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Abstract-Chip-MultiProcessor (CMP) architectures are becoming more and more popular as an alternative to the traditional processors that only extract instruction-level parallelism from an application. CMPs introduce complexities when accounting CPU utilization. This is due to the fact that the progress done by an application during an interval of time highly depends on the activity of the other applications it is co-scheduled with.In this paper, we identify how an inaccurate measurement of the CPU utilization affects several key aspects of the system like the application scheduling or the charging mechanism in data centers. We propose a new hardware CPU accounting mechanism to improve the accuracy when measuring the CPU utilization in CMPs and compare it with the previous accounting mechanisms. Our results show that currently known mechanisms lead to a 19% average error when it comes to CPU utilization accounting. Our proposal reduces this error to less than 1% in a modeled 4-core processor system.

Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this application into standard diabetic care protocols requires to overcome technical issues, particularly the foot sole segmentation. In this work we implemented and evaluated several segmentation approaches which include conventional and Deep Learning methods. Multimodal images, constituted by registered visual-light, infrared and depth images, were acquired for 37 healthy subjects. The segmentation methods explored were based on both visual-light as well as infrared images, and optimization was achieved using the spatial information provided by the depth images. Furthermore, a ground truth was established from the manual segmentation performed by two independent researchers. Overall, the performance level of all the implemented approaches was satisfactory. Although the best performance, in terms of spatial overlap, accuracy, and precision, was found for the Skin and U-Net approaches optimized by the spatial information. However, the robustness of the U-Net approach is preferred.

Processor architectures combining several paradigms of Thread-Level Parallelism (TLP), such as CMP processors in which each core is SMT, are becoming more and more popular as a way to improve performance at a moderate cost. However, the complex interaction between running tasks in hardware shared resources in multi-TLP architectures introduces complexities when accounting CPU time (or CPU utilization) to tasks. The CPU utilization accounted to a task depends on both the time it runs in the processor and the amount of processor hardware resources it receives. Deploying systems with accurate CPU accounting mechanisms is necessary to increase fairness. Moreover, it will allow users to be fairly charged on a shared data center, facilitating server consolidation in future systems.In this article we analyze the accuracy and hardware cost of previous CPU accounting mechanisms for pure-CMP and pure-SMT processors and we show that they are not adequate for CMP+SMT processors. Consequently, we propose a new accounting mechanism for CMP+SMT processors which: (1) increases the accuracy of accounted CPU utilization; (2) provides much more stable results over a wide range of processor setups; and (3) does not require tracking all hardware shared resources, significantly reducing its implementation cost. In particular, previous proposals lead to inaccuracies between 21% and 79% when measuring CPU utilization in an 8-core 2-way SMT processor, while our proposal reduces this inaccuracy to less than 5.0%.

This work presents a revision of four different registration methods for thermal infrared and visible images captured by a camera-based prototype for the remote monitoring of diabetic foot. This prototype uses low cost and off-the-shelf available sensors in thermal infrared and visible spectra. Four different methods (Geometric Optical Translation, Homography, Iterative Closest Point, and Affine transform with Gradient Descent) have been implemented and analyzed for the registration of images obtained from both sensors. All four algorithms’ performances were evaluated using the Simultaneous Truth and Performance Level Estimation (STAPLE) together with several overlap benchmarks as the Dice coefficient and the Jaccard index. The performance of the four methods has been analyzed with the subject at a fixed focal plane and also in the vicinity of this plane. The four registration algorithms provide suitable results both at the focal plane as well as outside of it within 50 mm margin. The obtained Dice coefficients are greater than 0.950 in all scenarios, well within the margins required for the application at hand. A discussion of the obtained results under different distances is presented along with an evaluation of its robustness under changing conditions.