Mohamed F. Abu-Elyazeed

FPGA Implementation of an Efficient 3D-WT Temporal Decomposition Algorithm for Video Compression

In this paper, the hardware design and FPGA implementation of a new efficient three-dimensional wavelet transform (3D-WT) algorithm for video compression is presented. This algorithm performs the temporal decomposition of a video sequence in a more efficient way than the classical 3D-WT algorithm. It exhibits lower memory demands and lower latencies for the compression and decompression processes than the classical one. This makes the addressed algorithm fits better for real-time video processing. The hardware design is based on the use of the transposed-form FIR filter structure which is hereby compared to the direct-form FIR filter. The former is found to exhibit less clock latency and less chip area utilization. The reference design is made scalable to any wavelet filter coefficients and to fit for any frame size. The chip area utilization is compared upon the FPGA implementation at different frame sizes. The designed system can be used for real-time video applications.

Generalized fractional logistic map suitable for data encryption

This paper presents a generalized form of the fractional logistic map. Two general parameters a and b are added to the classical fractional logistic equation. The effect of such parameters on the map is studied explicitly, in combination with the fractional order parameter α, which offers an extra degree of freedom increasing the design flexibility and adding more controllability on the design. The vertical and the zooming map are two special maps that arise as a result of the added parameters. Moreover, different design problems are offered in this work, as a resultant of the control of all these parameters at hand. This shows that any application specific map can be designed, highlighting the flexibility and integrity of the design. The combination of the added extra parameters a and b in addition to the system parameter ρ and the initial condition x0, as well as the fractional order parameter α makes the proposed generalized fractional logistic map the most favorable in constructing more efficient encryption keys.

Adaptive reduced-set matching pursuit for compressed sensing recovery

Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Various greedy recovery algorithms have been proposed to achieve a lower computational complexity compared to the optimal ℓ1 minimization, while maintaining a good reconstruction accuracy. We propose a new greedy recovery algorithm for compressed sensing, called the Adaptive Reduced-set Matching Pursuit (ARMP). Our algorithm achieves higher reconstruction accuracy at a significantly low computational complexity compared to existing greedy recovery algorithms. It is even superior to ℓ1 minimization in terms of the normalized time-error product, a metric that we introduced to measure the trade-off between the reconstruction time and error.

RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction.

Graphical abstract

Generalized double-humped logistic map-based medical image encryption

Graphical abstract

Biomedical image encryption based on double-humped and fractional logistic maps

This paper presents a secured highly sensitive image encryption system suitable for biomedical applications. The pseudo random number generator of the presented system is based on two discrete logistic maps. The employed maps are: the double humped logistic map as well as the fractional order logistic map. The mixing of the map parameters and the initial conditions x0, offers a great variety for constructing more efficient encryption keys. Different analyses are introduced to measure the performance of the proposed encryption system such as: histogram analysis, correlation coefficients, MAE, NPCR as well as UACI measurements. The encryption system is proven to be highly sensitive to ±0.001% perturbation of the logistic maps parameters. The system is tested on medical images of knee MRI and a lung X-rays.

Accurate dynamic power estimation for CMOS combinational logic circuits with real gate delay model

Dynamic power estimation is essential in designing VLSI circuits where many parameters are involved but the only circuit parameter that is related to the circuit operation is the nodes’ toggle rate. This paper discusses a deterministic and fast method to estimate the dynamic power consumption for CMOS combinational logic circuits using gate-level descriptions based on the Logic Pictures concept to obtain the circuit nodes’ toggle rate. The delay model for the logic gates is the real-delay model. To validate the results, the method is applied to several circuits and compared against exhaustive, as well as Monte Carlo, simulations. The proposed technique was shown to save up to 96% processing time compared to exhaustive simulation.

Fast matching pursuit for sparse representation-based face recognition

Even though face recognition is one of the most studied pattern recognition problems, most existing solutions still lack efficiency and high speed. Here, the authors present a new framework for face recognition which is efficient, fast, and robust against variations of illumination, expression, and pose. For feature extraction, the authors propose extracting Gabor features in order to be resilient to variations in illumination, facial expressions, and pose. In contrast to the related literature, the authors then apply supervised locality-preserving projections (SLPP) with heat kernel weights. The authors’ feature extraction approach achieves a higher recognition rate compared to both traditional unsupervised LPP and SLPP with constant weights. For classification, the authors use the recently proposed sparse representation-based classification (SRC). However, instead of performing SRC using the computationally expensive minimisation, the authors propose performing SRC using fast matching pursuit, which considerably improves the classification performance. The authors’ proposed framework achieves ∼99% recognition rate using four benchmark face databases, significantly faster than related frameworks.

Comparative Mechanical Analysis of Deep Brain Stimulation Electrodes

The new field of neuro-prosthetics focuses on the design and implementation of neural prostheses to restore some of the lost neural functions. The electrode-tissue contacts remain one of the major obstacles of neural prostheses microstructure. Recently, Microelectrode fabrication techniques have been developed to have a long-term and stable interface with the brain. In this paper, a comparative analysis of finite element models (FEM) for several electrode layouts is conducted. FEM involves parametric and sensitivity analysis to show the effects of the different design parameters on the electrode mechanical performance. These parameters include electrode dimensions, geometry, and materials. The electrodes mechanical performance is evaluated with various analysis techniques including: linear buckling analysis, stationary analysis with axial and shear loading, and failure analysis for brittle and ductile materials. Finally, a novel figure of merit (FOM) is presented and dedicated to the various electrodes prototypes. The proposed designs take into account mechanical performance, fabrication cost, and cross sectional area of the electrode. The FOM provides important design insights to help the electrodes designers to select the best electrode design parameters that meet their design constraints.

Image encryption based on double-humped and delayed logistic maps for biomedical applications

This paper presents a secured highly sensitive image encryption system suitable for biomedical applications. The pseudo random number generator of the presented system is based on two discrete logistic maps. The employed maps are: the one dimensional double humped logistic map as well as the two-dimensional delayed logistic map. Different analyses are introduced to measure the performance of the proposed encryption system such as: histogram analysis, correlation coefficients, MAE, NPCR as well as UACI measurements. The encryption system is proven to be highly sensitive to ±0.001% perturbation of the logistic maps parameters. The system is tested on medical images of palm print as well as Parkinson disease MRI images.