Osama A. Omer

Adaptive Hybrid PAPR Reduction in OFDM System

High Peak to Average Power Ratio (PAPR) is still one of the most important challenges in Orthogonal Frequency Division Multiplexing (OFDM) system. In this paper, we propose a novel scheme based on Selected Mapping (SLM) technique for PAPR reduction in OFDM system. The proposed scheme is an adaptive hybrid scheme, which is based on the SLM technique and multiple transformation techniques. In the proposed scheme, instead of using only Discrete Fourier Transform (DFT) in OFDM system, we suggest to use Discrete Sine Transform (DST) and Discrete Cosine Transform (DCT) as well to get the lowest PAPR. In addition, the multiplied phase in the SLM is adapted to reduce the PAPR to an acceptable low PAPR level. Simulation results show that the proposed scheme technique can reduce the PAPR to about 4 dB in case of 256 sub-carriers and in case of 512 sub-carriers at clipping probability of 10 -3 .

Compressive sensing MRI using dual tree complex wavelet transform with wavelet tree sparsity

Magnetic resonance imaging is one of the most accurate imaging techniques that can be used to detect several diseases, where other imaging methodologies fail. Long scanning time is one of most serious drawback of the MRI modality. Compressed sensing contributed in solving this drawback and decrease the acquisition time of MRI images by reducing the quantity of the measured data that are desirable for reconstruction of an image. In this paper, a new scheme has been realized to reconstruct a high-quality image from smaller amount of measured data. The realized algorithm opportunists the sparsity of the finite difference and the wavelet tree sparsity side by side with the dual-tree wavelet transform as sparsifying transform, by manipulating them in the reconstruction problem as regularization terms. Indeed, exploiting the sparsity of wavelet tree achieves further lessening in the amount of measured data that are needed for the reconstruction, while the utilization of the dual tree wavelet transform as sparsifying transform mitigates the shortcomings of the usage of conventional wavelet transforms in the reconstruction problem. Our technique boosts the signal-to-noise ratio of the image to be reconstructed against the state-of-the-art methods.

A Framework for Satellite Image Enhancement Using Quantum Genetic and Weighted IHS+Wavelet Fusion Method

this paper examined the applicability of quantum genetic algorithms to solve optimization problems posed by satellite image enhancement techniques, particularly super-resolution, and fusion. We introduce a framework starting from reconstructing the higher-resolution panchromatic image by using the subpixel-shifts between a set of lower-resolution images (registration), then interpolation, restoration, till using the higher-resolution image in pan-sharpening a multispectral image by weighted IHS+Wavelet fusion technique. For successful super-resolution, accurate image registration should be achieved by optimal estimation of subpixel-shifts. Optimal-parameters blind restoration and interpolation should be performed for the optimal quality higher-resolution image. There is a trade-off between spatial and spectral enhancement in image fusion; it is difficult for the existing methods to do the best in both aspects. The objective here is to achieve all combined requirements with optimal fusion weights, and use the parameters constraints to direct the optimization process. QGA is used to estimate the optimal parameters needed for each mathematic model in this framework “Super-resolution and fusion.” The simulation results show that the QGA-based method can be used successfully to estimate automatically the approaching parameters which need the maximal accuracy, and achieve higher quality and efficient convergence rate more than the corresponding conventional GA-based and the classic computational methods.

Simultaneous Reconstruction and Resolution Enhancement for Magnetic Particle Imaging

Spatial resolution is an essential parameter for magnetic particle imaging (MPI). The spatial resolution of MPI depends, among other things, on the particles diameter and sampling frequency. The spatial resolution increases when increasing the particle diameter. However, large particles suffer from relaxation effects and are not preferred in some applications. On the other hand, spatial resolution increases with the sampling frequency, which in turn increases the number of sampling points. As an alternative solution for resolution enhancement, super-resolution (SR) is proved to be beneficial in improving the image quality of many medical imaging systems without the need for significant hardware alteration. In this paper, we propose to use small particle diameter and low sampling frequency to obtain multiple low-resolution (LR) magnetic particle measurements and apply a resolution enhancement technique to reconstruct a higher resolution magnetic particle concentration. Unlike the conventional SR techniques, we propose to reconstruct a high-resolution concentration from the measured LR signals instead of reconstructing LR concentrations and then post-process these concentrations to get a higher resolution concentration. Simulation results show that the resolution of MPI can be increased by incorporating resolution enhancement technique without increasing the particle diameter. In addition, simulation results show that using simultaneous reconstruction and resolution enhancement results in sharper concentrations and that this procedure is more robust against noise than using SR as a post-process.

