set of systematic effects and as such is an excellent probe into the source of the deviation from unitarity. The parameter b, the Fierz interference term, appears as a distortion in the mea- sured electron energy spectra from beta decay. This parameter, if non-zero, would indicate the existence of Scalar and/or Tensor couplings in the Weak interaction which according to the Standard Model is purely Vector minus Axial-Vector. This is therefore a search for physics beyond the standard model, BSM, physics search. The Nab and Calcium-45 experiments probe these parameters with a combination of elegant experimental design and brute force collection and analysis of large amounts of digitized detector data. These datasets, particularly in the case of the Nab experiment, are anticipated to span multiple petabytes of data and will require high performance online analysis and precision offline analysis routines in order to reach the experimental goals. Of particular note are the requirements for better than 3 keV energy resolution and an understanding of the uncertainty in the mean timing bias for the detected particles within 300 ps. Presented in this dissertation is an overview of the experiments and their design, a description of the data acquisition systems and analysis routines that have been developed to support the experiments, and a discussion of the data analysis performed for the Calcium-45 experiment

Techniques d'abstraction pour l'analyse et la mitigation des effets dus à la radiation

The main objective of this thesis is to develop techniques that can beused to analyze and mitigate the effects of radiation-induced soft errors in industrialscale integrated circuits. To achieve this goal, several methods have been developedbased on analyzing the design at higher levels of abstraction. These techniquesaddress both sequential and combinatorial SER.Fault-injection simulations remain the primary method for analyzing the effectsof soft errors. In this thesis, techniques which significantly speed-up fault-injectionsimulations are presented. Soft errors in flip-flops are typically mitigated by selectivelyreplacing the most critical flip-flops with hardened implementations. Selectingan optimal set to harden is a compute intensive problem and the second contributionconsists of a clustering technique which significantly reduces the number offault-injections required to perform selective mitigation.In terrestrial applications, the effect of soft errors in combinatorial logic hasbeen fairly small. It is known that this effect is growing, yet there exist few techniqueswhich can quickly estimate the extent of combinatorial SER for an entireintegrated circuit. The third contribution of this thesis is a hierarchical approachto combinatorial soft error analysis.Systems-on-chip are often developed by re-using design-blocks that come frommultiple sources. In this context, there is a need to develop and exchange reliabilitymodels. The final contribution of this thesis consists of an application specificmodeling language called RIIF (Reliability Information Interchange Format). Thislanguage is able to model how faults at the gate-level propagate up to the block andchip-level. Work is underway to standardize the RIIF modeling language as well asto extend it beyond modeling of radiation-induced failures.In addition to the main axis of research, some tangential topics were studied incollaboration with other teams. One of these consisted in the development of a novelapproach for protecting ternary content addressable memories (TCAMs), a specialtype of memory important in networking applications. The second supplementalproject resulted in an algorithm for quickly generating approximate redundant logicwhich can protect combinatorial networks against permanent faults. Finally anapproach for reducing the detection time for errors in the configuration RAM forField-Programmable Gate-Arrays (FPGAs) was outlined.Les effets dus à la radiation peuvent provoquer des pannes dans des circuits intégrés. Lorsqu'une particule subatomique, fait se déposer une charge dans les régions sensibles d'un transistor cela provoque une impulsion de courant. Cette impulsion peut alors engendrer l'inversion d'un bit ou se propager dans un réseau de logique combinatoire avant d'être échantillonnée par une bascule en aval.Selon l'état du circuit au moment de la frappe de la particule et selon l'application, cela provoquera une panne observable ou non. Parmi les événements induits par la radiation, seule une petite portion génère des pannes. Il est donc essentiel de déterminer cette fraction afin de prédire la fiabilité du système. En effet, les raisons pour lesquelles une perturbation pourrait être masquée sont multiples, et il est de plus parfois difficile de préciser ce qui constitue une erreur. A cela s'ajoute le fait que les circuits intégrés comportent des milliards de transistors. Comme souvent dans le contexte de la conception assisté par ordinateur, les approches hiérarchiques et les techniques d'abstraction permettent de trouver des solutions.Cette thèse propose donc plusieurs nouvelles techniques pour analyser les effets dus à la radiation. La première technique permet d'accélérer des simulations d'injections de fautes en détectant lorsqu'une faute a été supprimée du système, permettant ainsi d'arrêter la simulation. La deuxième technique permet de regrouper en ensembles les éléments d'un circuit ayant une fonction similaire. Ensuite, une analyse au niveau des ensemble peut être faite, identifiant ainsi ceux qui sont les plus critiques et qui nécessitent donc d'être durcis. Le temps de calcul est ainsi grandement réduit.La troisième technique permet d'analyser les effets des fautes transitoires dans les circuits combinatoires. Il est en effet possible de calculer à l'avance la sensibilité à des fautes transitoires de cellules ainsi que les effets de masquage dans des blocs fréquemment utilisés. Ces modèles peuvent alors être combinés afin d'analyser la sensibilité de grands circuits. La contribution finale de cette thèse consiste en la définition d'un nouveau langage de modélisation appelé RIIF (Reliability Information Ineterchange Format). Ce langage permet de décrire le taux des fautes dans des composants simples en fonction de leur environnement de fonctionnement. Ces composants simples peuvent ensuite être combinés permettant ainsi de modéliser la propagation de leur fautes vers des pannes au niveau système. En outre, l'utilisation d'un langage standard facilite l'échange de données de fiabilité entre les partenaires industriels.Au-delà des contributions principales, cette thèse aborde aussi des techniques permettant de protéger des mémoires associatives ternaires (TCAMs). Les approches classiques de protection (codes correcteurs) ne s'appliquent pas directement. Une des nouvelles techniques proposées consiste à utiliser une structure de données qui peut détecter, d'une manière statistique, quand le résultat n'est pas correct. La probabilité de détection peut être contrôlée par le nombre de bits alloués à cette structure. Une autre technique consiste à utiliser un détecteur de courant embarqué (BICS) afin de diriger un processus de fond directement vers le région touchée par une erreur. La contribution finale consiste en un algorithme qui permet de synthétiser de la logique combinatoire afin de protéger des circuits combinatoires contre les fautes transitoires.Dans leur ensemble, ces techniques facilitent l'analyse des erreurs provoquées par les effets dus à la radiation dans les circuits intégrés, en particulier pour les très grands circuits composés de blocs provenant de divers fournisseurs. Des techniques pour mieux sélectionner les bascules/flip-flops à durcir et des approches pour protéger des TCAMs ont étés étudiées

