Head portraits are popular in traditional painting. Automating portrait painting is challenging as the human visual system is sensitive to the slightest irregularities in human faces. Applying generic painting techniques often deforms facial structures. On the other hand portrait painting techniques are mainly designed for the graphite style and/or are based on image analogies; an example painting as well as its original unpainted version are required. This limits their domain of applicability. We present a new technique for transferring the painting from a head portrait onto another. Unlike previous work our technique only requires the example painting and is not restricted to a specific style. We impose novel spatial constraints by locally transferring the color distributions of the example painting. This better captures the painting texture and maintains the integrity of facial structures. We generate a solution through Convolutional Neural Networks and we present an extension to video. Here motion is exploited in a way to reduce temporal inconsistencies and the shower-door effect. Our approach transfers the painting style while maintaining the input photograph identity. In addition it significantly reduces facial deformations over state of the art.

In this paper, we present a spectrum monitoring framework for the detection of radar signals in spectrum sharing scenarios. The core of our framework is a deep convolutional neural network (CNN) model that enables Measurement Capable Devices (MCDs) to identify the presence of radar signals in the radio spectrum, even when these signals are overlapped with other sources of interference, such as commercial Long-Term Evolution (LTE) and Wireless Local Area Network (WLAN). We collected a large dataset of RF measurements, which include the transmissions of multiple radar pulse waveforms, downlink LTE, WLAN, and thermal noise. We propose a pre-processing data representation that leverages the amplitude and phase shifts of the collected samples. This representation allows our Convolutional Neural Network (CNN) model to achieve a classification accuracy of 99.6% on our testing dataset. The trained CNN model is then tested under various SNR values, outperforming other models, such as spectrogram-based CNN models.

The concept of cancellation carriers (CCs) has been proposed in the literature for sidelobe suppression for orthogonal frequency division multiplexing (OFDM) systems. Subcarriers at the edges of the OFDM spectrum are used for sidelobe reduction while the remaining subcarriers are used for data transmission. Existing CCs techniques require performing complex optimization that should be applied for each OFDM symbol which is not suitable for real-time applications. In this paper, we propose a heuristic approach for CCs. The proposed algorithm involves few computations compared with all other techniques proposed in the literature. Simulation results show that sidelobe reduction performance can be close to the optimal. Moreover, the proposed algorithm is implemented in a software defined radio and implementation results prove that it can be introduced for real-time applications.

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