Abstract

Abstract

Abstract

Abstract
Real-Time For the Masses (RTFM) is a set of languages and tools being developed to facilitate embedded software development and provide highly efficient implementations geared to static verification. The RTFM-kernel is an architecture designed to provide highly efficient and predicable Stack Resource Policy based scheduling, targeting bare metal (singlecore) platforms. We contribute beyond prior work by introducing a platform independent timer abstraction that relies on existing RTFM-kernel primitives. We develop two alternative implementations for the ARM Cortex-M family of MCUs: a generic implementation, using the ARM defined SysTick- /DWT hardware; and a target specific implementation, using the match compare/free running timers. While sacrificing generality, the latter is more exible and may reduce overall overhead. Invariants for correctness are presented, and methods to static and run-time verification are discussed. Overhead is bound and characterized. In both cases the critical section from release time to dispatch is less than 2us on a 100MHz MCU. Queue and timer mechanisms are directly implemented in the RTFM-core language and can be included in system-wide scheduling analysis.

Abstract

Abstract
Roads have multiple effects on wildlife; amphibians are one of the groups more intensely affected by roadkills. Monitoring roadkills is expensive and time consuming. Automated mapping systems for detecting roadkills, based on robotic computer vision techniques, are largely necessary. Amphibians can be recognised by a set of features as shape, size, colouration, habitat and location. This species identification by using multiple features at the same time is known as "jizz". In a similar way to human vision, computer vision algorithms must incorporate a prioritisation process when analysing the objects in an image. Our main goal here was to give a numerical priority sequence of particular characteristics of roadkilled amphibians to improve the computing and learning process of algorithms. We asked hundred and five amateur and professional herpetologists to answer a simple test of five sets with ten images each of roadkilled amphibians, in order to determine which body parts or characteristics (body form, colour, and other patterns) are used to identify correctly the species. Anura was the group most easily identified when it was roadkilled and Caudata was the most difficult. The lower the taxonomic level of amphibian, the higher the difficulty of identifying them, both in Anura and Caudata. Roadkilled amphibians in general and Anura group were mostly identified by the Form, by the combination of Form and Colour, and finally by Colour. Caudata was identified mainly on Form and Colour and on Colour. Computer vision algorithms must incorporate these combinations of features, avoiding to work exclusively in one specific feature.