Joint source-channel-network coding in wireless mesh networks with temporal reuse

Technological innovation that empowers tiny low-cost transceivers to operate with a high degree of utilisation efficiency in multihop wireless mesh networks is contributed in this dissertation. Transmission scheduling and joint source-channel-network coding are two of the main aspects that are addressed. This work focuses on integrating recent enhancements such as wireless network coding and temporal reuse into a cross-layer optimisation framework, and to design a joint coding scheme that allows for space-optimal transceiver implementations. Link-assigned transmission schedules with timeslot reuse by multiple links in both the space and time domains are investigated for quasi-stationary multihop wireless mesh networks with both rate and power adaptivity. Specifically, predefined cross-layer optimised schedules with proportionally fair end-to-end flow rates and network coding capability are constructed for networks operating under the physical interference model with single-path minimum hop routing. Extending transmission rights in a link-assigned schedule allows for network coding and temporal reuse, which increases timeslot usage efficiency when a scheduled link experiences packet depletion. The schedules that suffer from packet depletion are characterised and a generic temporal reuse-aware achievable rate region is derived. Extensive computational experiments show improved schedule capacity, quality of service, power efficiency and benefit from opportunistic bidirectional network coding accrued with schedules optimised in the proposed temporal reuse-aware convex capacity region. The application of joint source-channel coding, based on fountain codes, in the broadcast timeslot of wireless two-way network coding is also investigated. A computationally efficient subroutine is contributed to the implementation of the fountain compressor, and an error analysis is done. Motivated to develop a true joint source-channel-network code that compresses, adds robustness against channel noise and network codes two packets on a single bipartite graph and iteratively decodes the intended packet on the same Tanner graph, an adaptation of the fountain compressor is presented. The proposed code is shown to outperform a separated joint source-channel and network code in high source entropy and high channel noise regions, in anticipated support of dense networks that employ intelligent signalling. AFRIKAANS : Tegnologiese innovasie wat klein lae-koste kommunikasie toestelle bemagtig om met ’n hoë mate van benuttings doeltreffendheid te werk word bygedra in hierdie proefskrif. Transmissie-skedulering en gesamentlike bron-kanaal-netwerk kodering is twee van die belangrike aspekte wat aangespreek word. Hierdie werk fokus op die integrasie van onlangse verbeteringe soos draadlose netwerk kodering en temporêre herwinning in ’n tussen-laag optimaliserings raamwerk, en om ’n gesamentlike kodering skema te ontwerp wat voorsiening maak vir spasie-optimale toestel implementerings. Skakel-toegekende transmissie skedules met tydgleuf herwinning deur veelvuldige skakels in beide die ruimte en tyd domeine word ondersoek vir kwasi-stilstaande, veelvuldige-sprong draadlose rooster netwerke met beide transmissie-spoed en krag aanpassings. Om spesifiek te wees, word vooraf bepaalde tussen-laag geoptimiseerde skedules met verhoudings-regverdige punt-tot-punt vloei tempo’s en netwerk kodering vermoë saamgestel vir netwerke wat bedryf word onder die fisiese inmengings-model met enkel-pad minimale sprong roetering. Die uitbreiding van transmissie-regte in ’n skakel-toegekende skedule maak voorsiening vir netwerk kodering en temporêre herwinning, wat tydgleuf gebruiks-doeltreffendheid verhoog wanneer ’n geskeduleerde skakel pakkie-uitputting ervaar. Die skedules wat ly aan pakkie-uitputting word gekenmerk en ’n generiese temporêre herwinnings-bewuste haalbare transmissie-spoed gebied word afgelei. Omvattende berekenings-eksperimente toon verbeterde skedulerings kapasiteit, diensgehalte, krag doeltreffendheid asook verbeterde voordeel wat getrek word uit opportunistiese tweerigting netwerk kodering met die skedules wat geoptimiseer word in die temporêre herwinnings-bewuste konvekse transmissie-spoed gebied. Die toepassing van gesamentlike bron-kanaal kodering, gebaseer op fontein kodes, in die uitsaai-tydgleuf van draadlose tweerigting netwerk kodering word ook ondersoek. ’n Berekenings-effektiewe subroetine word bygedra in die implementering van die fontein kompressor, en ’n foutanalise word gedoen. Gemotiveer om ’n ware gesamentlike bron-kanaal-netwerk kode te ontwikkel, wat robuustheid byvoeg teen kanaal geraas en twee pakkies netwerk kodeer op ’n enkele bipartiete grafiek en die beoogde pakkie iteratief dekodeer op dieselfde Tanner grafiek, word ’n aanpassing van die fontein kompressor aangebied. Dit word getoon dat die voorgestelde kode ’n geskeide gesamentlike bron-kanaal en netwerk kode in hoë bron-entropie en ho¨e kanaal-geraas gebiede oortref in verwagte ondersteuning van digte netwerke wat van intelligente sein-metodes gebruik maak.Dissertation (MEng)--University of Pretoria, 2011.Electrical, Electronic and Computer Engineeringunrestricte

