Efficient and Reliable Simulation, Memory Protection, and Driver Generation in Embedded Network Systems

Embedded systems are widely used, from consumer electronics, to industrial equipment, to spacecraft. With embedded systems becoming more complex, new challenges are presented to application developers. In this dissertation, we focus on three of the most important: (i) Network simulation tools are widely used for sensor network testing and evaluation. Simulation performance affects the efficiency of the application developers who use these tools. The performance of a single host system represents a performance bottleneck for large-scale network simulation. A distributed simulator offering higher performance is needed to support fast, large-scale network simulation. (ii) Single event upsets (SEUs), which occur when a high-energy ionizing particle passes through an integrated circuit, can change the value of a single bit, causing damage and potentially catastrophic system failures. Modern SEU detection and correction approaches typically introduce additional hardware, increasing execution overhead and cost. Given the nature of resource-lean embedded systems, a software-based protection approach must be lightweight. (iii) Writing device drivers for serial-based peripherals is a repetitive task, given that microprocessors operate most such devices in the same way, issuing commands and parsing corresponding responses. A serial device driver generation tool must be capable of accommodating various microprocessors and devices with varying characteristics (e.g., UART settings, device response times, etc.), while producing drivers that offer performance at least as good as functionally equivalent, handwritten drivers. In this dissertation, we focus on the design and implementation of approaches to distributed sensor network simulation, embedded memory protection, and automated serial device driver generation. The first challenge is to effectively emulate sensor network systems with high fidelity using a distributed simulation system. This is achieved by developing a distributed version of SnapSim, D-SnapSim, which runs on a cluster. D-SnapSim relies on multiple physical systems to achieve enhanced speed and scalability, while providing flexibility to execute on clusters of varying size and computational power. The performance of D-SnapSim is evaluated as a function of network size, bitrate, and cluster configuration relative to SnapSim. The second challenge is to protect embedded system memory from SEUs with a software-only approach. Traditional SEU prevention and correction strategies rely on hardware extensions to the target system. We present a software-only approach that detects and corrects SEUs in RAM. This is achieved by extending the AVR-GCC compiler to protect the system stack from SEUs through duplication, validation, and recovery. Four applications are used to verify our approach, and the time and space overhead characteristics are evaluated. The third challenge is to automatically generate serial device drivers, eliminating the repetitive, error-prone work involved in serial device driver development. We present DriverGen, a configuration-based tool developed to provide automated serial device driver generation. Three applications are used to evaluate the performance of the generated drivers, both in terms of space and execution time. A user study is conducted to evaluate the usability of our tool in comparison with driver development in C

Association of clinical outcomes and the predictive value of T lymphocyte subsets within colorectal cancer patients

IntroductionTumor immunity is a hot topic in tumor research today, and human immunity is closely related to tumor progression. T lymphocyte is an important component of human immune system, and the changes in their subsets may influence the progression of colorectal cancer (CRC) to some extent. This clinical study systematically describes and analyzes the association of CD4+ and CD8+ T-lymphocyte content and CD4+/CD8+ T-lymphocyte ratio with CRC differentiation, clinical pathological stage, Ki67 expression, T-stage, N-stage, carcinoembryonic antigen (CEA) content, nerve and vascular infiltration, and other clinical features, as well as preoperative and postoperative trends. Furthermore, a predictive model is constructed to evaluate the predictive value of T-lymphocyte subsets for CRC clinical features.MethodsStrict inclusion and exclusion criterion were formulated to screen patients, preoperative and postoperative flow cytometry and postoperative pathology reports from standard laparoscopic surgery were assessed. PASS and SPSS software, R packages were invoked to calculate and analyze.ResultsWe found that a high CD4+ T-lymphocyte content in peripheral blood and a high CD4+/CD8+ ratio were associated with better tumor differentiation, an earlier clinical pathological stage, lower Ki67 expression, shallower tumor infiltration, a smaller number of lymph node metastases, a lower CEA content, and a lower likelihood of nerve or vascular infiltration (P < 0.05). However, a high CD8+ T-lymphocyte content indicated an unpromising clinical profile. After effective surgical treatment, the CD4+ T-lymphocyte content and CD4+/CD8+ ratio increased significantly (P < 0.05), while the CD8+ T-lymphocyte content decreased significantly (P < 0.05). Further, we comprehensively compared the merits of CD4+ T-lymphocyte content, CD8+ T-lymphocyte content, and CD4+/CD8+ ratio in predicting the clinical features of CRC. We then combined the CD4+ and CD8+ T-lymphocyte content to build models and predict major clinical characteristics. We compared these models with the CD4+/CD8+ ratio to explore their advantages and disadvantages in predicting the clinical features of CRC.DiscussionOur results provide a theoretical basis for the future screening of effective markers in reflecting and predicting the progression of CRC. Changes in T lymphocyte subsets affect the progression of CRC to a certain extent, while their changes also reflect variations in the human immune system

