Publications

2021

1. 

   CARACAL: A versatile passive acoustic monitoring tool for wildlife research and conservation

   Wijers, Matthew, Loveridge, Andrew, Macdonald, David W, and Markham, Andrew

   Bioacoustics 2021

   Abstract HTML Github

   Acoustic localisation technology has been widely tested and applied for passive acoustic monitoring and ecological research, however, hardware costs of commercially available devices limit scalability. Furthermore, few studies have explored its use with low-density arrays. We present a low-cost, custom-designed hardware and software system termed ‘CARACAL’ that is able to extract and localise weak acoustic signals. The key to this is the use of four microphones on each logger, allowing for phase-based measurements and the ability to enhance signal-to-noise ratio through beamforming. As a proof of concept, we test the functionality of the CARACAL system by conducting a gunshot localisation experiment and demonstrate animal call detection and localisation from a lion predation event. Results show the system could locate gunshots with an average accuracy of 33.2 ± 15.3 m within an array of 7 stations 500 m apart. When applied to animal call positioning, we show long range (> 1 km) localisation of three different species’ calls, Cape buffalo, chacma baboon and spotted hyaena. With a cost of approximately £150 per unit, the CARACAL system provides a cost-effective solution for acoustic localisation over large areas. The system is open source and can be customised to suit a variety of wildlife research applications.

2. 

   The influence of spatial features and atmospheric conditions on African lion vocal behaviour

   Wijers, Matthew, Trethowan, Paul, Preez, Byron, Chamaillé-Jammes, Simon, Loveridge, Andrew J, Macdonald, David W, and Markham, Andrew

   Animal Behaviour 2021

   Abstract HTML

   Long-distance vocalization is a characteristic of African lion, Panthera leo, behaviour and is important for maintaining territorial boundaries as well as locating distant group members. Vocal signalling is, however, a flexible behaviour that involves varying costs and benefits depending on environmental, social and spatial factors. Motivated by previous data collection limitations, we developed a novel approach to investigate the influence of atmospheric conditions and animal home range geography on lion vocal behaviour using acoustic and accelerometer biologgers. To compensate for the short lifetime of the acoustic biologger, we trained a machine-learning model to detect lion roars from long-term acceleration signals which yielded over 500 nights of data from seven individual lions. Analysis of detected roar events revealed that vocalizations occurred mainly at night with a peak just before dawn. The relative likelihood of vocalization was negatively related to wind speed and temperature and positively related to absolute humidity suggesting that lions preferred to roar under conditions that reduce sound attenuation and thereby maximize calling area. Roar occurrence was found to be dependent on an animal’s location relative to its home range with lions demonstrating an apparent avoidance for vocalizing beyond the home range boundary. Lions were also more likely to roar repeatedly while closer to rivers and water points within their home range. This study is the first of its kind and not only improves the understanding of lion vocal behaviour but can also inform new approaches for recording animal vocalizations remotely.

3. 

   iMag+: An Accurate and Rapidly Deployable Inertial Magneto-Inductive SLAM System

   Wei, Bo, Trigoni, Niki, and Markham, Andrew

   IEEE Transactions on Mobile Computing 2021

   Abstract HTML

   Localisation is an important part of many applications. Our motivating scenarios are short-term construction work and emergency rescue. These scenarios also require rapid setup and robustness to environmental conditions additional to localisation accuracy. These requirements preclude the use of many traditional high-performance methods, e.g. vision-based, laser-based, Ultra-wide band (UWB) and Global Positioning System (GPS)-based localisation systems. To overcome these challenges, we introduce iMag+, an accurate and rapidly deployable inertial magneto-inductive (MI) mapping and localisation system, which only requires monitored workers to carry a single MI transmitter and an inertial measurement unit in order to localise themselves with minimal setup effort. However, one major challenge is to use distorted and ambiguous MI location estimates for localisation. To solve this challenge, we propose a novel method to use MI devices for sensing environmental distortions for accurate closing inertial loops. We also suggest a robust and efficient first quadrant estimator to sanitise the ambiguous MI estimates. By applying robust simultaneous localisation and mapping (SLAM), our proposed localisation method achieves excellent tracking accuracy and can improve performance significantly compared with only using a Magneto-inductive device or inertial measurement unit (IMU) for localisation.

4. 

   Human tracking and identification through a millimeter wave radar

   Zhao, Peijun, Lu, Chris Xiaoxuan, Wang, Jianan, Chen, Changhao, Wang, Wei, Trigoni, Niki, and Markham, Andrew

   Ad Hoc Networks 2021

   Abstract HTML

   The key to offering personalized services in smart spaces is knowing where a particular person is with a high degree of accuracy. Visual tracking is one such solution, but concerns arise around the potential leakage of raw video information and many people are not comfortable accepting cameras in their homes or workplaces. We propose a human tracking and identification system (mID) based on millimeter wave radar which has a high tracking accuracy, without being visually compromising. Using a low-cost, commercial, off-the-shelf radar, we first obtain sparse point clouds and form temporally associated trajectories. With the aid of a deep recurrent network, we identify individual users and show how to detect intruders. We evaluate and demonstrate our system, showing median position errors of 0.16 m, identification accuracy of 89% and intruder detection accuracy of 73% for 12 insiders. By increasing observation time from 2 s to 7 s, identification accuracy rises to 99%.

5. 

   Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling

   Hu, Qingyong, Yang, Bo, Xie, Linhai, Rosa, Stefano, Guo, Yulan, Wang, Zhihua, Trigoni, Niki, and Markham, Andrew

   IEEE Transactions on Pattern Analysis and Machine Intelligence 2021

   Abstract HTML Github

   We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing steps, most existing approaches are only able to be trained and operate over small-scale point clouds. In this paper, we introduce RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Although remarkably computation and memory efficient, random sampling can discard key features by chance. To overcome this, we introduce a novel local feature aggregation module to progressively increase the receptive field for each 3D point, thereby effectively preserving geometric details. Comparative experiments show that our RandLA-Net can process 1 million points in a single pass with up to 200X faster than existing approaches. Moreover, extensive experiments on several large-scale point cloud datasets, including Semantic3D, SemanticKITTI, Toronto3D, S3DIS and NPM3D, demonstrate the state-of-the-art semantic segmentation performance of our RandLA-Net.

6. 

