Laurence S. Dooley

Combined SINR Based Vertical Handoff Algorithm for Next Generation Heterogeneous Wireless Networks

Next generation heterogeneous wireless networks offer the end users with assurance of QoS inside each access network as well as during vertical handoff between them. For guaranteed QoS, the vertical handoff algorithm must be QoS aware, which cannot be achieved with the use of traditional RSS as the vertical handoff criteria. In this paper, we propose a novel vertical handoff algorithm which uses received SINR from various access networks as the handoff criteria. This algorithm consider the combined effects of SINR from different access networks with SINR value from one network being converted to equivalent SINR value to the target network, so the handoff algorithm can have the knowledge of achievable bandwidths from both access networks to make handoff decisions with QoS consideration. Analytical results confirm that the new SINR based vertical handoff algorithm can consistently offer the end user with maximum available bandwidth during vertical handoff contrary to the RSS based vertical handoff, whose performance differs under different network conditions. System level simulations also reveal the improvement of overall system throughputs using SINR based vertical handoff, comparing with the RSS based vertical handoff.

Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data

MOTIVATION Microarray data are used in a range of application areas in biology, although often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible before using these algorithms. While many imputation algorithms have been proposed, more robust techniques need to be developed so that further analysis of biological data can be accurately undertaken. In this paper, an innovative missing value imputation algorithm called collateral missing value estimation (CMVE) is presented which uses multiple covariance-based imputation matrices for the final prediction of missing values. The matrices are computed and optimized using least square regression and linear programming methods. RESULTS The new CMVE algorithm has been compared with existing estimation techniques including Bayesian principal component analysis imputation (BPCA), least square impute (LSImpute) and K-nearest neighbour (KNN). All these methods were rigorously tested to estimate missing values in three separate non-time series (ovarian cancer based) and one time series (yeast sporulation) dataset. Each method was quantitatively analyzed using the normalized root mean square (NRMS) error measure, covering a wide range of randomly introduced missing value probabilities from 0.01 to 0.2. Experiments were also undertaken on the yeast dataset, which comprised 1.7% actual missing values, to test the hypothesis that CMVE performed better not only for randomly occurring but also for a real distribution of missing values. The results confirmed that CMVE consistently demonstrated superior and robust estimation capability of missing values compared with other methods for both series types of data, for the same order of computational complexity. A concise theoretical framework has also been formulated to validate the improved performance of the CMVE algorithm. AVAILABILITY The CMVE software is available upon request from the authors.

A Generic Fuzzy Rule Based Image Segmentation Algorithm

Fuzzy rule based image segmentation techniques tend in general, to be application dependent with the structure of the membership functions being predefined and in certain cases, the corresponding parameters being manually determined. The net result is that the overall performance of the segmentation technique is very sensitive to parameter value selections. This paper addresses these issues by introducing a generic fuzzy rule based image segmentation (GFRIS) algorithm, which is both application independent and exploits inter-pixel spatial relationships. The GFRIS algorithm automatically approximates both the key weighting factor and threshold value in the definitions of the fuzzy rule and neighbourhood system, respectively. A quantitative evaluation is presented between the segmentation results obtained using GFRIS and the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms. The results demonstrate that GFRIS exhibits a considerable improvement in performance compared to both FCM and PCM, for many different image types.

A real-time pattern selection algorithm for very low bit-rate video coding using relevance and similarity metrics

Very low bit-rate video coding using regularly shaped patterns to represent moving regions in macroblocks has good potential for improved coding efficiency. This paper presents a real-time pattern selection (RTPS) algorithm, which uses a pattern relevance and similarity metric to achieve faster pattern selection from a large codebook. For each applicable macroblock, the relevance metric is applied to create a customized pattern codebook (CPC) from which the best pattern is selected using the similarity metric. The CPC size is adapted to facilitate real-time selection. Results prove the quantitative and perceptual performance of RTPS is superior to both the Fixed-8 algorithm and H.263.

A Guided Genetic Algorithm for Protein Folding Prediction Using 3D Hydrophobic-Hydrophilic Model

In this paper, a Guided Genetic Algorithm (GGA) has been presented for protein folding prediction (PFP) using 3D Hydrophobic-Hydrophilic (HP) model. Effective strategies have been formulated utilizing the core formation of the globular protein, which provides the guideline for the Genetic Algorithm (GA) while predicting protein folding. Building blocks containing Hydrophobic (H) -Hydrophilic (P or Polar) covalent bond are utilized such a way that it helps form a core that maximizes the fitness. A series of operators are developed including Diagonal Move and Tilt Move to assist in implementing the building blocks in three-dimensional space. The GGA outperformed Ungers GA in 3D HP model. The overall strategy incorporates a swing function that provides a mechanism to enable the GGA to test more potential solutions and also prevent it from developing a schema that may cause it to become trapped in local minima. Further, it helps the guidelines remain non-rigid. GGA provides improved and robust performance for PFP.

