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Modeled Microbial Dynamics Explain the Apparent Temperature Sensitivity of Wetland Methane Emissions

Methane emissions from natural wetlands tend to increase with temperature and therefore may lead to a positive feedback under future climate change. However, their temperature response includes confounding factors and appears to differ on different time scales. Observed methane emissions depend strongly on temperature on a seasonal basis, but if the annual mean emissions are compared between sites

Separating direct and indirect effects of rising temperatures on biogenic volatile emissions in the Arctic

Plants release to the atmosphere reactive gases, so-called volatile organic compounds (VOCs). The release of VOCs from vegetation is temperature-dependent and controlled by vegetation composition because different plant species release a distinct blend of VOCs. We used modelling approaches on ecosystem VOC release data collected across the Arctic, which is experiencing both rapid warming and veget

A Result for Orthogonal Plus Rank-1 Matrices

In this paper the sum of an orthogonal matrix and an outer product is studied, and a relation between the norms of the vectors forming the outer product and the singular values of the resulting matrix is presented. The main result may be found in Theorem 1.

Operator splitting performance estimation : Tight contraction factors and optimal parameter selection

We propose a methodology for studying the performance of common splitting methods through semidefinite programming. We prove tightness of the methodology and demonstrate its value by presenting two applications of it. First, we use the methodology as a tool for computerassisted proofs to prove tight analytical contraction factors for Douglas-Rachford splitting that are likely too complicated for a

MSE-optimal measurement dimension reduction in gaussian filtering

We present a framework for measurement dimension reduction in Gaussian filtering, defined in terms of a linear operator acting on the measurement vector. This operator is optimized to minimize the Cramér-Rao bound of the estimate's mean squared error (MSE), yielding a measurement subspace from which elements minimally worsen the filter MSE performance, as compared to filtering with the original me

Wireless channel dynamics for relay selection under ultra-reliable low-latency communication

Ultra-reliable, low-latency communication (URLLC) is being developed to support critical control applications over wireless networks. Exploiting spatial diversity through relays is a promising technique for achieving the stringent requirements of URLLC, but coordinating relays reliably and with low overhead is a challenge. Adaptive relay selection techniques have been proposed as a way to simplify

INTERNET OF THINGS AS A COMPLEMENT TO INCREASE SAFETY

Safety evaluations made in the city Helsingborg indicate a decreased risk of being exposed to crime, but an increased feeling of unsafe. Damage in the form of scribbling (graffiti) is one of several indicators contributing to feeling unsafe. In this paper we investigate whether the use of Internet­-of-­things technology, where a sensor monitors a walking and cycling tunnel, makes it possible to re

A Domain-Specific Language for Filtering in Application-Level Gateways

Application-level packet filtering is a technique for network access control in which an “application-level gateway” intercepts network packets at the application level (e.g., HTTP, FTP), scans them for security concerns and optionally logs, rewrites or discards them. Existing application-level filters express their filtering rules in general-purpose languages, which limits the correctness guarant

On LQG-Optimal Event-Based Sampling

Event-based control is a promising concept for the design of resource-efficient feedback systems, where events such as sampling, actuation, and data transmissions are triggered reactively based on monitored control performance rather than a periodic timer. In this thesis, we investigate how sampling and communication events should be triggered to fully exploit the potential of event-based control

Neural-Network-Based Adaptive Control for Bilateral Teleoperation with Multiple Slaves under Round-Robin Scheduling Protocol

A neural-network-based adaptive control scheme is developed for bilateral teleoperation systems with single-master-multiple-slaves in the presence of dynamic uncertainties and communication constraint. Discrete-time data transmitting communication network with bandwidth limitation and time-varying communication delays is considered and the Round-Robin scheduling protocol is used to orchestrate the

Methods for identifying aged ship plumes and estimating contribution to aerosol exposure downwind of shipping lanes

Ship traffic is a major source of aerosol particles, particularly near shipping lanes and harbours. In order to estimate the contribution to exposure downwind of a shipping lane, it is important to be able to measure the ship emission contribution at various distances from the source. We report on measurements of atmospheric particles 7-20 km downwind of a shipping lane in the Baltic Se

Realizability and internal model control on networks

It is proved that network realizability of controllers can be enforced without conservatism using convex constraints on the closed loop transfer function. Once a network realizable closed loop transfer matrix has been found, a corresponding controller can be implemented using a network structured version of Internal Model Control.

Optimality interpretations for atomic norms

Atomic norms occur frequently in data science and engineering problems such as matrix completion, sparse linear regression, system identification and many more. These norms are often used to convexify non-convex optimization problems, which are convex apart from the solution lying in a non-convex set of so-called atoms. For the convex part being a linear constraint, the ability of several atomic n

Sparsity-constrained optimization of inputs to second-order systems

We propose an efficient algorithm, that given a strictly proper, second-order system, finds a sparse input signal so that the system's output optimally approximates a given trajectory in least-squares sense. As an illustration, we apply the algorithm to an estimation problem from medicine.

Using Radial Basis Functions to Approximate the LQG-Optimal Event-Based Sampling Policy

A numerical method based on radial basis functions (RBF) has been developed to find the optimal event-based sampling policy in an LQG problem setting. The optimal sampling problem can be posed as a stationary partial differential equation with a free boundary, which is solved by reformulatingthe optimal RBF approximation as a linear complementarity problem (LCP). The LCP can be efficiently solved

Towards real-time ADMM for linear MPC

We present a novel predictive control scheme for linear constrained systems that uses the alternating direction method of multipliers (ADMM) for online optimization. In contrast to existing works on ADMM-based model predictive control (MPC), we only consider a single ADMM-iteration in every time step. The resulting real-time ADMM scheme is tailored for embedded and fast MPC implementations. The ma

The Potential of Using Large Antenna Arrays on Intelligent Surfaces

In this paper, we consider capacities of single-antenna terminals communicating to large antenna arrays that are deployed on surfaces. That is, the entire surface is used as an intelligent receiving antenna array. Under the condition that the surface area is sufficiently large, the received signal after matched-filtering (MF) can be well approximated by an intersymbol interference (ISI) channel wh

Multiuser Bandwidth Minimization with Individual Rate Requirements for Non-Orthogonal Multiple Access

Non-Orthogonal Multiple Access (NOMA) for a multi-user single- input single-output (SISO) setup is studied in the power and the frequency domains simultaneously. The problem of sum-bandwidth minimization under perfect channel state information is solved for various combinations of rate requirements and user pairings. In this process, an iterative Tabu-search based algorithm is applied to avoid an

Bandwidth Minimization under Probabilistic Constraints and Statistical CSI for NOMA

Non-Orthogonal Multiple Access (NOMA) is studied under statistical Channel State Information (CSI) and probabilistic constraints. Unlike the conventional power domain only NOMA, the definition of NOMA here considers both the power and the frequency domains simultaneously, where a flat power spectrum is assumed for each UE. This increases the capacity region when compared to the conventional definiti