Efficient Visual Ego-Motion Estimation for Agile Flying Robots

Micro air vehicles (MAVs) have shown significant potential in modern society. The development in robotics and automation is changing the roles of MAVs from remotely controlled machines requiring human pilots to autonomous and intelligent robots. There is an increasing number of autonomous MAVs involved in outdoor operations. In contrast, the...

DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features

Outstanding users (OUs) denote the influential, 'core' or 'bridge' users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on...

Hover and fast flight of minimum-mass mission-capable flying robots

Highly automated Unmanned Aerial Vehicles (UAVs) or "flying robots" are rapidly becoming an important asset to society. The last decade has seen the advent of an impressive number of new UAV types and applications. For many applications, the UAVs need to be safe, highly automated, and versatile. Safety is a prerequisite to allowing their use in...

Earthquake Prediction: A MLP & SVM Comparison

Multi-Layer Perceptron and Support Vector Machine have both been widely used in machine learning. In this research paper, these models have been applied to binary classification on an individual time series basis. The goal was to see whether they can predict earthquakes, using earthquakes measured at specific stations across New Zealand. As it...

Fault detection and diagnosis to enhance safety in digitalized process system

The increased complexity of digitalized process systems requires advanced tools to detect and diagnose faults early to maintain safe operations. This study proposed a hybrid model that consists of Kernel Principal Component Analysis (kPCA) and DNNs that can be applied to detect and diagnose faults in various processes. The complex data is...

Accurate and Energy-efficient ECG Classification using RRAM based DNN Architecture

Cardiac arrhythmia characterized by irregular heartbeats is a prevalent problem among people suffering from cardiovascular diseases (CVD). Abnormalities in the heartbeats manifested in the electrocardiogram (ECG) signal are traditionally analysed by expert cardiologists or semi-automated computer aided techniques, which can be time consuming....

Multi-AL: Robust Active learning for Multi-label Classifier

Multi-label learning is becoming more and moreimportant as real-world data often contains multi-ple labels. The dataset used for learning such aclassifier is of great importance. Acquiring a cor-rectly labelled dataset is however a difficult task.Active learning is a method which can, given anoisy dataset, identify important instances for...

Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators

Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels. Not only acquiring a clean and fully labeled dataset in multi-label learning is extremely expensive, but also many of the actual labels are corrupted or missing due to the automated or non-expert annotation techniques. Noisy...

Evaluating the Supervised Video Summarization Model VASNet on an Action Localization Dataset

There is growing research on automated video summarization following the rise of video content. However, the subjectivity of the task itself is still an issue to address. This subjectivity stems from the fact that there can be different summaries for the same video depending on which parts one considers important. Supervised models especially...

Automated Bone Age Assessment based on DXA scans for a variety of ethnicities using Deep Transfer Learning

A child’s bone age is important for the diagnosis of a wide range of growth disorders. The most often used manual method for bone age assessment (BAA) consists of comparing hand-wrist radiographs with ’ground-truth’ atlasses. This method is criticised for being time-invasive, prone to inter- and intra-observer variability and not applicable to...

Modeling Inference Time of Deep Neural Networks on Memory-constrained Systems

Deep neural networks have revolutionized multiple fields within computer science. It is important to have a comprehensive understanding of the memory requirements and performance of deep networks on low-resource systems. While there have been efforts to this end, the effects of severe memory limits and heavy swapping are understudied. We have...

Towards Natural Language Understanding using Multimodal Deep Learning

This thesis describes how multimodal sensor data from a 3D sensor and microphone array can be processed with deep neural networks such that its fusion, the trained neural network, is a) more robust to noise, b) outperforms unimodal recognition and c) enhances unimodal recognition in absence of multimodal data. We built a framework for a complete...