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

Abstract
In recent years, deep neural networks have shown significant progress in computer vision due to their large generalization capacity; however, the overfitting problem ubiquitously threatens the learning process of these highly nonlinear architectures. Dropout is a recent solution to mitigate overfitting that has witnessed significant success in various classification applications. Recently, many efforts have been made to improve the Standard dropout using an unsupervised merit-based semantic selection of neurons in the latent space. However, these studies do not consider the task-relevant information quality and quantity and the diversity of the latent kernels. To solve the challenge of dropping less informative neurons in deep learning, we propose an efficient end-to-end dropout algorithm that selects the most informative neurons with the highest correlation with the target output considering the sparsity in its selection procedure. First, to promote activation diversity, we devise an approach to select the most diverse set of neurons by making use of determinantal point process (DPP) sampling. Furthermore, to incorporate task specificity into deep latent features, a mutual information (MI)-based merit function is developed. Leveraging the proposed MI with DPP sampling, we introduce the novel DPPMI dropout that adaptively adjusts the retention rate of neurons based on their contribution to the neural network task. Empirical studies on real-world classification benchmarks including, MNIST, SVHN, CIFAR10, CIFAR100, demonstrate the superiority of our proposed method over recent state-of-the-art dropout algorithms in the literature.