Zaki Nossair

An image watermarking scheme based on multiresolution analysis

A robust blind image-watermarking algorithm is proposed. The proposed algorithm is based on both the Discrete Wavelet Transform (DWT) and the Wavelet Packet Transform (WPT). The main idea of the proposed algorithm is to decompose the host image using DWT and WPT according to the size of the watermark. The watermark is embedded in the fine-scale bands of the WPT of the fine-scale bands of the last DWT decomposition level of the host image. Each pixel in the watermark is split into three parts, and the One-Bit per Coefficient (OBC) approach is applied for embedding the pixel. The final step is to compute the inverses - IWPT and IDWT - to obtain the watermarked image. In this algorithm, the obtained watermarked image has high Peak Signal to Noise Ratio (PSNR) and the extracted watermark has very high Normalized Correlation (NC). The proposed algorithm is robust to a variety of signal operations.

Prediction of subcutaneous glucose concentration for type-1 diabetic patients using a feed forward neural network

Insulin Dependent Diabetes Mellitus (IDDM) is a chronic disease characterized by the inability of the pancreas to produce sufficient amount of insulin. Daily compensation of the deficiency requires 4–6 insulin injections to be taken every day. The aim of this insulin therapy is to maintain normoglycemia—i.e., a blood glucose level between 4–7 [mmol/L]. To determine the quantity and timing of these injections, several different approaches are used. Prediction of future glucose values can be used for early hypoglycemic/hyperglycemic alarms for adjustment of insulin injections or insulin infusion rates of manual or automated pumps. Recent developments in continuous glucose monitoring (CGM) devices open new opportunities for glycemia management of diabetic patients. CGM technologies provide glucose readings at high frequency and consequently detailed insight into the subjects glucose variations. The objective of this research is to use glucose readings that are obtained from CGM devices, to develop a feed forward neural network model (NNM) to predict future glucose values. This NNM can be used in model predictive control systems to automatically adjust the glucose level in type-1 diabetic patients. The results of our research indicate that the NNM can be used to accurately predict future glucose values for prediction horizons of 30 minutes or less without time delay between the predicted output and the real glucose samples.

Blood glucose regulation using a neural network predictor with a fuzzy logic controller

Current insulin therapy for patients with type 1 diabetes often results in high variability in blood glucose concentration and may cause hyper-and hypoglycemic episodes. Closing the glucose control loop with a fully automated control system improves the quality of life for insulin-dependent patients. This paper presents a nonlinear model predictive control technique for glucose regulation in type 1 diabetic patients. The proposed technique uses a neural network as a nonlinear model for prediction of future glucose values and a fuzzy logic controller FLC to determine the insulin dose required to regulate the blood glucose level, especially after unmeasured meals. In the proposed technique, to avoid errors of meal estimation, the patient is not required to enter any data such as the meal time and size which was, in previous systems, necessary to determine the insulin bolus. The use of neural networks in predicting future glucose levels helps the proposed control strategy to handle delays associated with insulin absorption and time-lag between subcutaneous glucose readings and the plasma glucose level. The FLC uses the predicted glucose values to determine the required insulin bolus. A feed forward neural network FFNN and a recurrent neural network RNN are tested and evaluated as nonlinear glucose prediction models. Simulation results for three meal challenges are demonstrated. our results indicate that, the use of a neural network as a predictor along with a FL controller can decrease the postprandial glucose concentration, avoids hyper glycemia, and dynamically responds to glycemic challenges. The simulation results also indicate that, the use of a RNN in glucose prediction gives better results than the use of a FFNN. The RNN provides much better prediction performance than the FFNN especially at longer prediction horizons.