   VMLoc: Variational Fusion For Learning-Based Multimodal Camera Localization

   Zhou, Kaichen, Chen, Changhao, Wang, Bing, Saputra, Muhamad Risqi U, Trigoni, Niki, and Markham, Andrew

   In 2021

   Abstract Github

   Recent learning-based approaches have achieved impressive results in the field of single-shot camera localization. However, how best to fuse multiple modalities (e.g., image and depth) and to deal with degraded or missing input are less well studied. In particular, we note that previous approaches towards deep fusion do not perform significantly better than models employing a single modality. We conjecture that this is because of the naive approaches to feature space fusion through summation or concatenation which do not take into account the different strengths of each modality. To address this, we propose an end-to-end framework, termed VMLoc, to fuse different sensor inputs into a common latent space through a variational Product-of-Experts (PoE) followed by attention-based fusion. Unlike previous multimodal variational works directly adapting the objective function of vanilla variational auto-encoder, we show how camera localization can be accurately estimated through an unbiased objective function based on importance weighting. Our model is extensively evaluated on RGB-D datasets and the results prove the efficacy of our model. The source code is available at https://github.com/Zalex97/VMLoc.

7. 

   SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration

   Ao, Sheng, Hu, Qingyong, Yang, Bo, Markham, Andrew, and Guo, Yulan

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021

   Abstract arXiv Github

   Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either sensitive to rotation transformations, or rely on classical handcrafted features which are neither general nor representative. In this paper, we introduce a new, yet conceptually simple, neural architecture, termed SpinNet, to extract local features which are rotationally invariant whilst sufficiently informative to enable accurate registration. A Spatial Point Transformer is first introduced to map the input local surface into a carefully designed cylindrical space, enabling end-to-end optimization with SO (2) equivariant representation. A Neural Feature Extractor which leverages the powerful point-based and 3D cylindrical convolutional neural layers is then utilized to derive a compact and representative descriptor for matching. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities.

8. 

   Towards semantic segmentation of urban-scale 3d point clouds: A dataset, benchmarks and challenges

   Hu, Qingyong, Yang, Bo, Khalid, Sheikh, Xiao, Wen, Trigoni, Niki, and Markham, Andrew

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021

   Abstract arXiv Github

   An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets. However, publicly available datasets are either in relatively small spatial scales or have limited semantic annotations due to the expensive cost of data acquisition and data annotation, which severely limits the development of fine-grained semantic understanding in the context of 3D point clouds. In this paper, we present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is three times the number of labeled points than the existing largest photogrammetric point cloud dataset. Our dataset consists of large areas from three UK cities, covering about 7.6 km^ 2 of the city landscape. In the dataset, each 3D point is labeled as one of 13 semantic classes. We extensively evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results. In particular, we identify several key challenges towards urban-scale point cloud understanding. The dataset is available at https://github. com/QingyongHu/SensatUrban.

9. 

   SoundDet: Polyphonic Moving Sound Event Detection and Localization from Raw Waveform

   He, Yuhang, Trigoni, Niki, and Markham, Andrew

   In International Conference on Machine Learning 2021

   Abstract arXiv PDF

   We present a new end-to-end trainable and light-weight framework, SoundDet, for polyphonic moving sound event detection and localization. Prior methods typically approach this problem by preprocessing raw waveforms into time-frequency representations, which are more amenable to process with well-established image processing pipelines. In addition, detection is performed in a segment-wise manner, leading to incomplete and partial detections. SoundDet takes a novel approach and directly consumes the raw, multichannel waveform and treats the spatio-temporal sound event as a complete “sound-object" to be detected. Specifically, SoundDet consists of a backbone neural network and two parallel heads for temporal detection and spatial localization. Given the high sampling rates of raw waveforms, the backbone network first learns a set of phase-sensitive and frequency-selective filters to explicitly retain direction-of-arrival information, whilst being highly computationally and parametrically efficient in comparison to standard 1D/2D convolution. A dense sound event proposal map is then constructed to handle the challenges of predicting events with a wide distribution of temporal durations. Accompanying the dense proposal map are a temporal overlapness map and a motion smoothness map that measure a proposal’s confidence to represent an event from temporal detection accuracy and movement consistency perspective respectively. Using the two maps simultaneously allows SoundDet to be trained in a unified manner over space and time. Experimental results on the public DCASE dataset show the advantage of SoundDet on both segment-based and our newly proposed event-based evaluation metrics.

2020

1. 

   Robust attentional aggregation of deep feature sets for multi-view 3D reconstruction

   Yang, Bo, Wang, Sen, Markham, Andrew, and Trigoni, Niki

   International Journal of Computer Vision 2020

   Abstract HTML Github

   We study the problem of recovering an underlying 3D shape from a set of images. Existing learning based approaches usually resort to recurrent neural nets, e.g., GRU, or intuitive pooling operations, e.g., max/mean poolings, to fuse multiple deep features encoded from input images. However, GRU based approaches are unable to consistently estimate 3D shapes given different permutations of the same set of input images as the recurrent unit is permutation variant. It is also unlikely to refine the 3D shape given more images due to the long-term memory loss of GRU. Commonly used pooling approaches are limited to capturing partial information, e.g., max/mean values, ignoring other valuable features. In this paper, we present a new feed-forward neural module, named AttSets, together with a dedicated training algorithm, named FASet, to attentively aggregate an arbitrarily sized deep feature set for multi-view 3D reconstruction. The AttSets module is permutation invariant, computationally efficient and flexible to implement, while the FASet algorithm enables the AttSets based network to be remarkably robust and generalize to an arbitrary number of input images. We thoroughly evaluate FASet and the properties of AttSets on multiple large public datasets. Extensive experiments show that AttSets together with FASet algorithm significantly outperforms existing aggregation approaches.

2. 

   DeepTIO: A deep thermal-inertial odometry with visual hallucination

   Saputra, Muhamad Risqi U, Gusmao, Pedro PB, Lu, Chris Xiaoxuan, Almalioglu, Yasin, Rosa, Stefano, Chen, Changhao, Wahlström, Johan, Wang, Wei, Markham, Andrew, and Trigoni, Niki

   IEEE Robotics and Automation Letters 2020

   Abstract arXiv PDF

   Visual odometry shows excellent performance in a wide range of environments. However, in visually-denied scenarios (e.g. heavy smoke or darkness), pose estimates degrade or even fail. Thermal cameras are commonly used for perception and inspection when the environment has low visibility. However, their use in odometry estimation is hampered by the lack of robust visual features. In part, this is as a result of the sensor measuring the ambient temperature profile rather than scene appearance and geometry. To overcome this issue, we propose a Deep Neural Network model for thermal-inertial odometry (DeepTIO) by incorporating a visual hallucination network to provide the thermal network with complementary information. The hallucination network is taught to predict fake visual features from thermal images by using Huber loss. We also employ selective fusion to attentively fuse the features from three different modalities, i.e thermal, hallucination, and inertial features. Extensive experiments are performed in hand-held and mobile robot data in benign and smoke-filled environments, showing the efficacy of the proposed model.

