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.

Currently, a high percentage of the world’s population lives in urban areas, and this proportion will increase in the coming decades. In this context, indoor positioning systems (IPSs) have been a topic of great interest for researchers. On the other hand, Visible Light Communication (VLC) systems have advantages over RF technologies; for instance, they do not need satellite signals or the absence of electromagnetic interference to achieve positioning. Nowadays, in the context of Indoor Positioning (IPS), Visible Light Positioning (VLP) systems have become a strong alternative to RF-based systems, allowing the reduction in costs and time to market. This paper shows a low cost VLP solution for indoor systems. This includes multiple programmable beacons and a receiver which can be plugged to a smartphone running a specific app. The position information will be quickly and securely available through the interchange between the receiver and any configurable LED-beacon which is strategically disposed in an area. The implementation is simple, inexpensive, and no direct communication with any data server is required.

This paper describes a methodological study carried out between 2018 and 2022, at Rey Juan Carlos University, focused on the subject monitoring and control systems within a master's program in Industrial Engineering. The study proposes an innovative teaching strategy using problem‐based learning and project‐based learning methodologies. The projects undertaken are based on Internet of Things (IoT) systems aimed at enhancing weather stations, services and facilitating real‐time decision‐making. Inspired by our experience in the development of Industry 4.0 projects, we have designed a methodological strategy for this subject that focuses on providing students with the necessary knowledge and skills in the field of Control and Monitoring Systems and the IoT to develop real monitoring and control systems. The approach emphasizes interdisciplinary problem‐solving, with students working collaboratively in stable teams. Throughout the 16‐week course, tasks of increasing complexity are completed, resulting in the development of a complete system. The practical approach of the course and its relation to real applications motivates students, resulting in better performance. The acquired techniques and skills from the course are broadly applicable across engineering disciplines.

Image transmission by incoherent optical fiber bundles (IOFBs) requires prior calibration to obtain the spatial in-out fiber correspondence to reconstruct the image captured by the pseudocamera. This information is recorded in a lookup table (LUT), which is later used for reordering the fiber positions and reconstructing the original image. This paper shows how to apply a fiber detection process to minimize the calibration time and improve the quality of the recovered image. Two different fiber detection methods were developed. The former uses the circular Hough transform algorithm based on the image gradient. The second algorithm combines a number of morphological transformations with distance transform. The results demonstrate that this technique provides a remarkable reduction in the processing time while improving fiber detection accuracy.

Relearning to walk requires progressive training in real scenarios—overground—along with assistance in basic tasks, such as balancing. In addition, user ability must be maximized through compliant robotic assistance as needed. Despite decades of research, gait rehabilitation robotic devices yield controversial results. This article presents the conceptual design of a novel walking assistance and rehabilitation robot, the NIMBLE robot, aimed at providing ambulatory, bodyweight-supported gait training, assisting the user’s center of mass trajectory to aid weight transfer and dynamic balance during walking. NIMBLE consists of a robotic mobile frame, a partial bodyweight support (PBWS) system, an ambulatory lower-limb exoskeleton (Exo-H3) and a cable-driven pelvis-assisting robot. Designed as a modular structure, it differentiates hierarchical communication levels through a Robot Operating System (ROS) 2 network. We present the mechatronic design and experimental results assessing the impact of the mechatronic coupling between the robotic modules on the walking kinematics and the frame movement control performance. The robotic frame hardly affects the walking kinematics up to 2 degrees in both the sagittal and frontal planes, making it feasible for lateral balance and weight translation training. Moreover, it successfully tracks and follows user trajectories. The NIMBLE robotic frame assessment shows promising results for ambulatory gait rehabilitation.

Image transmission using incoherent optical fiber bundles (IOFB) requires prior calibration to obtain the spatial in-out fiber correspondence in order to reconstruct the image captured by the pseudo-sensor. This information is recorded in a Look-Up Table (LUT), used later for reordering the fiber positions and reconstructing the original image. This paper presents a method based on line-scan to obtain the in-out correspondence. The results demonstrate that this technique yields a remarkable reduction in processing time and increased image quality by introducing a fiber detection algorithm, an intensity compensation process and finally, a single interpolation algorithm.