A COMPUTATIONAL FRAMEWORK FOR NEONATAL BRAIN MRI STRUCTURE SEGMENTATION AND CLASSIFICATION

Deep Learning is increasingly being used in both supervised and unsupervised learning to derive complex patterns from data. However, the successful implementation of deep learning using medical imaging requires careful consideration for the quality and availability of data. Infants diagnosed with CHD are at a higher risk for neurodevelopmental impairment. Many of these deficits may be attenuated by early detection and intervention. However, we currently lack effective diagnostic tools for the reliable detection of these disorders at the neonatal period. We believe that the structural correlates of the cognitive deficits associated with developmental abnormalities can be measured within the first few months of life. Based on this assumption, we hypothesize that we can use an atlas registration based structural segmentation pipeline to sufficiently reduce the search space of neonatal structural brain MRI to viably implement convolutional neural networks for dysplasia classification. Secondly, we hypothesize that convolutional neural networks can successfully identify morphological biomarkers capable of detecting structurally abnormal brain substructures. In this study, we develop a computational framework for the automated classification of dysplastic substructures from neonatal MRI. We validate our implementation on a dataset of neonates born with CHD, as this is a vulnerable population for structural dysmaturation. We chose the cerebellum as the initial test substructure because of its relatively simple structure and known vulnerability to structural dysplasia in infants born with CHD. We then apply the same method to the hippocampus, a more challenging substructure due to its complex morphological properties. We attempt to overcome the limited availability of clinical data in neonatal populations by first extracting each brain substructure of interest and individually registering them into a standard space. This greatly reduces the search space required to learn the subtle abnormalities associated with a given pathology, making it feasible to implement a 3-D CNN as the classification algorithm. We achieved excellent classification accuracy in detecting dysplastic cerebelli, and demonstrate a viable computational framework for search space reduction using limited clinical datasets. All methods developed in this work are designed to be extensible, reproducible, and generalizable diagnostic tools for future neuroimaging problems

