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.

New emerging concepts as ldquowireless hospitalrdquo, ldquomobile healthcarerdquo or ldquowearable telemonitoringrdquo require the development of bio-signal acquisition devices to be easily integrated into the clinical routine. In this work, we present a new system for electrocardiogram (ECG) acquisition and its processing, with wireless transmission on demand (either the complete ECG or only one alarm message, just in case a pathological heart rate detected). Size and power consumption are optimized in order to provide mobility and comfort to the patient. We have designed a modular hardware system and an autonomous platform based on a field-programmable gate array (FPGA) for developing and debugging. The modular approach allows to redesign the system in an easy way. Its adaptation to a new biomedical signal would only need small changes on it. The hardware system is composed of three layers that can be plugged/unplugged: communication layer, processing layer and sensor layer. In addition, we also present a general purpose end-user application developed for mobile phones or personal digital assistant devices (PDAs).

Autofocus systems are essential in optical microscopy. These systems typically sweep the sample through the focal range and apply an algorithm to determine the contrast value of each image, where the highest value indicates the optimal focus position. As the optimal algorithm may vary according to the images' content, we evaluate the 15 most used algorithms in the field using 150 stacks of images from four different kinds of tissue. We use four measuring criteria and two types of analysis and propose a general methodology to apply to select the best fitting algorithm for any given application. In this paper, we present the results of this evaluation and a detailed discussion of different features: the threshold used for the algorithms, the criteria parameters, the analysis used, the bit depth of the images, their magnification, and the type of tissue, reaching the conclusion that some of these parameters are more relevant to the study than others, and the implementation of the proposed methodology can lead to a fast and reliable autofocus system capable of performing an analysis and selection of algorithms with no supervision required.

Impairment of the olfactory sensibility can be an indicator of neurode generative disorders, such as Alzheimer's disease and Parkinson's disease. The problem lies in obtaining an objective quantitative analysis of olfactory response. For this task, we will use functional magnetic resonance imaging (fMRI) and a device that will provide a selective and controlled stimulation of the olfactory system. The novel issues of our design are synchronization between the acquisition and the olfactory task, and automated control of experimental parameters, odorants sequences, and frequency. We present a review of the basic fMRI experimental design of event-related stimulus paradigms versus block design experiments, and their use in olfactory experiments. Finally, we present the preliminary results obtained on a real 3-T magnetic resonance imaging (MRI) scanner.

In this paper we present our experience in teaching digital electronic circuit and system design with FPGAs using VHDL. The course follows a project based learning methodology, in which the students learn how to design digital circuits and systems in a practical way. During the course, students design electronic circuits of incremental complexity. At the end of the course they are capable of implementing relatively complex projects, such as image processing systems and videogames.