Improving the reconstruction efficiency of sparsity adaptive matching pursuit based on the Wilkinson matrix

Sparsity adaptive matching pursuit (SAMP) is a greedy reconstruction algorithm for compressive sensing signals. SAMP reconstructs signals without prior information of sparsity and presents better reconstruction performance for noisy signals compared to other greedy algorithms. However, SAMP still suffers from relatively poor reconstruction quality especially at high compression ratios. In the proposed research, the Wilkinson matrix is used as a sensing matrix to improve the reconstruction quality and to increase the compression ratio of the SAMP technique. Furthermore, the idea of block compressive sensing (BCS) is combined with the SAMP technique to improve the performance of the SAMP technique. Numerous simulations have been conducted to evaluate the proposed BCS-SAMP technique and to compare its results with those of several compressed sensing techniques. Simulation results show that the proposed BCS-SAMP technique improves the reconstruction quality by up to six decibels (dB) relative to the conventional SAMP technique. In addition, the reconstruction quality of the proposed BCS-SAMP is highly comparable to that of iterative techniques. Moreover, the computation time of the proposed BCS-SAMP is less than that of the iterative techniques, especially at lower measurement fractions.

Improved CNFET performance based on genetic algorithm parameters optimization

The performance of Carbon Nanotube Field Effect Transistors (CNFETs) depends critically on device parameters such as CNT diameter, number of nanotubes, and inter-nanotube spacing. To achieve a minimum Power-Delay Product (PDP) of the CNFET based digital design, the Genetic Algorithm (GA) is used in this paper to optimize CNFET device parameters. The results of GA optimization are found to be 10 carbon nanotubes with 1.33nm CNT diameter and 4nm spacing between them. The performance of CNFET digital circuits (such as XOR2, 2×1 MUX and D-latch) based on these optimum values is compared with that designed based on typical parameters. The simulation results demonstrate better performance of proposed CNFET based circuits with the significant reduction of PDP values in comparison with previous designs.

C22. Filterbank-Enhanced IHS Transform Method for Satellite Image Fusion

Image fusion is a basic tool for combining low spatial resolution multi-spectral image and high spatial resolution panchromatic image using advanced image processing techniques. Many approaches have been developed to combine these images to obtain a fused one. Intensity-hue-saturation IHS transform is one of the widespread image fusion methods in the remote sensing community. However, this method introduces color distortion; therefore many papers have investigated modifications for the IHS method that can reduce this problem. In this paper, a filterbank-enhanced IHS method is developed for fusing satellite images. In this method a group of filters (a low pass filter, a high pass filter and several band pass filters) are used for filtering the panchromatic image and the intensity component of the original multispectral image. The resultant filtered images are combined to obtain a fused image. Experimental results indicate that filterbank-enhanced IHS method gives improved results relative to other fusion techniques.

Direction of Arrival Tracking under Various Degrees of Correlation

The tracking devices in the cellular networks or the tracking radars prefer to have an estimated covariance matrix in a single snapshot to estimate the next direction of the moving target.They have achieved great success in tracking when the targets are uncorrelated. Recent research provides the deflation approach to estimate the direction of arrival (DOA) of stationary targets by using symmetric uniform linear arrays (ULAs) under different unknown fading conditions. In this paper, a modified deflation approach using an adaptive signal processing is proposed to achieve DOA tracking capability for moving targets under various degrees of correlation. Computer simulation results are provided to verify the theoritical analysis of the proposed method.

Comparative Study on Super-Resolution of Images

Super-resolution of images has become a very important research topic nowadays. There are many algorithms that have been developed to enhance the resolution of images. In this paper, we undertake a study for evaluating and comparing three of these algorithms. These three algorithms are: neural network algorithm, wavelet extrema extrapolation algorithm, and hallucinating faces algorithm. Our study indicated that: the better performance comes at the expense of higher complexity, large database, and more computational time. The hallucinating faces algorithm gives the largest peak signal to noise ratio (PSNR) when magnifying low dimensional faces and gives better output when the database contains larger number of images. The neural network algorithm gives better results for high dimensional faces, but it needs long time for training. The wavelet extrema extrapolation algorithm gives better results for high dimensional faces than for low dimensional faces. The performance of these three algorithms gets better as the dimension of input faces gets higher and only the hallucinating faces can give good results for lower dimensional faces such as 64times48 pixels