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks

Protecting Systems From Exploits Using Language-Theoretic Security

Any computer program processing input from the user or network must validate the input. Input-handling vulnerabilities occur in programs when the software component responsible for filtering malicious input---the parser---does not perform validation adequately. Consequently, parsers are among the most targeted components since they defend the rest of the program from malicious input. This thesis adopts the Language-Theoretic Security (LangSec) principle to understand what tools and research are needed to prevent exploits that target parsers. LangSec proposes specifying the syntactic structure of the input format as a formal grammar. We then build a recognizer for this formal grammar to validate any input before the rest of the program acts on it. To ensure that these recognizers represent the data format, programmers often rely on parser generators or parser combinators tools to build the parsers. This thesis propels several sub-fields in LangSec by proposing new techniques to find bugs in implementations, novel categorizations of vulnerabilities, and new parsing algorithms and tools to handle practical data formats. To this end, this thesis comprises five parts that tackle various tenets of LangSec. First, I categorize various input-handling vulnerabilities and exploits using two frameworks. First, I use the mismorphisms framework to reason about vulnerabilities. This framework helps us reason about the root causes leading to various vulnerabilities. Next, we built a categorization framework using various LangSec anti-patterns, such as parser differentials and insufficient input validation. Finally, we built a catalog of more than 30 popular vulnerabilities to demonstrate the categorization frameworks. Second, I built parsers for various Internet of Things and power grid network protocols and the iccMAX file format using parser combinator libraries. The parsers I built for power grid protocols were deployed and tested on power grid substation networks as an intrusion detection tool. The parser I built for the iccMAX file format led to several corrections and modifications to the iccMAX specifications and reference implementations. Third, I present SPARTA, a novel tool I built that generates Rust code that type checks Portable Data Format (PDF) files. The type checker I helped build strictly enforces the constraints in the PDF specification to find deviations. Our checker has contributed to at least four significant clarifications and corrections to the PDF 2.0 specification and various open-source PDF tools. In addition to our checker, we also built a practical tool, PDFFixer, to dynamically patch type errors in PDF files. Fourth, I present ParseSmith, a tool to build verified parsers for real-world data formats. Most parsing tools available for data formats are insufficient to handle practical formats or have not been verified for their correctness. I built a verified parsing tool in Dafny that builds on ideas from attribute grammars, data-dependent grammars, and parsing expression grammars to tackle various constructs commonly seen in network formats. I prove that our parsers run in linear time and always terminate for well-formed grammars. Finally, I provide the earliest systematic comparison of various data description languages (DDLs) and their parser generation tools. DDLs are used to describe and parse commonly used data formats, such as image formats. Next, I conducted an expert elicitation qualitative study to derive various metrics that I use to compare the DDLs. I also systematically compare these DDLs based on sample data descriptions available with the DDLs---checking for correctness and resilience

Opinions and Outlooks on Morphological Computation

Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon system – and plants, but it has also been observed at the cellular and even at the molecular level – as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others

Phlebot: The Robotic Phlebotomist

Phlebotomy is a routine task, performed over a billion times annually in the United States alone, that is essential for proper diagnosis and treatment. We designed and constructed Phlebot, a robotic assistive device that uses near- infrared imaging and force-feedback to guide a needle into a forearm vein for blood sample collection or intravenous catheterization. Through initial validation on phantoms, we show that it is feasible to automate phlebotomy reliably. We envision the device to be a first major step towards more affordable point-of-care testing and diagnostic healthcare systems. In the long term, we expect that Phlebot will expedite healthcare delivery and drastically reduce needle stick injuries, instances of hemolysis, and infections caused by blood-borne pathogens