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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
In this paper, we consider precoder design for multiuser multiple-input-multiple-output (MIMO) Gaussian broadcast (BC) channels and propose a generalized zero-forcing (GZF) precoder based on successive dirty-paper coding (DPC), i.e., the GZF-DP precoder. The GZF-DP precoder is an extension of the GZF-DP precoder designed earlier for multi-input-single-output broadcast (MISO-BC) channels, and also
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
Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each re
We consider the potential for positioning with a system where antenna arrays are deployed as a large intelligent surface (LIS). We derive Fisher-informations and Cram\'{e}r-Rao lower bounds (CRLB) in closed-form for terminals along the central perpendicular line (CPL) of the LIS for all three Cartesian dimensions. For terminals at positions other than the CPL, closed-form expressions for the Fishe
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
Microseismic monitoring of hydrofrac is an actively developing technology utilizing various acquizition arrays. In this paper we consider processing of microseismic data recorded by specific surface network geometry-patch arrays (far separated local receiver groups). The project aim is to produce an optimal partial stacking of the data within patches for improving a signal to noise ratio for micro
We present a novel approach to model the diffusion of a mutant in a geographical network. This is a key issue in the control of epidemics, since many diseases are transmitted by intermediate hosts that could be substituted with genetically modified organisms (GMOs) with a mutation that prevents them from spreading the pathogen. The main strength of our model lies in making analytically tractable t
Remote sensing is a key tool for precision agriculture applications as it is capable of capturing spatial and temporal variations in crop status. However, satellites often have an inadequate spatial resolution for precision agriculture applications. High-resolution Unmanned Aerial Vehicles (UAV) imagery can be obtained at flexible dates, but operational costs may limit the collection frequency. Th
The concept of Channel Shortening (CS) is a well-known technique that has a rich history over 40 years. CS transfers linear vector channels such as multi-input multi-output (MIMO) and intersymbol interference (ISI) channels into ``shortened'' versions, for the purpose of reducing demodulation-complexity and improving data-transmission performance. The original CS idea can trace back to the minimum
Astrocytic glutamate (Glt) uptake keeps brain interstitial Glt levels low. Within the astrocytes Glt is converted to glutamine (Gln), which is released and reconverted to Glt in neurons. The Glt-Gln cycle is energy demanding and impaired energy metabolism has been suggested to cause low interstitial Gln/Glt ratios. Using microdialysis (MD) measurements from visually noninjured cortex in 33 neuroin
