Ahmed Selim

Painting style transfer for head portraits using convolutional neural networks

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.

Efficient sidelobe suppression for OFDM systems using advanced cancellation carriers

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.

Real-time sidelobe suppression for OFDM systems using advanced subcarrier weighting

The concept of subcarrier weighting (SW) has been proposed in the literature for sidelobe suppression for OFDM systems. Each OFDM subcarrier is multiplied with a real-valued weight that is determined in order not to interfere with adjacent users. The problem with the SW technique is involving a very complex optimization that has to be performed for each OFDM symbol which limits the suitability for real-time applications. In this paper, we propose a heuristic approach called advanced subcarrier weighting (ASW). It can achieve considerable sidelobe suppression while requiring significantly less computational resources than the optimal solution. Simulation results show that the performance of the proposed algorithm can be close to the optimal performance. Moreover, the proposed ASW algorithm is implemented in a software defined radio. Implementation results prove that it can be introduced for real-time transmissions.

Efficient sidelobe suppression for OFDM systems with peak-to-average power ratio reduction

This paper describes a practical method for sidelobe suppression for orthogonal frequency-division multiplexing (OFDM) signals. Most of the existing sidelobe suppression techniques are computationally expensive. The proposed technique is simpler in terms of computational complexity, and it enables high suppression capability. In addition, most of the related work deals with either sidelobe suppression or peak-to-average power ratio (PAPR) reduction which are mutually exclusive in some cases. However, the proposed technique is compatible with some PAPR reduction techniques. Simulation results show that the proposed technique can achieve significant sidelobe suppression. In addition, under adjacent channel interference constraints, the proposed algorithm outperforms the conventional OFDM transmissions in terms of bit-error rate (BER). Moreover, the proposed algorithm is implemented on a software defined radio and tested for practical and real-time transmissions.

Efficient Cyclic Prefix Reconstruction for Shaped OFDM Systems without Cyclic Prefix

This paper describes a method to mitigate interference when the cyclic prefix (CP) is totally removed for shaped orthogonal frequency division multiplexing (OFDM) signals. The CP is removed to increase system throughput while shaping is applied to increase the spectral efficiency. Samples in the shaping extension are used to restore part of the cyclicity information missing in the

Context-aware cognitive radio using deep learning

i

A method for reducing the out-of-band emissions for OFDM systems

th received symbol. Furthermore, optimally weighted samples of the

Improved Out-of-Band Emissions Reduction for OFDM Systems

(i+1)

A software radio LTE network testbed for video quality of experience experimentation

th symbol are used to restore the remaining part of the cyclicity information with some residual error. Simulation results show that the bit error rate (BER) can be very close to the optimal bound in certain scenarios, while the complexity added to the standard OFDM receiver is low.

Out-of-Band Interference Reduction Using Subcarrier Weighting and Cancellation Carriers

This paper describes the design, experimental assessmant and Software Defined Radio (SDR) implementation of a Secondary User (SU) link for the IEEE DySPAN Challenge 2017. The objective is to successfully discern the behavior of and coexist with a Primary User (PU), whose channel access patterns vary over time. For that end, we utilize sensing, deep learning and dynamic optimization.