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The global carbon cycle is an important component of the Earth system and it interacts with the hydrology, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification of measures to reduce anthropogenic greenhous

There is a strong industrial drive to use cloud computing technologies and concepts for providing timing sensitive services in the networking domain since it would provide the means to share the physical resources among multiple users and thus increase the elasticity and reduce the costs. In this work, we develop a mathematical model for user-stateless virtual network functions forming a forwardin

Operating systems code is often developed according to principles like simplicity, low overhead, and low memory footprint. Schedulers are no exceptions. A scheduler is usually developed with flexibility in mind, and this restricts the ability to provide real-time guarantees. Moreover, even when schedulers can provide real-time guarantees, it is unlikely that these guarantees are properly quantifie

Level crossing sampling (LCS) is a power-efficient analog-to-digital conversion scheme for spikelike signals that arise in many Internet of Things-enabled automotive and environmental monitoring applications. However, LCS scheme requires a dedicated time-to-digital converter with large dynamic range specifications. In this paper, we present a compressed LCS that exploits the signal sparsity in the

Timely loss-of-contact detection between devices in pervasive systems using mobile networks is an important aspect for both functionality and user experience. Traditional schemes where time-to-live is defined by the service provider are not adequate in mobile networks where communication failures are frequent. On the other hand, schemes using keep-alive messaging tend to increase the communication

This brief presents an on-chip memory subsystem for massive multiple-input-multiple-output (MIMO) baseband processing at the base station. In massive MIMO systems, the required memory bandwidth and capacity are orders of magnitude higher than those used in conventional wireless systems, due to the large number of serving antennas. These are further combined with design targets on low access latenc

We develop a simple routine unifying the analysis of several important recently-developed stochastic optimization methods including SAGA, Finito, and stochastic dual coordinate ascent (SDCA). First, we show an intrinsic connection between stochastic optimization methods and dynamic jump systems, and propose a general jump system model for stochastic optimization methods. Our proposed model recover

We consider the problem of finding an event-based sampling scheme that optimizes the trade-off between average sampling rate and control performance in a linear-quadratic-Gaussian (LQG) control problem setting with output feedback. Our analysis is based on a recently presented sampled-data controller structure, which remains LQG-optimal for any choice of sampling scheme. We show that optimization

Resilience has become a key aspect in the design of contemporary infrastructure networks. This comes as a result of ever-increasing loads, limited physical capacity, and fast-growing levels of interconnectedness and complexity due to the recent technological advancements. The problem has motivated a considerable amount of research within the last few years, particularly focused on the dynamical as

This paper presents the mathematical conditions and the associated design methodology of an active fault diagnosis technique for continuous-time linear systems. Given a set of faults known a priori, the system is modeled by a finite family of linear time-invariant systems, accounting for one healthy and several faulty configurations. By assuming bounded disturbances and using a residual generator,

We consider flow-inducing networks, a class of models that are well-suited to describe important biochemical systems, including the MAPK pathway and the interactions at the trans-Golgi network. A flow-inducing network is given by the interconnection of subsystems (modules), each associated with a stochastic state matrix whose entries depend on the state variables of other modules. This results in

The figure-ground segmentation of humans in images captured in natural environments is an outstanding open problem due to the presence of complex backgrounds, articulation, varying body proportions, partial views and viewpoint changes. In this work we propose classspecific segmentation models that leverage parametric max-flow image segmentation and a large dataset of human shapes. Our contribution

Automatic video captioning is challenging due to the complex interactions in dynamic real scenes. A comprehensive system would ultimately localize and track the objects, actions and interactions present in a video and generate a description that relies on temporal localization in order to ground the visual concepts. However, most existing automatic video captioning systems map from raw video data

In modern day industry, robots are indispensable for achieving high production rates and competitiveness. In small and medium scale enterprises, where the production may shift rapidly, it is vital to be able to reprogram robots quickly. Kinesthetic teaching, also known as lead-through programming (LTP), provides a fast approach for teaching a trajectory. In this approach, a trajectory is demonstra

We present a data-driven interpolation method, based on extended structure tensors. The method uses directional information, extracted from the crossline derivative of the pressure field, to interpolate both pressure field and its normal (depth) derivative. Here we focus on the interpolation of the normal derivative.

We present a data-driven method, based on extended structure tensors, that interpolates multicomponent, marine seismic data. The method is designed to interpolate pressure measurements and their normal derivatives using directional information from the crossline derivative of the pressure field.

In this paper we propose an autotuning method that combines a setup for decentralized relay autotuning of two-input two-output systems with an identification method that uses short experiments to estimate up to second-ordertime-delayed systems. A small modification of the experiment gives better low-frequency excitation and improved models. The method is successfully demonstrated in simulations an

Marine seismic data are distorted by ghosts as waves propagating upwards reflect downwards from the sea surface. Ghosts appear both on the source-side as well as on the receiver-side. However, whereas the receiver-side ghost problem has been studied in detail and many different solutions have been proposed and implemented commercially, the source-side ghost problem has remained largely "unsolved"

In wavefield signal apparition, acquisition with periodic variations in source activation parameters shifts all or part of a signal cone out to, e.g., the Nyquist wavenumber, enabling perfect separation of simultaneous-source (sim-source) data below a certain temporal frequency. Cyclic convolution in the spatial frequency domain is used to generalise the signal apparition concept to the case of ap