Open source hardware for scientific equipment needs to provide source files and enough documentation to allow the study, replication and modification of the design. In addition, parametric modeling is encouraged in order to facilitate customization for other experiments. Parametric design using a solid modeling programming language allows customization and provides a source file for the design. OpenSCAD is the most widely used scripting tool for parametric modeling of open source labware. However, OpenSCAD lacks the ability to export to standard parametric formats; thus, the parametric dimensional information of the model is lost. This is an important deficiency because it is key to share the design in the most accessible formats with no information loss. In this work we analyze OpenSCAD and compare it with FreeCAD Python scripts. We have created a parametric open source hardware design to compare these tools. Our findings show that although Python for FreeCAD is more arduous to learn, its advantages counterbalance the initial difficulties. The main benefits are being able to export to standard parametric models; using Python language with its libraries; and the ability to use and integrate the models in its graphical interface. Thus, making it more appropriate to design open source hardware for scientific equipment.

Medical imaging is considered one of the most important advances in the history of medicine and has become an essential part of the diagnosis and treatment of patients. Earlier prediction and treatment have been driving the acquisition of higher image resolutions as well as the fusion of different modalities, raising the need for sophisticated hardware and software systems for medical image registration, storage, analysis, and processing. In this scenario and given the new clinical pipelines and the huge clinical burden of hospitals, these systems are often required to provide both highly accurate and real-time processing of large amounts of imaging data. Additionally, lowering the prices of each part of imaging equipment, as well as its development and implementation, and increasing their lifespan is crucial to minimize the cost and lead to more accessible healthcare. This paper focuses on the evolution and the application of different hardware architectures (namely, CPU, GPU, DSP, FPGA, and ASIC) in medical imaging through various specific examples and discussing different options depending on the specific application. The main purpose is to provide a general introduction to hardware acceleration techniques for medical imaging researchers and developers who need to accelerate their implementations.

Recently, technology has become more attractive for industry by paving the way to new applications, which go well beyond traditional sensor applications. These applications can be classified in different categories: monitoring, alerting, healthcare assistance, and actuating, among others. A Wireless Sensor Network (WSN) is feasible for the heritage scenarios protection and for monitoring because its specificity, autonomy, self-configurability, lasting lifetime, and mobility. However, their services, protocols, and architectures have constraints to support new issues for monitoring cultural assets: geographical dispersion, insufficient resources, and not enough staff to protect all the monuments and to provide tourist information broadcasting. We propose a modular system. It consists of three elements: a WSN, a “Central System,” and a novel platform named “Local Node Gateway.” It allows the coexistence of different wireless technologies in order to provide actuation, processing and communication functionalities. This proposal takes a new approach to solve these heritage-monitoring problems.

This paper presents a case study analyzing the advantages and disadvantages of using project-based learning (PBL) combined with collaborative learning (CL) and industry best practices, integrated with information communication technologies, open-source software, and open-source hardware tools, in a specialized microcontroller and embedded systems engineering Master's course. In addition to addressing industry requirements in both contents and methodology, the course develops capabilities and competencies in problem solving, independent learning, teamwork, and technical knowledge. Since PBL methodology alone does not ensure teamwork, it was complemented with CL. Design review meetings (as described in IEC 61160), deliverables, and organizational resources were also introduced to mirror industry demands. This structure integrated course content and student academic achievement in a simulated industrial environment. The course had students build a modular management system for home appliances, implementing control software on the “Arduino” open-source platform, as well as using wireless communications. The results show that teaching, learning, and student assessment processes can be improved by using PBL with CL. In addition, the introduction of industry practices, such us peer review meetings, brings academia closer to a real-world context.