A new guided genetic algorithm for 2D hydrophobic-hydrophilic model to predict protein folding

This paper presents a novel guided genetic algorithm (GGA) for protein folding prediction (PFP) in 2D hydrophobic-hydrophilic (HP) by exploring the protein core formation concept. A proof of the shape for an optimal core is provided and a set of highly probable sub-conformations are defined which help to establish the guidelines to form the core boundary. A series of new operators including diagonal move and tilt move are defined to assist in implementing the guidelines. The underlying reasons for the failure in the folding prediction of relatively long sequences using Ungers genetic algorithm (GA) in 2D HP model are analysed and the new GGA is shown to overcome these limitations. The overall strategy incorporates a swing function that provides a mechanism to enable the GGA to test more potential solutions and also prevent it from developing a schema that may cause it to become trapped in local minima. While the guidelines do not force particular conformations, the result is a number of conformations for particular putative ground energy and superior prediction accuracy, endorsing the improved performance compared with other well established nondeterministic search approaches

Texture classification based on DCT and soft computing

We present a classification method based on the discrete cosine transform (DCT) coefficients of texture image. Since the DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images with DCT, we used two popular soft computing techniques, namely neurocomputing and neuro-fuzzy computing. We used a feedforward neural network trained by backpropagation algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. We also analyzed the effects of prolonged training of neural networks. It was observed that the proposed neuro-fuzzy model performed better than neural network.

A hybrid genetic algorithm for 2d FCC hydrophobic-hydrophilic lattice model to predict protein folding

This paper presents a Hybrid Genetic Algorithm (HGA) for the protein folding prediction (PFP) applications using the 2D face-centred-cube (FCC) Hydrophobic-Hydrophilic (HP) lattice model. This approach enhances the optimal core formation concept and develops effective and efficient strategies to implement generalized short pull moves to embed highly probable short motifs or building blocks and hence forms the hybridized GA for FCC model. Building blocks containing Hydrophobic (H) – Hydrophilic (P or Polar) covalent bonds are utilized such a way as to help form a core that maximizes the |fitness|. The HGA helps overcome the ineffective crossover and mutation operations that traditionally lead to the stuck condition, especially when the core becomes compact. PFP has been strategically translated into a multi-objective optimization problem and implemented using a swing function, with the HGA providing improved performance in the 2D FCC model compared with the Simple GA.

New mobility based call admission control with on-demand borrowing scheme for QOS provisioning

In this paper, an advanced call admission control strategy is proposed in which bandwidth is allocated more efficiently and effectively to neighbouring cells by exploiting key mobility parameters to provide consistent Quality of Service (QoS) guarantees for multimedia traffic. Concomitantly, to ensure continuity of on-going calls with better utilization of resources, bandwidth is borrowed from existing adaptive calls without affecting the minimum QoS guarantee. The performance of the scheme is compared with other techniques including the rate-based borrowing scheme and implicit QoS provisioning strategy. Simulation results prove that this new scheme offers significant improvements in the requisite performance metrics of call blocking probability, call dropping probability, and bandwidth utilization, under a variety of differing traffic conditions.

Dynamic fractional frequency reuse based hybrid resource management for femtocell networks

Femtocell access points are inexpensive, plug and play home base stations designed to extend radio coverage and increase capacity within indoor environments. Their inherent uncoordinated and overlaid deployment however, means existing radio resource management (RRM) techniques are often ineffectual. Recent advances in dynamic RRM have emphasised the need for more efficient resource management strategies. While centralised resource management offers improved coordination and operator control giving better interference management, it is not scalable for increasing nodes. Distributed management techniques in contrast, do afford scaled deployment, but at higher node densities incur performance degradation in both system throughput and link-quality because of poor coordination. The level of spectrum sharing mandated by macro-femto deployment also impacts on system throughput and is scenario dependant. This paper presents a new hybrid resource management algorithm( HRMA) for down-links in orthogonal frequency division multiple access-based systems, with the model analysed for a range of macro-femto deployment scenarios. HRMA employs a dynamic fractional frequency reuse scheme for macro-cell deployment with frequency reuse defined for femto users depending on their location by making certain frequencies locally available based on macro-femto tier information sharing and efficient localised spectrum utilisation. Quantitative performance results confirm the efficacy of the HRMA strategy for various key system metrics including interference minimisation, outage probability and throughput.