3. 

   FootSLAM meets adaptive thresholding

   Wahlström, Johan, Markham, Andrew, and Trigoni, Niki

   IEEE Sensors Journal 2020

   Abstract arXiv HTML

   Calibration of the zero-velocity detection threshold is an essential prerequisite for zero-velocity-aided inertial navigation. However, the literature is lacking a self-contained calibration method, suitable for large-scale use in unprepared environments without map information or pre-deployed infrastructure. In this paper, the calibration of the zero-velocity detection threshold is formulated as a maximum likelihood problem. The likelihood function is approximated using estimation quantities readily available from the FootSLAM algorithm. Thus, we obtain a method for adaptive thresholding that does not require map information, measurements from supplementary sensors, or user input. Experimental evaluations are conducted using data with different gait speeds, sensor placements, and walking trajectories. The proposed calibration method is shown to outperform fixed-threshold zero-velocity detectors and a benchmark using a speed-based threshold classifier.

4. 

   Deep learning for fast simulation of seismic waves in complex media

   Moseley, Ben, Nissen-Meyer, Tarje, and Markham, Andrew

   Solid Earth 2020

   Abstract PDF Github

   The simulation of seismic waves is a core task in many geophysical applications. Numerical methods such as finite difference (FD) modelling and spectral element methods (SEMs) are the most popular techniques for simulating seismic waves, but disadvantages such as their computational cost prohibit their use for many tasks. In this work, we investigate the potential of deep learning for aiding seismic simulation in the solid Earth sciences. We present two deep neural networks which are able to simulate the seismic response at multiple locations in horizontally layered and faulted 2-D acoustic media an order of magnitude faster than traditional finite difference modelling. The first network is able to simulate the seismic response in horizontally layered media and uses a WaveNet network architecture design. The second network is significantly more general than the first and is able to simulate the seismic response in faulted media with arbitrary layers, fault properties and an arbitrary location of the seismic source on the surface of the media, using a conditional autoencoder design. We test the sensitivity of the accuracy of both networks to different network hyperparameters and show that the WaveNet network can be retrained to carry out fast seismic inversion in the same media. We find that are there are challenges when extending our methods to more complex, elastic and 3-D Earth models; for example, the accuracy of both networks is reduced when they are tested on models outside of their training distribution. We discuss further research directions which could address these challenges and potentially yield useful tools for practical simulation tasks.

5. 

   Deep-Learning-Based Pedestrian Inertial Navigation: Methods, Data Set, and On-Device Inference

   Chen, Changhao, Zhao, Peijun, Lu, Chris Xiaoxuan, Wang, Wei, Markham, Andrew, and Trigoni, Niki

   IEEE Internet of Things Journal 2020

   Abstract arXiv Website Dataset

   Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. Recently, there has been a growing interest in applying deep neural networks (DNNs) to motion sensing and location estimation. However, the lack of sufficient labelled data for training and evaluating architecture benchmarks has limited the adoption of DNNs in IMU-based tasks. In this paper, we present and release the Oxford Inertial Odometry Dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research, with fine-grained ground-truth on all sequences. Furthermore, to enable more efficient inference at the edge, we propose a novel lightweight framework to learn and reconstruct pedestrian trajectories from raw IMU data. Extensive experiments show the effectiveness of our dataset and methods in achieving accurate data-driven pedestrian inertial navigation on resource-constrained devices.

6. 

   Learning With Stochastic Guidance for Robot Navigation

   Xie, Linhai, Miao, Yishu, Wang, Sen, Blunsom, Phil, Wang, Zhihua, Chen, Changhao, Markham, Andrew, and Trigoni, Niki

   IEEE transactions on neural networks and learning systems 2020

   Abstract arXiv HTML

   Due to the sparse rewards and high degree of environmental variation, reinforcement learning approaches, such as deep deterministic policy gradient (DDPG), are plagued by issues of high variance when applied in complex real-world environments. We present a new framework for overcoming these issues by incorporating a stochastic switch, allowing an agent to choose between high- and low-variance policies. The stochastic switch can be jointly trained with the original DDPG in the same framework. In this article, we demonstrate the power of the framework in a navigation task, where the robot can dynamically choose to learn through exploration or to use the output of a heuristic controller as guidance. Instead of starting from completely random actions, the navigation capability of a robot can be quickly bootstrapped by several simple independent controllers. The experimental results show that with the aid of stochastic guidance, we are able to effectively and efficiently train DDPG navigation policies and achieve significantly better performance than state-of-the-art baseline models.

7. 

   Milli-rio: Ego-motion estimation with low-cost millimetre-wave radar

   Almalioglu, Yasin, Turan, Mehmet, Lu, Chris Xiaoxuan, Trigoni, Niki, and Markham, Andrew

   IEEE Sensors Journal 2020

   Abstract arXiv HTML

   Robust indoor ego-motion estimation has attracted significant interest in the last decades due to the fast-growing demand for location-based services in indoor environments. Among various solutions, frequency-modulated continuous-wave (FMCW) radar sensors in millimeter-wave (MMWave) spectrum are gaining more prominence due to their intrinsic advantages such as penetration capability and high accuracy. Single-chip low-cost MMWave radar as an emerging technology provides an alternative and complementary solution for robust ego-motion estimation, making it feasible in resource-constrained platforms thanks to low-power consumption and easy system integration. In this paper, we introduce Milli-RIO, an MMWave radar-based solution making use of a single-chip low-cost radar and inertial measurement unit sensor to estimate six-degrees-of-freedom ego-motion of a moving radar. Detailed quantitative and qualitative evaluations prove that the proposed method achieves precisions on the order of few centimeters for indoor localization tasks.

8. 

