Small Unmanned Aerial Systems (UASs) provide significant benefits to economies across the world on a daily basis but increased usage brings a number of security challenges. For example, identifying small UASs operating with malicious intent in a restricted airspace. Supervised learning techniques applied to radio frequency (RF) signals have been considered for the classification of UAS type with high accuracy but due to labelled data assume the UAS signal is already known. Unsupervised learning algorithms such as K-means clustering provide a potential for identifying small UAS signals which have not been seen before. The use of transfer learning and CNN feature extraction (FE) with spectrogram graphical signal representations have been successfully used in a supervised manner for medical diagnosis and audio classification. This research is the first application of transfer learning and CNN FE as a pre-cursor to an unsupervised learning algorithm. This paper shows that clustering graphical representations of the signal and utilising CNN FE with transfer learning produces the highest v-measure score 0.814 but at a cost of 6s in time. Small UASs can travel at speeds of 45 mph so timely detection is essential in many use cases. A decrease in 0.2 v-measure score using PSD graphical image representations of the RF signal and PCA initialisation allows the clustering time to complete in under 0.3s even in environments with active interference in the same band. This timely result could provide effective early warning with the cueing of a secondary sensor or supervised algorithm with higher classification accuracy.

Agents need a formal representation of knowledge. This is modelled in an ontology. We present a survey of ontologies in the area of QoS management. From the survey we identified that improvements can be done in the Ontology of the Quality of Experience framework. We believe that with the extension full QoS Management capabilities can be then supported in the context of Quality of Experience. We focus in the appropriate QoS mechanism selection, network monitoring and QoS adaptation. With the additional concepts and actions the QoE ontology meets three key requirements for a QoS ontology: (i) decide which QoS mechanisms is better to fits the user needs; (ii) perform QoS monitoring and detection of SLA violations; and (iii) carry out QoS adaptation. Two experimental scenarios are currently being conducted. In scenario 1 the original ontology is used whilst for scenario 2 the extended version is employed. An initial comparative analysis is performed.

In this work, we demonstrate a systematic way to analyze a binary partition tree representation of natural images for the purposes of archiving and segmentation. Within the tree structure, these problems are transformed into locating prevalent tree branches. With a user interface these points can be found manually by browsing branches. However, tree visualization is difficult due to the high node density. A simpler version of the tree is desired which facilitates subsequent retrieval whilst maintaining as much semantic detail as possible. By studying the evolution of region statistics, our method highlights nodes which represent the boundary between salient detail and provide a set of tree levels from which simplifications and segmentations can be derived. A series of subjective tests are performed to demonstrate the effectiveness of using the simplified trees for object extraction. Segmentation results are compared to ground truths showing semantic content is maintained

Pyramid image representations via tree structures are recognized methods for region-based image analysis. Binary partition trees can be applied which document the merging process with small details found at the bottom levels and larger ones close to the root. Hindsight of the merging process is stored within the tree structure and provides the change histories of an image property from the leaf to the root node. In this work, the change histories are modelled by evolvement functions and their second order statistics are analyzed by using a knee function. Knee values show the reluctancy of each merge. We have systematically formulated these findings to provide a novel framework for binary partition tree analysis, where tree simplification is demonstrated. Based on an evolvement function, for each upward path in a tree, the tree node associated with the first reluctant merge is considered as a pruning candidate. The result is a simplified version providing a reduced solution space and still complying with the definition of a binary tree. The experiments show that image details are preserved whilst the number of nodes is dramatically reduced. An image filtering tool also results which preserves object boundaries and has applications for segmentation.

Tracking objects in motion is often done by imposing the constraints of kinematics and local image properties onto the objects. In this work, we propose a novel tracking algorithm which uses the surrounding information of the object to construct the feature profiles. The object feature profiles are then compared across consecutive frames to locate the targets. The feature profiles possess two important properties, distinctive-ness and coherence, which make them robust to measurement noises, short occlusions and false targets. The matching cost function is formulated under a Bayesian framework that enables the algorithm to capture the properties in the form of probabilities. The algorithm is also self-initializing. The computation of the feature profiles is fast due to their simple definition; and the comparison between two profiles can also be done efficiently.

This paper explores the idea that it may be possible to combine two ideas - UAV flocking, and wireless cluster computing - in a single system, the UltraSwarm. The possible advantages of such a system are considered, and solutions to some of the technical problems are identified. Initial work on constructing such a system based around miniature electric helicopters is described.

This paper introduces techniques for segmentation and tracking based on analysis of region derived descriptors. By partitioning the image into a series of homogeneous regions, the problem of object extraction changes from pixel based to database analysis. A region based approach has distinct advantages over pixel. In particular, it has low dimensionality, is resilient to rotation, shear, and photometric changes, preserves boundaries, gives implied segmentation, provides comprehensive description, has reduced drift, and permits detailed analysis. The method is amenable to the introduction of prior knowledge permitting the simplification of segmentation, which is otherwise an ill-posed problem. Objects are defined as collections of contiguous regions with known statistics, allowing their identification by performing correlations in the database. The interframe difference of region based sequences is high for low-motion scenes due to segmentation noise, but performs consistently better for high motion, making it suitable for tracking. When applied to tracking the method proves robust, occlusion insensitive, and unlike the other techniques, it re-establishes lost lock.

This paper proposes using a novel region space technique to track sport persons for the purpose of extracting their shirt numbers and use this to provide augmented information to the viewer. The region adjacency graph and picture trees are used to perform a search for an object using prior knowledge from a scene description. Once the candidate object has been extracted the sub-space is examined for alphanumeric characters, which are then characterized by optical character recognition. Rogue candidates may be removed based on the recognition histograms with improved robustness using temporal analysis.