35,635 research outputs found
Process of designing robust, dependable, safe and secure software for medical devices: Point of care testing device as a case study
This article has been made available through the Brunel Open Access Publishing Fund.Copyright © 2013 Sivanesan Tulasidas et al. This paper presents a holistic methodology for the design of medical device software, which encompasses of a new way of eliciting requirements, system design process, security design guideline, cloud architecture design, combinatorial testing process and agile project management. The paper uses point of care diagnostics as a case study where the software and hardware must be robust, reliable to provide accurate diagnosis of diseases. As software and software intensive systems are becoming increasingly complex, the impact of failures can lead to significant property damage, or damage to the environment. Within the medical diagnostic device software domain such failures can result in misdiagnosis leading to clinical complications and in some cases death. Software faults can arise due to the interaction among the software, the hardware, third party software and the operating environment. Unanticipated environmental changes and latent coding errors lead to operation faults despite of the fact that usually a significant effort has been expended in the design, verification and validation of the software system. It is becoming increasingly more apparent that one needs to adopt different approaches, which will guarantee that a complex software system meets all safety, security, and reliability requirements, in addition to complying with standards such as IEC 62304. There are many initiatives taken to develop safety and security critical systems, at different development phases and in different contexts, ranging from infrastructure design to device design. Different approaches are implemented to design error free software for safety critical systems. By adopting the strategies and processes presented in this paper one can overcome the challenges in developing error free software for medical devices (or safety critical systems).Brunel Open Access Publishing Fund
Enhanced Parallel Generation of Tree Structures for the Recognition of 3D Images
Segmentations of a digital object based on a connectivity
criterion at n-xel or sub-n-xel level are useful tools in image topological
analysis and recognition. Working with cell complex analogous of digital
objects, an example of this kind of segmentation is that obtained from
the combinatorial representation so called Homological Spanning Forest
(HSF, for short) which, informally, classifies the cells of the complex as
belonging to regions containing the maximal number of cells sharing the
same homological (algebraic homology with coefficient in a field) information.
We design here a parallel method for computing a HSF (using
homology with coefficients in Z/2Z) of a 3D digital object. If this object
is included in a 3D image of m1 × m2 × m3 voxels, its theoretical time
complexity order is near O(log(m1 + m2 + m3)), under the assumption
that a processing element is available for each voxel. A prototype implementation
validating our results has been written and several synthetic,
random and medical tridimensional images have been used for testing.
The experiments allow us to assert that the number of iterations in which
the homological information is found varies only to a small extent from
the theoretical computational time.Ministerio de Economía y Competitividad MTM2016-81030-
Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation
Combinatorial interaction testing is an important software testing technique
that has seen lots of recent interest. It can reduce the number of test cases
needed by considering interactions between combinations of input parameters.
Empirical evidence shows that it effectively detects faults, in particular, for
highly configurable software systems. In real-world software testing, the input
variables may vary in how strongly they interact, variable strength
combinatorial interaction testing (VS-CIT) can exploit this for higher
effectiveness. The generation of variable strength test suites is a
non-deterministic polynomial-time (NP) hard computational problem
\cite{BestounKamalFuzzy2017}. Research has shown that stochastic
population-based algorithms such as particle swarm optimization (PSO) can be
efficient compared to alternatives for VS-CIT problems. Nevertheless, they
require detailed control for the exploitation and exploration trade-off to
avoid premature convergence (i.e. being trapped in local optima) as well as to
enhance the solution diversity. Here, we present a new variant of PSO based on
Mamdani fuzzy inference system
\cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive
selection of its global and local search operations. We detail the design of
this combined algorithm and evaluate it through experiments on multiple
synthetic and benchmark problems. We conclude that fuzzy adaptive selection of
global and local search operations is, at least, feasible as it performs only
second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the
best mean test suite size, the fuzzy adaptation even outperforms DPSO
occasionally. We discuss the reasons behind this performance and outline
relevant areas of future work.Comment: 21 page
A Fast and Accurate Cost Model for FPGA Design Space Exploration in HPC Applications
Heterogeneous High-Performance Computing
(HPC) platforms present a significant programming challenge,
especially because the key users of HPC resources are scientists,
not parallel programmers. We contend that compiler technology
has to evolve to automatically create the best program variant
by transforming a given original program. We have developed a
novel methodology based on type transformations for generating
correct-by-construction design variants, and an associated
light-weight cost model for evaluating these variants for
implementation on FPGAs. In this paper we present a key
enabler of our approach, the cost model. We discuss how we
are able to quickly derive accurate estimates of performance
and resource-utilization from the design’s representation in our
intermediate language. We show results confirming the accuracy
of our cost model by testing it on three different scientific
kernels. We conclude with a case-study that compares a solution
generated by our framework with one from a conventional
high-level synthesis tool, showing better performance and
power-efficiency using our cost model based approach
- …