   Wirelessly Powered Embedded Sensor Nodes for Internal Structural Health Monitoring

   Wang, Zhihua, and Markham, Andrew

   IEEE Transactions on Industrial Electronics 2020

   Abstract HTML

   Monitoring large civil structures during construction and over their entire lifetime is key to being able to predict structural weaknesses and provide predictive maintenance. Existing approaches to structural health monitoring typically rely on wired or wireless sensors placed on the exterior of the structure. Although these approaches, being easy to power and replace, provide useful information, they fall short of measuring what is happening deep within the structure itself, e.g. within foundations or within supporting beams. Wireless sensors embedded inside are particularly attractive for these applications, but lifetime is limited by the battery capacity. Charging these sensors has been the bottleneck. Existing wireless power transfer approaches based on electromagnetic propagation suffer heavy attenuation due to the high dielectric absorption of the dense concrete. To resolve the issue, the limitation of dielectric absorption is tackled with a novel physical layer. In this work, power transfer is built on the fact that nearly all concrete structures are reinforced with steel bars, to provide tensile strength. Instead of transferring power over a few centimetres of concrete with a large transmitter as previous work has attempted to do, the reinforcing steel can act as a conduit for extremely low-frequency magneto-inductive fields.

9. 

   Learning distributed communication and computation in the IoT

   Abudu, Prince, and Markham, Andrew

   Computer Communications 2020

   Abstract HTML

   In distributed, cooperative Internet of Things (IoT) settings, sensing devices must communicate in a resource-aware fashion to achieve a diverse set of tasks, (i.e., event detection, image classification). In such settings, we continue to see a shift from reliance on cloud-centric to edge-centric architectures for data processing, inference and actuation. Distributed edge inference techniques address real-time, connectivity, network bandwidth and latency challenges in spatially distributed IoT applications. Achieving efficient, resource-aware communication in such systems is a longstanding challenge. Many current approaches require complex, hand-engineered communication protocols. In this paper, we present a novel scalable, data-driven and communication-efficient Convolutional Recurrent Neural Network (C-RNN) framework for distributed tasks. We provide empirical and systematic analyses of model convergence, node scalability, computation-cost and communication-cost based on dynamic network graphs. Further to this, we show that our framework is able to solve distributed image classification tasks via automatically learned communication.

10. 

    Vocal discrimination of African lions and its potential for collar-free tracking

    Wijers, Matthew, Trethowan, Paul, Du Preez, Byron, Chamaillé-Jammes, Simon, Loveridge, Andrew J, Macdonald, David W, and Markham, Andrew

    Bioacoustics 2020

    Abstract HTML

    Previous research has shown that African lions (Panthera leo) have the ability to discriminate between conspecific vocalisations, but little is known about how individual identity is conveyed in the spectral structure of roars. Using acoustic – accelerometer biologgers that allow vocalisations to be reliably associated with individual identity, we test for vocal individuality in the fundamental frequency (f0) of roars from 5 male lions, firstly by comparing simple f0 summary features and secondly by modelling the temporal pattern of the f0 contour. We then assess the application of this method for discriminating between individuals using passive acoustic monitoring. Results indicate that f0 summary features only allow for vocal discrimination with 70.7% accuracy. By comparison, vocal discrimination can be achieved with an accuracy of 91.5% based on individual differences in the temporal pattern of the f0 sequence. We further demonstrate that passively recorded lion roars can be localised and differentiated with similar accuracy. The existence of individually unique f0 contours in lion roars and their relatively lower attenuation indicates a likely mechanism enabling individual lions to identify conspecifics over long distances. These differences can be exploited by researchers to track individuals across the landscape and thereby supplement conventional lion monitoring approaches.

11. 

    Atloc: Attention guided camera localization

    Wang, Bing, Chen, Changhao, Lu, Chris Xiaoxuan, Zhao, Peijun, Trigoni, Niki, and Markham, Andrew

    In Proceedings of the AAAI Conference on Artificial Intelligence 2020

    Abstract arXiv Github

    Deep learning has achieved impressive results in camera localization, but current single-image techniques typically suffer from a lack of robustness, leading to large outliers. To some extent, this has been tackled by sequential (multi-images) or geometry constraint approaches, which can learn to reject dynamic objects and illumination conditions to achieve better performance. In this work, we show that attention can be used to force the network to focus on more geometrically robust objects and features, achieving state-of-the-art performance in common benchmark, even if using only a single image as input. Extensive experimental evidence is provided through public indoor and outdoor datasets. Through visualization of the saliency maps, we demonstrate how the network learns to reject dynamic objects, yielding superior global camera pose regression performance.

12. 

    CVPR ORAL Randla-net: Efficient semantic segmentation of large-scale point clouds

    Hu, Qingyong, Yang, Bo, Xie, Linhai, Rosa, Stefano, Guo, Yulan, Wang, Zhihua, Trigoni, Niki, and Markham, Andrew

    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020

    Abstract arXiv Github

    We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing steps, most existing approaches are only able to be trained and operate over small-scale point clouds. In this paper, we introduce RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Although remarkably computation and memory efficient, random sampling can discard key features by chance. To overcome this, we introduce a novel local feature aggregation module to progressively increase the receptive field for each 3D point, thereby effectively preserving geometric details. Extensive experiments show that our RandLA-Net can process 1 million points in a single pass with up to 200X faster than existing approaches. Moreover, our RandLA-Net clearly surpasses state-of-the-art approaches for semantic segmentation on two large-scale benchmarks Semantic3D and SemanticKITTI.

13. 

    SnapNav: Learning Mapless Visual Navigation with Sparse Directional Guidance and Visual Reference

    Xie, Linhai, Markham, Andrew, and Trigoni, Niki

    In 2020 IEEE International Conference on Robotics and Automation (ICRA) 2020

    Abstract HTML PDF

    Learning-based visual navigation still remains a challenging problem in robotics, with two overarching issues: how to transfer the learnt policy to unseen scenarios, and how to deploy the system on real robots. In this paper, we propose a deep neural network based visual navigation system, SnapNav. Unlike map-based navigation or Visual-Teach-and-Repeat (VT&R), SnapNav only receives a few snapshots of the environment combined with directional guidance to allow it to execute the navigation task. Additionally, SnapNav can be easily deployed on real robots due to a two-level hierarchy: a high level commander that provides directional commands and a low level controller that provides real-time control and obstacle avoidance. This also allows us to effectively use simulated and real data to train the different layers of the hierarchy, facilitating robust control. Extensive experimental results show that SnapNav achieves a highly autonomous navigation ability compared to baseline models, enabling sparse, map-less navigation in previously unseen environments.

14. 

