ImageJ2: ImageJ for the next generation of scientific image data

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs

Intuitive Instruction of Industrial Robots : A Knowledge-Based Approach

With more advanced manufacturing technologies, small and medium sized enterprises can compete with low-wage labor by providing customized and high quality products. For small production series, robotic systems can provide a cost-effective solution. However, for robots to be able to perform on par with human workers in manufacturing industries, they must become flexible and autonomous in their task execution and swift and easy to instruct. This will enable small businesses with short production series or highly customized products to use robot coworkers without consulting expert robot programmers. The objective of this thesis is to explore programming solutions that can reduce the programming effort of sensor-controlled robot tasks. The robot motions are expressed using constraints, and multiple of simple constrained motions can be combined into a robot skill. The skill can be stored in a knowledge base together with a semantic description, which enables reuse and reasoning. The main contributions of the thesis are 1) development of ontologies for knowledge about robot devices and skills, 2) a user interface that provides simple programming of dual-arm skills for non-experts and experts, 3) a programming interface for task descriptions in unstructured natural language in a user-specified vocabulary and 4) an implementation where low-level code is generated from the high-level descriptions. The resulting system greatly reduces the number of parameters exposed to the user, is simple to use for non-experts and reduces the programming time for experts by 80%. The representation is described on a semantic level, which means that the same skill can be used on different robot platforms. The research is presented in seven papers, the first describing the knowledge representation and the second the knowledge-based architecture that enables skill sharing between robots. The third paper presents the translation from high-level instructions to low-level code for force-controlled motions. The two following papers evaluate the simplified programming prototype for non-expert and expert users. The last two present how program statements are extracted from unstructured natural language descriptions

Designing and Implementing a Framework for Real-time Robot Controller Clients

This research paper designs a framework for developing real-time clients that communicate with robot controllers built by students at the University of Stavanger. The framework provides patterns that handle time-sensitive components and demonstrates a technique for functional scalability. A brief section introduces design metrics for user experience. The report presents three use cases covering functionality for actuating a robot and reading its movements in real time. This thesis implemented the three use cases in the spring semester of 2022, and the results show that it is possible to model use cases, but implementing complex use cases requires more effort

API2MoL: Automating the building of bridges between APIs and Model-Driven Engineering

International audienceContext: A software artefact typically makes its functionality available through a specialized Application Programming Interface (API) describing the set of services offered to client applications. In fact, building any software system usually involves managing a plethora of APIs, which complicates the development process. In Model-Driven Engineering (MDE), where models are the key elements of any software engineering activity, this API management should take place at the model level. Therefore, tools that facilitate the integration of APIs and MDE are clearly needed. Objective: Our goal is to automate the implementation of API-MDE bridges for supporting both the creation of models from API objects and the generation of such API objects from models. In this sense, this paper presents the API2MoL approach, which provides a declarative rule-based language to easily write mapping definitions to link API specifications and the metamodel that represents them. These definitions are then executed to convert API objects into model elements or vice versa. The approach also allows both the metamodel and the mapping to be automatically obtained from the API specification (bootstrap process). Method: After implementing the API2MoL engine, its correctness was validated using several APIs. Since APIs are normally large, we then developed a tool to implement the bootstrap process, which was also validated. Results: We provide a toolkit (language and bootstrap tool) for the creation of bridges between APIs and MDE. The current implementation focuses on Java APIs, although its adaptation to other statically typed object-oriented languages is straightforward. The correctness, expressiveness and completeness of the approach have been validated with the Swing, SWT and JTwitter APIs. Conclusion: API2MoL frees developers from having to manually implement the tasks of obtaining models from API objects and generating such objects from models. This helps to manage API models in MDE-based solutions

A framework for domain-specific modeling on graph databases

La complexité du logiciel augmente tout le temps: les systèmes deviennent plus grands et plus complexes. La modélisation est un élément central de génie logicielle pour relever les défis de la complexité. Cependant, un défi majeur auquel est confronté le développement de logiciels axés sur les modèles est l'évolutivité des outils de modélisation avec une taille croissante de modèles. Certaines initiatives ont commencé à explorer la modélisation tout en stockant des modèles dans une base de données de graphes. Dans cette thèse, nous présentons NMF, un framework pour créer et éditer des modèles dans une base de données Neo4j élevée à l'abstraction du langage de modélisation.Software complexity increases all the time: systems become larger and more complex. Modeling is a central part of software engineering to tackle challenges of complexity. However, a prominent challenge model-driven software development is facing is scalability of modeling tools with a growing size of models. Some initiatives started exploring modeling while storing models in a graph database. In this thesis, we present NMF, a framework to create and edit MDE models in a Neo4j database lifted to the abstraction of the modeling language

A formal architecture-centric and model driven approach for the engineering of science gateways

From n-Tier client/server applications, to more complex academic Grids, or even the most recent and promising industrial Clouds, the last decade has witnessed significant developments in distributed computing. In spite of this conceptual heterogeneity, Service-Oriented Architecture (SOA) seems to have emerged as the common and underlying abstraction paradigm, even though different standards and technologies are applied across application domains. Suitable access to data and algorithms resident in SOAs via so-called ‘Science Gateways’ has thus become a pressing need in order to realize the benefits of distributed computing infrastructures.In an attempt to inform service-oriented systems design and developments in Grid-based biomedical research infrastructures, the applicant has consolidated work from three complementary experiences in European projects, which have developed and deployed large-scale production quality infrastructures and more recently Science Gateways to support research in breast cancer, pediatric diseases and neurodegenerative pathologies respectively. In analyzing the requirements from these biomedical applications the applicant was able to elaborate on commonly faced issues in Grid development and deployment, while proposing an adapted and extensible engineering framework. Grids implement a number of protocols, applications, standards and attempt to virtualize and harmonize accesses to them. Most Grid implementations therefore are instantiated as superposed software layers, often resulting in a low quality of services and quality of applications, thus making design and development increasingly complex, and rendering classical software engineering approaches unsuitable for Grid developments.The applicant proposes the application of a formal Model-Driven Engineering (MDE) approach to service-oriented developments, making it possible to define Grid-based architectures and Science Gateways that satisfy quality of service requirements, execution platform and distribution criteria at design time. An novel investigation is thus presented on the applicability of the resulting grid MDE (gMDE) to specific examples and conclusions are drawn on the benefits of this approach and its possible application to other areas, in particular that of Distributed Computing Infrastructures (DCI) interoperability, Science Gateways and Cloud architectures developments