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In this paper, we present a heuristic method for adapting a networked linear feedback controller to improve its robustness to timing complications, such as long delays, aborted computations, and dropped packets. The core concept of the approach is to log successful sampling and actuation events and then, at discrete time-points, use non-convex parametric optimization to improve the expected perfor
Support for Machine Learning (ML) applications in networking has significantly improved over the last decade. The availability of public datasets and programmable switching fabrics (including low-level languages to program them) presents a full-stack to the programmer for deploying in-network ML. However, the diversity of tools involved, coupled with complex optimization tasks of ML model design a
A memetic framework for optimal inverse design is proposed by combining a local gradient-based procedure and a robust global scheme. The procedure is based on method-of-moments matrices and does not demand full inversion of a system matrix. Fundamental bounds are evaluated for all optimized metrics in the same manner, providing natural stopping criteria and quality measures for realized devices. C
The nonlinear, or warped, resolvent recently explored by Giselsson and Bùi-Combettes has been used to model a large set of existing and new monotone inclusion algorithms. To establish convergent algorithms based on these resolvents, corrective projection steps are utilized in both works. We present a different way of ensuring convergence by means of a nonlinear momentum term, which in many cases l
The human vestibulo-ocular reflex (VOR) leads to maintenance of the acuity of an image on the retina and contributes to the perception of orientation during high acceleration head movements. Our objective was to determine whether vision affects the horizontal VOR by assessing and comparing the performance at the boundaries of contribution of: (a) unrestricted visual information and (b) no visual i
This article presents a fully integrated millimeter-wave (mmW) transceiver front end covering 24.25–29.5 GHz. It features a wideband Doherty power amplifier utilizing adaptive bias and a transmit/receive switch (TRX-switch) that has embedded low noise amplifier to antenna matching. The phase shift of 90
With the growth of the cloud industry in recent years, the energy consumption of the underlying infrastructure is a major concern.The need for energy efficient resource management and control in the cloud becomes increasingly important as one part of the solution, where the other is to reduce the energy consumption of the hardware itself.Resource management in the cloud is typically done using rel
This paper explores acceleration of the finite element-boundary integral hybrid method using the adaptive cross approximation. Our code implementing the hybrid method in Python based on open source packages is briefly presented. A simple one-level version of the adaptive cross approximation is described and it is used to accelerate the boundary integral matrices. We present results for scattering
We designed and simulated a 1 GHz planar dipole antenna that is cloaked in a band around 10 GHz. Relative to an uncloaked antenna it has a reduced extinction cross-section for the co-antenna polarization, at the cost of an increased cross-polarization. This reduced extinction cross-section characterizes a reduction in scattering and absorption, and so reduced interference to electromagnetic waves
The exponential growth of converging technologies, including augmented reality, autonomous vehicles, machine-to-machine and machine-to-human interactions, biomedical and environmental sensory systems, and artificial intelligence, is driving the need for robust infrastructural systems capable of handling vast data volumes between end users and service providers. This demand has prompted a significa
We study intervention design problems for general finite non-binary super-modular games. The considered interventions consist in constraining or incentivizing the players to play actions above designed lower bounds, with a cost for the system planner that is a separable increasing function of such bounds. We study the intervention of minimum cost for which a best response learning algorithm leads
FPGAs play an increasing role in the reconfgurable accelerator landscape. A key challenge in designing FPGA-based systems is partitioning computation between processor cores and FPGAs. An appropriate division of labor is difcult to predict in advance and requires experiments and measurements. When an investigation requires rewriting part of the system in a new language or with a new programming mo
Stream processing applications are naturally described as dataflow programs. Dataflow programs modelled as actor networks are well suited to describe concurrent and computationally intensive problems. Realistic dataflow programs are typically characterized by highly dynamic behaviour, limiting the applicability of static analysis techniques. In this work we explore using dynamic analyses of datafl
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A unification of characteristic mode decomposition for all method-of-moment formulations of field integral equations describing free-space scattering is derived. The work is based on an algebraic link between impedance and transition matrices, the latter of which was used in early definitions of characteristic modes and is uniquely defined for all scattering scenarios. This also makes it possible
Quaternary Asian low-latitude hydroclimate cyclicity has long been attributed to insolation forcing, in contrast to the dominant ice-sheet and CO2 controls identified in mid-high-latitude regions. However, debates exist regarding these rainfall variations and forcings due to inconsistent reconstructions and simulations. Here, by combining rainfall proxy records with multi-model simulations, domina
This paper presents a four-stage algorithm for the realization of multi-input/multi-output (MIMO) switched linear systems (SLSs) from Markov parameters. In the first stage, a linear time-varying (LTV) realization that is topologically equivalent to the true SLS is derived from the Markov parameters assuming that the discrete states have a common MacMillan degree and a mild condition on their dwell
We train and compare the performance of two machine learning methods, a multi-variate regression network and a ResNet-50-based neural network, to learn and forecast plant biomass as well as the relative growth rate from a short sequence of temporal images from plants in aeroponic cultivation. The training dataset consists of images of 57 plants taken from two different angles every hour during a 5
We train and compare the performance of two different machine learning algorithms to learn changes in winter wheat production for fields from the southwest of Sweden. As input to these algorithms, we use cloud-penetrating Sentinel-1 polarimetry radar data together with respective field topography and local weather over four different years. We note that all of the input data were freely available.
We study the problem of estimating the semi-generalized pose of a partially calibrated camera, i.e., the pose of a perspective camera with unknown focal length w.r.t. a generalized camera, from a hybrid set of 2D-2D and 2D-3D point correspondences. We study all possible camera configurations within the generalized camera system. To derive practical solvers to previously unsolved challenging config