    Heart rate sensing with a robot mounted mmwave radar

    Zhao, Peijun, Lu, Chris Xiaoxuan, Wang, Bing, Chen, Changhao, Xie, Linhai, Wang, Mengyu, Trigoni, Niki, and Markham, Andrew

    In 2020 IEEE International Conference on Robotics and Automation (ICRA) 2020

    Abstract HTML PDF

    Heart rate monitoring at home is a useful metric for assessing health e.g. of the elderly or patients in post-operative recovery. Although non-contact heart rate monitoring has been widely explored, typically using a static, wall-mounted device, measurements are limited to a single room and sensitive to user orientation and position. In this work, we propose mBeats, a robot mounted millimeter wave (mmWave) radar system that provide periodic heart rate measurements under different user poses, without interfering in a users daily activities. mBeats contains a mmWave servoing module that adaptively adjusts the sensor angle to the best reflection pro le. Furthermore, mBeats features a deep neural network predictor, which can estimate heart rate from the lower leg and additionally provides estimation uncertainty. Through extensive experiments, we demonstrate accurate and robust operation of mBeats in a range of scenarios. We believe by integrating mobility and adaptability, mBeats can empower many down-stream healthcare applications at home, such as palliative care, post-operative rehabilitation and telemedicine.

15. 

    See through smoke: robust indoor mapping with low-cost mmWave radar

    Lu, Chris Xiaoxuan, Rosa, Stefano, Zhao, Peijun, Wang, Bing, Chen, Changhao, Stankovic, John A, Trigoni, Niki, and Markham, Andrew

    In Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services (MobiSys) 2020

    Abstract arXiv HTML Github

    This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility environments to assist in emergency response. A unique feature of milliMap is that it only leverages a low-cost, off-the-shelf mmWave radar, but can reconstruct a dense grid map with accuracy comparable to lidar, as well as providing semantic annotations of objects on the map. milliMap makes two key technical contributions. First, it autonomously overcomes the sparsity and multi-path noise of mmWave signals by combining cross-modal supervision from a co-located lidar during training and the strong geometric priors of indoor spaces. Second, it takes the spectral response of mmWave reflections as features to robustly identify different types of objects e.g. doors, walls etc. Extensive experiments in different indoor environments show that milliMap can achieve a map reconstruction error less than 0.2m and classify key semantics with an accuracy of   90%, whilst operating through dense smoke.

16. 

    Deep Emergent Communication for the IoT

    Abudu, Prince, and Markham, Andrew

    In 2020 IEEE International Conference on Smart Computing (SMARTCOMP) 2020

    Abstract HTML

    Learning emergent communication remains a longstanding challenge in distributed Internet of Things (IoT) settings. The need to overcome tedious, complex design of hand-engineered communication protocols coupled with superior prediction and classification capabilities, make Deep Networks attractive for distributed, cooperative IoT settings. In such settings, sensing devices must sense, communicate and provide actuation whilst executing a resource-aware operation. Reliance on the Cloud for knowledge discovery is fraught with latency, connectivity, and bandwidth issues. We continue to see the emergence of edge-centric paradigms in which sensing devices at the network edge are endowed with intelligence. In turn, these devices are equipped with self-organization capabilities, robust real-time capabilities, reduced bandwidth requirements and greater context awareness. In this paper, we propose a novel, scalable communicating Convolutional Recurrent Neural Network (C-RNN) architecture for distributed IoT settings. Our framework automatically learns emergent communication in a purely data-driven way. Extensive experimental evaluation shows that our framework can learn to solve distributed image classification tasks, optimises for communication cost, is robust to lossy-links and can scale to multiple nodes.

17. 

    milliEgo: single-chip mmWave radar aided egomotion estimation via deep sensor fusion

    Lu, Chris Xiaoxuan, Saputra, Muhamad Risqi U, Zhao, Peijun, Almalioglu, Yasin, Gusmao, Pedro PB, Chen, Changhao, Sun, Ke, Trigoni, Niki, and Markham, Andrew

    In Proceedings of the 18th Conference on Embedded Networked Sensor Systems (SenSys) 2020

    Abstract arXiv HTML Github

    Robust and accurate trajectory estimation of mobile agents such as people and robots is a key requirement for providing spatial awareness for emerging capabilities such as augmented reality or autonomous interaction. Although currently dominated by optical techniques e.g., visual-inertial odometry these suffer from challenges with scene illumination or featureless surfaces. As an alternative, we propose milliEgo, a novel deep-learning approach to robust egomotion estimation which exploits the capabilities of low-cost mm Wave radar. Although mmWave radar has a fundamental advantage over monocular cameras of being metric i.e., providing absolute scale or depth, current single chip solutions have limited and sparse imaging resolution, making existing point-cloud registration techniques brittle. We propose a new architecture that is optimized for solving this challenging pose transformation problem. Secondly, to robustly fuse mmWave pose estimates with additional sensors, e.g. inertial or visual sensors we introduce a mixed attention approach to deep fusion. Through extensive experiments, we demonstrate our proposed system is able to achieve 1.3% 3D error drift and generalizes well to unseen environments. We also show that the neural architecture can be made highly efficient and suitable for real-time embedded applications.

2019

1. Deep neural network based inertial odometry using low-cost inertial measurement units

   Chen, Changhao, Lu, Xiaoxuan, Wahlstrom, Johan, Markham, Andrew, and Trigoni, Niki

   IEEE Transactions on Mobile Computing 2019
2. Sensor Fusion for Magneto-Inductive Navigation

   Wahlström, Johan, Kok, Manon, Gusmao, Pedro Porto Buarque, Abrudan, Traian E, Trigoni, Niki, and Markham, Andrew

   IEEE Sensors Journal 2019
3. Zero-Velocity Detection-A Bayesian Approach to Adaptive Thresholding

   Wahlstrom, Johan, Skog, Isaac, Gustafsson, Fredrik, Markham, Andrew, and Trigoni, Niki

   IEEE Sensors Letters 2019
4. Autonomous Learning of Speaker Identity and WiFi Geofence From Noisy Sensor Data

   Lu, Chris Xiaoxuan, Xiangli, Yuanbo, Zhao, Peijun, Chen, Changhao, Trigoni, Niki, and Markham, Andrew

   IEEE Internet of Things Journal 2019
5. Learning monocular visual odometry through geometry-aware curriculum learning

   Saputra, Muhamad Risqi U, Gusmao, Pedro PB, Wang, Sen, Markham, Andrew, and Trigoni, Niki

