Recurrent neural networks for hydrodynamic imaging using a 2D-sensitive artificial lateral line

The lateral line is a mechanosensory organ found in fish and amphibians that allows them to sense and act on their near-field hydrodynamic environment. We present a 2D-sensitive Artificial lateral line (ALL) comprising eight all-optical flow sensors, which we use to measure hydrodynamic velocity profiles along the sensor array in response to a moving object in its vicinity. We then use the measured velocity profiles to reconstruct the objects location, via two types of neural networks: feed-forward and recurrent. Several implementations of feed-forward neural networks for ALL source localisation exist, while recurrent neural networks may be more appropriate for this task. The performance of a recurrent neural network (the Long Short-Term Memory, LSTM) is compared to that of a feed-forward neural network (the Online-Sequential Extreme Learning Machine, OS-ELM) via localizing a 6 cm sphere moving at 13 cm/s. Results show that, in a 62 cm × 9.5 cm area of interest, the LSTM outperforms the OS-ELM with an average localisation error of 0.72 cm compared to 4.27 cm respectively. Furthermore, the recurrent network is relatively less affected by noise, indicating that recurrent connections can be beneficial for hydrodynamic object localisation

Localisation and navigation in GPS-denied environments using RFID tags

Includes bibliographical references.This dissertation addresses the autonomous localisation and navigation problem in the context of an underground mining environment. This kind of environment has little or no features as well as no access to GPS or stationary towers, which are usually used for navigation. In addition dust and debris may hinder optical methods for ranging. This study looks at the feasibility of using randomly distributed RFID tags to autonomously navigate in this environment. Clustering of observed tags are used for localisation, subsequently value iteration is used to navigate to a defined goal. Results are presented, concluding that it is feasible to localise and navigate using only RFID tags, in simulation. Localisation feasibility is also confirmed by experimental measurements

Information-theoretic Reasoning in Distributed and Autonomous Systems

The increasing prevalence of distributed and autonomous systems is transforming decision making in industries as diverse as agriculture, environmental monitoring, and healthcare. Despite significant efforts, challenges remain in robustly planning under uncertainty. In this thesis, we present a number of information-theoretic decision rules for improving the analysis and control of complex adaptive systems. We begin with the problem of quantifying the data storage (memory) and transfer (communication) within information processing systems. We develop an information-theoretic framework to study nonlinear interactions within cooperative and adversarial scenarios, solely from observations of each agent's dynamics. This framework is applied to simulations of robotic soccer games, where the measures reveal insights into team performance, including correlations of the information dynamics to the scoreline. We then study the communication between processes with latent nonlinear dynamics that are observed only through a filter. By using methods from differential topology, we show that the information-theoretic measures commonly used to infer communication in observed systems can also be used in certain partially observed systems. For robotic environmental monitoring, the quality of data depends on the placement of sensors. These locations can be improved by either better estimating the quality of future viewpoints or by a team of robots operating concurrently. By robustly handling the uncertainty of sensor model measurements, we are able to present the first end-to-end robotic system for autonomously tracking small dynamic animals, with a performance comparable to human trackers. We then solve the issue of coordinating multi-robot systems through distributed optimisation techniques. These allow us to develop non-myopic robot trajectories for these tasks and, importantly, show that these algorithms provide guarantees for convergence rates to the optimal payoff sequence

AN ENERGY EFFICIENT CROSS-LAYER NETWORK OPERATION MODEL FOR MOBILE WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) are modern technologies used to sense/control the environment whether indoors or outdoors. Sensor nodes are miniatures that can sense a specific event according to the end user(s) needs. The types of applications where such technology can be utilised and implemented are vast and range from households’ low end simple need applications to high end military based applications. WSNs are resource limited. Sensor nodes are expected to work on a limited source of power (e.g., batteries). The connectivity quality and reliability of the nodes is dependent on the quality of the hardware which the nodes are made of. Sensor nodes are envisioned to be either stationary or mobile. Mobility increases the issues of the quality of the operation of the network because it effects directly on the quality of the connections between the nodes

Antenna array calibration in wireless communications

Imperial Users onl

Prostate cancer treated with brachytherapy; an exploratory study of dose-dependent biomarkers and quality of life

