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We deal with the construction of optimal channel shortening, also known as combined linear Viterbi detection, algorithms for ISI and MIMO channels. In the case of MIMO channel shortening, the tree structure to represent MIMO signals is replaced by a trellis. The optimization is performed from an information theoretical perspective and the achievable information rates of the shortened models are de
The ecosystem-atmosphere flux of biogenic volatile organic compounds (BVOCs) has important impacts on tropospheric oxidative capacity and the formation of secondary organic aerosols, influencing air quality and climate. Here we present within-canopy measurements of a set of dominant BVOCs in a managed spruce- and pine-dominated boreal forest located at the ICOS (Integrated Carbon Observation Syste
The rapidly evolving domain of network systems poses complex challenges, especially when considering scalability and transient behaviors. This thesis aims to address these challenges by offering insights into the transient analysis and control design tailored for large-scale network systems. The thesis consists of three papers, each of which contributes to the overarching goal of this work.The fir
In recent years, shared micro-mobility services (e.g., bikes, e-bikes, and e-scooters) have been popularized at a rapid pace worldwide, which provide more choices for people’s short and medium-distance travel. Accurately modeling the choice of these shared micro-mobility services is important for their regulation and management. However, little attention has been paid to modeling their choice, esp
Shared electric scooters (e-scooters) have been rapidly growing in popularity across Europe over the past three years, which can bring various environmental and socioeconomic benefits. However, how to further improve the usage efficiency of shared e-scooters is still a major concern for micro-mobility operators and city planners. This paper proposes a machine learning based approach to predict the
This report presents showcases of active teaching and learning in spatial data infrastructure education in the SPIDER partner universities. It includes detailed descriptions of the practices that have been implemented, as well as the results of the evaluation of the practices from an active teaching learning perspective.
This report looks at different methods of active teaching and learning and the application of these methods at the partner universities of the SPIDER project. Different methods of on-campus and online teaching are presented and reports on experiences in their application at the partner universities are discussed. In combination with the results from Intellectual Output (IO) 4, this results in best
As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused
Gaussian models are frequently used for road elevations. However, these models are often only valid for short sections of the road. Here we present a comprehensive approach to describe various aspects of road surface/elevation by using extensions of Gaussian models arising from random gamma distributed variances. These random variances result in the Laplace distribution and thus we refer to the so
Rhombomys opimus (R. opimus), a social rodent, is the main reservoir host for zoonotic cutaneous leishmaniasis (ZCL) in most parts of the Middle East and central Asia. The difficulties in monitoring rodent population patterns have hindered the effective application of preventive measures of ZCL. This study presents a spatially explicit agent-based simulation model of R. opimus behaviors that is in
A major problem in CTG analysis is that detectionof a suspicious pattern in short intervals so that one can reducethe damage caused by a delay of an automatic monitoringsystem. In this paper, we aim for improving intrapartumsurveillance based on signal processing and machine learningtechniques. We evaluate a classification method on a real dataset.
In this conceptual paper we reimagine the Capitalocentric present. To do so, we (1) critique corporate fantasies about ethical consumption; (2) offer alternative relational ethics of consumption, with special attention to concrete neo-animist practices to imagine alternative futures to the Capitalocentric present, and (3) invite imaginative theorization as a bridge between present and future.
In this paper, a novel decentralized leader-follower control scheme for multi–agent systems is devised, where each agent communicates only with a subset of neighboring mates. The goal is to track assigned trajectories for the centroid and the formation of the system. The desired trajectories are known only by a subset of agents, named leaders: the other agents, the followers, are required to estim
Symmetry and dominance breaking can be crucial for solving hard combinatorial search and optimisation problems, but the correctness of these techniques sometimes relies on subtle arguments. For this reason, it is desirable to produce efficient, machine-verifiable certificates that solutions have been computed correctly. Building on the cutting planes proof system, we develop a certification method
Background: In Northern Province, Rwanda, stunting is common among children aged under 5 years. However, previous studies on spatial analysis of childhood stunting in Rwanda did not assess its randomness and clustering, and none were conducted in Northern Province. We conducted a spatial-pattern analysis of childhood undernutrition to identify stunting clusters and hotspots for targeted interventi
Background: Taxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability. Methods: This study proposed a rapid assessme