   In 2019 International Conference on Robotics and Automation (ICRA) 2019
6. Map-aided navigation for emergency searches

   Wahlström, Johan, Gusmao, Pedro Porto Buarque, Markham, Andrew, and Trigoni, Niki

   In 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) 2019
7. mid: Tracking and identifying people with millimeter wave radar

   Zhao, Peijun, Lu, Chris Xiaoxuan, Wang, Jianan, Chen, Changhao, Wang, Wei, Trigoni, Niki, and Markham, Andrew

   In 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) 2019
8. Map-aided navigation for emergency searches

   Wahlström, Johan, Gusmão, Pedro Porto Buarque, Markham, Andrew, and Trigoni, Niki

   In 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) 2019
9. Distilling knowledge from a deep pose regressor network

   Saputra, Muhamad Risqi U, Gusmao, Pedro PB, Almalioglu, Yasin, Markham, Andrew, and Trigoni, Niki

   In Proceedings of the IEEE International Conference on Computer Vision 2019
10. Seismic wave propagation and inference using deep learning, numerical methods and wave physics

    Nissen-Meyer, Tarje, Szenicer, Alexandre, Moseley, Benjamin, Leng, Kuangdai, and Markham, Andrew

    In AGU Fall Meeting 2019 2019
11. Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues

    Lu, Chris Xiaoxuan, Kan, Xuan, Du, Bowen, Chen, Changhao, Wen, Hongkai, Markham, Andrew, Trigoni, Niki, and Stankovic, John

    In The World Wide Web Conference 2019
12. Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents

    Everett, Richard, Cobb, Adam, Markham, Andrew, and Roberts, Stephen

    In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems 2019
13. Selective sensor fusion for neural visual-inertial odometry

    Chen, Changhao, Rosa, Stefano, Miao, Yishu, Lu, Chris Xiaoxuan, Wu, Wei, Markham, Andrew, and Trigoni, Niki

    In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019

2018

1. Dense 3D object reconstruction from a single depth view

   Yang, Bo, Rosa, Stefano, Markham, Andrew, Trigoni, Niki, and Wen, Hongkai

   IEEE transactions on pattern analysis and machine intelligence 2018
2. Listening to lions: animal-borne acoustic sensors improve bio-logger calibration and behaviour classification performance

   Wijers, Matthew, Trethowan, Paul, Markham, Andrew, Du Preez, Byron, Chamaillé-Jammes, Simon, Loveridge, Andrew, and Macdonald, David

   Frontiers in Ecology and Evolution 2018
3. Visual SLAM and structure from motion in dynamic environments: A survey

   Saputra, Muhamad Risqi U, Markham, Andrew, and Trigoni, Niki

   ACM Computing Surveys (CSUR) 2018
4. In situ behavioral plasticity as compensation for weather variability: implications for future climate change

   Noonan, Michael J, Newman, Chris, Markham, Andrew, Bilham, Kirstin, Buesching, Christina D, and Macdonald, David W

   Climatic change 2018
5. Snoopy: Sniffing your smartwatch passwords via deep sequence learning

   Lu, Chris Xiaoxuan, Du, Bowen, Wen, Hongkai, Wang, Sen, Markham, Andrew, Martinovic, Ivan, Shen, Yiran, and Trigoni, Niki

   Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018
6. Learning 3D scene semantics and structure from a single depth image

   Yang, Bo, Lai, Zihang, Lu, Xiaoxuan, Lin, Shuyu, Wen, Hongkai, Markham, Andrew, and Trigoni, Niki

   In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 2018
7. Learning with training wheels: speeding up training with a simple controller for deep reinforcement learning

   Xie, Linhai, Wang, Sen, Rosa, Stefano, Markham, Andrew, and Trigoni, Niki

   In 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018
8. iMag: Accurate and rapidly deployable inertial magneto-inductive localisation

   Wei, Bo, Trigoni, Niki, and Markham, Andrew

   In 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018
9. Defo-Net: Learning body deformation using generative adversarial networks

   Wang, Zhihua, Rosa, Stefano, Xie, Linhai, Yang, Bo, Wang, Sen, Trigoni, Niki, and Markham, Andrew

   In 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018
10. Learning the intuitive physics of non-rigid object deformations

    Wang, Zhihua, Rosa, Stefano, and Markham, Andrew

    In Neural Information Processing Systems (NIPS) Workshops 2018
11. Automatic Face Recognition Adaptation via Ambient Wireless Identifiers

    Lu, Chris Xiaoxuan, Zhao, Peijun, Du, Bowen, Wen, Hongkai, Markham, Andrew, Rosa, Stefano, and Trigoni, Niki

    In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems 2018
12. Deepauth: in-situ authentication for smartwatches via deeply learned behavioural biometrics

    Lu, Chris Xiaoxuan, Du, Bowen, Zhao, Peijun, Wen, Hongkai, Shen, Yiran, Markham, Andrew, and Trigoni, Niki

    In Proceedings of the 2018 ACM International Symposium on Wearable Computers 2018
13. Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus

    Cobb, Adam D, Everett, Richard, Markham, Andrew, and Roberts, Stephen J

    In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018
14. IONet: Learning to cure the curse of drift in inertial odometry

    Chen, Changhao, Lu, Xiaoxuan, Markham, Andrew, and Trigoni, Niki

    In Thirty-Second AAAI Conference on Artificial Intelligence 2018
15. Towards an enterprise architecture controlling framework

    Arriola, Lennin, and Markham, Andrew

    In Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings 2018

2017

1. Getting to the core: Internal body temperatures help reveal the ecological function and thermal implications of the lions’ mane

   Trethowan, Paul, Fuller, Andrea, Haw, Anna, Hart, Tom, Markham, Andrew, Loveridge, Andrew, Hetem, Robyn, Preez, Byron, and Macdonald, David W

   Ecology and evolution 2017
2. An active-radio-frequency-identification system capable of identifying co-locations and social-structure: Validation with a wild free-ranging animal

   Ellwood, Stephen A, Newman, Chris, Montgomery, Robert A, Nicosia, Vincenzo, Buesching, Christina D, Markham, Andrew, Mascolo, Cecilia, Trigoni, Niki, Pasztor, Bence, Dyo, Vladimir, and others,

   Methods in Ecology and Evolution 2017
3. Magneto-Inductive Tracking in Underground Environments

