WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

SMT goes ABMS: Developing Strategic Management Theory using Agent-Based Modelling and Simulation.

For the emerging complexity theory of strategy (CTS), organizations are complex adaptive systems able to co-evolve with their dynamic environments through interaction and response, rather than purely analysis and planning. A promising approach within the CTS context, is to focus on a strategic logic of opportunity pursuit, one in which the distributed decision-makers behave audaciously despite unpredictable, unstable environments. Although there is only emergent support for it, intriguingly organizations can perform better when these decision-makers ‘throw caution to the wind’ even at their own possible expense. Since traditional research methods have had difficulty showing how this can work over time, this research adopts a complementary method, agent-based modelling and simulation (ABMS), to examine this phenomenon. The simulation model developed here, CTS-SIM, is based on quite simple constructs, but it introduces a rich and novel externally driven environment and represents individual decision-makers as having autonomous perceptions but constrainable decision-making freedom. Its primary contribution is the illumination of core dynamics and causal mechanisms in the opportunity-transitioning process. During model construction the apparently simple concept of opportunity-transitioning turns out to be complex, and the apparently complex integration of exogenous and endogenous environments with all three views of opportunity pursuit in the entrepreneurship literature, turns out to be relatively simple. Simulation outcomes using NetLogo contribute to CTS by confirming the positive effects on agent performance of opportunistic transitioning among opportunities in highly dynamic environments. The simulations also reveal tensions among some of the chosen variables and tipping points in emergent behaviours, point to areas where theoretical clarity is currently lacking, provoke some interesting questions and open up useful avenues for future research and data collection using other methods and models. Guidance through numerous stylized facts, flexible methods, careful documentation and description are all intended to inspire interest and facilitate critical discussion and ongoing scientific work

The application of ocean front metrics for understanding habitat selection by marine predators

Marine predators such as seabirds, cetaceans, turtles, pinnipeds, sharks and large teleost fish are essential components of healthy, biologically diverse marine ecosystems. However, intense anthropogenic pressure on the global ocean is causing rapid and widespread change, and many predator populations are in decline. Conservation solutions are urgently required, yet only recently have we begun to comprehend how these animals interact with the vast and dynamic oceans that they inhabit. A better understanding of the mechanisms that underlie habitat selection at sea is critical to our knowledge of marine ecosystem functioning, and to ecologically-sensitive marine spatial planning. The collection of studies presented in this thesis aims to elucidate the influence of biophysical coupling at oceanographic fronts – physical interfaces at the transitions between water masses – on habitat selection by marine predators. High-resolution composite front mapping via Earth Observation remote sensing is used to provide oceanographic context to several biologging datasets describing the movements and behaviours of animals at sea. A series of species-habitat models reveal the influence of mesoscale (10s to 100s of kilometres) thermal and chlorophyll-a fronts on habitat selection by taxonomically diverse species inhabiting contrasting ocean regions; northern gannets (Morus bassanus; Celtic Sea), basking sharks (Cetorhinus maximus; north-east Atlantic), loggerhead turtles (Caretta caretta; Canary Current), and grey-headed albatrosses (Thalassarche chrysostoma; Southern Ocean). Original aspects of this work include an exploration of quantitative approaches to understanding habitat selection using remotely-sensed front metrics; and explicit investigation of how the biophysical properties of fronts and species-specific foraging ecology interact to influence associations. Main findings indicate that front metrics, particularly seasonal indices, are useful predictors of habitat preference across taxa. Moreover, frontal persistence and spatiotemporal predictability appear to mediate the use of front-associated foraging habitats, both in shelf seas and in the open oceans. These findings have implications for marine spatial planning and the design of protected area networks, and may prove useful in the development of tools supporting spatially dynamic ocean management

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

Personalised service discovery in mobile environments

In recent years, some trends have emerged that pertain both to mobile devices and the Web. On one side, mobile devices have transitioned from being simple wireless phones to become ubiquitous Web-enabled users' companions. On the other side, the Web has evolved from an online one-size-fits-all collection of interlinked documents to become an open platform of personalised services and content. It will not be long before these trends will converge and create a Seamless Web: an integrated environment where, besides traditional services delivered by powerful server machines accessible via wide area networks, new services and content will be offered by users to users via their portable devices. As a result, mobile users will soon be exposed - in addition to traditional "on-line" Web services/content - to a parallel universe of pervasive "off-line" services provided by devices in their surroundings. Such circumstances will raise new challenges when it comes to selecting the services to rely on, that will require solutions grounded on the characteristics of mobile environments. Two aspects will require particular attention: first, users will have access to a countless multitude of services impossible to explore; they will need assistance to identify, among this multitude, those services they are most likely to enjoy. Secondly, if today's services (and their providers) are always-on, `static' and aiming at Five 9s availability, tomorrow's pervasive services will be mobile (as devices move), fine-grained, increasingly composite (to provide richer functionalities) and so more unreliable by nature. Our research tackles the problem of service discovery in pervasive environments in two ways: on one hand, we support personalised discovery by means of a mobile recommender system, easing the discovery of pervasive services appealing to end-users. On the other hand, we enable reliable discovery, by reasoning on the composite nature of pervasive services and the physical availability of their component providers. Overall, we provide a discovery method that enables 'better' pervasive services, where by 'better' we mean both `more interesting' to the user and 'more reliable'

The Evolution of Smart Buildings: An Industrial Perspective of the Development of Smart Buildings in the 2010s

Over the course of the 2010s, specialist research bodies have failed to provide a holistic view of the changes in the prominent reason (as driven by industry) for creating a smart building. Over the 2010s, research tended to focus on remaining deeply involved in only single issues or value drivers. Through an analysis of the author’s peer reviewed and published works (book chapters, articles, essays and podcasts), supplemented with additional contextual academic literature, a model for how the key drivers for creating a smart building have evolved in industry during the 2010s is presented. The critical research commentary within this thesis, tracks the incremental advances of technology and their application to the built environment via academic movements, industrial shifts, or the author’s personal contributions. This thesis has found that it is demonstrable, through the chronology and publication dates of the included research papers, that as the financial cost and complexity of sensors and cloud computing reduced, smart buildings became increasingly prevalent. Initially, sustainability was the primary focus with the use of HVAC analytics and advanced metering in the early 2010s. The middle of the decade saw an economic transformation of the commercial office sector and the driver for creating a smart building was concerned with delivering flexible yet quantifiably used space. Driven by society’s emphasis on health, wellbeing and productivity, smart buildings pivoted their focus towards the end of the 2010s. Smart building technologies were required to demonstrate the impacts of architecture on the human. This research has evidenced that smart buildings use data to improve performance in sustainability, in space usage or for humancentric outcomes