Deep Room Recognition Using Inaudible Echos

Recent years have seen the increasing need of location awareness by mobile applications. This paper presents a room-level indoor localization approach based on the measured room's echos in response to a two-millisecond single-tone inaudible chirp emitted by a smartphone's loudspeaker. Different from other acoustics-based room recognition systems that record full-spectrum audio for up to ten seconds, our approach records audio in a narrow inaudible band for 0.1 seconds only to preserve the user's privacy. However, the short-time and narrowband audio signal carries limited information about the room's characteristics, presenting challenges to accurate room recognition. This paper applies deep learning to effectively capture the subtle fingerprints in the rooms' acoustic responses. Our extensive experiments show that a two-layer convolutional neural network fed with the spectrogram of the inaudible echos achieve the best performance, compared with alternative designs using other raw data formats and deep models. Based on this result, we design a RoomRecognize cloud service and its mobile client library that enable the mobile application developers to readily implement the room recognition functionality without resorting to any existing infrastructures and add-on hardware. Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and 89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in a quiet museum, and 15 spots in a crowded museum, respectively. Compared with the state-of-the-art approaches based on support vector machine, RoomRecognize significantly improves the Pareto frontier of recognition accuracy versus robustness against interfering sounds (e.g., ambient music).Comment: 29 page

Machine learning techniques to forecast non-linear trends in smart environments

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Contributions to channel modelling and performance estimation of HAPS-based communication systems regarding IEEE Std 802.16TM

New and future telecommunication networks are and will be broadband type. The existing terrestrial and space radio communication infrastructures might be supplemented by new wireless networks that make and will make use of aeronautics-technology. Our study/contribution is referring to radio communications based on radio stations aboard a stratospheric platform named, by ITU-R, HAPS (High Altitude Platform Station). These new networks have been proposed as an alternative technology within the ITU framework to provide various narrow/broadband communication services. With the possibility of having a payload for Telecommunications in an aircraft or a balloon (HAPS), it can be carried out radio communications to provide backbone connections on ground and to access to broadband points for ground terminals. The latest implies a complex radio network planning. Therefore, the radio coverage analysis at outdoors and indoors becomes an important issue on the design of new radio systems. In this doctoral thesis, the contribution is related to the HAPS application for terrestrial fixed broadband communications. HAPS was hypothesised as a quasi-static platform with height above ground at the so-called stratospheric layer. Latter contribution was fulfilled by approaching via simulations the outdoor-indoor coverage with a simple efficient computational model at downlink mode. This work was assessing the ITU-R recommendations at bands recognised for the HAPS-based networks. It was contemplated the possibility of operating around 2 GHz (1820 MHz, specifically) because this band is recognised as an alternative for HAPS networks that can provide IMT-2000 and IMT-Advanced services. The global broadband radio communication model was composed of three parts: transmitter, channel, and receiver. The transmitter and receiver parts were based on the specifications of the IEEE Std 802.16TM-2009 (with its respective digital transmission techniques for a robust-reliable link), and the channel was subjected to the analysis of radio modelling at the level of HAPS and terrestrial (outdoors plus indoors) parts. For the channel modelling was used the two-state characterisation (physical situations associated with the transmitted/received signals), the state-oriented channel modelling. One of the channel-state contemplated the environmental transmission situation defined by a direct path between transmitter and receiver, and the remaining one regarded the conditions of shadowing. These states were dependent on the elevation angle related to the ray-tracing analysis: within the propagation environment, it was considered that a representative portion of the total energy of the signal was received by a direct or diffracted wave, and the remaining power signal was coming by a specular wave, to last-mentioned waves (rays) were added the scattered and random rays that constituted the diffuse wave. At indoors case, the variations of the transmitted signal were also considering the following matters additionally: the building penetration, construction material, angle of incidence, floor height, position of terminal in the room, and indoor fading; also, these indoors radiocommunications presented different type of paths to reach the receiver: obscured LOS, no LOS (NLOS), and hard NLOS. The evaluation of the feasible performance for the HAPS-to-ground terminal was accomplished by means of thorough simulations. The outcomes of the experiment were presented in terms of BER vs. Eb/N0 plotting, getting significant positive conclusions for these kind of system as access network technology based on HAPS