   Abrudan, Traian E, Kypris, Orfeas, Trigoni, Niki, and Markham, Andrew

   Underground Sensing: Monitoring and Hazard Detection for Environment and Infrastructure 2017
4. Snoopy: Sniffing Your Passwords from Motion Sensors on Smartwatches

   Lu, Chris Xiaoxuan, Du, Bowen, Wen, Hongkai, Wang, Sen, Markham, Andrew, Martinovic, Ivan, Shen, Yiran, and Trigoni, Niki

   Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 2017
5. 3d object reconstruction from a single depth view with adversarial learning

   Yang, Bo, Wen, Hongkai, Wang, Sen, Clark, Ronald, Markham, Andrew, and Trigoni, Niki

   In Proceedings of the IEEE International Conference on Computer Vision 2017
6. GraphTinker: Outlier rejection and inlier injection for pose graph SLAM

   Xie, Linhai, Wang, Sen, Markham, Andrew, and Trigoni, Niki

   In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2017
7. Evolutionary machine learning for RTS game StarCraft

   Wu, Lianlong, and Markham, Andrew

   In Thirty-First AAAI Conference on Artificial Intelligence 2017
8. SCAN: Learning Associations between Noisy Sensor Sets

   Lu, Xiaoxuan, Wen, Hongkai, Wang, Sen, Markham, Andrew, and Trigoni, Niki

   In Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks 2017
9. SCAN: learning speaker identity from noisy sensor data

   Lu, Chris Xiaoxuan, Wen, Hongkai, Wang, Sen, Markham, Andrew, and Trigoni, Niki

   In Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks 2017
10. Towards self-supervised face labeling via cross-modality association

    Lu, Chris Xiaoxuan, Kan, Xuan, Rosa, Stefano, Du, Bowen, Wen, Hongkai, Markham, Andrew, and Trigoni, Niki

    In Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems 2017
11. VeriNet: user verification on smartwatches via behavior biometrics

    Lu, Chris Xiaoxuan, Du, Bowen, Kan, Xuan, Wen, Hongkai, Markham, Andrew, and Trigoni, Niki

    In Proceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications 2017
12. Interpreting lion behaviour with nonparametric probabilistic programs

    Dhir, Neil, Wood, Frank, Vákár, Matthijs, Markham, Andrew, Wijers, Matthew, Trethowan, Paul, Du Preez, Byron, Loveridge, Andrew, and Macdonald, David

    In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 2017
13. Vinet: Visual-inertial odometry as a sequence-to-sequence learning problem

    Clark, Ronald, Wang, Sen, Wen, Hongkai, Markham, Andrew, and Trigoni, Niki

    In Thirty-First AAAI Conference on Artificial Intelligence 2017
14. Vidloc: A deep spatio-temporal model for 6-dof video-clip relocalization

    Clark, Ronald, Wang, Sen, Markham, Andrew, Trigoni, Niki, and Wen, Hongkai

    In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017
15. Underground Sensing: Monitoring and Hazard Detection for Environment and Infrastructure

    Abrudan, Traian E, Kypris, Orfeas, Trigoni, Niki, and Markham, Andrew

    2017

2016

1. Tracking people in highly dynamic industrial environments

   Papaioannou, Savvas, Markham, Andrew, and Trigoni, Niki

   IEEE Transactions on mobile computing 2016
2. Use of tri-axial accelerometers to assess terrestrial mammal behaviour in the wild

   Lush, L, Ellwood, S, Markham, A, Ward, AI, and Wheeler, P

   Journal of Zoology 2016
3. 3-D displacement measurement for structural health monitoring using low-frequency magnetic fields

   Kypris, Orfeas, and Markham, Andrew

   IEEE Sensors Journal 2016
4. Magnetic induction-based positioning in distorted environments

   Kypris, Orfeas, Abrudan, Traian E, and Markham, Andrew

   IEEE Transactions on Geoscience and Remote Sensing 2016
5. Underground incrementally deployed magneto-inductive 3-D positioning network

   Abrudan, Traian E, Xiao, Zhuoling, Markham, Andrew, and Trigoni, Niki

   IEEE Transactions on Geoscience and Remote Sensing 2016
6. Impact of rocks and minerals on underground magneto-inductive communication and localization

   Abrudan, Traian E, Kypris, Orfeas, Trigoni, Niki, and Markham, Andrew

   IEEE Access 2016
7. RePWR: Wireless Power Transfer within Reinforced Concrete

   Wang, Zhihua, Kypris, Orfeas, and Markham, Andrew

   In Proceedings of the 4th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems 2016
8. Increasing the efficiency of 6-DoF visual localization using multi-modal sensory data

   Clark, Ronald, Wang, Sen, Wen, Hongkai, Trigoni, Niki, and Markham, Andrew

   In 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids) 2016
9. System for providing information and associated devices

   Markham, Andrew, Trigoni, Niki, and Ven, Jean-Paul

   2016 US Patent 9,351,106

2015

1. Robust indoor positioning with lifelong learning

   Xiao, Zhuoling, Wen, Hongkai, Markham, Andrew, and Trigoni, Niki

   IEEE Journal on Selected Areas in Communications 2015
2. Indoor tracking using undirected graphical models

   Xiao, Zhuoling, Wen, Hongkai, Markham, Andrew, and Trigoni, Niki

   IEEE Transactions on Mobile Computing 2015
3. A new Magneto-Inductive tracking technique to uncover subterranean activity: what do animals do underground?

   Noonan, Michael J, Markham, Andrew, Newman, Chris, Trigoni, Niki, Buesching, Christina D, Ellwood, Stephen A, and Macdonald, David W

   Methods in Ecology and Evolution 2015
4. Distortion rejecting magneto-inductive three-dimensional localization (MagLoc)

   Abrudan, Traian E, Xiao, Zhuoling, Markham, Andrew, and Trigoni, Niki

   IEEE Journal on Selected Areas in Communications 2015
5. Accurate positioning via cross-modality training

   Papaioannou, Savvas, Wen, Hongkai, Xiao, Zhuoling, Markham, Andrew, and Trigoni, Niki

   In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems 2015
6. A realistic evaluation and comparison of indoor location technologies: experiences and lessons learned

   Lymberopoulos, Dimitrios

   In IPSN 2015 2015
7. Reducing magneto-inductive positioning errors in a metal-rich indoor environment

