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A major challenge for a person with diabetes is to adapt insulin dosage regimens and food intake to keep blood glucose within tolerable limits during daily life activities. The accurate prediction of blood glucose levels in response to inputs would support the patients with invaluable information for appropriate on-the-spot decision making concerning the management of the disease. Against this bac
Novel methods for the inference of radiation intensityfunctions defined over known surfaces are proposed, intendedfor use in surveying applications with mobile spectrometers.Previous approaches, based on the maximum likelihoodexpectation maximization (ML-EM) framework with Poissonlikelihoods, are extended to better handle spatially continuousintensity statistics using ideas from Gaussian filtering
This article addresses the distributed cooperative control design for a class of sampled-data teleoperation systems with multiple slave mobile manipulators grasping an object in the presence of communication bandwidth limitation and time delays. Discrete-time information transmission with time-varying delays is assumed, and the Round-Robin (RR) scheduling protocol is used to regulate the data tran
A 32 Kb dual-port low-voltage SRAM in 28 nm FD-SOI, featuring foundry supplied high-density 6T bitcells, is presented. Dual-port configurability is realized by a unique dual-rail architecture, utilizing boost techniques that guarantee reliable operation in low-voltage. The area cost of the array is 62% lower, compared to widely used 8T two-port or dual-port SRAM arrays. The SRAM reliably operates
In inverse optimal control, an optimal controller is synthesized with respect to a meaningful, a posteriori defined, cost functional. Our work illustrates the usefulness of this approach in the control of converter-based power systems and networked systems in general, and thereby in finding controllers with topological structure and known optimality properties. In particular, we design an inverse
Low-rank inducing unitarily invariant norms have been introduced to convexify problems with a low-rank/sparsity constraint. The most well-known member of this family is the so-called nuclear norm. To solve optimization problems involving such norms with proximal splitting methods, efficient ways of evaluating the proximal mapping of the low-rank inducing norms are needed. This is known for the nuc
We prove that for k ≫; 4√n regular resolution requires length nω(k) to establish that an ErdÅ's-Rényi graph with appropriately chosen edge density does not contain a k-clique. This lower bound is optimal up to the multiplicative constant in the exponent and also implies unconditional nω(k) lower bounds on running time for several state-of-the-art algorithms for finding maximum cliques in graphs.
Engineers require high-quality data for the design and implementation of today’s software, especially in the context of machine learning (ML). This puts an emphasis on the need for the publication and sharing of data from and between organizations, public as well as private. Following the paradigm of open innovation, open data provide a mechanism to increase the availability of information, offeri
State estimators are crucial components of anomaly detectors that are used to monitor cyber-physical systems. Many frequently-used state estimators are suscepti- ble to model risk as they rely critically on the availability of an accurate state-space model. Modeling errors make it more difficult to distinguish whether deviations from expected behavior are due to anomalies or simply a lack of knowl
In order to improve the energy efficiency in district heating networks and the comfort of their customers, these networks need to overcome the problem of unfair heat distribution under heat deficits. This paper introduces a new strategy to achieve this thermal fairness objective: it is lowcost in terms of communication and computation. The proposed approach is illustrated on a simulation example.
Background: The fractional exhaled nitric oxide (FENO) test is a point-of-care test that is used in the assessment of asthma. Objective: To provide evidence-based clinical guidance on whether FENO testing is indicated to optimize asthma treatment in patients with asthma in whom treatment is being considered. Methods: An international, multidisciplinary panel of experts was convened to form a conse
IoT networks are getting overcrowded following the vast increase in number of Internet-of-Things (IoT) devices and connections. Networks can be extended with more gateways, increasing the number of supported devices. However, as investigated in this work, massive MIMO has the potential to increase the number of simultaneous connections and moreover lower the energy expenditure of these devices. We
We extend the generalized information criteria for high-dimensional penalizedmodels to sparse statistical jump models, a new class of statistically robust and computationally efficient alternatives to hidden Markov models. In a simulation study, we demonstrate that the new generalized information criteria selects the correct hyperparameters with high probability. Finally, providing an empirical ap
In recent years, the combination of the digitization of the field of pathology and increased computational power has led to a big increase in research of computer-aided diagnostics using systems based on artificial intelligence (AI). This includes detection and classification of prostate cancer, where several studies have shown great promise in automated prostate cancer grading using deep learning
Medical research generates a large number of publications with the PubMed database already containing >35 million research articles. Integration of the knowledge scattered across this large body of literature could provide key insights into physiological mechanisms and disease processes leading to novel medical interventions. However, it is a great challenge for researchers to utilize this informa
Antennas are one of the key components that empower a new generation of wireless technologies, such as 5G and new radar systems. It has been shown that antenna design strategies based on modal theories represent a powerful systematic approach to design practical antenna systems with high performance. In this thesis, several innovative multi-antenna systems are proposed for wireless applications in
