W.G.J. Howells

SiTra: simple transformations in Java

A number of different Model Transformation Frameworks (MTF) are being developed, each of them requiring a user to learn a different language and each possessing its own specific language peculiarities, even if they are based on the QVT standard. To write even a simple transformation, these MTFs require a large amount of learning time. We describe in this paper a minimal, Java based, library that can be used to support the implementation of many practical transformations. Use of this library enables simple transformations to be implemented simply, whilst still providing some support for more complex transformations.

A Model-Driven Development Approach to Mapping UML State Diagrams to Synthesizable VHDL

With the continuing rise in the complexity of embedded systems, there is an emerging need for a higher level modeling environment that facilitates efficient handling of this complexity. The aim here is to produce such a high-level environment using model- driven development (MDD) techniques that map a high-level abstract description of an electronic embedded system into its low-level implementation details. The Unified Modeling Language (UML) is a high-level graphical-based language that is broad enough in scope to model embedded systems hardware circuits. The authors have developed a framework for deriving very high speed integrated circuit hardware description language (VHDL) code from UML state diagrams and defined a set of rules that enable automated generation of synthesizable VHDL code from UML specifications using MDD techniques. By adopting the techniques and tools described in this paper, the design and implementation of complex state-based systems is greatly simplified.

ESPACENET: A Framework of Evolvable and Reconfigurable Sensor Networks for Aerospace–Based Monitoring and Diagnostics

There is an increasing need to develop flexible, reconfigurable, and intelligent multi-spacecraft sensing networks for aerospace-based monitoring and diagnostics. Technical advancements in ad hoc networking, MEMS devices, low-power electronics, adaptive and reconfigurable hardware, micro-spacecraft, and micro-sensors have enabled the design and development of such highly integrated space wireless sensor networks. This paper proposes the framework for an evolvable sensor network architecture, investigated as part of the ESPACENET project, collocated at the University of Edinburgh, Essex, Kent and Surrey. The aim is to design a flexible and intelligent embedded network of reconfigurable piconodes optimised by a hierarchical multi-objective algorithm. Although the project is targeted at aerospace applications, the same intelligent network can be used for many earth bound applications such as environmental and medical diagnostics

A Novel Weightless Artificial Neural Based Multi-Classifier for Complex Classifications

Artificial neural systems in general and weightless systems in particular, have traditionally struggled in performance terms when confronted with problem domains such as possessing a large number of independent pattern classes and pattern classes with non-standard distributions. A multi-classifier is proposed which explores problem domains with a large number of independent pattern classes typically found in forensic and security databases. Specifically, the multi-classifier system is demonstrated on the exemplar of fingerprint identification problem typical to forensic, biometric, and security. Furthermore, the multi-classifier is able to provide a reasonable solution to benchmark problems from medicinal and physical (science) fields, which are determining the health, state of thyroid glands and determining whether or not there is a structure in the ionosphere, respectively.

Ensuring data integrity via ICmetrics based security infrastructure

Society has grown to trust and depend on electronic systems. However, rising complexity, reduced time to market and increased commercial pressure contribute to eroding dependability. At the same time the value of data and high social dependence on the now ubiquitous embedded systems that underlie most products and equipment attract malicious exploits. Tampering of electronic systems to compromise their integrity and the security of their stored and transmitted data is a growing problem in todays connected devices. This manuscript describes research relating to a new concept called ICmetrics capable of improving the security of system-on- chip (SoC) based devices and their data by providing a reliable means of identifying each device and verifying the absence of tampering using each devices unique features and properties. Specifically, this manuscript outlines a methodology to extract low level feature values from a SoC via reconfiguration of its existing debug support circuits that were originally intended to aid system-level development and monitoring. This novel approach to feature extraction yields an area saving of over 30% percent compared with dedicated circuits.

A Fingerprint Identification System Using Adaptive FPGA-Based Enhanced Probabilistic Convergent Network

This paper explores the biometric identification and verification of human subjects via fingerprints utilising an adaptive FPGA-based weightless neural networks. The exploration espoused here is a hardware-based system motivated by the need for accurate and rapid response to identification of fingerprints which may be lacking in other alternative systems such as software based neural networks. The fingerprints are pre-processed and binarized, and the binarized fingerprints are partitioned into train- and test-set for the FPGA-based neural network. The neural network employed in this exploration is known as Enhanced Convergent Network (EPCN). The results obtained are compared to other alternative systems. They demonstrate the suitability of the FPGA-based EPCN for such tasks.

A Framework for Self-Diagnosis and Condition Monitoring for Embedded Systems Using a SOM-Based Classifier

This paper presents a system level framework for system-on-chip (SoC) based embedded devices that may include adaptive and reconfigurable elements. Current development support and debugging solutions are highly dependant on off-line post-mortem style inspection, and even those that utilise tracing for real-time and schedule-critical systems rely on external development tools and environments. This new framework introduces an AI-lead infrastructure that has the potential to reduce much of the development effort while complementing existing debugging circuits. Specifically this paper investigates how to use a Kohonen self-organising map (SOM) as a classifier, and shows a preliminary investigation into how to determine the quality of a map after training. This classifier is a first step in diagnosing failure, degradation and anomalies (i.e. provides condition monitoring) in an embedded system from a system level point of view, and in the larger task of self-diagnosis of an embedded system.

Adaptive Online Profiling Hardware for ICmetrics Based Security

Monitoring circuitry is presented that extracts properties and features from a complex system based on a system-on-chip based device to support ICmetrics, a novel security concept that aims to uniquely identify and secure an embedded system based on its own behavioural identity. The circuits utilise a novel approach to profiling the instruction fetches and data accesses associated with each of the systempsilas component software tasks so that a representation of their address distributions can be generated online when required. By using profiling circuits with adaptive allocation of counters and by exploiting existing debugging infrastructure circuits, the overall resource requirement is kept sufficiently low for real-world applications. Experimental results are provided to illustrate the nature of features that can be obtained.

I^2 S^3 the Integrated Intelligent Secure Sensor Systems Project

Secure systems are of growing importance for protecting the assets and rights of business and individuals, who are increasingly reliant on surveillance equipment for deterrence of criminals and enforcement of justice. The Integrated Intelligent Secure Sensor Systems project (I2S3) integrates the novel technology necessary to extend the capabilities of surveillance equipment to provide preemptive warnings by detection of suspicious activity. Advanced reconfigurable hardware provides an ultra low power platform to underlie the image analysis and novel remote biometric identification algorithms. Each of the distributed sensor nodes is secured through a novel technique named ICmetrics which exploits synergies between biological and artificial systems.

A System Level Framework for Monitoring and Self Diagnosis in ESPACENET

This paper introduces a framework for system level debugging and monitoring for advanced embedded System-on-Chip architectures that may include evolvable and reconfigurable elements and have very tight power constraints. While current debug solutions are highly dependant on off-line configuration and interrogation through a developer, the new framework introduces an Al-lead infrastructure that can potentially reduce much of the development effort. The framework complements the debug circuits in the current generation while enhancing them with a higher level functionality that examines and monitors system activity on-line.