Occupancy Patterns Scoping Review Project

Understanding the occupancy and heating patterns of UK domestic consumers is important for understanding the role of demand-side technologies, such as occupancy-based smart heating controls to manage energy consumption more efficiently.The research undertakes a systematic scoping review to identify and assess the quality of the UK and international evidence on occupancy patterns, to critically review the common methods of measuring occupancy, and to discuss the potential role of occupancy-based smart heating controls in meeting energy savings, thermal comfort and usability requirements.This report was prepared by a team at the University of Southampton and commissioned by the former Department of Energy and Climate Change (DECC).<br/

A model personal energy meter

Every day each of us consumes a significant amount of energy, both directly through transport, heating and use of appliances, and indirectly from our needs for the production of food, manufacture of goods and provision of services. This dissertation investigates a personal energy meter which can record and apportion an individual's energy usage in order to supply baseline information and incentives for reducing our environmental impact. If the energy costs of large shared resources are split evenly without regard for individual consumption each person minimises his own losses by taking advantage of others. Context awareness offers the potential to change this balance and apportion energy costs to those who cause them to be incurred. This dissertation explores how sensor systems installed in many buildings today can be used to apportion energy consumption between users, including an evaluation of a range of strategies in a case study and elaboration of the overriding principles that are generally applicable. It also shows how second-order estimators combined with location data can provide a proxy for fine-grained sensing. A key ingredient for apportionment mechanisms is data on energy usage. This may come from metering devices or buildings directly, or from profiling devices and using secondary indicators to infer their power state. A mechanism for profiling devices to determine the energy costs of specific activities, particularly applicable to shared programmable devices is presented which can make this process simpler and more accurate. By combining crowdsourced building-inventory information and a simple building energy model it is possible to estimate an individual's energy use disaggregated by device class with very little direct sensing. Contextual information provides crucial cues for apportioning the use and energy costs of resources, and one of the most valuable sources from which to infer context is location. A key ingredient for a personal energy meter is a low cost, low infrastructure location system that can be deployed on a truly global scale. This dissertation presents a description and evaluation of the new concept of inquiry-free Bluetooth tracking that has the potential to offer indoor location information with significantly less infrastructure and calibration than other systems. Finally, a suitable architecture for a personal energy meter on a global scale is demonstrated using a mobile phone application to aggregate energy feeds based on the case studies and technologies developed

Event Detection and Predictive Maintenance using Component Echo State Networks

With a growing number of sensors collecting information about systems in indus- try and infrastructure, one wants to extract useful information from this data. The goal of this project is to investigate the applicability of Echo State Net- work techniques to time-varying classification of multivariate time series from primarily mechanical and electrical systems. Two relevant technical problems are predicting impending failure of systems (predictive maintenance), and clas- sifying a common event related to the system (event detection). In this project, they are formulated as a supervised machine learning problem on a multivariate time series. For this problem, Echo State Networks (ESN) have proven effective. However, applying these algorithms to new data sets involves a lot of guesswork as to how the algorithm should be configured to model the data effectively. In this work, a modification of the Echo State Network (ESN) model is presented, that helps to remove some of this guesswork. The new algorithm uses specifically structured components in order to facilitate the generation of relevant features by the ESN. The algorithm is tested on two easy event detection data sets, and one hard predictive maintenance data set. The results are compared to Support Vector Machine and Multilayer Perceptron classifiers, as well as to a basic ESN, which is also implemented as a reference. The component ESN successfully generates promising features, and outperforms the minimum complexity ESN as well as the standard classifiers