Applications of a TransFLash in an embedded system debugging

In this paper, the hardware and software design for using a TF card in debugging an embedded system are described. The used hardware platform is designed based on a PXA310 application processor. The Android open source operating system is used as the software platform. The design of the connection circuit between the application processor and the TF card is introduced first. Secondly, the design of the TF card driver program and the method for Android system to mount the TF card are described. In designing the TF driver program, an SPI operation mode and FAT32 file system are used. The transplant of the FAT32 file system is presented more detail. Finally, the paper introduced the system debugging and the test results are given for the TF card used in a video data acquisition unit of a video monitoring. It is shown that high speed data exchange and good universal property can be obtained by using a TF card to download a system image during developing and debugging. The TF card used in debugging can be used as a mass storage in the embedded product without the need of changing the design for debugging the system and it is also convenient for a user to upgrade operating system.

Non-referenced genome assembly from epigenomic short-read data

Current computational methods used to analyze changes in DNA methylation and chromatin modification rely on sequenced genomes. Here we describe a pipeline for the detection of these changes from short-read sequence data that does not require a reference genome. Open source software packages were used for sequence assembly, alignment, and measurement of differential enrichment. The method was evaluated by comparing results with reference-based results showing a strong correlation between chromatin modification and gene expression. We then used our de novo sequence assembly to build the DNA methylation profile for the non-referenced Psammomys obesus genome. The pipeline described uses open source software for fast annotation and visualization of unreferenced genomic regions from short-read data.

Extending support to customised multi-point haptic devices in CHAI3D

 CHAI3D is a widely accepted haptic SDK in the society because it is open-source and provides support to devices from different vendors. In many cases, CHAI3D and its related demos are used for benchmarking various haptic collision and rendering algorithms. However, CHAI3D is designed for off-the-shelf single-point haptic devices only, and it does not provide native support to customised multi-point haptic devices. In this paper, we aim to extend the existing CHAI3D framework and provide a standardized routine to support customised, single/multi-point haptic devices. Our extension aims at two issues: Intra-device communication and Inter-device communication. Therefore, our extension includes an HIP wrapper layer to concurrently handle multiple HIPs of a single device, and a communication layer to concurrently handle multiple position, orientation and force calculations of multiple haptic devices. Our extension runs on top of a custom-built 8-channel device controller, although other offthe shelf controllers can also be integrated easily. Our extension complies with the CHAI3D design framework and advanced provide inter-device communication capabilities for multi-device operations. With straightforward conversion routines, existing CHAI3D demos can be adapted to multi-point demos, supporting real-time parallel collision detection and force rendering.

Characterization and prediction of issue-related risks in software projects

Identifying risks relevant to a software project and planning measures to deal with them are critical to the success of the project. Current practices in risk assessment mostly rely on high-level, generic guidance or the subjective judgements of experts. In this paper, we propose a novel approach to risk assessment using historical data associated with a software project. Specifically, our approach identifies patterns of past events that caused project delays, and uses this knowledge to identify risks in the current state of the project. A set of risk factors characterizing “risky” software tasks (in the form of issues) were extracted from five open source projects: Apache, Duraspace, JBoss, Moodle, and Spring. In addition, we performed feature selection using a sparse logistic regression model to select risk factors with good discriminative power. Based on these risk factors, we built predictive models to predict if an issue will cause a project delay. Our predictive models are able to predict both the risk impact (i.e. the extend of the delay) and the likelihood of a risk occurring. The evaluation results demonstrate the effectiveness of our predictive models, achieving on average 48%-81% precision, 23%-90% recall, 29%-71% F-measure, and 70%-92% Area Under the ROC Curve. Our predictive models also have low error rates: 0.39-0.75 for Macro-averaged Mean Cost-Error and 0.7-1.2 for Macro-averaged Mean Absolute Error.

CISR-ODE, A C plus plus framework with ODE solver for code based system dynamics simulation

Ordinary differential equations are used for modelling a wide range of dynamic systems. Even though there are many graphical software applications for this purpose, a fully customised solution for all problems is code-level programming of the model and solver. In this project, a free and open source C++ framework is designed to facilitate modelling in native code environment and fulfill the common simulation needs of control and many other engineering and science applications. The solvers of this project are obtained from ODEINT and specialised for Armadillo matrix library to provide an easy syntax and a fast execution. The solver code is minimised and its modification for users have become easier. There are several features added to the solvers such as controlling maximum step size, informing the solver about sudden input change and forcing custom times into the results and calling a custom method at these points. The comfort of the model designer, code readability, extendibility and model isolation have been considered in the structure of this framework. The application manages the output results, exporting and plotting them. Modifying the model has become more practical and a portion of corresponding codes are updated automatically. A set of libraries is provided for generation of output figures, matrix hashing, control system functions, profiling, etc. In this paper, an example of using this framework for a classical washout filter model is explained.

