Abstract
A new methodology for fault detection on wearable medical devices is proposed. The main strategy relies on correctly classifying the captured physiological signals, in order to distinguish whether the actual cause is a wearer health abnormality or a system functional flaw. Data fusion techniques, namely fuzzy logic, are employed to process the captured data, like the electrocardiogram and blood pressure, to increase the trust levels with which diagnostics are made. Concerning the wearer condition, additional information is provided after classifying the set of signals into normal or abnormal (e.g. arrhythmia, chest angina, and stroke). As for the monitoring system, once an abnormal situation is detected in its operation or in the sensors, a set of tests is run to check if actually the wearer shows a degradation of his health condition or if the system is reporting erroneous values.

Abstract
The present work addresses the development of a smart orchard irrigation system (SOIS) that performs the estimation of orchard evapotranspiration and the estimation of the soil salinization risk. Measurements of heat transfer are made to compute tree transpiration rate and soil water evaporation. The soil electrical conductivity is measured to compute the soil salinization risk. An inferential fuzzy algorithm is used to process data. This paper describes the physical principles underlining these estimations, the architecture of the data acquisition interface, and the construction and characterization of the probes used to perform the temperature measurements. The preliminary results shown here address the experimental evaluation of the performance of the probes inserted in the trees. Relative measurements with a precision of 0.2 degrees C were obtained which are in agreement with the minimum required for these applications.

Abstract
Wearable vital signals monitoring systems are promoting new assistance approaches in the healthcare system and are essential for the future connected health paradigm. The present work addresses the design of a cardiac and coronary monitoring system taking into consideration dependability and autonomy issues. A fuzzy logic approach is used to determine, in case deviations in the captured electrocardiogram are detected, whether these are occurring in the patient or in the system. As for autonomy, the use of data compression versus extra data processing balance is analysed to find operating conditions for which compression is worth to be used.

Abstract
This paper presents the design and implementation of a GaN-HEMT, class-J power amplifier suitable for cognitive radio transceivers, i.e., which presents high-efficiency and wideband characteristics, being these maintained for large load variations. Simulation results are presented which show large-signal measurement results of 30 dB gain with 60%-76% power-added efficiency (PAE) over a band of 1.3-2.3 GHz. Adaptivity to load changes is being developed to ensure PAE above 70% for large load variations.

Abstract
Development time and accuracy are measures that need to be taken into account when devising device models for a new technology. If complex circuits need to be designed immediately, then it is very important to reduce the time taken to realize the model. Solely based on data measurements, artificial neural networks (ANNs) modeling methodologies are capable of capturing small and large signal behavior of the transistor, with good accuracy, thus becoming excellent alternatives to more strenuous modeling approaches, such as physical and semi-empirical. This paper then addresses a static modeling methodology for amorphous Gallium-Indium-Zinc-Oxide - Thin Film Transistor (a-GIZO TFT), with different ANNs, namely: multilayer perceptron (MLP), radial basis functions (RBF) and least squares-support vector machine (LS-SVM). The modeling performance is validated by comparing the model outcome with measured data extracted from a real device. In case of a single transistor modeling and under the same training conditions, all the ANN approaches revealed a very good level of accuracy for large- and small-signal parameters (g(m) and g(d)), both in linear and saturation regions. However, in comparison to RBF and LS-SVM, the MLP achieves a very acceptable degree of accuracy with lesser complexity. The impact on simulation time is strongly related with model complexity, revealing that MLP is the most suitable approach for circuit simulations among the three ANNs. Accordingly, MLP is then extended for multiple TFTs with different aspect ratios and the network implemented in Verilog-A to be used with electric simulators. Further, a simple circuit (inverter) is simulated from the developed model and then the simulation outcome is validated with the fabricated circuit response.

Abstract
The work reported in this paper introduces a periodic switching technique applied to continuous-time filters, whose outcome is an equivalent filter with scaled time-constants. The principle behind the method is based on a procedure that extends the integration time by periodically interrupting the normal integration of the filter. The net result is an up scaling of the time constant, inversely proportional to the switching duty-cycle. This is particularly suitable for reducing the area occupied by passive devices in integrated circuits, as well as to accurately calibrate the filter dynamics. Previous works have been following this concept in an entirely continuous-time perspective, either focusing on specific circuits or using approximations to provide an extended analysis. This paper includes input/output sampling to derive a closed-form representation for the scaling technique herein coined as 'Filter & Hold' (F&H). A detailed mathematical analysis is described, demonstrating that the F&H concept represents an exact filtering solution. Simulation results and experimental measurements are provided to further validate the theoretical analysis for an F&H vector-filter prototype. Copyright (C) 2014 John Wiley & Sons, Ltd.