Abstract
Resistive switching behaviour has been demonstrated to be a common characteristic to many materials. In this regard, research teams to date have produced a plethora of different devices exhibiting diverse behaviour, but when system design is considered, finding a 'one-model-fits-all' solution can be quite difficult, or even impossible. However, it is in the interest of the community to achieve more general modelling tools for design that allows a quick model update as devices evolve. Laying the grounds with such a principle, this paper presents an artificial neural network learning approach to resistive switching modelling. The efficacy of the method is demonstrated firstly with two simulated devices and secondly with a 4 mu m(2) amorphous IGZO device. For the amorphous IGZO device, a normalized root-mean-squared error (NRMSE) of 5.66 x 10(-3) is achieved with a [2, 50,50 ,1] network structure, representing a good balance between model complexity and accuracy. A brief study on the number of hidden layers and neurons and its effect on network performance is also conducted with the best NRMSE reported at 4.63 x 10(-3). The low error rate achieved in both simulated and real-world devices is a good indicator that the presented approach is flexible and can suit multiple device types.

Abstract
This paper reports the development of a power recycling network for a wireless radio-frequency (RF) transmitter combiner. The transmitter makes use of two RF power amplifiers (PAs) in an outphasing architecture, connected at the output by a 180-degree hybrid combiner. In general, to provide isolation between the PAs and prevent nonlinear distortion, an isolation resistor is usually applied at the four-port combiner. However, the main drawback of such approach is the power dissipated at the isolation port, which drastically reduces the overall power efficiency of the outphasing transmitter. In the present work, the isolation port is replaced by an active network that provides the required input impedance for isolation, at the same time it converts the RF signal into dc, feeding it back to the transmitter power supply. Hence, this way, one recycles the power that would be lost in the isolating resistor. The proposed active network comprises a circulator, a resonant rectifier and a dcdc converter that can be regulated by a maximum power point tracking (MPPT) algorithm. Simulation results for this power recycling system are provided, denoting 61-percent maximum efficiency achieved for an increase of 22-percent peak efficiency for QAM signals with a bandwidth of 250-kHz and carrier frequency equal to 250-MHz when operating at 41-miliwatt output power.

Abstract
The operational transconductance amplifier (OTA) is, probably, the most relevant building block in analog circuits. However, its design has become particularly difficult in deep nanoscale CMOS technologies. Consequently, during the past decade, several inverter-based continuous-time and switched-capacitor (SC) amplifier circuit solutions have been proposed to overcome the limitations imposed by deep-submicron processes. Inverters scale friendly with the technology downscaling, but their applicability depends on some key performance parameters such as, energy-efficiency, die area, low-frequency (DC) gain, gain-bandwidth product (GBW) and linearity versus output-swing (OS). This paper presents three inverter-based SC amplifiers, namely a single inverter, a three-stage inverter, and a three-stage inverter with a multipath. The key performance parameters are simulated and fairly compared. The impact of their linearity on systems, depending on the application, is also discussed.

Abstract
As a key technology to help bridging the gap between deaf and hearing people, sign language recognition (SLR) has become one of the most active research topics in the human-computer interaction field. Although several SLR methodologies have been proposed, the development of a real-world SLR system is still a very challenging task. One of the main challenges is related to the large intersigner variability that exists in the manual signing process of sign languages. To address this problem, we propose a novel end-to-end deep neural network that explicitly models highly discriminative signer-independent latent representations from the input data. The key idea of our model is to learn a distribution over latent representations, conditionally independent of signer identity. Accordingly, the learned latent representations will preserve as much information as possible about the signs, and discard signer-specific traits that are irrelevant for recognition. By imposing such regularization in the representation space, the result is a truly signer-independent model which is robust to different and new test signers. The experimental results demonstrate the effectiveness of the proposed model in several SLR databases.

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Abstract