Subsuming methods: finding new optimisation opportunities in object-oriented software

The majority of existing application profiling techniques ag- gregate and report performance costs by method or call- ing context. Modern large-scale object-oriented applications consist of thousands of methods with complex calling pat- terns. Consequently, when profiled, their performance costs tend to be thinly distributed across many thousands of loca- tions with few easily identifiable optimisation opportunities. However experienced performance engineers know that there are repeated patterns of method calls in the execution of an application that are induced by the libraries, design patterns and coding idioms used in the software. Automati- cally identifying and aggregating costs over these patterns of method calls allows us to identify opportunities to improve performance based on optimising these patterns. We have developed an analysis technique that is able to identify the entry point methods, which we call subsuming methods, of such patterns. Our ofiine analysis runs over previously collected runtime performance data structured in a calling context tree, such as produced by a large number of existing commercial and open source profilers. We have evaluated our approach on the DaCapo bench- mark suite, showing that our analysis significantly reduces the size and complexity of the runtime performance data set, facilitating its comprehension and interpretation. We also demonstrate, with a collection of case studies, that our analysis identifies new optimisation opportunities that can lead to significant performance improvements (from 20% to over 50% improvement in our case studies).

Adaptable, model-driven security engineering for SaaS cloud-based applications

Software-as-a-service (SaaS) multi-tenancy in cloud-based applications helps service providers to save cost, improve resource utilization, and reduce service customization and maintenance time. This is achieved by sharing of resources and service instances among multiple "tenants" of the cloud-hosted application. However, supporting multi-tenancy adds more complexity to SaaS applications required capabilities. Security is one of these key requirements that must be addressed when engineering multi-tenant SaaS applications. The sharing of resources among tenants - i.e. multi-tenancy - increases tenants' concerns about the security of their cloud-hosted assets. Compounding this, existing traditional security engineering approaches do not fit well with the multi-tenancy application model where tenants and their security requirements often emerge after the applications and services were first developed. The resultant applications do not usually support diverse security capabilities based on different tenants' needs, some of which may change at run-time i.e. after cloud application deployment. We introduce a novel model-driven security engineering approach for multi-tenant, cloud-hosted SaaS applications. Our approach is based on externalizing security from the underlying SaaS application, allowing both application/service and security to evolve at runtime. Multiple security sets can be enforced on the same application instance based on different tenants' security requirements. We use abstract models to capture service provider and multiple tenants' security requirements and then generate security integration and configurations at runtime. We use dependency injection and dynamic weaving via Aspect-Oriented Programming (AOP) to integrate security within critical application/service entities at runtime. We explain our approach, architecture and implementation details, discuss a usage example, and present an evaluation of our approach on a set of open source web applications.

A survey of recommendation techniques based on offline data processing

Recommendations based on offline data processing has attracted increasing attention from both research communities and IT industries. The recommendation techniques could be used to explore huge volumes of data, identify the items that users probably like, translate the research results into real-world applications and so on. This paper surveys the recent progress in the research of recommendations based on offline data processing, with emphasis on new techniques (such as temporal recommendation, graph-based recommendation and trust-based recommendation), new features (such as serendipitous recommendation) and new research issues (such as tag recommendation and group recommendation). We also provide an extensive review of evaluation measurements, benchmark data sets and available open source tools. Finally, we outline some existing challenges for future research.

Simplifying medical additive manufacturing: making the surgeon the designer

Additive Manufacturing, a technology which has been in existence since three decades, is now successfully being transitioned from a research setting to finding technologically and financially viable end-user applications. A key sector in which Additive Manufacturing is being used is the medical devices and healthcare sector. Drivers in this sector include the ability to create customized, patient specific devices and implants with quick turnaround time in a cost-effective manner. Doctors and surgeons are important change agents and innovators in the creation of new healthcare devices as well as surgical methods. Often times, they may find it necessary at first to build devices and plan surgeries which are not even being thought of or acted upon by the major healthcare companies. In this sense, they perform the roles of designers, creating new ideas and improving on them until they can be implemented and adopted by others. However, the scope for performing this creative activity is often limited in their workplaces, with resource, time and financial impediments often being present. Additive Manufacturing can be helpful to speed up the iterative process of designing such medical devices or planning surgeries as well as help convince people outside of the surgery room of the feasibility and business case for such innovations. This paper proposes to introduce a framework of design, processes and tools which will enable non-engineers (specifically surgeons) to create custom-built products. It is hoped that this paper will motivate more surgeons and non-engineers to get involved in the process of designing for additive manufacturing.

Virtual lumbar spine of multi-body model based on simbody

The spine is an important and complex skeletal structure in the human body. It is a vulnerable part of our skeleton that is open to many medical problems. Hence it is necessary to establish a virtual spine model to assist surgeons to understand biomechanics of the spine. In this study, we aim to propose a virtual spine multi-body model. The computational biomechanical modeling of the spine is based on the theory of multi-body dynamics and implemented with SimBody open-source SDK. Simbody was then used to solve the kinetic equations and simulate the movement of spine. The spine model was validated by comparing its simulation results with experimental results from literature. The spine model will be helpful to understand biomechanics of the spine under loading.