THE ASSESSMENT OF THE QUALITY OF WATER RESOURCES IN NORTHERN THAILAND: TOWARDS SAFE DRINKING-WATER ACCESS FOR VULNERABLE POPULATIONS

Ph.DDOCTOR OF PHILOSOPH

Reductive Dehalogenation of Chlorophenols by Anaerobic Microbial Consortia

Master'sMASTER OF ENGINEERIN

Water Leaders Summit 2016: Future of World’s Water beyond 2030 – a retrospective analysis

10.1080/07900627.2016.1244643International Journal of Water Resources Development331326-34

Governance of aquaculture water use

10.1080/07900627.2018.1457513International Journal of Water Resources Development3504659-68

Flood Mitigation, Climate Change Adaptation and Technological Lock-In in Assam

Climate change adaptation requires communities and policymakers to be flexible in order to cope with high levels of uncertainty in climate projections, particularly of precipitation, flood magnitude and frequency, and changing human exposure and vulnerability to floods—which are even less predictable than the climate. Most of the world’s major rivers are embanked to ―protect‖ communities from floods. Embankments—which represent a significant investment largely of public funds—are a manifestation of the professionalism of engineers and hydrologists. They are also the result of professional and political entrapment and a technological frame that grows in strength (probably non-linearly) by positive feedback to produce technological lock-in. This results in inertia in large socio-technological systems, with little incentive to adopt more adaptive and flexible solutions, including non-structural measures—such as land-use zoning—even in the face of evidence that structural measures do not always reduce damage and, in some cases, actually make it worse. Where embankment breaches are common, damage is likely to increase as climate change induces larger floods, and lock-in and path dependence increase risk. Therefore, there is an urgent need for the mitigation of floods through non-structural measures that complement embankments. While the phenomena we describe in this paper are common in many countries, as well as in many states in India, it will focus on data from the Brahmaputra River catchment in Assam.the National University of Singapore for funding

PCANet-Based Convolutional Neural Network Architecture for a Vehicle Model Recognition System

Vehicle model recognition plays a crucial role in intelligent transportation systems. Most of the existing vehicle model recognition methods focus on locating a large global feature or extracting more than one local subordinate-level feature from a vehicle image. In this paper, we propose the principal component analysis network-based convolutional neural network (PCNN) and pinpoint only one discriminative local feature of a vehicle, which is the vehicle headlamp, for vehicle model recognition. The proposed model eliminates the need for locating and segmenting the headlamp precisely. In particular, PCNN ascertains the effectiveness of both principal component analysis and CNN in extracting hierarchical features from a vehicle headlamp image and also reducing the computational complexity of the traditional CNN system. To further enhance the training procedure while still keeping the discriminative property of the network, the fully connected layer is updated by backpropagation optimized with stochastic gradient descent. The proposed method is validated using a data set that comprises 13 300 training images and 2660 testing images, respectively. The model is robust against various distortions. Experiments show that PCNN outperforms state-of-the-art techniques with an average accuracy of 99.51% over 38 vehicle makes and models using the PLUS data set. In addition, the effectiveness of the proposed method is also validated using the public CompCars data set, achieving 89.83% accuracy over 357 vehicle models. © 2000-2011 IEEE

Hyper-parameters optimisation of deep CNN architecture for vehicle logo recognition

The training of deep convolutional neural network (CNN) for classification purposes is critically dependent on the expertise of hyper-parameters tuning. This study aims to minimise the user variability in training CNN by automatically searching and optimising the CNN architecture, particularly in the field of vehicle logo recognition system. For this purpose, the architecture and hyper-parameters of CNN were selected according to the implementation of the stochastic method of particle swarm optimisation on the training-testing data. After obtaining the optimised hyper-parameters, the CNN is fine-tuned and trained to ensure better network convergence and classification performance. In this study, a total of 14,950 vehicle logo images are divided into two independent training and testing sets. In addition, these images are segmented coarsely, thus the requirement of precise logo segmentation is obviated in this work. The learned features of the CNN were sufficiently discriminative to be classified using multiclass Softmax classifier. With implementation using a graphics processing unit (GPU), the computation time of the proposed method is acceptable for real-time application. The experimental results explicitly prove that the authors' approach outperforms most of the state-of-the-art methods, achieving an accuracy of 99.1% over 13 vehicle manufacturers

Vehicle logo recognition using whitening transformation and deep learning

This paper presents a vehicle logo recognition using a deep convolutional neural network (CNN) method and whitening transformation technique to remove redundancy of adjacent image pixels. Backpropagation algorithm with stochastic gradient descent optimization technique has been deployed to train and obtain weight filters of the networks. Seven layers of our proposed CNN incorporating an input layer, five hidden layers and an output layer have been implemented to capture rich and discriminative information of vehicle logo images. Functioning as the output layer of the network, the softmax classifier is utilized to handle multiple classes of vehicle logo image. For a given vehicle logo image, the network provides the probability for each vehicle manufacturer to which the given logo image belongs. Unlike most of the common traditional methods that employ handcrafted visual features, our proposed method is able to automatically learn and extract high-level features for the classification task. The extracted features are discriminative sufficiently to perform well in various imaging conditions and complex scenes. We validate our proposed method by utilizing a public vehicle logo image dataset, which comprises 10,000 and 1500 vehicle logo images for training and validation objective, respectively. Experimental results based on our proposed method outperform other existing methods in terms of the computational cost and overall classification accuracy of 99.13%. © 2018, Springer-Verlag London Ltd., part of Springer Nature

Hyper-parameters optimisation of deep CNN architecture for vehicle logo recognition

The training of deep convolutional neural network (CNN) for classification purposes is critically dependent on the expertise of hyper-parameters tuning. This study aims to minimise the user variability in training CNN by automatically searching and optimising the CNN architecture, particularly in the field of vehicle logo recognition system. For this purpose, the architecture and hyper-parameters of CNN were selected according to the implementation of the stochastic method of particle swarm optimisation on the training-testing data. After obtaining the optimised hyper-parameters, the CNN is fine-tuned and trained to ensure better network convergence and classification performance. In this study, a total of 14,950 vehicle logo images are divided into two independent training and testing sets. In addition, these images are segmented coarsely, thus the requirement of precise logo segmentation is obviated in this work. The learned features of the CNN were sufficiently discriminative to be classified using multiclass Softmax classifier. With implementation using a graphics processing unit (GPU), the computation time of the proposed method is acceptable for real-time application. The experimental results explicitly prove that the authors' approach outperforms most of the state-of-the-art methods, achieving an accuracy of 99.1% over 13 vehicle manufacturers