Bayan S. Sharif

A computationally efficient Doppler compensation system for underwater acoustic communications

A Doppler compensation system is presented which is suitable for high-data-rate acoustic communication between rapidly moving platforms such as autonomous underwater vehicles. The proposed approach provides a generic preprocessor to conventional adaptive receiver structures with only a marginal increase in computational load and hardware cost. The preprocessor employs a novel Doppler estimation technique and efficient sample rate conversion to remove Doppler shift induced by platform velocity and acceleration. Performance predicted by simulation is compared to that of sea trials of a prototype communication system in the North Sea. Successful communication is demonstrated at 16 kbit/s with a transmitting platform moving at up to /spl plusmn/2.6 m/s.

Wireless Information and Power Transfer in Cooperative Networks With Spatially Random Relays

In this paper, the application of wireless information and power transfer to cooperative networks is investigated, where the relays in the network are randomly located and based on the decode-forward strategy. For the scenario with one source-destination pair, three different strategies for using the available relays are studied, and their impact on the outage probability and diversity gain is characterized by applying stochastic geometry. By using the assumptions that the path loss exponent is two and that the relay-destination distances are much larger than the source-relay distances, closed form analytical results can be developed to demonstrate that the use of energy harvesting relays can achieve the same diversity gain as the case with conventional self-powered relays. For the scenario with multiple sources, the relays can be viewed as a type of scarce resource, where the sources compete with each other to get help from the relays. Such a competition is modeled as a coalition formation game, and two distributed game theoretic algorithms are developed based on different payoff functions. Simulation results are provided to confirm the accuracy of the developed analytical results and facilitate a better performance comparison.

Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization

Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the need for energy efficient infrastructure is becoming increasingly more important since it impacts upon the network operational lifetime. Sensor node clustering is one of the techniques that can expand the lifespan of the whole network through data aggregation at the cluster head. In this paper, we present an energy-aware clustering for wireless sensor networks using particle swarm optimization (PSO) algorithm which is implemented at the base station. We define a new cost function, with the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. The performance of our protocol is compared with the well known cluster-based protocol developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH. Simulation results demonstrate that our proposed protocol can achieve better network lifetime and data delivery at the base station over its comparatives.

Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa

The development of an automated algorithm for the categorization of normal and cancerous colon mucosa is reported. Six features based on texture analysis were studied. They were derived using the co-occurrence matrix and were angular second moment, entropy, contrast, inverse difference moment, dissimilarity, and correlation. Optical density was also studied. Forty-four normal images and 58 cancerous images from sections of the colon were analyzed. These two groups were split equally into two subgroups: one set was used for supervised training and the other to test the classification algorithm. A stepwise selection procedure showed that correlation and entropy were the features that discriminated most strongly between normal and cancerous tissue (P<0.0001). A parametric linear-discriminate function was used to determine the classification rule. For the training set, a sensitivity and specificity of 93.1% and 81.8%, respectively, were achieved, with an overall accuracy of 88.2%. These results mere confirmed with the test set, with a sensitivity and specificity of 93.1% and 86.4%, respectively, and an overall accuracy of 90.2%.

Probe Fed Stacked Patch Antenna for Wideband Applications

A new design of a U-slot microstrip antenna with an E shaped stacked patch is presented that achieves an impedance bandwidth of 59.7%. Parameters such as substrate thickness, slot length, width are investigated and design results from parametric simulations are presented. The electric current distributions on the patch and the radiation patterns are also demonstrated in this paper.

Selected mapping without side information for PAPR reduction in OFDM

Selected mapping (SLM) is a technique used to reduce the peak-to-average power ratio (PAPR) in orthogonal frequency-division multiplexing (OFDM) systems. SLM requires the transmission of several side information bits for each data block, which results in some data rate loss. These bits must generally be channel-encoded because they are particularly critical to the error performance of the system. This increases the system complexity and transmission delay, and decreases the data rate even further. In this paper, we propose a novel SLM method for which no side information needs to be sent. By considering the example of several OFDM systems using either QPSK or 16-QAM modulation, we show that the proposed method performs very well both in terms of PAPR reduction and bit error rate at the receiver output provided that the number of subcarriers is large enough.

Secrecy Rate Optimizations for a MIMO Secrecy Channel With a Multiple-Antenna Eavesdropper

This paper studies different secrecy rate optimization problems for a multiple-input-multiple-output (MIMO) secrecy channel. In particular, we consider a scenario where a communication through a MIMO channel is overheard by a multiple-antenna eavesdropper. In this secrecy network, we first investigate two secrecy rate optimization problems: 1) power minimization and 2) secrecy rate maximization. These optimization problems are not convex due to the nonconvex secrecy rate constraint. However, by approximating this secrecy rate constraint based on Taylor series expansion, we propose iterative algorithms to solve these secrecy rate optimization problems. In addition, we provide the convergence analysis for the proposed algorithms. These iterative optimization approaches are developed under the assumption that the transmitter has perfect channel state information. However, there are practical difficulties in having perfect channel state information at the transmitter. Hence, robust secrecy rate optimization techniques based on the worst-case secrecy rate are considered by incorporating channel uncertainties. By exploiting the S-Procedure, we show that these robust optimization problems can be formulated into semidefinite programming at low signal-to-noise ratios (SNRs). Simulation results have been provided to validate the convergence of the proposed algorithms. In addition, numerical results show that the proposed robust optimization techniques outperform the nonrobust schemes in terms of the worst-case secrecy rates and the achieved secrecy rates.

A novel selected mapping technique for PAPR reduction in OFDM systems

Selected mapping (SLM) is a well-known method for reducing the peak-to-average power ratio (PAPR) in orthogonal frequency-division multiplexing (OFDM) systems. The main drawback of this technique is that, for each data block, it requires the transmission of several side information bits, which results in some data rate loss. These redundant bits are so critical to the error performance of the system that they need in practice to be protected by a powerful channel code. This increases the system complexity and transmission delay, and decreases the data rate even further. In this paper, we propose a novel SLM method for which no side information needs to be sent. By considering the example of an OFDM system using 16-QAM modulation, it is shown that the proposed method performs very well both in terms of PAPR reduction and bit error rate at the receiver output.

Performance Comparison of Optimization Algorithms for Clustering in Wireless Sensor Networks

Clustering in wireless sensor networks (WSNs) is one of the techniques that can expand the lifetime of the whole network through data aggregation at the cluster head. This paper presents performance comparison between particle swarm optimization (PSO) and genetic algorithms (GA) with a new cost function that has the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. Furthermore, a comparison is made with the well known cluster-based protocols developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH, as well as the traditional K-means clustering algorithm. Simulation results demonstrate that the proposed protocol using PSO algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station over its comparatives.

Skeletal growth estimation using radiographic image processing and analysis

An automated knowledge-based vision system for skeletal growth estimation in children is reported in this paper. Images were obtained from hand radiographs of 32 male and 25 female children of age 1-16 yr. Phalanx bones were automatically localized and segmented using hierarchical inferences and active shape models, respectively. A number of shape descriptors were obtained from the segmented bone contour to quantify skeletal growth. From these descriptors, a feature vector was selected for a regression model and a Bayesian estimator. The estimation accuracy was 84% for females and 82% for males. This level of accuracy is comparable to that of expert pediatric radiologists, which suggests that the proposed approach has a potential application in pediatric medicine.