Built Infrastructure Monitoring and Inspection Using UAVs and Vision-based Algorithms

This study presents an inspecting system using real-time control unmanned aerial vehicles (UAVs) to investigate structural surfaces. The system operates under favourable weather conditions to inspect a target structure, which is the Wentworth light rail base structure in this study. The system includes a drone, a GoPro HERO4 camera, a controller and a mobile phone. The drone takes off the ground manually in the testing field to collect the data requiring for later analysis. The images are taken through HERO 4 camera and then transferred in real time to the remote processing unit such as a ground control station by the wireless connection established by a Wi-Fi router. An image processing method has been proposed to detect defects or damages such as cracks. The method based on intensity histogram algorithms to exploit the pixel group related to the crack contained in the low intensity interval. Experiments, simulation and comparisons have been conducted to evaluate the performance and validity of the proposed system.

Dutch General Public Reaction on Governmental COVID-19 Measures and Announcements in Twitter Data

Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. While the staying-at-home policy makes face-to-face interactions and interviews challenging, analysing real-time Twitter data that reflects public opinion toward policy measures is a cost-effective way to access public sentiment. In this paper, we collect streaming data using the Twitter API starting from the COVID-19 outbreak in the Netherlands in February 2020, and track Dutch general public reactions on governmental measures and announcements. We provide temporal analysis of tweet frequency and public sentiment over the past four months. We also identify public attitudes towards the Dutch policy on wearing face masks in a case study. By presenting those preliminary results, we aim to provide visibility into the social media discussions around COVID-19 to the general public, scientists and policy makers. The data collection and analysis will be updated and expanded over time.

Accelerated learning algorithms of general fuzzy min-max neural network using a branch-and-bound-based hyperbox selection rule

This paper proposes a method to accelerate the training process of general fuzzy min-max neural network. The purpose is to reduce the unsuitable hyperboxes selected as the potential candidates of the expansion step of existing hyperboxes to cover a new input pattern in the online learning algorithms or candidates of the hyperbox aggregation process in the agglomerative learning algorithms. Our proposed approach is based on the mathematical formulas to form a branch-and-bound solution aiming to remove the hyperboxes which are certain not to satisfy expansion or aggregation conditions, and in turn decreasing the training time of learning algorithms. The efficiency of the proposed method is assessed over a number of widely used data sets. The experimental results indicated the significant decrease in training time of proposed approach for both online and agglomerative learning algorithms. Notably, the training time of the online learning algorithms is reduced from 1.2 to 12 times when using the proposed method, while the agglomerative learning algorithms are accelerated from 7 to 37 times on average.

Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks

With the growing significance of graphs as an effective representation of data in numerous applications, efficient graph analysis using modern machine learning is receiving a growing level of attention. Deep learning approaches often operate over the entire adjacency matrix -- as the input and intermediate network layers are all designed in proportion to the size of the adjacency matrix -- leading to intensive computation and large memory requirements as the graph size increases. It is therefore desirable to identify efficient measures to reduce both run-time and memory requirements allowing for the analysis of the largest graphs possible. The use of reduced precision operations within the forward and backward passes of a deep neural network along with novel specialised hardware in modern GPUs can offer promising avenues towards efficiency. In this paper, we provide an in-depth exploration of the use of reduced-precision operations, easily integrable into the highly popular PyTorch framework, and an analysis of the effects of Tensor Cores on graph convolutional neural networks. We perform an extensive experimental evaluation of three GPU architectures and two widely-used graph analysis tasks (vertex classification and link prediction) using well-known benchmark and synthetically generated datasets. Thus allowing us to make important observations on the effects of reduced-precision operations and Tensor Cores on computational and memory usage of graph convolutional neural networks -- often neglected in the literature.

Block-matching in FPGA

Block-matching and 3D filtering (BM3D) is an image denoising algorithm that works in two similar steps. Both of these steps need to perform grouping by block-matching. We implement the block-matching in an FPGA, leveraging its ability to perform parallel computations. Our goal is to enable other researchers to use our solution in the future for real-time video denoising in video cameras that use FPGAs (such as the AXIOM Beta).

