Information matrix for hidden Markov models with covariates

For a general class of hidden Markov models that may include time-varying covariates, we illustrate how to compute the observed information matrix, which may be used to obtain standard errors for the parameter estimates and check model identifiability. The proposed method is based on the Oakes’ identity and, as such, it allows for the exact computation of the information matrix on the basis of the output of the expectation-maximization (EM) algorithm for maximum likelihood estimation. In addition to this output, the method requires the first derivative of the posterior probabilities computed by the forward-backward recursions introduced by Baum and Welch. Alternative methods for computing exactly the observed information matrix require, instead, to differentiate twice the forward recursion used to compute the model likelihood, with a greater additional effort with respect to the EM algorithm. The proposed method is illustrated by a series of simulations and an application based on a longitudinal dataset in Health Economics

Experimental analysis of multistatic multiband radar signatures of wind turbines

This study presents the analysis of recent experimental data acquired using two radar systems at S-band and X-band to measure simultaneous monostatic and bistatic signatures of operational wind turbines near Shrivenham, UK. Bistatic and multistatic radars are a potential approach to mitigate the adverse effects of wind farm clutter on the performance of radar systems, which is a well-known problem for air traffic control and air defence radar. This analysis compares the simultaneous monostatic and bistatic micro-Doppler signatures of two operational turbines and investigates the key differences at bistatic angles up to 23°. The variations of the signature with different polarisations, namely vertical transmitted and vertical received and horizontal transmitted and horizontal received, are also discussed

Gravimetry through non-linear optomechanics

We propose a new method for measurements of gravitational acceleration using a quantum optomechanical system. As a proof-of-concept, we investigate the fundamental sensitivity for a cavity optomechanical system for gravitational accelerometry with a light-matter interaction of the canonical `trilinear' radiation pressure form. The phase of the optical output of the cavity encodes the gravitational acceleration $g$ and is the only component which needs to be measured to perform the gravimetry. We analytically show that homodyne detection is the optimal readout in our scheme, based on the cyclical decoupling of light and matter, and predict a fundamental sensitivity of $\Delta g = 10^{-15}$ ms$^{-2}$ for currently achievable optomechanical systems which could, in principle, surpass the best atomic interferometers even for low optical intensities. Our scheme is strikingly robust to the initial thermal state of the mechanical oscillator as the accumulated gravitational phase only depends on relative position separation between components of the entangled optomechanical state arising during the evolution.Comment: 14 pages, 15 figure

Quantum cooling and squeezing of a levitating nanosphere via time-continuous measurements

With the purpose of controlling the steady state of a dielectric nanosphere levitated within an optical cavity, we study its conditional dynamics under simultaneous sideband cooling and additional time-continuous measurement of either the output cavity mode or the nanosphere's position. We find that the average phonon number, purity and quantum squeezing of the steady-states can all be made more non-classical through the addition of time-continuous measurement. We predict that the continuous monitoring of the system, together with Markovian feedback, allows one to stabilize the dynamics for any value of the laser frequency driving the cavity. By considering state-of-the-art values of the experimental parameters, we prove that one can in principle obtain a non-classical (squeezed) steady-state with an average phonon number $n_{\sf ph}\approx 0.5$.Comment: 10 pages, 9 figures; v2: close to published versio

Recent Trends on Liquid Air Energy Storage: A Bibliometric Analysis

The increasing penetration of renewable energy has led electrical energy storage systems to have a key role in balancing and increasing the e ciency of the grid. Liquid air energy storage (LAES) is a promising technology, mainly proposed for large scale applications, which uses cryogen (liquid air) as energy vector. Compared to other similar large-scale technologies such as compressed air energy storage or pumped hydroelectric energy storage, the use of liquid air as a storage medium allows a high energy density to be reached and overcomes the problem related to geological constraints. Furthermore, when integrated with high-grade waste cold/waste heat resources such as the liquefied natural gas regasification process and hot combustion gases discharged to the atmosphere, LAES has the capacity to significantly increase the round-trip efficiency. Although the first document in the literature on the topic of LAES appeared in 1974, this technology has gained the attention of many researchers around the world only in recent years, leading to a rapid increase in a scientific production and the realization of two system prototype located in the United Kingdom (UK). This study aims to report the current status of the scientific progress through a bibliometric analysis, defining the hotspots and research trends of LAES technology. The results can be used by researchers and manufacturers involved in this entering technology to understand the state of art, the trend of scientific production, the current networks of worldwide institutions, and the authors connected through the LAES. Our conclusions report useful advice for the future research, highlighting the research trend and the current gaps.This work was partially funded by the Ministerio de Ciencia, Innovación y Universidades de España (RTI2018-093849-B-C31—MCIU/AEI/FEDER, UE). This work was partially funded by the Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (AEI) (RED2018-102431-T). The authors at the University of Lleida would like to thank the Catalan Government for the quality accreditation given to their research group GREiA (2017 SGR 1537). GREiA is a certified agent TECNIO in the category of technology developers from the Government of Catalonia. This work was partially supported by ICREA under the ICREA Academia program

Comparative Evaluation of Packet Classification Algorithms for Implementation on Resource Constrained Systems

This paper provides a comparative evaluation of a number of known classification algorithms that have been considered for both software and hardware implementation. Differently from other sources, the comparison has been carried out on implementations based on the same principles and design choices. Performance measurements are obtained by feeding the implemented classifiers with various traffic traces in the same test scenario. The comparison also takes into account implementation feasibility of the considered algorithms in resource constrained systems (e.g. embedded processors on special purpose network platforms). In particular, the comparison focuses on achieving a good compromise between performance, memory usage, flexibility and code portability to different target platforms

Design of additively manufactured moulds for expanded polymers

The traditional tools used in steam-chest moulding technologies for the shaping of expanded polymers can be replaced today by lighter moulds, accurately designed and produced exploiting the additive manufacturing technology. New paradigms have to be considered for mould design, assuming that additive manufacturing enables the definition of different architectures that are able to improve the performance of the moulding process. This work describes the strategies adopted for the design and manufacturing by Laser powder bed fusion of the moulds, taking into specific consideration their functional surfaces, which rule the heat transfer to the moulded material, hence the quality of the products and the overall performance of the steamchest process. The description of a case study and the comparison between the performance of the traditional solution and the new moulds are also presented to demonstrate the effectiveness of the new approach. This study demonstrates that the redesign and optimization of the mould shape can lead to a significant reduction of the energy demand of the process, thanks to a homogeneous delivery of the heating steam throughout the part volume, which also results in a remarkable cutting of the cycle time

Online Fault Classification in HPC Systems through Machine Learning

As High-Performance Computing (HPC) systems strive towards the exascale goal, studies suggest that they will experience excessive failure rates. For this reason, detecting and classifying faults in HPC systems as they occur and initiating corrective actions before they can transform into failures will be essential for continued operation. In this paper, we propose a fault classification method for HPC systems based on machine learning that has been designed specifically to operate with live streamed data. We cast the problem and its solution within realistic operating constraints of online use. Our results show that almost perfect classification accuracy can be reached for different fault types with low computational overhead and minimal delay. We have based our study on a local dataset, which we make publicly available, that was acquired by injecting faults to an in-house experimental HPC system.Comment: Accepted for publication at the Euro-Par 2019 conferenc