Search results

Autonomous vehicle functions in safety-critical situations show promise in reducing the risk and saving lives in accidents compared to existing safety systems. Consequently, it is from many perspectives advantageous to be able to quantify the potential benefits of new autonomous systems for vehicle maneuvers at-the-limit of tire friction. Here, to estimate the potential in terms of saved lives and

The eutrophication problems in lakes on the Yangtze Plain of China have attracted global concern. However, a comprehensive assessment of the eutrophication status and its evolution is still lacking for these regional lakes, mostly because of technical difficulties and/or insufficient data to cover the large region. Our study attempts to fill this knowledge gap by using the entire archive of remote

The paper focuses on analysis of macro‐ and micromorphological characteristics of relict slope deposits in Serra da Estrela (Portugal) to understand the significance of different slope processes and paleoenvironmental settings. Micromorphology is a useful sedimentology technique allowing significant advances compared to macroscopic techniques. Results show that different processes are involved in

We present a method for solving the general mixed constrained convex quadratic programming problem using an active set method on the dual problem. The approach is similar to existing active set methods, but we present a new way of solving the linear systems arising in the algorithm. There are two main contributions; we present a new way of factorizing the linear systems, and show how iterative ref

Event-based sampling has been proposed as a general technique for lowering the average communication rate, energy consumption and computational burden in remote state estimation. However, the design of the event trigger is critical for good performance. In this paper, we study the combination of innovation-based triggering and state estimation of nonlinear dynamical systems using the particle filt

Efficient unit propagation for clausal constraints is a core building block of conflict-driven clause learning (CDCL) Boolean satisfiability (SAT) and lazy clause generation constraint programming (CP) solvers. Conflict-driven pseudo-Boolean (PB) solvers extend the CDCL paradigm from clausal constraints to integer linear constraints, also known as (linear) PB constraints. For PB solvers, many diff

Background and objective: New proposals to improve the regulation of hypnosis in anaesthesia based on the development of advanced control structures emerge continuously. However, a fair study to analyse the real benefits of these structures compared to simpler clinically validated PID-based solutions has not been presented so far. The main objective of this work is to analyse the performance limit

Deep learning has proven itself to be a powerful tool to develop datadriven signal processing algorithms for challenging engineering problems. By learning the key features and characteristics of the input signals instead of requiring a human to first identify and model them, learned algorithms can beat many human-made algorithms. In particular, deep neural networks are capable of learning the comp

The damping ratio is a key performance measure in systems that can be modelled as networks of masses and springs. We derive a lower bound on this quantity that applies to such networks when the masses are subject to viscous damping. The result allows the size of the damping ratio to be understood as a function of the system parameters. We use this to derive a decentralised criterion which, if sati

In this paper, we address mixed-criticality applications characterized by their safety criticality and time-dependent performance, which are virtualized on a Fog Computing Platform (FCP). The FCP is implemented as a set of interconnected multicore computing nodes, and brings computation and communication closer to the edge of the network, where the machines are located in industrial applications.

This paper investigates potential factors that control the El Niño–Southern Oscillation (ENSO) Spring Persistence Barrier (SPB) strength in two different ENSO regimes and apply it to explain the ENSO SPB strength modulation after the 21st century. In a damped, noise-driven model, the theoretical solution of SPB strength illustrates that a weaker ENSO growth rate strengthens SPB. In the self-sustai

Class-C operation is leveraged to implement a K-band CMOS voltage-controlled oscillator (VCO) where the upconversion of 1/f current noise from the cross-coupled transistors in the oscillator core is robustly contained at a very low level. Implemented in a bulk 28-nm CMOS technology, the 12%-tuning-range VCO shows a phase noise as low as -112 dBc/Hz at 1-MHz offset (-86 dBc/Hz at 100 kHz offset) fr

Assessments of long-term changes of air quality and global radiative forcing at a large scale heavily rely on satellite aerosol optical depth (AOD) datasets, particularly their temporal binning products. Although some attempts focusing on the validation of long-term satellite AOD have been conducted, there is still a lack of comprehensive quantification and understanding of the representativeness

Iterative learning control (ILC) is an approach to improve the performance of a system that repeats the sameoperation. In this paper, we apply the theory of pseudospectra to transient analysis of ILC. The focus of thispaper is on frequency-domain analysis of filter-based ILC. Moreover, the effect of finite trial length on thetransient growth is discussed.

We show that the structured singular value of a real matrix with respect to five full complex uncertainty blocks equals its convex upper bound. This is done by formulating the equality conditions as a feasibility SDP and invoking a result on the existence of a low-rank solution. A counterexample is given for the case of six uncertainty blocks. Known results are also revisited using the proposed ap

We characterize a class of systems for which the H-infinity optimal control problem can be simplified in a way that enables sparse solutions and efficient computation. For a subclass of the systems, an optimal controller can be explicitly expressed in terms of the matrices of the system's state-space representation. In many applications, the controller given by this formula, which is st

A novel method using the cloud to implement a variable horizon model predictive controller is presented. In case of sudden long delays and downtime, a graceful degradation is used. Robust, best effort strategies allow industrial grade use of the powerful, efficient, and quickly improving cloud ecosystems. The variable horizon strategy finds use in, for example, non-linear control problems, and the

A two-tier architecture for cloud-based MPC is presented consisting of a high rate MPC in the cloud and a low rate MPC on the local device. The system use the cloud MPC as the nominal controller but switches to local MPC in case of connectivity loss. The two MPCs are designed to be as similar to each other as possible except for the sampling rate. Different alternatives for when to execute the loc

In this contribution, a recently conducted measurement campaign for indoor millimeter-wave (mm-wave) propagation channels is introduced. A vector network analyzer (VNA)-based channel sounder was exploited to record the channel characteristics at the frequency band from 28-30 GHz. A virtual uniform circular array (UCA) with a radius of 0.25 m was formed using a rotator with 360 steps. Moreover, by