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Observer-based switched-linear system identification

In this paper, we present a framework to identify discrete-time, single-input/single-output, switched linear systems (SISO-SLSs) from input–output data measurements. Continuous state is not assumed to be measured. The key step is a deadbeat observer-based transformation of the SLS model to a switched auto-regressive with exogenous input (SARX) model. Discrete states are estimated by a three-stage

A 20-60ghz digitally controlled composite oscillator for 5G

This paper describes a frequency generator supporting over-an-octave tuning range for 5G receiver front-end. Generator is built by composition of smaller-range oscillators multiplexed to the common output that drives a downconversion mixer. Simulated in 28nm CMOS with full physical device models the composite oscillator exhibits a frequency tuning range from 21.5 to 60.7GHz (95.3%) dissipating les

A Tutorial on Positive Systems and Large Scale Control

In this tutorial paper we first present some foundational results regarding the theory of positive systems. In particular, we present fundamental results regarding stability, positive realization and positive stabilization by means of state feedback. Special attention is also paid to the system performance in terms of disturbance attenuation. Under the asymptotic stability assumption, such perform

Adaptive H-infinity Synthesis for Linear Systems with Uncertain Parameters

A performance-based approach is developed for adaptive robust control of linear systems with uncertain parameters. The dual control objective involves the disturbance attenuation and worst case identification of the unknown parameters. The proposed synthesis procedure relies on sufficient conditions, given in terms of suitable solutions of perturbed differential Riccati equations to exist. Althoug

Optimizing robust PID control of propofol anesthesia for children; design and clinical evaluation

Objective: The goal of this study was to optimize robust PID control for propofol anesthesia in children aged 5-10 years to improve performance, particularly to decrease the time of induction of anesthesia while maintaining robustness.Methods: We analyzed results of a previous study conducted by our group to identify opportunities for system improvement. Allometric scaling was introduced to reduce

Remote Operation of Unmanned Surface Vessel through Virtual Reality

An unmanned ship can be designed without considering humancomfort, and can thus be constructed lighter, smaller and less expensive.It can carry out missions in rough terrain or be in areaswhere it would be dangerous for a human to operate. By not havingto support a crew, lengthy missions can be accepted, enabling, e.g.reconnaissance missions, or reducing emissions by lowering thespeed.Breakthrough

Achieving predictable and low end-to-end latency for a network of smart services

To remain competitive in the field of manufacturing today, companies must constantly improve the automation loops within their production plants. This can be done by augmenting the automation applications with "smart services" such as supervisory-control applications or machine-learning inference algorithms. The downside is that these smart services are often hosted in a cloud infrastructure and t

Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground

ExtendJ : Extensible Java compiler

ExtendJ (formerly JastAddJ) is an extensible Java compiler, supporting full Java source-to-bytecode compilation. ExtendJ enables researchers and developers to easily build powerful language extensions, custom static analyses, and other tools for Java. ExtendJ is built with Reference Attribute Grammars and Aspect Oriented Programming, enabling easy extension development.

Statistical Modeling and Learning of the Environmental and Genetic Drivers of Variation in Human Immunity

During the last decade the variation in the human genome has been mapped in fine detail. Next generation sequencing has made it possible to cheaply and rapidly aquire vast amounts of biomolecular information on large cohorts of people. This have enabled large-scale epidemiological studies to investigate the relationships between environmental and genetic factors and human biomolecular traits. It i

On Distributed Optimal Control of Traffic Flows in Transportation Networks

We propose and analyze distributed computation algorithms for finite-horizon optimal control problems in transportation networks. We model traffic flow dynamics by the cell-transmission model and focus on two problems: system-optimum dynamic traffic assignment (where the routing is part of the optimization) and freeway network control (where the routing is exogenous and the optimization is confine

Improving Receiver Close-In Blocker Tolerance by Baseband Gm-C Notch Filtering

This paper presents a receiver front end with improved blocker handling implemented in a 65-nm CMOS technology. Since close-in blockers are challenging to reject at RF, the receiver features a baseband (BB) notch filter, which effectively sinks close-in blocker current directly from the output of an LNTA and passive mixer structure. The notch-filter frequency can be tuned to match the blocker offs

Hybrid tests of contact events in air-to-air refueling

Air-to-air refueling is a vital technique for extending the range and endurance of manned or unmanned aircraft, with over 1000 refueling procedures flown per day in military operations. The hardware used for air-to-air refueling needs to be tailored to the specific aircraft and airspeeds involved, performing markedly different for permutations of tanker and receiver craft. Extensive flight testing

Low-rank inducing norms with optimality interpretations

Optimization problems with rank constraints appear in many diverse fields such as control, machine learning, and image analysis. Since the rank constraint is nonconvex, these problems are often approximately solved via convex relaxations. Nuclear norm regularization is the prevailing convexifying technique for dealing with these types of problem. This paper introduces a family of low-rank inducing

End-to-end deadlines over dynamic topologies

Despite the creativity of the scientific community and the funding agencies, the underlying model of computation behind IoT, WSN, cloud, edge, fog, and mist is fundamentally the same; Computational nodes which are dynamically interconnected to form a system in where both processing capacity and connectivity may vary over time. On top of such a system, we consider applications that need packets to

On H-infinity Control and Large-Scale Systems

In this thesis, a class of linear time-invariant systems is identified for which a particular type of H-infinity optimal control problem can be solved explicitly. It follows that the synthesized controller can be given on a simple explicit form. More specifically, the controller can be written in terms of the matrices of the system’s state-space representation. The result has applications in the c

Design of an area efficient crypto processor for 3GPP-LTE NB-IoT devices

Providing information security is crucial for the Internet of Things (IoT) devices, platforms in which the available power budget is very limited. This paper tackles this challenge and presents a cryptographic processor compliant with the security algorithms specified by the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) NarrowBand IoT (NB-IoT) standard. The proposed processor