Urban environments are characterised by the presence of distinctive audio signals which alert the drivers to events that require prompt action. The detection and interpretation of these signals would be highly beneficial for smart vehicle systems, as it would provide them with complementary information to navigate safely in the environment. In this paper, we present a framework that spots the presence of acoustic events, such as horns and sirens, using a two-stage approach. We first model the urban soundscape and use anomaly detection to identify the presence of an anomalous sound, and later determine the nature of this sound. As the audio samples are affected by copious non-stationary and unstructured noise, which can degrade classification performance, we propose a noise-removal technique to obtain a clean representation of the data we can use for classification and waveform reconstruction. The method is based on the idea of analysing the spectrograms of the incoming signals as images and applying spectrogram segmentation to isolate and extract the alerting signals from the background noise. We evaluate our framework on four hours of urban sounds collected driving around urban Oxford on different kinds of road and in different traffic conditions. When compared to traditional feature representations, such as Mel-frequency cepstrum coefficients, our framework shows an improvement of up to 31% in the classification rate.

Smart health home systems and assisted living architectures rely on severely energy-constrained sensing devices, such as wearable sensors, for the generation of data and their reliable wireless communication to a central location. However, the need for recharging the battery regularly constitutes a maintenance burden that hinders the long-term cost-effectiveness of these systems, especially for health-oriented applications that target people in need, such as the elderly or the chronically ill. These sensing systems generate raw data that is processed into knowledge by reasoning and machine learning algorithms. This paper investigates the benefits of embedded machine learning, i.e. executing this knowledge extraction on the wearable sensor, instead of communicating abundant raw data over the low power network. Focusing on a simple classification task and using an accelerometer-based wearable sensor, we demonstrate that embedded machine learning has the potential to reduce the radio and processor duty cycle by several orders of magnitude; and, thus, substantially extend the battery lifetime of resourceconstrained wearable sensors.

This paper explores the idea of reducing a robot's energy consumption while following a trajectory by turning off the main localisation subsystem and switching to a lowerpowered, less accurate odometry source at appropriate times. This applies to scenarios where the robot is permitted to deviate from the original trajectory, which allows for energy savings. Sensor scheduling is formulated as a probabilistic belief planning problem. Two algorithms are presented which generate feasible perception schedules: the first is based upon a simple heuristic; the second leverages dynamic programming to obtain optimal plans. Both simulations and real-world experiments on a planetary rover prototype demonstrate over 50% savings in perception-related energy, which translates into a 12% reduction in total energy consumption. I. INTRODUCTIONRobots require energy to operate. Yet they only have access to limited energy storage during missions. As we extend the reach of autonomous systems to operate in remote locations, over long distances and for long periods of time, energy considerations are becoming increasingly important. To date, these considerations are often brought to bear in schemes where trajectories or speed profiles are optimised to minimise the energy required for actuation (see, for example, [1], [2], [3]). Here we take a different, yet complementary, approach in considering the energy expenditure for sensing (and, implicitly, computation) associated with navigation. In particular, our goal is to activate the perception system only as required to maintain the vehicle within a given margin around a predetermined path. As the main navigation sensors are switched off and the robot reverts to a lower-powered, less accurate odometry source for parts of the trajectory, any associated computation will also be reduced, leading to further savings in energy.Naively, such perception schedules could be constructed by switching sensors on and off randomly or according to, for example, a fixed frequency. This does, however, suffer the drawback that no heed is paid to drift in the robot's position with respect to the original trajectory: it may not be desirable to deviate by more than an allowed margin from the predetermined route. This arises, for example, in a planetary exploration scenario when conducting long traverses over featureless terrain. Other possible considerations include traversability, obstacles, and the robustness of the localisation system to deviations from the original path. Such naive approaches would also need to be tuned to individual trajectories as savings would depend significantly on trajectory shape. In this work we present two approaches which explicitly account for drift and trajectory shape (though the

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In this paper, we represent a new evaluation approach for missing data techniques (MDTs) where the efficiency of those are investigated using listwise deletion method as reference. We experiment on classification problems and calculate misclassification rates (MR) for different missing data percentages (MDP). We compare three MDTs: pairwise deletion (PW), mean imputation (MI) and a maximum likelihood method that we call complete expectation maximization (CEM). We use synthetic dataset, Iris dataset and Pima Indians Diabetes dataset. We train a Gaussian mixture model (GMM) with missing at random (MAR) data. We test the trained GMM for two cases, in which test dataset is missing or complete. The results show that CEM is the most efficient method in both cases while MI is the worst of the three. PW and CEM prove to be more stable with respect to especially higher MDP values than MI.

Driver identification constitutes an important enabling technology in intelligent transportation systems, allowing the development and the use of in-car personalised functionalities and thwarting unauthorised usage. In this work, we leverage the literature in authentication tasks (e.g. speaker recognition) and present a framework for driver identification which employs Support Vector Machine (SVM) and Universal Background Model schemes. Our framework operates on accelerator and break pedal signals, and thus augments other technologies, such as microphones or cameras, if present. Moreover, our framework is compatible with vehicles which are limited to traditional sensing modalities. We evaluate the framework on 15 hours of driving data for a total of 416 Km travelled, comprising of messages from the CAN bus of an electric vehicle and GPS traces from four different drivers travelling on the same route, obtaining an accuracy of over 95% in the identification rate. Furthermore, our evaluation shows that UBM schemes outperform classification approaches traditionally adopted in driver identification literature by a significant margin.

Energy consumption represents one of the most basic constraints for mobile robot autonomy. We propose a new framework to predict energy consumption using information extracted from publicly available maps. This method avoids having to model internal robot configurations, which are often unavailable, while still providing invaluable predictions for both explored and unexplored trajectories. Our approach uses a heteroscedastic Gaussian Process to model the power consumption, which explicitly accounts for variance due to exogenous latent factors such as traffic and weather conditions. We evaluate our framework on 30km of data collected from a city centre environment with a mobile robot travelling on pedestrian walkways. Results across five different test routes show an average difference between predicted and measured power consumption of 3.3%, leading to an average error of 6.6% on predictions of energy consumption. The distinct advantage of our model is our ability to predict measurement variance. The variance predictions improved by 84.3% over a benchmark.

As a step toward the design of a robot that can take part to a conversation we propose a robotic system that, taking advantage of multiple perceptual capabilities, actively follows a conversation among several human subjects. The essential idea of our proposal is that the robot system can dynamically change the focus of its attention according to visual or audio stimuli to track the actual speaker throughout the conversation and infer her identity.