BACKGROUND: Low-dose-rate permanent prostate brachytherapy (PPB) is an attractive treatment option for patients with localised prostate cancer with excellent outcomes. As standard CT-based post-implant dosimetry often correlates poorly with late treatment-related toxicity, this exploratory (proof of concept) study was conducted to investigate correlations between radiation − induced DNA damage biomarker levels, and acute and late bowel, urinary, and sexual toxicity. METHODS: Twelve patients treated with (125)I PPB monotherapy (145Gy) for prostate cancer were included in this prospective study. Post-implant CT based dosimetry assessed the minimum dose encompassing 90% (D(90%)) of the whole prostate volume (global), sub-regions of the prostate (12 sectors) and the near maximum doses (D(0.1cc), D(2cc)) for the rectum and bladder. Six blood samples were collected from each patient; pre-treatment, 1 h (h), 4 h, 24 h post-implant, at 4 weeks (w) and at 3 months (m). DNA double strand breaks were investigated by staining the blood samples with immunofluorescence antibodies to γH2AX and 53BP1 proteins (γH2AX/53BP1). Patient self-scored quality of life from the Expanded Prostate Cancer Index Composite (EPIC) were obtained at baseline, 1 m, 3 m, 6 m, 9 m, 1 year (y), 2y and 3y post-treatment. Spearman’s correlation coefficients were used to evaluate correlations between temporal changes in γH2AX/53BP1, dose and toxicity. RESULTS: The minimum follow up was 2 years. Population mean prostate D(90%) was 144.6 ± 12.1 Gy and rectal near maximum dose D(0.1cc) = 153.0 ± 30.8 Gy and D(2cc) = 62.7 ± 12.1 Gy and for the bladder D(0.1cc) = 123.1 ± 27.0 Gy and D(2cc) = 70.9 ± 11.9 Gy. Changes in EPIC scores from baseline showed high positive correlation between acute toxicity and late toxicity for both urinary and bowel symptoms. Increased production of γH2AX/53BP1 at 24 h relative to baseline positively correlated with late bowel symptoms. Overall, no correlations were observed between dose metrics (prostate global or sector doses) and γH2AX/53BP1 foci counts. CONCLUSIONS: Our results show that a prompt increase in γH2AX/53BP1foci at 24 h post-implant relative to baseline may be a useful measure to assess elevated risk of late RT − related toxicities for PPB patients. A subsequent investigation recruiting a larger cohort of patients is warranted to verify our findings. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13014-017-0792-1) contains supplementary material, which is available to authorized users

Indoor Localisation of Scooters from Ubiquitous Cost-Effective Sensors: Combining Wi-Fi, Smartphone and Wheel Encoders

Indoor localisation of people and objects has been a focus of research studies for several decades because of its great advantage to several applications. Accuracy has always been a challenge because of the uncertainty of the employed sensors. Several technologies have been proposed and researched, however, accuracy still represents an issue. Today, several sensor technologies can be found in indoor environments, some of which are economical and powerful, such as Wi-Fi. Meanwhile, Smartphones are typically present indoors because of the people that carry them along, while moving about within rooms and buildings. Furthermore, vehicles such as mobility scooters can also be present indoor to support people with mobility impairments, which may be equipped with low-cost sensors, such as wheel encoders. This thesis investigates the localisation of mobility scooters operating indoor. This represents a specific topic as most of today's indoor localisation systems are for pedestrians. Furthermore, accurate indoor localisation of those scooters is challenging because of the type of motion and specific behaviour. The thesis focuses on improving localisation accuracy for mobility scooters and on the use of already available indoor sensors. It proposes a combined use of Wi-Fi, Smartphone IMU and wheel encoders, which represents a cost-effective energy-efficient solution. A method has been devised and a system has been developed, which has been experimented on different environment settings. The outcome of the experiments are presented and carefully analysed in the thesis. The outcome of several trials demonstrates the potential of the proposed solutions in reducing positional errors significantly when compared to the state-of-the-art in the same area. The proposed combination demonstrated an error range of 0.35m - 1.35m, which can be acceptable in several applications, such as some related to assisted living. 3 As the proposed system capitalizes on the use of ubiquitous technologies, it opens up to a potential quick take up from the market, therefore being of great benefit for the target audience

Tracking the Fine Scale Movements of Fish using Autonomous Maritime Robotics: A Systematic State of the Art Review

This paper provides a systematic state of the art review on tracking the fine scale movements of fish with the use of autonomous maritime robotics. Knowledge of migration patterns and the localization of specific species of fish at a given time is vital to many aspects of conservation. This paper reviews these technologies and provides insight into what systems are being used and why. The review results show that a larger amount of complex systems that use a deep learning techniques are used over more simplistic approaches to the design. Most results found in the study involve Autonomous Underwater Vehicles, which generally require the most complex array of sensors. The results also provide insight into future research such as methods involving swarm intelligence, which has seen an increase in use in recent years. This synthesis of current and future research will be helpful to research teams working to create an autonomous vehicle with intentions to track, navigate or survey