2,048 research outputs found
Spreadsheet-based Configuration of Families of Real-Time Specifications
Model checking real-time systems is complex, and requires a careful trade-off
between including enough detail to be useful and not too much detail to avoid
state explosion. This work exploits variability of the formal model being
analysed and the requirements being checked, to facilitate the model-checking
of variations of real-time specifications. This work results from the
collaboration between academics and Alstom, a railway company with a concrete
use-case, in the context of the VALU3S European project. The configuration of
the variability of the formal specifications is described in MS Excel
spreadsheets with a particular structure, making it easy to use also by
developers. These spreadsheets are processed automatically by our prototype
tool that generates instances and runs the model checker. We propose the
extension of our previous work by exploiting analysis over valid combination of
features, while preserving the simplicity of a spreadsheet-based interface with
the model checker.Comment: In Proceedings TiCSA 2023, arXiv:2310.1872
Near-Optimal Motion Planning Algorithms Via A Topological and Geometric Perspective
Motion planning is a fundamental problem in robotics, which involves finding a path for an autonomous system, such as a robot, from a given source to a destination while avoiding collisions with obstacles. The properties of the planning space heavily influence the performance of existing motion planning algorithms, which can pose significant challenges in handling complex regions, such as narrow passages or cluttered environments, even for simple objects. The problem of motion planning becomes deterministic if the details of the space are fully known, which is often difficult to achieve in constantly changing environments. Sampling-based algorithms are widely used among motion planning paradigms because they capture the topology of space into a roadmap. These planners have successfully solved high-dimensional planning problems with a probabilistic-complete guarantee, i.e., it guarantees to find a path if one exists as the number of vertices goes to infinity. Despite their progress, these methods have failed to optimize the sub-region information of the environment for reuse by other planners. This results in re-planning overhead at each execution, affecting the performance complexity for computation time and memory space usage.
In this research, we address the problem by focusing on the theoretical foundation of the algorithmic approach that leverages the strengths of sampling-based motion planners and the Topological Data Analysis methods to extract intricate properties of the environment. The work contributes a novel algorithm to overcome the performance shortcomings of existing motion planners by capturing and preserving the essential topological and geometric features to generate a homotopy-equivalent roadmap of the environment. This roadmap provides a mathematically rich representation of the environment, including an approximate measure of the collision-free space. In addition, the roadmap graph vertices sampled close to the obstacles exhibit advantages when navigating through narrow passages and cluttered environments, making obstacle-avoidance path planning significantly more efficient.
The application of the proposed algorithms solves motion planning problems, such as sub-optimal planning, diverse path planning, and fault-tolerant planning, by demonstrating the improvement in computational performance and path quality. Furthermore, we explore the potential of these algorithms in solving computational biology problems, particularly in finding optimal binding positions for protein-ligand or protein-protein interactions.
Overall, our work contributes a new way to classify routes in higher dimensional space and shows promising results for high-dimensional robots, such as articulated linkage robots. The findings of this research provide a comprehensive solution to motion planning problems and offer a new perspective on solving computational biology problems
Finding The Lazy Programmer's Bugs
Traditionally developers and testers created huge numbers of explicit tests, enumerating interesting cases, perhaps
biased by what they believe to be the current boundary conditions of the function being tested. Or at
least, they were supposed to.
A major step forward was the development of property testing. Property testing requires the user to write a few
functional properties that are used to generate tests, and requires an external library or tool to create test data
for the tests. As such many thousands of tests can be created for a single property. For the purely functional
programming language Haskell there are several such libraries; for example QuickCheck [CH00], SmallCheck
and Lazy SmallCheck [RNL08].
Unfortunately, property testing still requires the user to write explicit tests. Fortunately, we note there are
already many implicit tests present in programs. Developers may throw assertion errors, or the compiler may
silently insert runtime exceptions for incomplete pattern matches.
We attempt to automate the testing process using these implicit tests. Our contributions are in four main
areas: (1) We have developed algorithms to automatically infer appropriate constructors and functions needed
to generate test data without requiring additional programmer work or annotations. (2) To combine the
constructors and functions into test expressions we take advantage of Haskell's lazy evaluation semantics by
applying the techniques of needed narrowing and lazy instantiation to guide generation. (3) We keep the type
of test data at its most general, in order to prevent committing too early to monomorphic types that cause
needless wasted tests. (4) We have developed novel ways of creating Haskell case expressions to inspect elements
inside returned data structures, in order to discover exceptions that may be hidden by laziness, and to make
our test data generation algorithm more expressive.
In order to validate our claims, we have implemented these techniques in Irulan, a fully automatic tool for
generating systematic black-box unit tests for Haskell library code. We have designed Irulan to generate high
coverage test suites and detect common programming errors in the process
Performance and area optimization for reliable FPGA-based shifter design
This thesis addresses the problem of implementing reliable FPGA-based shifters. An FPGA-based design requires optimization between performance and resource utilization, and an effective verification methodology to validate design behavior. The FPGA-based implementation of a large shifter design is restricted by an I/O resource bottleneck. The verification of the design behavior presents a further challenge due to the \u27black-box\u27 nature of FPGAs. To tackle these design challenges, we propose a novel approach to implement FPGA-based shifters. The proposed design alleviates the I/O bottleneck while significantly reducing the logic resources required. This is achieved with a minimal increase in the design delay. The design is seamlessly scalable to a multi-FPGA chip setup to improve performance or to implement larger shifters. It is configured using assertion checkers for efficient design verification. The assertion-based design is further optimized to alleviate the performance degradation caused by the assertion checkers
Forensic Video Analytic Software
Law enforcement officials heavily depend on Forensic Video Analytic (FVA)
Software in their evidence extraction process. However present-day FVA software
are complex, time consuming, equipment dependent and expensive. Developing
countries struggle to gain access to this gateway to a secure haven. The term
forensic pertains the application of scientific methods to the investigation of
crime through post-processing, whereas surveillance is the close monitoring of
real-time feeds.
The principle objective of this Final Year Project was to develop an
efficient and effective FVA Software, addressing the shortcomings through a
stringent and systematic review of scholarly research papers, online databases
and legal documentation. The scope spans multiple object detection, multiple
object tracking, anomaly detection, activity recognition, tampering detection,
general and specific image enhancement and video synopsis.
Methods employed include many machine learning techniques, GPU acceleration
and efficient, integrated architecture development both for real-time and
postprocessing. For this CNN, GMM, multithreading and OpenCV C++ coding were
used. The implications of the proposed methodology would rapidly speed up the
FVA process especially through the novel video synopsis research arena. This
project has resulted in three research outcomes Moving Object Based Collision
Free Video Synopsis, Forensic and Surveillance Analytic Tool Architecture and
Tampering Detection Inter-Frame Forgery.
The results include forensic and surveillance panel outcomes with emphasis on
video synopsis and Sri Lankan context. Principal conclusions include the
optimization and efficient algorithm integration to overcome limitations in
processing power, memory and compromise between real-time performance and
accuracy.Comment: The Forensic Video Analytic Software demo video is available
https://www.youtube.com/watch?v=vsZlYKQxSk
Kelly F. Pearson v. Kimberlee Y. Pearson; Pete D. Thanos : Brief of Appellant
BRIEF OF APPELLANTS APPEAL FROM THE UTAH COURT OF APPEAL
Advanced Family Law South
Meeting proceedings of a seminar by the same name, held July 14-15, 2022
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