Methods and Tools for Monitoring Driver's Behavior

In-vehicle sensing technology has gained tremendous attention due to its ability to support major technological developments, such as connected vehicles and self-driving cars. In-vehicle sensing data are invaluable and important data sources for traffic management systems. In this paper we propose an innovative architecture of unobtrusive in-vehicle sensors and present methods and tools that are used to measure the behavior of drivers. The proposed architecture including methods and tools are used in our NIH project to monitor and identify older drivers with early dementi

Supporting the Specification and Runtime Validation of Asynchronous Calling Patterns in Reactive Systems

Wireless sensor networks (“sensornets”) are highly distributed and concurrent, with program actions bound to external stimuli. They exemplify a system class known as reactive systems, which comprise execution units that have “hidden” layers of control flow. A key obstacle in enabling reactive system developers to rigorously validate their implementations has been the absence of precise software component specifications and tools to assist in leveraging those specifications at runtime. We address this obstacle in three ways: (i) We describe a specification approach tailored for reactive environments and demonstrate its application in the context of sensornets. (ii) We describe the design and implementation of extensions to the popular nesC tool-chain that enable the expression of these specifications and automate the generation of runtime monitors that signal violations, if any. (iii) Finally, we apply the specification approach to a significant collection of the most commonly used software components in the TinyOS distribution and analyze the overhead involved in monitoring their correctness

Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data

The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost algorithm directly to imbalanced data since it is designed mainly for processing misclassified samples rather than samples of minority classes. To better process imbalanced data, this paper introduces the indicator Area Under Curve (AUC) which can reflect the comprehensive performance of the model, and proposes an improved AdaBoost algorithm based on AUC (AdaBoost-A) which improves the error calculation performance of the AdaBoost algorithm by comprehensively considering the effects of misclassification probability and AUC. To prevent redundant or useless weak classifiers the traditional AdaBoost algorithm generated from consuming too much system resources, this paper proposes an ensemble algorithm, PSOPD-AdaBoost-A, which can re-initialize parameters to avoid falling into local optimum, and optimize the coefficients of AdaBoost weak classifiers. Experiment results show that the proposed algorithm is effective for processing imbalanced data, especially the data with relatively high imbalances

Feasibility of Tunnel TEM Advanced Prediction: A 3D Forward Modeling Study

The transient electromagnetic (TEM) method has long been applied in tunnel advanced prediction. However, it remains questionable to what extent a geologic anomaly body will influence the induced electromagnetic response in front of the heading face. The dilemma is partly because observed TEM data are frequently interpreted by empirical formulas or proportional relationships, and a quantitative measurement has not been established. In this paper, we strive to understand the TEM characteristics from a 3D finite-element time-domain (FETD) modeling aspect. The modeling algorithm is based on unstructured space meshing and unconditional stable time discretization, which ensures its accuracy and stability. The modeling algorithm is verified by a half-space model, in which the misfit of late-time channels that we are concerned with is generally below 1%. The algorithm has also been utilized to carry out the TEM response of tunnel models with different types of TEM devices. Through model studies, we find that both the traditional central-loop device and the recently developed weak-coupling opposing-coil device are feasible in tunnel advanced detection. Nevertheless, the latter type of device better distinguishes low-resistivity anomalies at 30 m ahead of the heading face with a relative difference (between models with and without the anomaly) of more than 1000% at certain time channels, compared with only a 10% difference of the central-loop device. Also, we conclude that the vertical electromagnetic field component should be recorded and interpreted together with the horizontal field to provide more convincing results