Methods and ideas for the creation of 'transparent' music in the classroom

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Methods and ideas for the creation of ‘transparent’ music in the classroom The aims of this port-folio are as follows; - To provide a coherent sequence of pieces and methods which can be used to create music in an educational context and also encourage students and teachers to develop their own creativity. - To provide pieces which develop student’s confidence in their own ability to create music in a variety of ways including composition, improvisation and creative leadership. - To provide exercises and pieces which help to develop the listening and appreciation skills essential for ensemble musicmaking. - To provide methods that enable the creation of ‘transparent music’. This is music in which the some, or all, of the decision making involved in the creation of a piece is accessible and apparent to an audience during its performance. This submission consists of a teaching book containing thirteen pieces/exercises, instructions giving guidance on their possible use in a teaching context and recorded examples. Also included are separate instructions where appropriate for the use of pieces in a concert or other non-educational setting and two essays giving context and background information on the ideas behind the pieces

IUPUC Spatial Innovation Lab

During the summer of 2016 the IUPUC ME Division envi-sioned the concept of an “Imagineering Lab” based largely on academic makerspace concepts. Important sub-sections of the Imagineering Lab are its “Actualization Lab” (mecha-tronics, actuators, sensors, DAQ devices etc.) and a “Spatial Innovation Lab” (SIL) based on developing “dream stations” (computer work stations) equipped with exciting new tech-nology in intuitive 2D and 3D image creation and Virtual Reality (VR) technology. The objective of the SIL is to cre-ate a work flow converting intuitively created imagery to an-imation, engineering simulation and analysis and computer driven manufacturing interfaces. This paper discusses the challenges and methods being used to create a sustainable Spatial Innovation Lab

Avian communities and ecoacoustics in a tropical human-modified landscape

Large areas of the tropics have been cleared of forest and converted to agriculture. The consequent human-modified landscapes (HMLs) comprise a heterogenous mix of habitats; forest fragments and riparian strips are embedded in a matrix of cattle pasture, non-native timber plantations, and urban centres. These habitat changes can have dramatic consequences for wildlife, leading to range shifts and extirpations. In turn, this can influence the integrity of ecosystem services such as frugivory and seed dispersal. Understanding how habitat conversion affects natural ecosystems is critical to inform conservation interventions, but requires long-term biodiversity monitoring and detailed knowledge of species-level responses to HMLs. The research presented in this thesis was conducted in the Emparador HML, in central Republic of Panama. In Chapter 2, we show that the regional avian community is shaped by extent of forest cover across the landscape, and to a lesser degree, extent of forest fragmentation and distance to core forest. Effects of forest cover and fragmentation were examined at local (10 ha) and landscape (500 ha) scales. Species-level responses to these factors varied widely; while abundance of many species increased with greater local-scale forest cover, greater landscape-scale forest cover was often associated with declines. Generalist species that readily persist in HMLs still responded positively to local-scale forest cover, suggesting that even smaller forest fragments in these landscapes are important for maintaining diverse avian assemblages. Critically, we found that species’ responses were not associated with particular traits such as dietary composition or forest dependence, highlighting that species may often exhibit idiosyncratic responses to landscape structure. Chapters 3 and 4 address the wider issue of long-term monitoring, and the potential for data collection over large spatiotemporal scales using remote audio recorders. Ecoacoustics, the study of environmental sound is a relatively new discipline, and as such there is still considerable uncertainty surrounding best-practice for collecting and processing recordings. One of the most straightforward means of utilising audio recordings for environmental monitoring is via acoustic indices. These are objective measures of sound based on features such as pitch and amplitude. To date, attempts to use these indices have been hindered by inconsistent or inappropriate methodologies. In Chapter 3, we determine how many recordings are required to comprehensively capture a soundscape, the acoustic energy of a location. Furthermore, we demonstrate that there are habitat-specific patterns in acoustic indices values, suggesting that these indices reflect differences in vegetation structure and wildlife. We develop this further in Chapter 4, where we show that avian species richness and abundance are clearly linked to patterns in acoustic indices values. Critically, these patterns were coherent among habitat types emphasising their potential for monitoring. Acoustic indices sensitive to the frequencies occupied by bird song have the greatest potential for monitoring an avian community. The results from these two chapters suggest that acoustic indices can be effective tools for monitoring biodiversity, with values reflecting consistent differences across habitats, and among avian assemblages. Audio recordings are a source of permanent, verifiable evidence that can be collected at much greater spatiotemporal scales than traditional biodiversity monitoring data. As the use of audio recorders grows, it is important to compare their efficacy with standard methods of data collection. In Chapter 5, we contrast data derived from audio recordings with that gathered using standard point count methods, and consider whether recorders are a feasible means of surveying antbirds (Thamnophilidae), a disturbance-sensitive avian taxon. Both approaches revealed species’ responses to landscape structure, with qualitatively similar patterns in response to forest cover and vegetation quality. We show that common species can be readily monitored using audio recorders, with greater levels of detectability compared with point counts. However, rarer species were more likely to be detected using point counts. The work presented in this thesis helps to explain the patterns seen in avian responses to Neotropical HMLs. In particular we emphasise the importance of forest cover for maintaining bird assemblages in these landscapes. We demonstrate the utility of audio recorders for data collection, and highlight their potential for future biodiversity monitoring. In the face of human population growth, and ongoing habitat disturbance and agricultural intensification, conservation efforts are essential to avoid widespread species extinctions and ecosystem collapse. Interventions must take place in HMLs, to bolster ecosystem services, provide buffer zones for protected areas, and improve connectivity in the wider landscape

IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

Two main approaches exist when deploying a Convolutional Neural Network (CNN) on resource-constrained IoT devices: either scale a large model down or use a small model designed specifically for resource-constrained environments. Small architectures typically trade accuracy for computational cost by performing convolutions as depth-wise convolutions rather than standard convolutions like in large networks. Large models focus primarily on state-of-the-art performance and often struggle to scale down sufficiently. We propose a new model, namely IoTNet, designed for resource-constrained environments which achieves state-of-the-art performance within the domain of small efficient models. IoTNet trades accuracy with computational cost differently from existing methods by factorizing standard 3 × 3 convolutions into pairs of 1 × 3 and 3 × 1 standard convolutions, rather than performing depth-wise convolutions. We benchmark IoTNet against state-of-the-art efficiency-focused models and scaled-down large architectures on data sets which best match the complexity of problems faced in resource-constrained environments. We compare model accuracy and the number of floating-point operations (FLOPs) performed as a measure of efficiency. We report state-of-the-art accuracy improvement over MobileNetV2 on CIFAR-10 of 13.43 with 39 fewer FLOPs, over ShuffleNet on Street View House Numbers (SVHN) of 6.49 with 31.8 fewer FLOPs and over MobileNet on German Traffic Sign Recognition Benchmark (GTSRB) of 5 with 0.38 fewer FLOPs

A distributed programming environment for Ada

Despite considerable commercial exploitation of fault tolerance systems, significant and difficult research problems remain in such areas as fault detection and correction. A research project is described which constructs a distributed computing test bed for loosely coupled computers. The project is constructing a tool kit to support research into distributed control algorithms, including a distributed Ada compiler, distributed debugger, test harnesses, and environment monitors. The Ada compiler is being written in Ada and will implement distributed computing at the subsystem level. The design goal is to provide a variety of control mechanics for distributed programming while retaining total transparency at the code level

Smart Connected Homes: Integrating Sensor, Occupant and BIM data for Building Performance Analysis

Buildings produce huge volumes of data such as BIM, sensor, occupant and building maintenance data. Data is spread across multiple disconnected systems in numerous formats, making it difficult to identify performance gaps between building design and use. Better methods for gathering and analysing data can be used to support building managers with managing building performance. The knowledge can also be fed back to designers and contractors to help close the performance gaps. We have developed a platform to integrate BIM, sensor and occupant data for providing actionable advice for building managers. A social housing organisation is acting as a use case for the platform. A methodology for developing the information needs to support data capture across disconnected systems is proposed and the challenges of bringing data-sets together to provide meaningful information to building owners and managers are presented

Evolving and Ensembling Deep CNN Architectures for Image Classification

Deep Convolutional Neural Networks (CNNs) have traditionally been hand-designed owing to the complexity of their construction and the computational requirements of their training. Recently however, there has been an increase in research interest towards automatically designing deep CNNs for specific tasks. Ensembling has been shown to effectively increase the performance of deep CNNs, although usually with a duplication of work and therefore a large increase in computational resources required. In this paper we present a method for automatically designing and ensembling deep CNN models with a central weight repository to avoid work duplication. The models are trained and optimised together using particle swarm optimisation (PSO), with architecture convergence encouraged. At the conclusion of the joint optimisation and training process a base model nomination method is used to determine the best candidates for the ensemble. Two base model nomination methods are proposed, one using the local best particle positions from the PSO process, and one using the contents of the central weight repository. Once the base model pool has been created, the individual models inherit their parameters from the central weight repository and are then finetuned and ensembled in order to create a final system. We evaluate our system on the CIFAR-10 classification dataset and demonstrate improved results over the single global best model suggested by the optimisation process, with a minor increase in resources required by the finetuning process. Our system achieves an error rate of 4.27% on the CIFAR-10 image classification task with only 36 hours of combined optimisation and training on a single NVIDIA GTX 1080Ti GPU