Dataset shift in land-use classification for optical remote sensing

Multimodal dataset shifts consisting of both concept and covariate shifts are addressed in this study to improve texture-based land-use classification accuracy for optical panchromatic and multispectral remote sensing. Multitemporal and multisensor variances between train and test data are caused by atmospheric, phenological, sensor, illumination and viewing geometry differences, which cause supervised classification inaccuracies. The first dataset shift reduction strategy involves input modification through shadow removal before feature extraction with gray-level co-occurrence matrix and local binary pattern features. Components of a Rayleigh quotient-based manifold alignment framework is investigated to reduce multimodal dataset shift at the input level of the classifier through unsupervised classification, followed by manifold matching to transfer classification labels by finding across-domain cluster correspondences. The ability of weighted hierarchical agglomerative clustering to partition poorly separated feature spaces is explored and weight-generalized internal validation is used for unsupervised cardinality determination. Manifold matching solves the Hungarian algorithm with a cost matrix featuring geometric similarity measurements that assume the preservation of intrinsic structure across the dataset shift. Local neighborhood geometric co-occurrence frequency information is recovered and a novel integration thereof is shown to improve matching accuracy. A final strategy for addressing multimodal dataset shift is multiscale feature learning, which is used within a convolutional neural network to obtain optimal hierarchical feature representations instead of engineered texture features that may be sub-optimal. Feature learning is shown to produce features that are robust against multimodal acquisition differences in a benchmark land-use classification dataset. A novel multiscale input strategy is proposed for an optimized convolutional neural network that improves classification accuracy to a competitive level for the UC Merced benchmark dataset and outperforms single-scale input methods. All the proposed strategies for addressing multimodal dataset shift in land-use image classification have resulted in significant accuracy improvements for various multitemporal and multimodal datasets.Thesis (PhD)--University of Pretoria, 2016.National Research Foundation (NRF)University of Pretoria (UP)Electrical, Electronic and Computer EngineeringPhDUnrestricte

Transmission scheduling for wireless mesh networks with temporal reuse

Link-assigned transmission schedules with timeslot reuse by multiple links in both the space and time domains are investigated in this study for stationary multihop wireless mesh networks with both rate and power adaptivity. Specifically, cross-layer optimised schedules with proportionally fair end-to-end flow rates and network coding capability are constructed for networks operating under the physical interference model with single-path minimum hop routing. Extending transmission rights in a link-assigned schedule allows for network coding and temporal reuse, which increases timeslot usage efficiency when a scheduled link experiences packet depletion. The schedules that suffer from packet depletion are characterised, and a generic temporal reuse-aware achievable rate region is derived. Extensive computational experiments show improved schedule capacity, quality of service, power efficiency and benefit from network coding accrued with schedules optimised in the proposed temporal reuseaware convex rate region.http://jwcn.eurasipjournals.com/content/2011/1/8

Adaptive threshold-based shadow masking for across-date settlement classification of panchromatic QuickBird images

Multitemporal land-use analysis is becoming increasingly important for the effective management of earth resources. Despite that, consistent differences in the viewing and illumination geometry in satellite-borne imagery introduce some issues in the creation of land-use classification maps. The focus of this study is settlement classification with high-resolution panchromatic acquisitions, using texture features to distinguish between settlement classes. The important multitemporal variance component of shadow is effectively removed before feature determination, which allows for minimum-supervision across-date classification. Shadow detection based on local adaptive thresholding is employed and experimentally shown to outperform existing fixed threshold shadow detectors in increasing settlement classification accuracy. Both same and across-date settlement accuracies are significantly improved with shadow masking during feature calculation. A statistical study was performed and found to support the hypothesis that the increased accuracy is due to shadow masking specifically.National Research Foundationhttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859hb2014ai201

The effects of segmentation-based shadow removal on across-date settlement type classification of panchromatic QuickBird images

Settlement classifiers for multitemporal satellite image analysis have to overcome numerous difficulties related to across-date variances in viewing- and illumination geometry. Shadow anisotropy is a prominent contributing factor in classifier inaccuracy, so methods are introduced in this study to enable minimum-supervision classifier design that mitigate the effects of shadow profile differences. A segmentation-based shadow detector is proposed that utilizes a panchromatic segment merging algorithm with parameters that are robust against dynamic range variances seen in multitemporal imagery. The proposed shadow detector improves on the settlement classification accuracy achieved by fixed threshold detection paired with shadow removal in the presented case-study. The relationship between shadow detection accuracy and settlement classification accuracy is investigated, and it is shown that shadow removal produces greater settlement accuracy improvements for across-date experiments specifically.National Research Foundation (NRF)http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?reload=true&punumber=4609443hb2013ai201

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

Universal decremental redundancy compression with fountain codes

A new universal noise-robust lossless compression algorithm based on a decremental redundancy approach with fountain codes is proposed. The binary entropy code is harnessed to compress complex sources with the addition of a preprocessing system in this paper. Both the whole binary entropy range compression performance and the noise-robustness of an existing incremental redundancy fountain code compression technique are exceeded. A new autocorrelation-based symbol length estimator, the Burrows-Wheeler block sorting transform (BWT) and Move-to-Front transformation (MTF) with a new entropy ordered MTF indices transformation reduces the binary entropy of a universal data source. The preprocessed input source is coded with a new modified incremental degree LT-code (Luby Transform) and a low-complexity decremental redundancy algorithm is used to compress the Fountain-coded source. The improved compression and robustness against transmission errors with our novel incremental degree puncturing decremental redundancy algorithm is shown. The universal (complex memory source) compression performance of the proposed system is shown to achieve appreciable compression