Cyber-Physical Systems are experiencing a paradigm shift in which processing has been relocated to the distributed sensing layer and is no longer performed in a centralized manner. This approach, usually referred to as Edge Computing, demands the use of hardware platforms that are able to manage the steadily increasing requirements in computing performance, while keeping energy efficiency and the adaptability imposed by the interaction with the physical world. In this context, SRAM-based FPGAs and their inherent run-time reconfigurability, when coupled with smart power management strategies, are a suitable solution. However, they usually fail in user accessibility and ease of development. In this paper, an integrated framework to develop FPGA-based high-performance embedded systems for Edge Computing in Cyber-Physical Systems is presented. This framework provides a hardware-based processing architecture, an automated toolchain, and a runtime to transparently generate and manage reconfigurable systems from high-level system descriptions without additional user intervention. Moreover, it provides users with support for dynamically adapting the available computing resources to switch the working point of the architecture in a solution space defined by computing performance, energy consumption and fault tolerance. Results show that it is indeed possible to explore this solution space at run time and prove that the proposed framework is a competitive alternative to software-based edge computing platforms, being able to provide not only faster solutions, but also higher energy efficiency for computing-intensive algorithms with significant levels of data-level parallelism.

Introduction The presence of erectile dysfunction (ED) could be a warning of vascular disease in different arterial territories. Aim The aim of this study was to investigate the association between ED and the presence of atherosclerosis in 2 different vascular beds: carotid and lower limbs. Methods A total of 614 volunteers between 45 and 74 years of age (mean age 61.0 years) were randomly selected from the general population. ED was assessed using the International Index of Erectile Function (IIEF-5). Ankle-brachial index (ABI) measurement and carotid atherosclerosis were evaluated by echo-Doppler. Main Outcome Measures Mean carotid intima-media thickness (IMT), prevalence of carotid plaques, mean ABI, and prevalence of ABI < 0.9 were the main outcome measures. Results ED was present in 373 subjects (59.7%). Mean carotid IMT was significantly higher in men with ED (0.762 ± 0.151 mm vs 0.718 ± 0.114 mm, P < .001). Also the global prevalence of carotid plaques was more frequent in men with ED (63.8% vs 44.8%, P < .001), even after adjusting by age, cardiovascular risk factors, and ongoing treatment (P = .039). Both the IMT and the prevalence of carotid plaques increased significantly with ED severity (P trend .004 and <.001, respectively). There were no significant differences between groups neither in mean ABI nor in the prevalence of subjects with ABI < 0.9. However, there was a trend to a lower ABI and a higher prevalence of ABI < 0.9 with increasing ED severity. Conclusion In the general population, the presence of ED identifies subjects with higher atherosclerosis burden in carotid arteries but not in the lower extremities.

Combined cataract and glaucoma surgery with intraoperative 5-FU was associated with good long-term IOP control similar to that after phacoemulsification with intraoperative 5-FU in eyes with previous trabeculectomy.

Hyperspectral data processing is a computationally intensive task that is usually performed in high-performance computing clusters. However, in remote sensing scenarios, where communications are expensive, a compression stage is required at the edge of data acquisition before transmitting information to ground stations for further processing. Moreover, hyperspectral image compressors need to meet minimum performance and energy-efficiency levels to cope with the real-time requirements imposed by the sensors and the available power budget. Hence, they are usually implemented as dedicated hardware accelerators in expensive space-grade electronic devices. In recent years though, these devices have started to coexist with low-cost commercial alternatives in which unconventional techniques, such as run-time hardware reconfiguration are evaluated within research-oriented space missions (e.g., CubeSats). In this paper, a run-time reconfigurable implementation of a low-complexity lossless hyperspectral compressor (i.e., CCSDS 123) on a commercial off-the-shelf device is presented. The proposed approach leverages an FPGA-based on-board processing architecture with a data-parallel execution model to transparently manage a configurable number of resource-efficient hardware cores, dynamically adapting both throughput and energy efficiency. The experimental results show that this solution is competitive when compared with the current state-of-theart hyperspectral compressors and that the impact of the parallelization scheme on the compression rate is acceptable when considering the improvements in terms of performance and energy consumption. Moreover, scalability tests prove that run-time adaptation of the compression throughput and energy efficiency can be achieved by modifying the number of hardware accelerators, a feature that can be useful in space scenarios, where requirements change over time (e.g., communication bandwidth or power budget). INDEX TERMS Data compression, dynamic and partial reconfiguration, FPGAs, high-performance embedded computing, hyperspectral images, on-board processing.