Optimizing LDPC codes for a mobile WiMAX system with a saturated transmission amplifier

In mobile communication, the user’s information is transmitted through a wireless communication link that is subjected to a range of deteriorating effects. The quality of the transmission can be presented by the rate of transfer and the reliability of the received stream. The capacity of the communication link can be reached through the use of channel coding. Channel coding is the method of adding redundant information to the user’s information to mitigate the deteriorating effects of the communication link. Mobile WiMAX is a technology that makes use of orthogonal frequency division multiplexing (OFDM) modulation to transmit information over a wireless communication channel. The OFDM physical layer has a high peak average to power ratio (PAPR) characteristic that saturates the transmitter’s amplifier quite easily when proper backoff is not made in the transmission power. In this dissertation an optimized graph code was used as an alternative solution to improve the system’s performance in the presence of a saturated transmission’s amplifier. The graph code was derived from a degree distribution given by the density evolution algorithm and provided no extra network overhead to implement. The performance analysis resulted in a factor of 10 improvement in the error floor and a coding gain of 1.5 dB. This was all accomplished with impairments provided by the mobile WiMAX standard in the construction of the graph code.Dissertation (MEng)--University of Pretoria, 2009.Electrical, Electronic and Computer Engineeringunrestricte

Improved hyper-temporal feature extraction methods for land cover change detection in satellite time series

The growth in global population inevitably increases the consumption of natural resources. The need to provide basic services to these growing communities leads to an increase in anthropogenic changes to the natural environment. The resulting transformation of vegetation cover (e.g. deforestation, agricultural expansion, urbanisation) has significant impacts on hydrology, biodiversity, ecosystems and climate. Human settlement expansion is the most common driver of land cover change in South Africa, and is currently mapped on an irregular, ad hoc basis using visual interpretation of aerial photographs or satellite images. This thesis proposes several methods of detecting newly formed human settlements using hyper-temporal, multi-spectral, medium spatial resolution MODIS land surface reflectance satellite imagery. The hyper-temporal images are used to extract time series, which are analysed in an automated fashion using machine learning methods. A post-classification change detection framework was developed to analyse the time series using several feature extraction methods and classifiers. Two novel hyper-temporal feature extraction methods are proposed to characterise the seasonal pattern in the time series. The first feature extraction method extracts Seasonal Fourier features that exploits the difference in temporal spectra inherent to land cover classes. The second feature extraction method extracts state-space vectors derived using an extended Kalman filter. The extended Kalman filter is optimised using a novel criterion which exploits the information inherent in the spatio-temporal domain. The post-classification change detection framework was evaluated on different classifiers; both supervised and unsupervised methods were explored. A change detection accuracy of above 85% with false alarm rate below 10% was attained. The best performing methods were then applied at a provincial scale in the Gauteng and Limpopo provinces to produce regional change maps, indicating settlement expansion.Thesis (PhD(Eng))--University of Pretoria, 2012.Electrical, Electronic and Computer Engineeringunrestricte

Manifold adaptation for constant false alarm rate ship detection in South African oceans

The detection of ships at sea is a difficult task made more so by uncooperative ships, especially when using transponder based ship detection systems. Synthetic Aperture Radar imagery provides a means of observation independent of the ships cooperation and over the years a vast amount of research has gone into the detection of ships using this imagery. One of the most common methods used for ship detection in Synthetic Aperture Radar imagery is the Cell-Averaging Constant False Alarm Rate prescreening method. It uses a scalar threshold value to determine how bright a pixel needs to be in order to be classified as a ship and thus inversely how many false alarms are permitted. This paper presents by a method of converting the scalar threshold into a threshold manifold. The manifold is adjusted using a Simulated Annealing algorithm to optimally fit to information provided by the ship distribution map which is generated from transponder data. By carefully selecting the input solution and threshold boundaries, much of the computational inefficiencies usually associated with Simulated Annealing can be avoided. The proposed method was tested on six ASAR images against five other methods and had a reported detection accuracy of 85:2% with a corresponding false alarm rate of 1:01 10-7 .http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?reload=true&punumber=4609443hb201

Rapid detection of new and expanding human settlements in the Limpopo province of South Africa using a spatio-temporal change detection method

Recent development has identified the benefits of using hyper-temporal satellite time series data for land cover change detection and classification in South Africa. In particular, the monitoring of human settlement expansion in the Limpopo province is of relevance as it is the one of the most pervasive forms of land-cover change in this province which covers an area of roughly 125 000km2. In this paper, a spatiotemporal autocorrelation change detection (STACD) method is developed to improve the performance of a pixel based temporal Autocorrelation change detection (TACD) method previously proposed. The objective is to apply the algorithm to large areas to detect the conversion of natural vegetation to settlement which is then validated by an operator using additional data (such as high resolution imagery). Importantly, as the objective of the method is to indicate areas of potential change to operators for further analysis, a low false alarm rate is required while achieving an acceptable probability of detection. Results indicate that detection accuracies of 70% of new settlement instances are achievable at a false alarm rate of less than 1% with the STACD method, an improvement of up to 17% compared to the original TACD formulation.http://www.elsevier.com/locate/jag2016-08-30hb201

