An Efficient B-tree Implementation for Memory-Constrained Embedded Systems

Embedded devices collect and process significant amounts of data in a variety of applications including environmental monitoring, industrial automation and control, and other Internet of Things (IoT) applications. Storing data efficiently is critically important, especially when the device must perform local processing on the data. The most widely used data structure for high performance query and insert is the B-tree. However, existing implementations consume too much memory for small embedded devices and often rely on operating system support. This work presents an extremely memory efficient implementation of B-trees for embedded devices that functions on the smallest devices and does not require an operating system. Experimental results demonstrate that the B-tree implementation can run on devices with as little as 4 KB of RAM while efficiently processing thousands of records.Comment: Published in the 19th International Conference on Embedded Systems, Cyber-physical Systems, and Applications (ESCS'21). Code is available at https://github.com/ubco-d

Estimation of Average Annual Daily Bicycle Count Using Bike-Share GPS Data and Bike Counter Data for an Urban Active Transportation Network

In 2018, the City of Kelowna entered into a license agreement with Dropbike to operate a dockless bike-share pilot in and around the downtown core. The bikes were tracked by the user's cell phone GPS through the Dropbike app. The City's Active Transportation team recognized that this GPS data could help understand the routes used by cyclists which would then inform decision-making for infrastructure improvements. Using OSMnx and NetworkX, the map of Kelowna was converted into a graph network to map inaccurate, infrequent GPS points to the nearest street intersection, calculate the potential paths taken by cyclists and count the number of trips by street segment though the comparison of different path-finding models. Combined with the data from four counters around downtown, a mixed effects statistical model and a least squares optimization were used to estimate a relationship between the different traffic patterns of the bike-share and counter data. Using this relationship based on sparse data input from physical counting stations and bike share data, estimations and visualizations of the annual daily bicycle volume in downtown Kelowna were produced. The analysis, modelling and visualization helped to better understand how the bike network was being used in the urban center, including non-traditional routes such as laneways and highway crossings.Comment: Published in 17th International Conference on Data Science (ICDATA'21

An Application of Deep Learning for Sweet Cherry Phenotyping using YOLO Object Detection

Tree fruit breeding is a long-term activity involving repeated measurements of various fruit quality traits on a large number of samples. These traits are traditionally measured by manually counting the fruits, weighing to indirectly measure the fruit size, and fruit colour is classified subjectively into different color categories using visual comparison to colour charts. These processes are slow, expensive and subject to evaluators' bias and fatigue. Recent advancements in deep learning can help automate this process. A method was developed to automatically count the number of sweet cherry fruits in a camera's field of view in real time using YOLOv3. A system capable of analyzing the image data for other traits such as size and color was also developed using Python. The YOLO model obtained close to 99% accuracy in object detection and counting of cherries and 90% on the Intersection over Union metric for object localization when extracting size and colour information. The model surpasses human performance and offers a significant improvement compared to manual counting.Comment: Published in 25th International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'21

Resilience, apps and reluctant individualism: Technologies of self in the neoliberal academy

This paper is concerned with the deep crisis affecting universities, as large scale institutional and structural transformations produce a psychosocial and somatic catastrophe amongst academics (and other university workers) that manifests in experiences of chronic stress, anxiety, exhaustion, insomnia and spiralling rates of physical and mental illness. Elsewhere these have been discussed as the 'hidden injuries of the neoliberal university' (Gill, 2010), highlighting the ways in which such experiences are simultaneously acknowledged and recognised by university staff, yet silenced and exorcised from formal spaces of the contemporary academy and without 'proper channels' of expression - being the subject of conference coffee breaks but not keynotes, of after seminar drinks but not departmental meetings, committee minutes or Senate or Council documentation. For future historians seeking to understand through such official records something about the texture of experience of current academic life, the archives will offer no insights.However, in the last few years, this paper suggests, such injuries have moved from being almost completely silenced within universities to becoming the subject of a variety of new spaces and services designed with 'academics in crisis' at their heart. These include the rolling out of 'well-being' services within universities of programmes for stress management, mindfulness and resilience, the development of new 'apps' designed for busy or overworked people, and the rapidly expanding blogosphere which has become a key site for 'naming' and sharing such experiences of distress/injury. The paper looks critically at these three sites. It argues that whilst they recognise at least some aspects of the subjective experience of contemporary academic labouring, they remain locked into a profoundly individualist framework that turns away from systemic or collective politics to offer instead a set of individualised tools by which to 'cope' with the strains of the neoliberal academy

