A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Automatic Relative Radiometric Normalization of Bi-Temporal Satellite Images Using a Coarse-to-Fine Pseudo-Invariant Features Selection and Fuzzy Integral Fusion Strategies

Relative radiometric normalization (RRN) is important for pre-processing and analyzing multitemporal remote sensing (RS) images. Multitemporal RS images usually include different land use/land cover (LULC) types; therefore, considering an identical linear relationship during RRN modeling may result in potential errors in the RRN results. To resolve this issue, we proposed a new automatic RRN technique that efficiently selects the clustered pseudo-invariant features (PIFs) through a coarse-to-fine strategy and uses them in a fusion-based RRN modeling approach. In the coarse stage, an efficient difference index was first generated from the down-sampled reference and target images by combining the spectral correlation, spectral angle mapper (SAM), and Chebyshev distance. This index was then categorized into three groups of changed, unchanged, and uncertain classes using a fast multiple thresholding technique. In the fine stage, the subject image was first segmented into different clusters by the histogram-based fuzzy c-means (HFCM) algorithm. The optimal PIFs were then selected from unchanged and uncertain regions using each cluster’s bivariate joint distribution analysis. In the RRN modeling step, two normalized subject images were first produced using the robust linear regression (RLR) and cluster-wise-RLR (CRLR) methods based on the clustered PIFs. Finally, the normalized images were fused using the Choquet fuzzy integral fusion strategy for overwhelming the discontinuity between clusters in the final results and keeping the radiometric rectification optimal. Several experiments were implemented on four different bi-temporal satellite images and a simulated dataset to demonstrate the efficiency of the proposed method. The results showed that the proposed method yielded superior RRN results and outperformed other considered well-known RRN algorithms in terms of both accuracy level and execution time.publishedVersio

Densification of spatially-sparse legacy soil data at a national scale: a digital mapping approach

Digital soil mapping (DSM) is a viable approach to providing spatial soil information but its adoption at the national scale, especially in sub-Saharan Africa, is limited by low spread of data. Therefore, the focus of this thesis is on optimizing DSM techniques for densification of sparse legacy soil data using Nigeria as a case study. First, the robustness of Random Forest model (RFM) was tested in predicting soil particle-size fractions as a compositional data using additive log-ratio technique. Results indicated good prediction accuracy with RFM while soils are largely coarse-textured especially in the northern region. Second, soil organic carbon (SOC) and bulk density (BD) were predicted from which SOC density and stock were calculated. These were overlaid with land use/land cover (LULC), agro-ecological zone (AEZ) and soil maps to quantify the carbon sequestration of soils and their variation across different AEZs. Results showed that 6.5 Pg C with an average of 71.60 Mg C ha–1 abound in the top 1 m soil depth. Furthermore, to improve the performance of BD and effective cation exchange capacity (ECEC) pedotransfer functions (PTFs), the inclusion of environmental data was explored using multiple linear regression (MLR) and RFM. Results showed an increase in performance of PTFs with the use of soil and environmental data. Finally, the application of Choquet fuzzy integral (CI) technique in irrigation suitability assessment was assessed. This was achieved through multi-criteria analysis of soil, climatic, landscape and socio-economic indices. Results showed that CI is a better aggregation operator compared to weighted mean technique. A total of 3.34 x 106 ha is suitable for surface irrigation in Nigeria while major limitations are due to topographic and soil attributes. Research findings will provide quantitative basis for framing appropriate policies on sustainable food production and environmental management, especially in resource-poor countries of the world

Doctor of Philosophy

dissertationThe goal of machine learning is to develop efficient algorithms that use training data to create models that generalize well to unseen data. Learning algorithms can use labeled data, unlabeled data or both. Supervised learning algorithms learn a model using labeled data only. Unsupervised learning methods learn the internal structure of a dataset using only unlabeled data. Lastly, semisupervised learning is the task of finding a model using both labeled and unlabeled data. In this research work, we contribute to both supervised and semisupervised learning. We contribute to supervised learning by proposing an efficient high-dimensional space coverage scheme which is based on the disjunctive normal form. We use conjunctions of a set of half-spaces to create a set of convex polytopes. Disjunction of these polytopes can provide desirable coverage of space. Unlike traditional methods based on neural networks, we do not initialize the model parameters randomly. As a result, our model minimizes the risk of poor local minima and higher learning rates can be used which leads to faster convergence. We contribute to semisupervised learning by proposing 2 unsupervised loss functions that form the basis of a novel semisupervised learning method. The first loss function is called Mutual-Exclusivity. The motivation of this method is the observation that an optimal decision boundary lies between the manifolds of different classes where there are no or very few samples. Decision boundaries can be pushed away from training samples by maximizing their margin and it is not necessary to know the class labels of the samples to maximize the margin. The second loss is named Transformation/Stability and is based on the fact that the prediction of a classifier for a data sample should not change with respect to transformations and perturbations applied to that data sample. In addition, internal variations of a learning system should have little to no effect on the output. The proposed loss minimizes the variation in the prediction of the network for a specific data sample. We also show that the same technique can be used to improve the robustness of a learning model with respect to adversarial examples

Evaluating the suitability of marginal land for a perennial energy crop on the Loess Plateau of China

Abstract With a large marginal land area, the Loess Plateau in China holds great potential for biomass production and environmental improvement. Identifying suitable locations for biomass production on marginal land is important for decision‐makers from the viewpoint of land‐use planning. However, there is limited information on the suitability of marginal land within the Loess Plateau for biomass production. Therefore, this study aims to evaluate the suitability of the promising perennial energy crop switchgrass (Panicum virgatum L.) on marginal land across the Loess Plateau. A fuzzy logical model was developed and validated based on field trials on the Loess Plateau and applied to the marginal land of this region, owing to its ability of dealing with the continuous nature of soil, landscape variations, and uncertainties of the input data. This study identified that approximately 12.8–20.8 Mha of the Loess Plateau as available marginal land, of which 2.8–4.7 Mha is theoretically suitable for switchgrass cultivation. These parts of the total marginal land are mainly distributed in northeast and southwest of the Loess Plateau. The potential yield of switchgrass ranges between 44 and 77 Tg. This study showed that switchgrass can grow on a large proportion of the marginal land of the Loess Plateau and therefore offers great potential for biomass provision. The spatial suitability maps produced in this study provide information to farmers and policymakers to enable a more sustainable development of biomass production on the Loess Plateau. In addition, the fuzzy‐theory‐based model developed in this study provided a good framework for evaluating the suitability of marginal land