Application of Bayesian Networks to Coverage Directed Test Generation for the Verification of Digital Hardware Designs

Functional verification is generally regarded as the most critical phase in the successful development of digital integrated circuits. The increasing complexity and size of chip designs make it more challenging to find bugs and meet test coverage goals in time for market demands. These challenges have led to more automated methods of simulation with constrained random test generation and coverage analysis. Recent goals in industry have focused on improving the process further by applying Coverage Directed Test Generation (CDG) to automate the feedback from coverage analysis to test input generation. Previous research has presented Bayesian networks as a way to achieve CDG. Bayesian networks provide a means of capturing behaviors of a design under verification and making predictions to help guide test input generation to reach coverage goals more quickly. Previous research has shown methods for defining a Bayesian network for a design domain and generating input parameters for dynamic simulation. This thesis demonstrates that existing commercial verification tools can be combined with a Bayesian inference engine as a feasible solution for creating a fully automated CDG environment. This solution is demonstrated using methods from previous research for applying Bayesian networks to verification. The CDG framework was implemented by combining the Questa verification platform with the Bayesian inference engine SMILE (Structural Modeling, Inference, and Learning Engine) in a single simulation environment. SystemVerilog testbenches and custom software were created to automatically find coverage holes, predict test input parameters, and dynamically change these parameters to complete verification with a fewer number of test cases. The CDG framework was demonstrated by performing verification on both a combinational logic design and a sequential logic design. The results show that Bayesian networks can be successfully used to improve the efficiency and quality of the verification process

Status and Prospects of Top-Quark Physics

The top quark is the heaviest elementary particle observed to date. Its large mass of about 173 GeV/c^2 makes the top quark act differently than other elementary fermions, as it decays before it hadronises, passing its spin information on to its decay products. In addition, the top quark plays an important role in higher-order loop corrections to standard model processes, which makes the top quark mass a crucial parameter for precision tests of the electroweak theory. The top quark is also a powerful probe for new phenomena beyond the standard model. During the time of discovery at the Tevatron in 1995 only a few properties of the top quark could be measured. In recent years, since the start of Tevatron Run II, the field of top-quark physics has changed and entered a precision era. This report summarises the latest measurements and studies of top-quark properties and gives prospects for future measurements at the Large Hadron Collider (LHC).Comment: 76 pages, 35 figures, submitted to Progress in Particle and Nuclear Physic

Bayesian methods for small molecule identification

Confident identification of small molecules remains a major challenge in untargeted metabolomics, natural product research and related fields. Liquid chromatography-tandem mass spectrometry is a predominant technique for the high-throughput analysis of small molecules and can detect thousands of different compounds in a biological sample. The automated interpretation of the resulting tandem mass spectra is highly non-trivial and many studies are limited to re-discovering known compounds by searching mass spectra in spectral reference libraries. But these libraries are vastly incomplete and a large portion of measured compounds remains unidentified. This constitutes a major bottleneck in the comprehensive, high-throughput analysis of metabolomics data. In this thesis, we present two computational methods that address different steps in the identification process of small molecules from tandem mass spectra. ZODIAC is a novel method for de novo that is, database-independent molecular formula annotation in complete datasets. It exploits similarities of compounds co-occurring in a sample to find the most likely molecular formula for each individual compound. ZODIAC improves on the currently best-performing method SIRIUS; on one dataset by 16.5 fold. We show that de novo molecular formula annotation is not just a theoretical advantage: We discover multiple novel molecular formulas absent from PubChem, one of the biggest structure databases. Furthermore, we introduce a novel scoring for CSI:FingerID, a state-of-the-art method for searching tandem mass spectra in a structure database. This scoring models dependencies between different molecular properties in a predicted molecular fingerprint via Bayesian networks. This problem has the unusual property, that the marginal probabilities differ for each predicted query fingerprint. Thus, we need to apply Bayesian networks in a novel, non-standard fashion. Modeling dependencies improves on the currently best scoring