Researcher self‐care and caring in the research community

This paper seeks to begin a discussion on researcher self‐care in response to the state of contemporary academia, which sees increasing issues of academic stress and anxiety, and the growing use of facile metrics. Specifically, we wish to explore the potential a critical engagement with self‐care poses for ourselves as academics and the communities of which we are a part – what kinpaisby (2008) refers to as the “communiversity.” Our central argument is that self‐care may be regarded as a radical act that can push against the interests of the neoliberal university. We illustrate how researcher self‐care can be engaged as a reflexive process that operates to create and inform change within our communities through recognising ourselves as networked actors, rather than self‐contained individuals as the neoliberal ideology would have us believe. This paper is intended as an opening towards a much larger discussion regarding academia – of the communities, work environments, and “impacts” we wish to be a part of and how to begin working towards realising these

fMRI BOLD signal changes in elite swimmers while viewing videos of personal failure

Athletes who fail are susceptible to negative affect (NA) and impaired future performance. Functional magnetic resonance imaging (fMRI) studies have identified prefrontal, anterior cingulate, and limbic activations following negative mood provocation. Little is known about the neural correlates of negative self-reference (SR), especially in athletes. Even less is known about the neural correlates of the effects of cognitive intervention (CI) in modifying negative SR and NA in this population. In an fMRI study, 13 athletes watched a video of their own career-threatening defeat in two controlled blocks. Between fMRI blocks, they received a 20-min CI designed to assist in event reappraisal and planning for future performance. Relative increases post-CI were seen in premotor (BA6) and sensorimotor (BA4/1) cortices. Correlated with mood ratings, relatively higher pre-CI levels were seen in the ventromedial prefrontal cortex, the right dorsomedial prefrontal cortex (PFC; BA10), the right dorsolateral PFC (BA45), the anterior cingulate, and the right parahippocampus. CI may counteract the detrimental effects of NA and negative SR on premotor and motor activity.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83878/1/fMRI-BOLD-signal-changes-in-elite-swimmers-while-viewing-videos-of-personal-failure.pd

Science friction: The chair of the Commons science and technology committee is robust in his support of researchers and his scrutiny of how they are managed, but not everyone is happy with the way he goes about it

Article about Ian Gibson, chair of the UK House of Commons science and technology committee (1 page)

A low overhead and consistent flash translation layer for embedded devices utilizing serial NOR flash

In today's world, embedded devices are playing an ever increasing and important role in daily life. Recent interest has focused on how data from embedded devices can be harnessed. Whether it is complex environmental parameters or the average temperature in our homes, devices store and process data. Due to the vast amount of data that is generated by devices, the ability to process data on device is beneficial as it reduces the amount of data that must be transferred off the device. Flash memory is the most commonly found storage medium on embedded devices but presents challenges for developers. Flash memory is managed through the use of a flash translation layer (FTL) to address the physical limitations of memory. No FTL is currently available for the smallest of devices due to resource limitations. This thesis examines the features that are currently used in FTLs and highlights their shortcomings for use with resource constrained devices. In response to these challenges, this work introduces an FTL architecture with a minimal RAM footprint that is robust and fault-tolerant for resource constrained embedded systems. Utilizing attributes of the Adesto serial NOR Dataflash memory, a unique flash translation layer for use with serial NOR flash has been developed. Key features focus on minimizing data transfer between host and flash while maintaining persistent address translation. In addition to a low overhead and robust flash translation layer, the FTL contains a deterministic garbage collection and wear levelling strategy. This work introduces the technique of masked overwriting for NOR flash, which demonstrates the use page overwriting for modification of specific data in place. This technique offers savings in terms of write times, page erases and complexity in managing data pages, resulting in a novel FTL strategy suitable for resource constrained devices.Graduate Studies, College of (Okanagan)Graduat