553 research outputs found
Integration of tools for the Design and Assessment of High-Performance, Highly Reliable Computing Systems (DAHPHRS), phase 1
Systems for Space Defense Initiative (SDI) space applications typically require both high performance and very high reliability. These requirements present the systems engineer evaluating such systems with the extremely difficult problem of conducting performance and reliability trade-offs over large design spaces. A controlled development process supported by appropriate automated tools must be used to assure that the system will meet design objectives. This report describes an investigation of methods, tools, and techniques necessary to support performance and reliability modeling for SDI systems development. Models of the JPL Hypercubes, the Encore Multimax, and the C.S. Draper Lab Fault-Tolerant Parallel Processor (FTPP) parallel-computing architectures using candidate SDI weapons-to-target assignment algorithms as workloads were built and analyzed as a means of identifying the necessary system models, how the models interact, and what experiments and analyses should be performed. As a result of this effort, weaknesses in the existing methods and tools were revealed and capabilities that will be required for both individual tools and an integrated toolset were identified
Driver Assistance System and Feedback for Hybrid Electric Vehicles Using Sensor Fusion
abstract: Transportation plays a significant role in every human's life. Numerous factors, such as cost of living, available amenities, work style, to name a few, play a vital role in determining the amount of travel time. Such factors, among others, led in part to an increased need for private transportation and, consequently, leading to an increase in the purchase of private cars. Also, road safety was impacted by numerous factors such as Driving Under Influence (DUI), driver’s distraction due to the increase in the use of mobile devices while driving. These factors led to an increasing need for an Advanced Driver Assistance System (ADAS) to help the driver stay aware of the environment and to improve road safety.
EcoCAR3 is one of the Advanced Vehicle Technology Competitions, sponsored by the United States Department of Energy (DoE) and managed by Argonne National Laboratory in partnership with the North American automotive industry. Students are challenged beyond the traditional classroom environment in these competitions, where they redesign a donated production vehicle to improve energy efficiency and to meet emission standards while maintaining the features that are attractive to the customer, including but not limited to performance, consumer acceptability, safety, and cost.
This thesis presents a driver assistance system interface that was implemented as part of EcoCAR3, including the adopted sensors, hardware and software components, system implementation, validation, and testing. The implemented driver assistance system uses a combination of range measurement sensors to determine the distance, relative location, & the relative velocity of obstacles and surrounding objects together with a computer vision algorithm for obstacle detection and classification. The sensor system and vision system were tested individually and then combined within the overall system. Also, a visual and audio feedback system was designed and implemented to provide timely feedback for the driver as an attempt to enhance situational awareness and improve safety.
Since the driver assistance system was designed and developed as part of a DoE sponsored competition, the system needed to satisfy competition requirements and rules. This work attempted to optimize the system in terms of performance, robustness, and cost while satisfying these constraints.Dissertation/ThesisMasters Thesis Electrical Engineering 201
Aggregated Channels Network for Real-Time Pedestrian Detection
Convolutional neural networks (CNNs) have demonstrated their superiority in
numerous computer vision tasks, yet their computational cost results
prohibitive for many real-time applications such as pedestrian detection which
is usually performed on low-consumption hardware. In order to alleviate this
drawback, most strategies focus on using a two-stage cascade approach.
Essentially, in the first stage a fast method generates a significant but
reduced amount of high quality proposals that later, in the second stage, are
evaluated by the CNN. In this work, we propose a novel detection pipeline that
further benefits from the two-stage cascade strategy. More concretely, the
enriched and subsequently compressed features used in the first stage are
reused as the CNN input. As a consequence, a simpler network architecture,
adapted for such small input sizes, allows to achieve real-time performance and
obtain results close to the state-of-the-art while running significantly faster
without the use of GPU. In particular, considering that the proposed pipeline
runs in frame rate, the achieved performance is highly competitive. We
furthermore demonstrate that the proposed pipeline on itself can serve as an
effective proposal generator
mAPN: Modeling, Analysis, and Exploration of Algorithmic and Parallelism Adaptivity
Using parallel embedded systems these days is increasing. They are getting
more complex due to integrating multiple functionalities in one application or
running numerous ones concurrently. This concerns a wide range of applications,
including streaming applications, commonly used in embedded systems. These
applications must implement adaptable and reliable algorithms to deliver the
required performance under varying circumstances (e.g., running applications on
the platform, input data, platform variety, etc.). Given the complexity of
streaming applications, target systems, and adaptivity requirements, designing
such systems with traditional programming models is daunting. This is why
model-based strategies with an appropriate Model of Computation (MoC) have long
been studied for embedded system design. This work provides algorithmic
adaptivity on top of parallelism for dynamic dataflow to express larger sets of
variants. We present a multi-Alternative Process Network (mAPN), a high-level
abstract representation in which several variants of the same application
coexist in the same graph expressing different implementations. We introduce
mAPN properties and its formalism to describe various local implementation
alternatives. Furthermore, mAPNs are enriched with metadata to Provide the
alternatives with quantitative annotations in terms of a specific metric. To
help the user analyze the rich space of variants, we propose a methodology to
extract feasible variants under user and hardware constraints. At the core of
the methodology is an algorithm for computing global metrics of an execution of
different alternatives from a compact mAPN specification. We validate our
approach by exploring several possible variants created for the Automatic
Subtitling Application (ASA) on two hardware platforms.Comment: 26 PAGES JOURNAL PAPE
Intrusion Resilience Systems for Modern Vehicles
Current vehicular Intrusion Detection and Prevention Systems either incur
high false-positive rates or do not capture zero-day vulnerabilities, leading
to safety-critical risks. In addition, prevention is limited to few primitive
options like dropping network packets or extreme options, e.g., ECU Bus-off
state. To fill this gap, we introduce the concept of vehicular Intrusion
Resilience Systems (IRS) that ensures the resilience of critical applications
despite assumed faults or zero-day attacks, as long as threat assumptions are
met. IRS enables running a vehicular application in a replicated way, i.e., as
a Replicated State Machine, over several ECUs, and then requiring the
replicated processes to reach a form of Byzantine agreement before changing
their local state. Our study rides the mutation of modern vehicular
environments, which are closing the gap between simple and resource-constrained
"real-time and embedded systems", and complex and powerful "information
technology" ones. It shows that current vehicle (e.g., Zonal) architectures and
networks are becoming plausible for such modular fault and intrusion tolerance
solutions,deemed too heavy in the past. Our evaluation on a simulated
Automotive Ethernet network running two state-of-the-art agreement protocols
(Damysus and Hotstuff) shows that the achieved latency and throughout are
feasible for many Automotive applications
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