Security, trust and cooperation in wireless sensor networks

Wireless sensor networks are a promising technology for many real-world applications such as critical infrastructure monitoring, scientific data gathering, smart buildings, etc.. However, given the typically unattended and potentially unsecured operation environment, there has been an increased number of security threats to sensor networks. In addition, sensor networks have very constrained resources, such as limited energy, memory, computational power, and communication bandwidth. These unique challenges call for new security mechanisms and algorithms. In this dissertation, we propose novel algorithms and models to address some important and challenging security problems in wireless sensor networks. The first part of the dissertation focuses on data trust in sensor networks. Since sensor networks are mainly deployed to monitor events and report data, the quality of received data must be ensured in order to make meaningful inferences from sensor data. We first study a false data injection attack in the distributed state estimation problem and propose a distributed Bayesian detection algorithm, which could maintain correct estimation results when less than one half of the sensors are compromised. To deal with the situation where more than one half of the sensors may be compromised, we introduce a special class of sensor nodes called \textit{trusted cores}. We then design a secure distributed trust aggregation algorithm that can utilize the trusted cores to improve network robustness. We show that as long as there exist some paths that can connect each regular node to one of these trusted cores, the network can not be subverted by attackers. The second part of the dissertation focuses on sensor network monitoring and anomaly detection. A sensor network may suffer from system failures due to loss of links and nodes, or malicious intrusions. Therefore, it is critical to continuously monitor the overall state of the network and locate performance anomalies. The network monitoring and probe selection problem is formulated as a budgeted coverage problem and a Markov decision process. Efficient probing strategies are designed to achieve a flexible tradeoff between inference accuracy and probing overhead. Based on the probing results on traffic measurements, anomaly detection can be conducted. To capture the highly dynamic network traffic, we develop a detection scheme based on multi-scale analysis of the traffic using wavelet transforms and hidden Markov models. The performance of the probing strategy and of the detection scheme are extensively evaluated in malicious scenarios using the NS-2 network simulator. Lastly, to better understand the role of trust in sensor networks, a game theoretic model is formulated to mathematically analyze the relation between trust and cooperation. Given the trust relations, the interactions among nodes are modeled as a network game on a trust-weighted graph. We then propose an efficient heuristic method that explores network heterogeneity to improve Nash equilibrium efficiency

Development of a cognitive robotic system for simple surgical tasks

The introduction of robotic surgery within the operating rooms has significantly improved the quality of many surgical procedures. Recently, the research on medical robotic systems focused on increasing the level of autonomy in order to give them the possibility to carry out simple surgical actions autonomously. This paper reports on the development of technologies for introducing automation within the surgical workflow. The results have been obtained during the ongoing FP7 European funded project Intelligent Surgical Robotics (I-SUR). The main goal of the project is to demonstrate that autonomous robotic surgical systems can carry out simple surgical tasks effectively and without major intervention by surgeons. To fulfil this goal, we have developed innovative solutions (both in terms of technologies and algorithms) for the following aspects: fabrication of soft organ models starting from CT images, surgical planning and execution of movement of robot arms in contact with a deformable environment, designing a surgical interface minimizing the cognitive load of the surgeon supervising the actions, intra-operative sensing and reasoning to detect normal transitions and unexpected events. All these technologies have been integrated using a component-based software architecture to control a novel robot designed to perform the surgical actions under study. In this work we provide an overview of our system and report on preliminary results of the automatic execution of needle insertion for the cryoablation of kidney tumours

Report of the Higgs Working Group of the Tevatron Run 2 SUSY/Higgs Workshop

This report presents the theoretical analysis relevant for Higgs physics at the upgraded Tevatron collider and documents the Higgs Working Group simulations to estimate the discovery reach in Run 2 for the Standard Model and MSSM Higgs bosons. Based on a simple detector simulation, we have determined the integrated luminosity necessary to discover the SM Higgs in the mass range 100-190 GeV. The first phase of the Run 2 Higgs search, with a total integrated luminosity of 2 fb-1 per detector, will provide a 95% CL exclusion sensitivity comparable to that expected at the end of the LEP2 run. With 10 fb-1 per detector, this exclusion will extend up to Higgs masses of 180 GeV, and a tantalizing 3 sigma effect will be visible if the Higgs mass lies below 125 GeV. With 25 fb-1 of integrated luminosity per detector, evidence for SM Higgs production at the 3 sigma level is possible for Higgs masses up to 180 GeV. However, the discovery reach is much less impressive for achieving a 5 sigma Higgs boson signal. Even with 30 fb-1 per detector, only Higgs bosons with masses up to about 130 GeV can be detected with 5 sigma significance. These results can also be re-interpreted in the MSSM framework and yield the required luminosities to discover at least one Higgs boson of the MSSM Higgs sector. With 5-10 fb-1 of data per detector, it will be possible to exclude at 95% CL nearly the entire MSSM Higgs parameter space, whereas 20-30 fb-1 is required to obtain a 5 sigma Higgs discovery over a significant portion of the parameter space. Moreover, in one interesting region of the MSSM parameter space (at large tan(beta)), the associated production of a Higgs boson and a b b-bar pair is significantly enhanced and provides potential for discovering a non-SM-like Higgs boson in Run 2.Comment: 185 pages, 124 figures, 55 table

The AFIT ENgineer, Volume 2, Issue 4

In this issue: AFMC Spark Tank Semi-finalist New AFIT Patents 2020 Graduate School Award Winners Airmen and Artificial Intelligence Nuclear Treaty Monitorin

The AFIT ENgineer, Volume 2, Issue 4

In this issue: AFMC Spark Tank Semi-finalist New AFIT Patents 2020 Graduate School Award Winners Airmen and Artificial Intelligence Nuclear Treaty Monitorin