Hassan Ahmed

Vertical Handover Necessity Estimation Based on a New Dwell Time Prediction Model for Minimizing Unnecessary Handovers to a WLAN Cell

In this work, we devise a vertical handover necessity estimation (HNE) method to minimize unnecessary handovers for a mobile node (MN) entering a WLAN cell. The method relies on a new model for prediction of dwell time and computation of certain threshold values. By comparing the predicted dwell time with those thresholds, a MN is able to make decision whether it should perform handover to a WLAN cell, while keeping the probability of handover failure and probability of unnecessary handover within bounds. Simulation results obtained from Monte-Carlo experiments prove validity of the proposed model. We also compare this model with existing models for minimizing unnecessary handovers. We further enhance the analytical model by incorporating the throughput gain in HNE and show that this can further optimize handover decision in heterogenous networks.

An Approach to Real-time Color-based Object Tracking

Object tracking is of great interest in different areas of industry, security and defense. Tracking moving objects based on color information is more robust than systems utilizing motion cues. In order to maintain the lock on the object as the surrounding conditions vary, the color model needs to be adapted in real-time. In this paper an on-line learning method for the color model is implemented using fuzzy adaptive resonance theory (ART). Fuzzy ART is a type of neural network that is trained based on competitive learning principle. The color model of the target region is regularly updated based on the vigilance criteria (which is a threshold) applied to the pixel color information. The target location in the next frame is predicted using evolving extended Takagi-Sugeno (exTS) model to improve the tracking performance. The results of applying exTS for prediction of the position of the moving target were compared with the usually used solution based on Kalman filter. The experiments with real footage demonstrate over a variety of scenarios the superiority of the exTS as a predictor comparing to the Kalman filter. Further investigation concentrates on using evolving clustering for realizing computationally efficient simultaneous tracking of different segments in the object

Matrix-based memory efficient symmetric key generation and pre-distribution scheme for wireless sensor networks

As wireless sensor networks (WSN) have gained popularity due to their broader applications areas, so does the need for effective security mechanisms. Encryption can be used to protect data communication, but severe resource constraints in WSN make necessary key distribution very difficult. This study addresses the problem of key establishment in WSN using a key pre-distribution scheme. In this study, the authors have proposed a symmetric key generation and pre-distribution scheme, using a symmetric matrix and generator matrix of maximum rank distance (MRD) codes. Sensor nodes are divided into groups and some information is stored at each node to enable it to generate link keys. This node division substantially improves the memory required at each node, which is approximately equal to the group size. The proposed scheme also reduces the communication overhead to setup a link key. It requires only two messages to setup a link key between any two nodes. Furthermore this scheme also provides the highest level of network connectivity and scalability. Any two nodes in the network can establish a link key and new nodes can be added at any time without changing any information on previously deployed nodes.

Adaptive state dependent filters

An approach known as state dependent embedding for developing nonlinear adaptive filters is presented. Many types of nonlinear filters including Volterra, bilinear, and polynomial autoregressive (PAR) are unified under this method. By recognizing the functional relationships between the channels of an equivalent linearly embedded system, state dependent embedding creates much more efficient filters than previous approaches. A filter called the layered structure emerges from the embedding. Its virtues include low computation, modularity, and local adaptation, allowing nonlinear filters to be implemented with linear adaptive building blocks. The layered structure is very amenable to VLSI. The state dependent embedding can also be used to develop very efficient lattice filters.<<ETX>>

New Nonlinear Adaptive Filters with Applications to Chaos

We present a new approach to nonlinear adaptive filtering based on Successive Linearization. Our approach provides a simple, modular and unified implementation for a broad class of polynomial filters. We refer to this implementation as the layered structure and note that it offers substantial computational efficiency over previous methods. A new class of Polynomial Autoregressive filters is introduced which can model limit cycle and chaotic dynamics. Existing geometric methods for modeling and characterizing chaotic processes suffer from several drawbacks. They require a huge number of data points to reconstruct the attractor geometry and performance is severely limited by noisy experimental measurements. We present a new method for processing chaotic signals using nonlinear adaptive filters. We demonstrate the modeling, prediction and filtering of these signals. We also show how the prediction error growth rate can be used to estimate the effective Lyapunov exponent of the chaotic map. Our approach requires orders of magnitude fewer data points and is robust to noise in the experimental data. Although reconstruction of the attractor geometry is unnecessary, the adaptive filter contains most of the geometric information.