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

Vegetation encroachment in power transmission lines can cause outages, which may result in severe impact on economic of power utilities companies as well as the consumer. Vegetation detection and monitoring along the power line corridor right-of-way (ROW) are implemented to protect power transmission lines from vegetation penetration. There were various methods used to monitor the vegetation penetration, however, most of them were too expensive and time consuming. Satellite images can play a major role in vegetation monitoring, because it can cover high spatial area with relatively low cost. In this paper, the current techniques used to detect the vegetation encroachment using satellite images are reviewed and categorized into four sectors; Vegetation Index based method, object-based detection method, stereo matching based and other current techniques. However, the current methods depend usually on setting manually serval threshold values and parameters which make the detection process very static. Machine Learning (ML) and deep learning (DL) algorithms can provide a very high accuracy with flexibility in the detection process. Hence, in addition to review the current technique of vegetation penetration monitoring in power transmission, the potential of using Machine Learning based algorithms are also included.

Explainability for fair machine learning

As the decisions made or influenced by machine learning models increasingly impact our lives, it is crucial to detect, understand, and mitigate unfairness. But even simply determining what "unfairness" should mean in a given context is non-trivial: there are many competing definitions, and choosing between them often requires a deep understanding of the underlying task. It is thus tempting to use model explainability to gain insights into model fairness, however existing explainability tools do not reliably indicate whether a model is indeed fair. In this work we present a new approach to explaining fairness in machine learning, based on the Shapley value paradigm. Our fairness explanations attribute a model's overall unfairness to individual input features, even in cases where the model does not operate on sensitive attributes directly. Moreover, motivated by the linearity of Shapley explainability, we propose a meta algorithm for applying existing training-time fairness interventions, wherein one trains a perturbation to the original model, rather than a new model entirely. By explaining the original model, the perturbation, and the fair-corrected model, we gain insight into the accuracy-fairness trade-off that is being made by the intervention. We further show that this meta algorithm enjoys both flexibility and stability benefits with no loss in performance.

High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks

The future landscape of modern farming and plant breeding is rapidly changing due to the complex needs of our society. The explosion of collectable data has started a revolution in agriculture to the point where innovation must occur. To a commercial organization, the accurate and efficient collection of information is necessary to ensure that optimal decisions are made at key points of the breeding cycle. However, due to the shear size of a breeding program and current resource limitations, the ability to collect precise data on individual plants is not possible. In particular, efficient phenotyping of crops to record its color, shape, chemical properties, disease susceptibility, etc. is severely limited due to labor requirements and, oftentimes, expert domain knowledge. In this paper, we propose a deep learning based approach, named DeepStand, for image-based corn stand counting at early phenological stages. The proposed method adopts a truncated VGG-16 network as a backbone feature extractor and merges multiple feature maps with different scales to make the network robust against scale variation. Our extensive computational experiments suggest that our proposed method can successfully count corn stands and out-perform other state-of-the-art methods. It is the goal of our work to be used by the larger agricultural community as a way to enable high-throughput phenotyping without the use of extensive time and labor requirements.

A proposed method to extract maximum possible power in the shortest time on solar PV arrays under partial shadings using metaheuristic algorithms

The increasing use of fossil fuels to produce energy is leading to environmental problems. Hence, it has led the human society to move towards the use of renewable energies, including solar energy. In recent years, one of the most popular methods to gain energy is using photovoltaic arrays to produce solar energy. Skyscrapers and different weather conditions cause shadings on these PV arrays, which leads to less power generation. Various methods such as TCT and Sudoku patterns have been proposed to improve power generation for partial shading PV arrays, but these methods have some problems such as not generating maximum power and being designed for a specific dimension of PV arrays. Therefore, we proposed a metaheuristic algorithm-based approach to extract maximum possible power in the shortest possible time. In this paper, five algorithms which have proper results in most of the searching problems are chosen from different groups of metaheuristic algorithms. Also, four different standard shading patterns are used for more realistic analysis. Results show that the proposed method achieves better results in maximum power generation compared to TCT arrangement (18.53%) and Sudoku arrangement (4.93%). Also, the results show that GWO is the fastest metaheuristic algorithm to reach maximum output power in PV arrays under partial shading condition. Thus, the authors believe that by using metaheuristic algorithms, an efficient, reliable, and fast solution is reached to solve partial shading PV arrays problem

Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars

We propose a neural rendering-based system that creates head avatars from a single photograph. Our approach models a person's appearance by decomposing it into two layers. The first layer is a pose-dependent coarse image that is synthesized by a small neural network. The second layer is defined by a pose-independent texture image that contains high-frequency details. The texture image is generated offline, warped and added to the coarse image to ensure a high effective resolution of synthesized head views. We compare our system to analogous state-of-the-art systems in terms of visual quality and speed. The experiments show significant inference speedup over previous neural head avatar models for a given visual quality. We also report on a real-time smartphone-based implementation of our system.