Aerial Base Station Placement via Propagation Radio Maps

The technology of base stations on board unmanned aerial vehicles, also known as aerial base stations (ABSs), promises to deliver cellular connectivity in areas where the terrestrial infrastructure is overloaded, damaged, or inexistent. A central problem in this context is to determine the locations where these ABSs must be deployed to serve a set of users on the ground. However, nearly all existing schemes assume that the channel gain depends only on the length and (possibly) the elevation of the link. To alleviate this limitation, this paper proposes a scheme that accommodates arbitrary channel gains by means of a propagation radio map of the air-to-ground channel. The algorithm finds the locations of an approximately minimal number of ABSs to serve all ground terminals with a target rate while meeting the given constraints on the capacity of the backhaul links and respecting no-fly regions. A convex-relaxation formulation ensures convergence and the alternating-direction method of multipliers is utilized to derive an implementation whose complexity is linear in the number of ground terminals. Numerical results with three channel models corroborate the strengths of the proposed scheme

Economic assessment of the impacts of post-Brexit trade and policies on Scottish farms

This report presents an analysis on impacts of post-Brexit trade and domestic policy scenarios on eight different Scottish farming systems. A farm level model, ScotFarm and a microsimulation model, ScotMS are used to simulate the impacts at a farm level and at an aggregated national level respectively. The analysis is conducted under the post-Brexit free trade agreement between the UK and the EU and four alternative domestic policy scenarios (two different levels of removal of farm direct payments and two production impact assumptions under such farm direct payment removals). The price parameters under all scenarios used in this modelling work were taken from the AFBI Post-Brexit report. The models estimates that the free trade agreement have a very small impact on farm net profit for all Scottish farming systems. The production level on farms in each of the farming systems are also shown to have a negligible impact under this trade scenario. However, all farming systems are projected to be negatively affected by the removal of farm direct payment. The beef and sheep farming systems are the most affected farms suggesting their higher dependency on farm direct payment. Many of these farms become loss making farms when farm payments are removed. For instance, around one third of the farms in the specialist beef farming system go from being profitable to loss making farms when 50% of the farm direct payments are removed from the farms. The consequences of removal of direct payment on Scottish farms can also be observed on the changes in farm production levels. Farms in all farming systems are estimated to reduce production to different extent when farm direct payments are removed with the highest production reductions (> 10%) on farms within the lowland beef and mixed farming systems (Figure iii). Farms in these two farming system are also projected to have higher share of farms exiting farm production

Event-Based Angular Velocity Regression with Spiking Networks

Spiking Neural Networks (SNNs) are bio-inspired networks that process information conveyed as temporal spikes rather than numeric values. A spiking neuron of an SNN only produces a spike whenever a significant number of spikes occur within a short period of time. Due to their spike-based computational model, SNNs can process output from event-based, asynchronous sensors without any pre-processing at extremely lower power unlike standard artificial neural networks. This is possible due to specialized neuromorphic hardware that implements the highly-parallelizable concept of SNNs in silicon. Yet, SNNs have not enjoyed the same rise of popularity as artificial neural networks. This not only stems from the fact that their input format is rather unconventional but also due to the challenges in training spiking networks. Despite their temporal nature and recent algorithmic advances, they have been mostly evaluated on classification problems. We propose, for the first time, a temporal regression problem of numerical values given events from an event camera. We specifically investigate the prediction of the 3-DOF angular velocity of a rotating event camera with an SNN. The difficulty of this problem arises from the prediction of angular velocities continuously in time directly from irregular, asynchronous event-based input. Directly utilising the output of event cameras without any pre-processing ensures that we inherit all the benefits that they provide over conventional cameras. That is high-temporal resolution, high-dynamic range and no motion blur. To assess the performance of SNNs on this task, we introduce a synthetic event camera dataset generated from real-world panoramic images and show that we can successfully train an SNN to perform angular velocity regression

Physical activity in public space: insights from a global community of practice applying photovoice as a tool for digital participatory place analysis

Physical activity in open spaces is a crucial topic of urban health around the globe. As cities and everyday life differ a lot in specific urban contexts, places for physical activity may vary in shape, perception and use by local people. A group of researchers from eight countries in Europe, South America and Asia used the same online-photovoice application to explore places in their cities regarding physical activity in public spaces. Using the same application in eight countries with diverse local participants, we collected a rich basis for a reflection on methodological issues, the usability of the online-photovoice application and determinants of physical activity in public spaces in contrasting cities. The paper aims to provide traceable documentation of a collaborative learning activity with an online-photovoice application. In doing so, results are presented from researchers’ self-reflection as a global community of practice on how the online-photovoice approach can be applied to place analysis for healthy urban development at different places with practitioners, communities, and scientists from diverse backgrounds. The paper contributes to a broader problem understanding of physical activity in public open spaces. As one result, we find that including aspects of safety and conflict in public space is highly relevant

Physical activity in public space: insights from a global community of practice applying photovoice as a tool for digital participatory place analysis

Physical activity in open spaces is a crucial topic of urban health around the globe. As cities and everyday life differ a lot in specific urban contexts, places for physical activity may vary in shape, perception and use by local people. A group of researchers from eight countries in Europe, South America and Asia used the same online-photovoice application to explore places in their cities regarding physical activity in public spaces. Using the same application in eight countries with diverse local participants, we collected a rich basis for a reflection on methodological issues, the usability of the online-photovoice application and determinants of physical activity in public spaces in contrasting cities. The paper aims to provide traceable documentation of a collaborative learning activity with an online-photovoice application. In doing so, results are presented from researchers’ self-reflection as a global community of practice on how the online-photovoice approach can be applied to place analysis for healthy urban development at different places with practitioners, communities, and scientists from diverse backgrounds. The paper contributes to a broader problem understanding of physical activity in public open spaces. As one result, we find that including aspects of safety and conflict in public space is highly relevant