Adaptive signal processing techniques for chaotic systems

The issue of modeling chaotic systems is addressed. Present methods for treating chaotic dynamics are based on state space reconstruction through delay embedding. These approaches are computationally intensive and are adversely affected by noise in the experimental time series. The authors take a different approach and apply an adaptive layered structure for estimation of chaotic dynamics. They show that presently used spatial local approximations are not necessary and that their temporal adaptive local approximations perform better, are tolerant to noise factors, and save an order of magnitude in computations, and data requirements.<<ETX>>

Multi-model AAM framework for face image modeling

Active Appearance Modeling (AAM) offers acceptable face synthesis performance when applied to person-specific modeling applications. The aim of the work presented in this paper is to enable AAM to model and synthesize more accurately previously unseen face images. Thus a clustering process based on shape similarities is incorporated in the system and applied prior to conventional AAM modeling, to yield Multi-Model AAM. In this approach the wide appearance spectrum possible face images is decomposed into a number of cluster each containing similar shape faces. This allows AAM modeling per cluster to be applied and therefore the generation of several AAM models which capture more accurately variability between possible input faces. Experimental results show that, when dealing with previously unseen faces, models generated through this Multi-Model AAM framework can be significantly more effective in terms of both shape and texture, than the conventional single model AAM approach.

Multiple-input multiple-output ultra-wide band channel modelling method based on ray tracing

In this work, the authors have developed a deterministic Ultra Wide Band (UWB) channel model for indoor environment using both ray-tracing technique and the art of computer game technology in 3D Game Studio (game development tool). In the developed model, the characteristics of indoor environment such as texture, transparency etc. can be taken into consideration while indoor parameters such as room size, objects position etc. can be interactively changed. Each time, indoor environment is changed, the program is compiled and hence, the underlying ray-tracing captures the updated indoor environment. It is the key novelty of the authors’ developed model and it has been so incorporated to make the authors’ model independent of any fixed (pre-defined) indoor environment. The developed model is compared against the standard statistical UWB channel model based on certain parameters such as delay spread etc. to address its validity and accuracy. The model is then enhanced to use multiple antennas on both sides of the system and capture the channel response accordingly. Finally, the developed model has been tested over a range of frequencies to see frequency effect on the channel impulse response. The simulation results have been presented and discussed in the simulation section.

Modified Niederreiter type of GPT cryptosystem based on reducible rank codes

GPT public key cryptosystem was proposed by Gabidulin, Paramonov and Tretjakov in 1991. This cryptosystem is based on rank error correcting codes. The main advantage of using rank codes in cryptography is that, it has smaller key size as compared to other code based public key cryptosystems. Several attacks against this system were published and some modifications were also proposed withstanding these attacks. In this paper, we have proposed a modified Niederreiter type GPT cryptosystem based on reducible rank codes by properly choosing the column scrambler matrix to withstand these attacks. Although, the idea of choosing column scrambler matrix from extension field is not new but the approach proposed in this paper, provides more elements of column scrambler matrix from extension field as compared to any previous modifications which makes system more secure against attacks.

Hierarchical Classification Fusion framework

This paper presents a novel hierarchical Classification Fusion (CF) framework which operates on Abstract and Measurement levels simultaneously and thus exploits information patterns resulting from the output labels and posterior beliefs of individual classifiers. Furthermore the proposed classification fusion methodology allows for the decomposition of the input data, which is used to design individual classifiers, into subsets. This in turn permits individual classifiers to be re-designed per subset and in a manner that increases overall system classification performance. Experimental results are presented which demonstrate the potential of the proposed methodology in the case of multi-modal, multi-feature binary data classification problems. In addition the proposed CF design framework can be applied to multi class problems and is independent of the type of classifiers employed in the system.