Multiview deep learning for land-use classification

A multiscale input strategy for multiview deep learning is proposed for supervised multispectral land-use classification and it is validated on a well-known dataset. The hypothesis that simultaneous multiscale views can improve compositionbased inference of classes containing size-varying objects compared to single-scale multiview is investigated. The end-to-end learning system learns a hierarchical feature representation with the aid of convolutional layers to shift the burden of feature determination from hand-engineering to a deep convolutional neural network. This allows the classifier to obtain problemspecific features that are optimal for minimizing the multinomial logistic regression objective, as opposed to user-defined features which trades optimality for generality. A heuristic approach to the optimization of the deep convolutional neural network hyperparameters is used, based on empirical performance evidence. It is shown that a single deep convolutional neural network can be trained simultaneously with multiscale views to improve prediction accuracy over multiple single-scale views. Competitive performance is achieved for the UC Merced dataset where the 93.48% accuracy of multiview deep learning outperforms the 85.37% accuracy of SIFT-based methods and the 90.26% accuracy of unsupervised feature learning.National Research Foundation (NRF) of South Africahttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859hb201

An inductive approach to simulating multispectral MODIS surface reflectance time series

In this paper, a first order MODIS time series simulator, which uses a Colored Simple Harmonic Oscillator, is proposed. The simulated data can be used to augment data sets so that data intensive classification and change detection algorithms can be applied without enlarging the available ground truth data sets. The simulator’s validity is tested by simulating data sets of natural vegetation and human settlement areas and comparing it to the ground truth data in the Gauteng province located in South Africa. The difference found between the real and simulated data sets, which is reported in the experiments is negligent. The simulated and real world data sets are compared by using a wide selection of class and pixel metrics. In particular the average temporal Hellinger distance between the real and simulated data sets is 0.2364 and 0.2269 for the vegetation and settlement class respectively, while the average parameter Hellinger distance is 0.1835 and 0.2554 respectively.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859hb2013ai201

Cavalieri integration

We use Cavalieri’s principle to develop a novel integration technique which we call Cavalieri integration. Cavalieri integrals differ from Riemann integrals in that non-rectangular integration strips are used. In this way we can use single Cavalieri integrals to find the areas of some interesting regions for which it is difficult to construct single Riemann integrals. We also present two methods of evaluating a Cavalieri integral by first transforming it to either an equivalent Riemann or Riemann-Stieltjes integral by using special transformation functions h(x) and its inverse g(x), respectively. Interestingly enough it is often very difficult to find the transformation function h(x), whereas it is very simple to obtain its inverse g(x).http://www.tandfonline.com/loi/tqma20hb201

Using Page's cumulative sum test on MODIS time series to detect land-cover changes

Human settlement expansion is one of the most pervasive forms of land cover change in South Africa. The use of Page’s Cumulative Sum Test is proposed as a method to detect new settlement developments in areas that were previously covered by natural vegetation using 500 m MODIS time series satellite data. The method is a sequential per pixel change alarm algorithm that can take into account positive detection delay, probability of detection and false alarm probability to construct a threshold. Simulated change data was generated to determine a threshold during a preliminary off-line optimization phase. After optimization the method was evaluated on examples of known land cover change in the Gauteng and Limpopo provinces of South Africa. The experimental results indicated that CUSUM performs better than band differencing in the before mentioned study areas.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859hb2013ai201

The use of a multilayer perceptron for detecting new human settlements from a time series of MODIS images

This paper presents a novel land cover change detection method that employs a sliding window over hyper-temporal multi-spectral images acquired from the 7 bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance product. The method uses a Feedforward Multilayer Perceptron (MLP) for supervised change detection that operates on multi-spectral time series extracted with a sliding window from the dataset. The method was evaluated on both real and simulated land cover change examples. The simulated land cover change comprises of concatenated time series that are produced by blending actual time series of pixels from human settlements to those from adjacent areas covered by natural vegetation. The method employs an iteratively retrained MLP to capture all local patterns and to compensate for the time-varying climate in the geographical area. The iteratively retrained MLP was compared to a classical batch mode trained MLP. Depending on the length of the temporal sliding window used, an overall change detection accuracy between 83% and 90% was achieved. It is shown that a sliding window of 6 months using all 7 bands of MODIS data is sufficient to detect land cover change reliably. Window sizes of 18 months and longer provide minor improvements to classification accuracy and change detection performance at the cost of longer time delays.The CSIR Strategic Research Panelhttp://www.elsevier.com/locate/jagai201

Detecting land cover change using an extended Kalman filter on MODIS NDVI time-series data

A method for detecting land cover change using NDVI time series data derived from 500m MODIS satellite data is proposed. The algorithm acts as a per pixel change alarm and takes as input the NDVI time series of a 3x3 grid of MODIS pixels. The NDVI time series for each of these pixels was modeled as a triply (mean, phase and amplitude) modulated cosine function, and an extended Kalman Filter was used to estimate the parameters of the modulated cosine function through time. A spatial comparison between the center pixel of the the 3x3 grid and each of its neighboring pixel’s mean and amplitude parameter sequence was done to calculate a change metric which yields a change or no-change decision after thresholding. Although the development of new settlements is the most prevalent form of land cover change in South Africa, it is rarely mapped and known examples amounts to a limited number of changed MODIS pixels. Therefore simulated change data was generated and used for preliminary optimization of the change detection method. After optimization the method was evaluated on examples of known land cover change in the study area and experimental results indicate a 89% change detection accuracy, while a traditional annual NDVI differencing method could only achieve a 63% change detection accuracy.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=885