Enhanced MFSK spectral efficiency based on super-resolution spectral estimation

Single tone M-ary frequency shift keying (MFSK) enjoys constant envelop transmission at the expense of much lower spectral efficiency (SE). Many research efforts were given for enhancing the SE of MFSK. In this paper, SE of coherent single tone MFSK is improved by filling up the same allocated spectral band-width by larger number of uniformly spaced non-orthogonal tones. The main idea resides in two main points, first, regarding the detection task as an operation of super-resolution (SR) spectral line estimation, second, highlighting implicit sparsity construction of MFSK scheme. Compressive sensing (CS) based SR approaches are enabled for resolving activated atom from over-complete dictionary. Moreover, more general keying schemes can be performed on learned dictionary instead of the classical Fourier (sinusoidal) dictionary, where each atom may occupy the whole allocated spectrum instead of residing on a specific tone. This allows better diversity against narrow band interference and frequency selective fading channel under sever fading conditions but, on the cost of losing the constant envelop advantage.

Damping shift keying (Dsk): A new modulation space for single carrier communications

While the amplitude, frequency and phase, are the well-known carrier features that can be modulated according to digital data stream, there is still another unexploited sinusoidal feature. Sinusoidal damping factor is the suggested modulation technique that can be used in carrier communication. In this paper, damping shift keying (DSK) is proposed as a new modulation space that can be combined with conventional modulation techniques to increase the data rate per symbol. In addition to the complex amplitude modulation, the carrier can be modulated using multi-damping factors according to the data stream. Combination of DSK with quadrature amplitude modulation (QAM) increases the data rate of the so called Damped QAM (D-QAM). Simulation results measures the performance of the proposed D-4PSK (carrying 3 bits/symbol) against conventional QPSK (carrying 2 bits/symbol). The proposed scheme exhibits nearly the same bit-error-rate (BER) performance but with 50% increase in the data rate. Moreover, in this paper all-feature shift-keying (ALFSK) schemes will be defined which allows modulating of all sinusoidal features (amplitude, phase, frequency and damping factor) simultaneously.

Damped frequency division multiplexing: A super-resolution multicarrier scheme

Although OFDM has efficient spectrum usage, it still has about 25% of symbol time lost in the insertion of cyclic prefixes (CP) overhead between consecutive symbols for regarding Inter Symbol Interference (ISI). The proposed DFDM introduces spectral efficient multicarrier modulation scheme through getting rid of CP which represents loss in power and bandwidth, while accounting for ISI resulting from multi-path time dispersion. By employment of exponentially damped waveform, it is possible to have symbols with highly attenuated end (damped tail). Under time dispersion channel, the damped tail will cause minimal interference effect on the next symbol. DFDM can be incorporated as OFDM alternative for increasing data rate for local area communication (i.e., under high SNR).

High resolution magnetic particle imaging with low density trajectory

The spatial resolution is an important factor in MPI. The trajectory density is one of many parameters affecting the spatial resolution of MPI. To obtain a high-resolution (HR) concentration one can increase the trajectory density or increase particle diameters. However, increasing the trajectory density leads to increase of the computational complexity. Also, large particles suffer from relaxation effects. In addition, imaging with system matrix that exhibits high grid leads to higher resolution but requires much calibration time for the system matrix. This work suggests to use small particle diameters with low density trajectory while sampling with low dimension system matrix and fuses these low-resolution (LR) concentrations as a post process to get HR concentrations.

Modified LTE system with low PAPR and BER

In this paper, we propose a high performance LTE system. The performance of the LTE system is enhanced in two stages. The first stage is the reduction of the High Peak to Average Power Ratio (PAPR) of OFDM signal. The second stage is the improvement of the channel estimation. The PAPR reduction is based on selected mapping, where trigonometric transformation with minimum PAPR is selected for each partitioned block. The channel is estimated via enhanced least square estimator by using wavelet-based de-noising method to reduce additive white Gaussian noise and inter-carrier interference (ICI). The simulation results, based on ITU pedestrian Channel model, show that the proposed modified OFDM can reduce the PAPR with ~3 dB compared to traditional OFDM and improved the bit error rate (BER) for the system.

Satellite images registration approach using Lorentzian norm with an artificial immune system

Image registration is an important operation in many aerospace imaging applications, so that, in this paper a promising approach is proposed for satellite images registration. In this approach, we propose to use the Lorentzian norm as a fitness function with an artificial immune system optimization (AIS) technique to reduce the registration outlier and avoid trapping into local minima. To demonstrate the effectiveness of the proposed approach it is compared with the state-of-the-art Lukas-Kanade registration approach for many data sets.