Stable Hybrid Fuzzy Controller-based Architecture for Robotic Telesurgery Systems

Robotic surgery and remotely controlled teleoperational systems are on the rise. However, serious limitations arise on both the hardware and software side when traditional modeling and control approaches are taken. These limitations include the incomplete modeling of robot dynamics, tool–tissue interaction, human– machine interfaces and the communication channel. Furthermore, the inherent latency of long-distance signal transmission may endanger the stability of a robot controller. All of these factors contribute to the very limited deployment of real robotic telesurgery. This paper describes a stable hybrid fuzzy controller-based architecture that is capable of handling the basic challenges. The aim is to establish high fidelity telepresence systems for medical applications by easily handled modern control solution

Quality Properties of Execution Tracing, an Empirical Study

The authors are grateful to all the professionals who participated in the focus groups; moreover, they also express special thanks to the management of the companies involved for making the organisation of the focus groups possible.Data are made available in the appendix including the results of the data coding process.The quality of execution tracing impacts the time to a great extent to locate errors in software components; moreover, execution tracing is the most suitable tool, in the majority of the cases, for doing postmortem analysis of failures in the field. Nevertheless, software product quality models do not adequately consider execution tracing quality at present neither do they define the quality properties of this important entity in an acceptable manner. Defining these quality properties would be the first step towards creating a quality model for execution tracing. The current research fills this gap by identifying and defining the variables, i.e., the quality properties, on the basis of which the quality of execution tracing can be judged. The present study analyses the experiences of software professionals in focus groups at multinational companies, and also scrutinises the literature to elicit the mentioned quality properties. Moreover, the present study also contributes to knowledge with the combination of methods while computing the saturation point for determining the number of the necessary focus groups. Furthermore, to pay special attention to validity, in addition to the the indicators of qualitative research: credibility, transferability, dependability, and confirmability, the authors also considered content, construct, internal and external validity

An extended information system success model for mobile learning usage in Saudi Arabia universities

The emergence of 4G networks allows m-learning to be attractive for educational systems. Mobile devices have the potential to enhance accessibility and efficiency distribution of educational materials and information. Developing countries, especially in the Middle East, lag behind as they face difficulties in the adoption and use of m-learning. Previous researches stated that the studies in the success of m-learning are still insufficient in developing countries, particularly in Saudi Arabia where the number of students involved in m-learning also constitutes low percentages. Nine factors that influence the success of m-learning are incorporated and evaluated into a research model. A quantitative approach was used, where questionnaires were sent to three universities in KSA. The contributing factors and the relationships between them were evaluated using a Structural Equation Modelling technique. The research revealed that information quality, user satisfaction (US), trust in technology, attitude, organisation support, trust in organisation, and the net benefits of m-learning positively influence m-learning usage. In addition, the results confirmed that user satisfaction is positively affected by system quality (SEQ), service quality (SQ), and net benefits (NB) of using (U) the system. The results also showed that there is a significant relationship between NB and US for m-learning technology. This study extends the previous research by providing a conceptual model for the successful execution of m-learning services in universities. This mediating effect of US explains the impact of independent variables (IQ, SEQ, SQ) on U. It also examined the mediating effect of U in explaining the influence of US on the NB using m-learning services. The findings of this study are valuable as input for the Ministry of Higher Education and practitioners concerned with successful m-learning services. This study constructed a new model to enhance the mobile learning usage among students in universities

Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition

Recent advances in Convolutional Neural Networks (CNNs) have obtained promising results in difficult deep learning tasks. However, the success of a CNN depends on finding an architecture to fit a given problem. A hand-crafted architecture is a challenging, time-consuming process that requires expert knowledge and effort, due to a large number of architectural design choices. In this dissertation, we present an efficient framework that automatically designs a high-performing CNN architecture for a given problem. In this framework, we introduce a new optimization objective function that combines the error rate and the information learnt by a set of feature maps using deconvolutional networks (deconvnet). The new objective function allows the hyperparameters of the CNN architecture to be optimized in a way that enhances the performance by guiding the CNN through better visualization of learnt features via deconvnet. The actual optimization of the objective function is carried out via the Nelder-Mead Method (NMM). Further, our new objective function results in much faster convergence towards a better architecture. The proposed framework has the ability to explore a CNN architecture’s numerous design choices in an efficient way and also allows effective, distributed execution and synchronization via web services. Empirically, we demonstrate that the CNN architecture designed with our approach outperforms several existing approaches in terms of its error rate. Our results are also competitive with state-of-the-art results on the MNIST dataset and perform reasonably against the state-of-the-art results on CIFAR-10 and CIFAR-100 datasets. Our approach has a significant role in increasing the depth, reducing the size of strides, and constraining some convolutional layers not followed by pooling layers in order to find a CNN architecture that produces a high recognition performance. Moreover, we evaluate the effectiveness of reducing the size of the training set on CNNs using a variety of instance selection methods to speed up the training time. We then study how these methods impact classification accuracy. Many instance selection methods require a long run-time to obtain a subset of the representative dataset, especially if the training set is large and has a high dimensionality. One example of these algorithms is Random Mutation Hill Climbing (RMHC). We improve RMHC so that it performs faster than the original algorithm with the same accuracy

Mathematics in Software Reliability and Quality Assurance

This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment