Placing defects at the heart of high quality new homes: a learning perspective

The UK new-build housing sector is facing dual pressures to expand supply, whilst delivering against tougher planning and Building Regulation requirements; predominantly in the areas of sustainability. The sector is currently responding by significantly scaling up production and incorporating new technical solutions into new homes. This trajectory of up-scaling and technical innovation has been of research interest; but this research has primarily focus on the ‘upstream’ implications for house builders’ business models and standardised design templates. There has been little attention, though, to the potential ‘downstream’ implications of the ramping up of supply and the introduction of new technologies for build quality and defects. This paper contributes to our understanding of the ‘downstream’ implications through a synthesis of the current UK defect literature with respect to new-build housing. It is found that the prevailing emphasis in the literature is limited to the responsibility, pathology and statistical analysis of defects (and failures). The literature does not extend to how house builders individually and collectively, in practice, collect and learn from defects information. The paper concludes by describing an ongoing collaborative research programme with the National House Building Council (NHBC) to: (a) understand house builders’ localised defects analysis procedures, and their current knowledge feedback loops to inform risk management strategies; and, (b) building on this understanding, design and test action research interventions to develop new data capture, learning processes and systems to reduce targeted defects.

Interactive sensory objects for and by people with learning disabilities

This project engages people with learning disabilities as co-researchers and co-designers in the development of multisensory interactive artworks, with the aim of making museums or heritage sites more interesting, meaningful, and fun. This article describes our explorations, within this context, of a range of technologies including squishy circuits, littleBits, and easy-build websites, and presents examples of objects created by the co-researchers such as “sensory boxes” and interactive buckets, baskets, and boots. Public engagement is an important part of the project and includes an annual public event and seminar day, a blog rich with photos and videos of the workshops, and an activities book to give people ideas for creating their own sensory explorations of museums and heritage sites.

A survey of VANET technologies

Recent advancement in wireless communication technologies and automobiles have enabled the evolution of Intelligent Transport System (ITS) which addresses various vehicular traffic issues like traffic congestion, information dissemination, accident etc. Vehicular Ad-hoc Network (VANET) a distinctive class of Mobile ad-hoc Network (MANET) is an integral component of ITS in which moving vehicles are connected and communicate wirelessly. Wireless communication technologies play a vital role in supporting both Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication in VANET. This paper surveys some of the key vehicular wireless access technology standards such as 802.11p, P1609 protocols, Cellular System, CALM, MBWA, WiMAX, Microwave, Bluetooth and ZigBee which served as a base for supporting both Safety and Non Safety applications. It also analyses and compares the wireless standards using various parameters such as bandwidth, ease of use, upfront cost, maintenance, accessibility, signal coverage, signal interference and security. Finally, it discusses some of the issues associated with the interoperability among those protocols.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.

Mobile phones and radios: effects on transactions costs and market participation for households in Northern Ghana

The literature on agricultural markets suggests that transactions costs are the main obstacles preventing households from participating in agricultural markets. We examine the impact of the recent massive penetration of information communication technologies (ICTs), particularly mobile phones and radios, in developing countries to investigate the role of information in economic transactions and participation in food crop markets. To fully capture market participation behaviours, the current theoretical framework on market participation and transactions costs is extended to include those households that sell and buy in the same time period. We correct for endogeneity and selectivity throughout our models. We used a novel dataset of 393 households in northern Ghana with detailed information on market transactions and ICTs usage. Results show that receiving market information via mobile phones has a positive and significant impact on market participation, with a greater impact for households with a surplus of food crops. We find that radios have a larger impact on the quantity traded. This may reflect the nature of mobile phones in reducing searching costs, whereas radios provide an updated and regular flow of information which affects the pattern of crops consumed and sold. We also emphasise that the most significant factor is how ICTs are used, rather than their ownership.

Using mobile devices to enhance undergraduate field research

Mobile devices can enhance undergraduate research projects and students’ research capabilities. The use of mobile devices such as tablet computers will not automatically make undergraduates better researchers, but their use should make investigations, writing, and publishing more effective and may even save students time. We have explored some of the possibilities of using “tablets” and “smartphones” to aid the research and inquiry process in geography and bioscience fieldwork. We provide two case studies as illustration of how students working in small research groups use mobile devices to gather and analyze primary data in field-based inquiry. Since April 2010, Apple’s iPad has changed the way people behave in the digital world and how they access their music, watch videos, or read their email much as the entrepreneurs Steve Jobs and Jonathan Ive intended. Now with “apps” and “the cloud” and the ubiquitous references to them appearing in the press and on TV, academics’ use of tablets is also having an impact on education and research. In our discussion we will refer to use of smartphones such as the iPhone, iPod, and Android devices under the term “tablet”. Android and Microsoft devices may not offer the same facilities as the iPad/iphone, but many app producers now provide versions for several operating systems. Smartphones are becoming more affordable and ubiquitous (Melhuish and Falloon 2010), but a recent study of undergraduate students (Woodcock et al. 2012, 1) found that many students who own smartphones are “largely unaware of their potential to support learning”. Importantly, however, students were found to be “interested in and open to the potential as they become familiar with the possibilities” (Woodcock et al. 2012). Smartphones and iPads could be better utilized than laptops when conducting research in the field because of their portability (Welsh and France 2012). It is imperative for faculty to provide their students with opportunities to discover and employ the potential uses of mobile devices in their learning. However, it is not only the convenience of the iPad or tablet devices or smartphones we wish to promote, but also a way of thinking and behaving digitally. We essentially suggest that making a tablet the center of research increases the connections between related research activities.

Evaluating e-learning systems: an empirical investigation on students' perception in higher education area

In search of better, traditional learning universities have expanded their ways to deliver knowledge and integrate cost effective e-learning systems. Universities’ use of information and communication technologies has grown tremendously over the last decade. To ensure efficient use of the e-learning system, the Arab Open University (AOU) in Bahrain was the first to use e-learning system there, aimed to evaluate the good and bad practices, detect errors and determine areas for further improvements in usage. This study critically evaluated the students’ perception of the elearning system in Bahrain and recommended changes to improve students’ e-learning usage. Results of the study indicated that, in general, students have favourable perceptions toward using the e-learning system. This study has shown that technology acceptance is the most variable, factor that contributes to students’ perception and satisfaction of the e-learning system.

Effectiveness of mobile apps in teaching field-based identification skills

It has been suggested that few students graduate with the skills required for many ecological careers, as field-based learning is said to be in decline in academic institutions. Here, we asked if mobile technology could improve field-based learning, using ability to identify birds as the study metric. We divided a class of ninety-one undergraduate students into two groups for field-based sessions where they were taught bird identification skills. The first group has access to a traditional identification book and the second group were provided with an identification app. We found no difference between the groups in the ability of students to identify birds after three field sessions. Furthermore, we found that students using the traditional book were significantly more likely to identify novel species. Therefore, we find no evidence that mobile technology improved students’ ability to retain what they experienced in the field; indeed, there is evidence that traditional field guides were more useful to students as they attempted to identify new species. Nevertheless, students felt positively about using their own smartphone devices for learning, highlighting that while apps did not lead to an improvement in bird identification ability, they gave greater accessibility to relevant information outside allocated teaching times.

Semantic mapping using mobile robots

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.

Is the unfolding of the group discussion off-pattern? Improving coordination support in educational forums using mobile devices

A forum is a valuable tool to foster reflection in an in-depth discussion; however, it forces the course mediator to continually pay close attention in order to coordinate learners` activities. Moreover, monitoring a forum is time consuming given that it is impossible to know in advance when new messages are going to be posted. Additionally, a forum may be inactive for a long period and suddenly receive a burst of messages forcing forum mediators to frequently log on in order to know how the discussion is unfolding to intervene whenever it is necessary. Mediators also need to deal with a large amount of messages to identify off-pattern situations. This work presents a piece of action research that investigates how to improve coordination support in a forum using mobile devices for mitigating mediator`s difficulties in following the status of a forum. Based on summarized information extracted from message meta-data, mediators consult visual information summaries on PDAs and receive textual notifications in their mobile phone. This investigation revealed that mediators used the mobile-based coordination support to keep informed on what is taking place within the forum without the need to log on their desktop computer. (C) 2009 Elsevier Ltd. All rights reserved.

Pedagogical Aspects of Using Mobile Devices in the Biology Classroom

The ubiquity and power of personal digital devices make them attractive tools for STEM instructors who would like to stimulate active learning. These devices offer both abundant pedagogical opportunities and worrisome challenges. We will discuss our two years of experience in using mobile devices to teach biology in a community college setting, as well as our observations on the best ways to organize digital-based activities to facilitate student active learning.

Deciding What to Use: Introducing Pieces of Digital Learning without Overwhelming the Students

There are many possible ways to introduce social media or academic technologies such as Blackboard, Collaborate, ePortfolio (Digication), blogs, wikis, tests, quizzes, Chalktalk, podcasting, etc. and those are just the ones we use at BCC! What is the best way to introduce these into the classroom and into the distance learning environment? Good question, and discussing it in fifteen minutes will be a GREAT starting point!.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.