Objective gait assessment in Parkinson’s Disease (PD) is limited by the absence of large, diverse, and clinically annotated motion datasets. We introduce Care-PD, the largest publicly available archive of 3D mesh gait data for PD, and the first multi-site collection spanning 9 cohorts from 8 clinical centers. All recordings (RGB video or motion capture) are converted into anonymized SMPL meshes via a harmonized preprocessing pipeline. Care-PD supports two key benchmarks: supervised clinical score prediction (estimating Unified Parkinson’s Disease Rating Scale, UPDRS, gait scores) and unsupervised motion pretext tasks (2D-to-3D keypoint lifting and full-body 3D reconstruction). Clinical prediction is evaluated under four generalization protocols: within-dataset, cross-dataset, leave-one-dataset-out, and multi-dataset in-domain adaptation.To assess clinical relevance, we compare state-of-the-art motion encoders with a traditional gait-feature baseline, finding that encoders consistently outperform handcrafted features. Pretraining on Care-PD reduces MPJPE (from 60.8mm to 7.5mm) and boosts PD severity macro-F1 by 17\%, underscoring the value of clinically curated, diverse training data. Care-PD and all benchmark code are released for non-commercial research (Code, Data).

Mobile communications, not infrequently, are disrupted by multipath propagation in the wireless channel. In this context, this paper proposes a new blind concurrent equalization approach that combines a Phase Transmittance Radial Basis Function Neural Network (PTRBFNN) and the classic Constant Modulus Algorithm (CMA) in a concurrent architecture, with a Fuzzy Controller (FC) responsible for adapting the PTRBFNN and CMA step sizes. Differently from the Neural Network (NN) based equalizers present in literature, the proposed Fuzzy Controller Concurrent Neural Network Equalizer (FC-CNNE) is a completely self-taught concurrent architecture that does not need any training. The Fuzzy Controller inputs are based on the estimated mean squared error of the equalization process and on its variation in time. The proposed solution has been evaluated over standard multipath VHF/UHF channels defined by the International Telecommunication Union. Results show that the FC-CNNE is able to achieve lower residual steady-state MSE value and/or faster convergence rate and consequently lower Bit Error Rate (BER) when compared to Constant Modulus Algorithm-Phase Transmittance Radial Basis Function Neural Network (CMA-PTRBFNN) equalizer.

Cloud computing has been touted as a revolutionary concept in computing in the Information Age, since it enhances the quality of communication and it is highly cost effective. Cloud computing market has attracted the interest of several providers and corporations, creating an environment in which the user's Quality of Experience (QoE) becomes a competitive advantage. Cloud services are often available as Web applications, since Web browsers may provide a more user friendly interface. Thus, Web Application Response plays a critical role in the perceptions of cloud service users. This article proposes a methodology to evaluate the user Quality of Experience of cloud services, with focus on web applications, using the MOS score in a user-centered approach. This methodology estimates the QoE from the end-to-end response time and adjusts the estimated score according to the evaluation context, thought maximum session time. Estimation of QoE is a differentiating factor in choosing cloud service providers and defining the form of implementing cloud applications (e.g., Through programming language, page type or application server). The proposed methodology has been applied to three cloud service servers, located in Brazil, Europe and USA and several case studies in business contexts have been evaluated, comparing different clients and server applications in a monitored environment. The results point to the crucial role that the evaluation period plays in the comparison of solutions.