   Kypris, Orfeas, Abrudan, Traian E, and Markham, Andrew

   In 2015 IEEE SENSORS 2015
8. Robust vision-based indoor localization

   Clark, Ronald, Trigoni, Niki, and Markham, Andrew

   In Proceedings of the 14th international conference on information processing in sensor networks 2015

2014

1. Non-line-of-sight identification and mitigation using received signal strength

   Xiao, Zhuoling, Wen, Hongkai, Markham, Andrew, Trigoni, Niki, Blunsom, Phil, and Frolik, Jeff

   IEEE Transactions on Wireless Communications 2014
2. Accuracy estimation for sensor systems

   Wen, Hongkai, Xiao, Zhuoling, Markham, Andrew, and Trigoni, Niki

   IEEE Transactions on Mobile Computing 2014
3. Climate and the individual: inter-annual variation in the autumnal activity of the European badger (Meles meles)

   Noonan, Michael J, Markham, Andrew, Newman, Chris, Trigoni, Niki, Buesching, Christina D, Ellwood, Stephen A, and Macdonald, David W

   PLoS One 2014
4. Does BTLE measure up against WiFi? A comparison of indoor location performance

   Zhao, Xiaojie, Xiao, Zhuoling, Markham, Andrew, Trigoni, Niki, and Ren, Yong

   In European Wireless 2014; 20th European Wireless Conference 2014
5. Robust pedestrian dead reckoning (R-PDR) for arbitrary mobile device placement

   Xiao, Zhuoling, Wen, Hongkai, Markham, Andrew, and Trigoni, Niki

   In 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN) 2014
6. Lightweight map matching for indoor localisation using conditional random fields

   Xiao, Zhuoling, Wen, Hongkai, Markham, Andrew, and Trigoni, Niki

   In Proceedings of the 13th international symposium on Information processing in sensor networks 2014
7. Fusion of radio and camera sensor data for accurate indoor positioning

   Papaioannou, Savvas, Wen, Hongkai, Markham, Andrew, and Trigoni, Niki

   In 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems 2014
8. Poster: Wi-Fi sensors meet visual tracking for an accurate positioning system

   Markham, A, Papaioannou, S, and others,

   In European Conference on Wireless Sensor Networks 2014
9. A Case for Magneto-Inductive Indoor Localization

   Abrudan, Traian E, Markham, Andrew, and Trigoni, Niki

   In The 11th European Conference on Wireless Sensor Networks 2014

2013

1. Human interactive secure key and identity exchange protocols in body sensor networks

   Huang, Xin, Chen, Bangdao, Markham, Andrew, Wang, Qinghua, Yan, Zheng, and Roscoe, Andrew William

   IET Information Security 2013
2. Identification and mitigation of non-line-of-sight conditions using received signal strength

   Xiao, Zhuoling, Wen, Hongkai, Markham, Andrew, Trigoni, Niki, Blunsom, Phil, and Frolik, Jeff

   In 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) 2013
3. Comparison of accuracy estimation approaches for sensor networks

   Wen, Hongkai, Xiao, Zhuoling, Symington, Andrew C, Markham, Andrew, and Trigoni, Niki

   In 2013 IEEE International Conference on Distributed Computing in Sensor Systems 2013

2012

1. Human interactive secure ID management in body sensor networks

   Huang, Xin, Ma, Xiao, Chen, Bangdao, Markham, Andrew, Wang, Qinghua, and Roscoe, A William

   Journal of Networks 2012
2. WILDSENSING: Design and deployment of a sustainable sensor network for wildlife monitoring

   Dyo, Vladimir, Ellwood, Stephen A, Macdonald, David W, Markham, Andrew, Trigoni, Niki, Wohlers, Ricklef, Mascolo, Cecilia, Pásztor, Bence, Scellato, Salvatore, and Yousef, Kharsim

   ACM Transactions on Sensor Networks (TOSN) 2012
3. Magneto-inductive networked rescue system (MINERS): taking sensor networks underground

   Markham, Andrew, and Trigoni, Niki

   In Proceedings of the 11th international conference on Information Processing in Sensor Networks 2012
4. Characterization of non-line-of-sight (NLOS) bias via analysis of clutter topology

   Hussain, Muzammil, Aytar, Yusuf, Trigoni, Niki, and Markham, Andrew

   In Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium 2012

2011

1. Underground localization in 3-D using magneto-inductive tracking

   Markham, Andrew, Trigoni, Niki, Macdonald, David W, and Ellwood, Stephen A

   IEEE Sensors Journal 2011
2. Body sensor network key distribution using human interactive channels

   Huang, Xin, Wang, Qinghua, Bangdao, Chen, Markham, Andrew, Jäntti, Riku, and Roscoe, AW

   In Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies 2011

2010

1. The automatic evolution of distributed controllers to configure sensor network operation

   Markham, Andrew, and Trigoni, Niki

   The Computer Journal 2010
2. Revealing the hidden lives of underground animals using magneto-inductive tracking

   Markham, Andrew, Trigoni, Niki, Ellwood, Stephen A, and Macdonald, David W

   In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems 2010
3. Magneto-inductive tracking of underground animals

   Markham, Andrew, Trigoni, Niki, Ellwood, Stephen A, and Macdonald, David W

   In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems 2010
4. Effect of rainfall on link quality in an outdoor forest deployment

   Markham, Andrew, Trigoni, Niki, and Ellwood, Stephen

   In 2010 International Conference on Wireless Information Networks and Systems (WINSYS) 2010
5. Discrete Gene Regulatory Networks (dGRNs): A novel approach to configuring sensor networks

   Markham, Andrew, and Trigoni, Niki

   In 2010 Proceedings IEEE INFOCOM 2010
6. Evolution and sustainability of a wildlife monitoring sensor network

   Dyo, Vladimir, Ellwood, Stephen A, Macdonald, David W, Markham, Andrew, Mascolo, Cecilia, Pásztor, Bence, Scellato, Salvatore, Trigoni, Niki, Wohlers, Ricklef, and Yousef, Kharsim

   In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems 2010

2009

1. Wildlife and environmental monitoring using RFID and WSN technology

   Dyo, Vladimir, Ellwood, Stephen A, Macdonald, David W, Markham, Andrew, Mascolo, Cecilia, Pásztor, Bence, Trigoni, Niki, and Wohlers, Ricklef

   In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems 2009

2008

2007

2006

1. The adaptive social hierarchy: A self organizing network based on naturally occurring structures

   Markham, Andrew, and Wilkinson, Andrew

   In Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems 2006