Post-Failure Reconfiguration of CSP Programs
IEEE Transactions on Software Engineering
A Generalized Message-Passing Mechanism for Communicating Sequential Processes
IEEE Transactions on Computers
Software Complexity and its Impact on Software Reliability
IEEE Transactions on Software Engineering
The C programming language
The nature of statistical learning theory
The nature of statistical learning theory
Using CSP to Detect Errors in the TMN Protocol
IEEE Transactions on Software Engineering
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Communicating sequential processes
Communications of the ACM
The Theory and Practice of Concurrency
The Theory and Practice of Concurrency
Performance evaluation of the object-relational transformation methodology
Data & Knowledge Engineering
A General Compiler Framework for Speculative Multithreaded Processors
IEEE Transactions on Parallel and Distributed Systems
Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers
Computer Methods and Programs in Biomedicine
Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages
Journal of Medical Systems
Building sparse multiple-kernel SVM classifiers
IEEE Transactions on Neural Networks
Systems engineering principles for the design of biomedical signal processing systems
Computer Methods and Programs in Biomedicine
Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review
Journal of Medical Systems
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
Reversible watermark using the difference expansion of a generalized integer transform
IEEE Transactions on Image Processing
Improved neural network for SVM learning
IEEE Transactions on Neural Networks
Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features
IEEE Transactions on Information Technology in Biomedicine
A new hybrid intelligent system for accurate detection of Parkinson's disease
Computer Methods and Programs in Biomedicine
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An embedded decision making is a key feature for many biomedical systems. In most cases human life directly depends on correct decisions made by these systems, therefore they have to work reliably. This paper describes how we applied systems engineering principles to design a high performance embedded classification system in a systematic and well structured way. We introduce the structured design approach by discussing requirements capturing, specifications refinement, implementation and testing. Thereby, we follow systems engineering principles and execute each of these processes as formal as possible. The requirements, which motivate the system design, describe an automated decision making system for diagnostic support. These requirements are refined into the implementation of a support vector machine (SVM) algorithm which enables us to integrate automated decision making in embedded systems. With a formal model we establish functionality, stability and reliability of the system. Furthermore, we investigated different parallel processing configurations of this computationally complex algorithm. We found that, by adding SVM processes, an almost linear speedup is possible. Once we established these system properties, we translated the formal model into an implementation. The resulting implementation was tested using XMOS processors with both normal and failure cases, to build up trust in the implementation. Finally, we demonstrated that our parallel implementation achieves the speedup, predicted by the formal model.