Gulistan Raja

Performance comparison of advanced video coding H.264 standard with baseline H.263 and H.263+ standards

The emerging ITU-T H.264 standard is designed to serve wide range of applications such as digital storage media, television broadcasting, and real time communication. It has significant improvement over all previous video compression standards. We provide an independent analysis of the coding performance improvement of H.264 over various other existing standards. Our test results shows that H.264 can achieve 4 dB PSNR gain over baseline H.263 and 3.7 dB PSNR gain over H.263+ for sequences of range of typical content for low and high latency applications.

Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images

Macular edema (ME) and central serous retinopathy (CSR) are two macular diseases that affect the central vision of a person if they are left untreated. Optical coherence tomography (OCT) imaging is the latest eye examination technique that shows a cross-sectional region of the retinal layers and that can be used to detect many retinal disorders in an early stage. Many researchers have done clinical studies on ME and CSR and reported significant findings in macular OCT scans. However, this paper proposes an automated method for the classification of ME and CSR from OCT images using a support vector machine (SVM) classifier. Five distinct features (three based on the thickness profiles of the sub-retinal layers and two based on cyst fluids within the sub-retinal layers) are extracted from 30 labeled images (10 ME, 10 CSR, and 10 healthy), and SVM is trained on these. We applied our proposed algorithm on 90 time-domain OCT (TD-OCT) images (30 ME, 30 CSR, 30 healthy) of 73 patients. Our algorithm correctly classified 88 out of 90 subjects with accuracy, sensitivity, and specificity of 97.77%, 100%, and 93.33%, respectively.

Real Time FFT Processor Implementation

This paper describes the implementation of a 64 point FFT processor in VHDL. The implemented architecture (E. Cetin, et al., 1997) is well suited for real-time spectrum analysis in instrumentation and measurement applications. It uses a single butterfly processor instead of a column of butterfly processors. The butterfly consists of one multiplier and two adders. The paper describes the VHDL implementation of various units of this processor

Fully automatic detection of lung nodules in CT images using a hybrid feature set

Purpose The aim of this study was to develop a novel technique for lung nodule detection using an optimized feature set. This feature set has been achieved after rigorous experimentation, which has helped in reducing the false positives significantly. Method The proposed method starts with preprocessing, removing any present noise from input images, followed by lung segmentation using optimal thresholding. Then the image is enhanced using multiscale dot enhancement filtering prior to nodule detection and feature extraction. Finally, classification of lung nodules is achieved using Support Vector Machine (SVM) classifier. The feature set consists of intensity, shape (2D and 3D) and texture features, which have been selected to optimize the sensitivity and reduce false positives. In addition to SVM, some other supervised classifiers like K‐Nearest‐Neighbor (KNN), Decision Tree and Linear Discriminant Analysis (LDA) have also been used for performance comparison. The extracted features have also been compared class‐wise to determine the most relevant features for lung nodule detection. The proposed system has been evaluated using 850 scans from Lung Image Database Consortium (LIDC) dataset and k‐fold cross‐validation scheme. Results The overall sensitivity has been improved compared to the previous methods and false positives per scan have been reduced significantly. The achieved sensitivities at detection and classification stages are 94.20% and 98.15%, respectively, with only 2.19 false positives per scan. Conclusions It is very difficult to achieve high performance metrics using only a single feature class therefore hybrid approach in feature selection remains a better choice. Choosing right set of features can improve the overall accuracy of the system by improving the sensitivity and reducing false positives.

Microwave Attenuation and Prediction of Rain Outage for Wireless Networks in Pakistan's Tropical Region

The microwave attenuation due to rainfall in tropical regions has not been very widely studied yet. In Pakistans tropical environment, line-of-sight microwave communication links were set up and have been operated for several years to study the microwave attenuation characteristics due to tropical rainfall. In this paper the experimental results are presented, including the cumulative distributions of microwave attenuation and the relationship between specific attenuation and rainfall rate. In addition, a rain outage prediction model is proposed which not only predicts microwave radio link performance but will also be useful in calculating the link degradations due to interference issues. The main focus of this research work has been done keeping in mind the wireless networks of Pakistan. The results show that the rainfall rate, the microwave propagation characteristics, and outage predictions in Pakistan differ from the International Radio Consultative Committee predictions and ITU recommendation P530.7/8, respectively.

H.264/AVC deblocking filter based on motion activity in video sequences

A new approach of H.264/AVC deblocking filter based on motion activity in video sequences is proposed. Based on sum of motion vectors the thresholds have been set through experimentation for categorizing different sequences. In this approach, a modified deblocking filter is used for low to moderate motion video sequences. Various simulations are conducted to evaluate the candidacy of the anticipated technique. The subjective and objective results are in conformity of the original H.264/AVC deblocking filter for low and moderate motion video sequences. A significant reduction in average number of operations is achieved without losing quality of the video.

All Analog Processing of Speckle Affected Self-Mixing Interferometric Signals

Self-mixing (SM) or optical feedback interferometry has been extensively used for high precision displacement and vibration sensing. However, presence of speckle can significantly degrade the SM interferometric signal and cause changes in signal amplitude as well as in the operating optical feedback regime, resulting in reduction in measurement precision. Previously, different advanced digital signal processing techniques have been proposed to undo the effects caused by speckle. However, their complex and computationally heavy nature inhibits their use for real-time, high bandwidth sensing applications. In this regard, an all analog signal processing algorithm has been presented in this paper, which allows real-time processing of speckle affected SM signal while using standard analog circuits. Various simulations indicated that it is able to correctly process speckle affected SM signals having amplitude variation of at least one order and optical feedback parameter

Adaptive Modified Hexagon Based Search Motion Estimation algorithm

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Fully automated assessment of Macular Edema using Optical Coherence Tomography (OCT) images

reduction until 0.5. This proposed algorithm has been tested on experimentally acquired speckle affected SM signals and found capable of dealing with variations in optical feedback regime and amplitude modulation of SM signals, in accordance with simulation results. The developed hardware prototype circuit measures a maximum displacement amplitude of 0.4 mm at a maximum target velocity of 8 mm/s for an SM sensor with a laser wavelength of 785 nm as long as

FPGA implementation of chaotic based AES image encryption algorithm

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