This paper proposes a scheduling strategy and an automatic scheduling flow that enable the simultaneous execution of multiple hard-real-time dataflow jobs. Each job has its own execution rate and starts and stops independently from other jobs, at instants unknown at compile-time, on a multiprocessor system-on-chip. We show how a combination of Time-Division Multiplex (TDM) and static-order scheduling can be modeled as additional nodes and edges on top of the dataflow representation of the job using SingleRate Dataflow semantics to enable tight worst-case temporal analysis. We also propose algorithms to find combined TDM/static order schedules for jobs that guarantee a requested minimum throughput and maximum latency, while minimizing the usage of processing resources. We illustrate the usage of these techniques for a combination of Wireless LAN and TD-SCDMA radio jobs running on a prototype Software-Defined Radio platform.

Mobile technologies are increasingly important components in telemedicine systems and are becoming powerful decision support tools. Universal access to data may already be achieved by resorting to the latest generation of tablet devices and smartphones. However, the protocols employed for communicating with image repositories are not suited to exchange data with mobile devices. In this paper, we present an extensible approach to solving the problem of querying and delivering data in a format that is suitable for the bandwidth and graphic capacities of mobile devices. We describe a three-tiered component-based gateway that acts as an intermediary between medical applications and a number of Picture Archiving and Communication Systems (PACS). The interface with the gateway is accomplished using Hypertext Transfer Protocol (HTTP) requests following a Representational State Transfer (REST) methodology, which relieves developers from dealing with complex medical imaging protocols and allows the processing of data on the server side.

The conception and deployment of cost effective Picture Archiving and Communication Systems (PACS) is a concern for small to medium medical imaging facilities, research environments, and developing countries' healthcare institutions. Financial constraints and the specificity of these scenarios contribute to a low adoption rate of PACS in those environments. Furthermore, with the advent of ubiquitous computing and new initiatives to improve healthcare information technologies and data sharing, such as IHE and XDS-i, a PACS must adapt quickly to changes. This paper describes Dicoogle, a software framework that enables developers and researchers to quickly prototype and deploy new functionality taking advantage of the embedded Digital Imaging and Communications in Medicine (DICOM) services. This full-fledged implementation of a PACS archive is very amenable to extension due to its plugin-based architecture and out-of-the-box functionality, which enables the exploration of large DICOM datasets and associated metadata. These characteristics make the proposed solution very interesting for prototyping, experimentation, and bridging functionality with deployed applications. Besides being an advanced mechanism for data discovery and retrieval based on DICOM object indexing, it enables the detection of inconsistencies in an institution's data and processes. Several use cases have benefited from this approach such as radiation dosage monitoring, Content-Based Image Retrieval (CBIR), and the use of the framework as support for classes targeting software engineering for clinical contexts.

Content-based image retrieval (CBIR) has been heralded as a mechanism to cope with the increasingly larger volumes of information present in medical imaging repositories. However, generic, extensible CBIR frameworks that work natively with Picture Archive and Communication Systems (PACS) are scarce. In this article we propose a methodology for parametric CBIR based on similarity profiles. The architecture and implementation of a profiled CBIR system, based on query by example, atop Dicoogle, an open-source, full-fletched PACS is also presented and discussed. In this solution, CBIR profiles allow the specification of both a distance function to be applied and the feature set that must be present for that function to operate. The presented framework provides the basis for a CBIR expansion mechanism and the solution developed integrates with DICOM based PACS networks where it provides CBIR functionality in a seamless manner.

The use of digital medical imaging systems in healthcare institutions has increased significantly, and the large amounts of data in these systems have led to the conception of powerful support tools: recent studies on content-based image retrieval (CBIR) and multimodal information retrieval in

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