Machine learning techniques combined with wearable electronics can deliver accurate short-term blood glucose level prediction models. These models can learn personalized glucose–insulin dynamics based on the sensor data collected by monitoring several aspects of the physiological condition and daily activity of an individual. Until now, the prevalent approach for developing data-driven prediction models was to collect as much data as possible to help physicians and patients optimally adjust therapy. The objective of this work was to investigate the minimum data variety, volume, and velocity required to create accurate person-centric short-term prediction models. We developed a series of these models using different machine learning time series forecasting techniques suitable for execution within a wearable processor. We conducted an extensive passive patient monitoring study in real-world conditions to build an appropriate data set. The study involved a subset of type 1 diabetic subjects wearing a flash glucose monitoring system. We comparatively and quantitatively evaluated the performance of the developed data-driven prediction models and the corresponding machine learning techniques. Our results indicate that very accurate short-term prediction can be achieved by only monitoring interstitial glucose data over a very short time period and using a low sampling frequency. The models developed can predict glucose levels within a 15-min horizon with an average error as low as 15.43 mg/dL using only 24 historic values collected within a period of sex hours, and by increasing the sampling frequency to include 72 values, the average error is reduced to 10.15 mg/dL. Our prediction models are suitable for execution within a wearable device, requiring the minimum hardware requirements while at simultaneously achieving very high prediction accuracy.

The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.

In this paper, the impact of considering diffuse multipath components at mm-wave frequencies as well as the significance of selecting appropriate diffuse scattering model parameters is shown. Two different diffuse models, namely, the Lambertian model and the directive model, have been parameterized for several materials typically present in indoor environments. These models are formulated to embed the diffuse scattering phenomenon easily into ray tracing tools. The estimation of the parameters has been performed by comparing measurements and simulations using the models. Once the best fitting parameters have been estimated, they are included in the diffuse components simulation section of a general ray tracing tool. This tool has been used to simulate the power delay profile at 60 GHz in an indoor scenario, including single and double bounce diffuse components. Thanks to the estimated model parameters, the wireless channel at the 60-GHz band can be analyzed, including the diffuse scattering phenomenon, without the need for any previous measurement or simulation. Thus, the channel analysis with ray tracing tools, including dense components, becomes easier, faster, and more reliable.

In this paper, a 4 times 4 indoor multiple-input multiple-output (MIMO) measurement campaign at a frequency of 2.45 GHz is presented. The main contribution of this work is the analysis of the impact of radio-wave polarization in MIMO systems operating at a typical indoor scenario through the calculation-from the measurements carried out-of a great deal of parameters such as the mean path loss, the cross polarization discrimination (XPD), and the RMS delay spread, which are all essential to estimate the performance of real MIMO systems. In this sense, some path loss models-which have been adjusted according to the measurements-are given, taking into account polarization, attenuation through walls, and the effect of T-junctions existing in the considered indoor scenario. Moreover, additional parameters such as the K-factor and statistical distribution, as well as spatial parameters, are discussed.

This letter presents an extensive multidimensional analysis of line-of-sight (LOS) experimental data and simulations at 60 GHz over a 9-GHz bandwidth. Numerical versions of the measured multiple-input-multiple-output (MIMO) channel transfer functions were obtained with a ray-tracing engine that includes single-order diffuse scattering. The received power, RMS delay spread (DS), and maximum excess delay (MED) computed from both measured and simulated data indicate that diffuse scattering improves ray-tracing-based modeling. Moreover, the multipath components (MPCs) extracted from both sets of data using the high-resolution estimator RiMAX were statistically compared. The analysis of the results shows that even a raw description of the environment can be used to predict millimeter-wave (mmW) propagation with ray tracing.