On the asymptotic optimality of a low-complexity coding strategy for WSS, MA, and AR vector sources

In this paper, we study the asymptotic optimality of a low-complexity coding strategy for Gaussian vector sources. Specifically, we study the convergence speed of the rate of such a coding strategy when it is used to encode the most relevant vector sources, namely wide sense stationary (WSS), moving average (MA), and autoregressive (AR) vector sources. We also study how the coding strategy considered performs when it is used to encode perturbed versions of those relevant sources. More precisely, we give a sufficient condition for such perturbed versions so that the convergence speed of the rate remains unaltered

Necessary and sufficient conditions for AR vector processes to be stationary: Applications in information theory and in statistical signal processing

As the correlation matrices of stationary vector processes are block Toeplitz, autoregressive (AR) vector processes are non-stationary. However, in the literature, an AR vector process of finite order is said to be stationary if it satisfies the so-called stationarity condition (i.e., if the spectral radius of the associated companion matrix is less than one). Since the term stationary is used for such an AR vector process, its correlation matrices should somehow approach the correlation matrices of a stationary vector process, but the meaning of somehow approach has not been mathematically established in the literature. In the present paper we give necessary and sufficient conditions for AR vector processes to be stationary. These conditions show in which sense the correlation matrices of an AR stationary vector process asymptotically behave like block Toeplitz matrices. Applications in information theory and in statistical signal processing of these necessary and sufficient conditions are also given

On the asymptotic optimality of a low-complexity coding strategy for WSS, MA, and AR vector sources

In this paper, we study the asymptotic optimality of a low-complexity coding strategy for Gaussian vector sources. Specifically, we study the convergence speed of the rate of such a coding strategy when it is used to encode the most relevant vector sources, namely wide sense stationary (WSS), moving average (MA), and autoregressive (AR) vector sources. We also study how the coding strategy considered performs when it is used to encode perturbed versions of those relevant sources. More precisely, we give a sufficient condition for such perturbed versions so that the convergence speed of the rate remains unaltered

Algorithm for the optimal design of a fault-tolerant aircraft power transmission network.

Aircraft manufacturers aim to decrease the fuel consumption based on reducing weight and increasing the subsystem efficiency. Hence, the electric power system (EPS) acquires great relevance because it must be efficient and lightweight. Any change in the EPS must not affect the aircraft’s electrical safety, which under a traditional decentralized EPS strategy is ensured by redundancy. Recently, several decentralized EPS strategies based on the introduction of multiport power converters have arisen. Such strategies meet the established safety goals since the aforementioned devices make it possible to recalculate the path to continue powering the loads in case of failure. However, the literature does not address how to connect such multiport power converters. The main contribution of this article is to present a low-complexity algorithm that minimizing the redundancy of wiring, provides a fault-tolerant power transmission network. This is done under a decentralized EPS strategy where multiport power converters are used. The proposed strategy is evaluated on Boeing 787 aircraft, where we compare the length of the cables both under a traditional decentralized network configuration (where the redundancy option is used to ensure the safety of operation) and in the network provided by our algorithm. A saving of 66.6% is obtained

In-network algorithm for passive sensors in structural health monitoring

Structural health monitoring (SHM) using wireless sensor networks (WSN) has become a popular implementation, due to low maintenance and installation costs. These networks commonly use a centralized approach and battery-powered sensors, leading to energy consumption limitations, in both the central unit and the sensors. Therefore, it is of interest to consider the use of passive sensors and distributed processing in the network. In this letter, we present a distributed algorithm for SHM using wireless passive sensor networks (WPSNs) that allows any passive sensor in the network to obtain the distance to its neighbours via backscattering, and hence to detect and signal changes in the monitored structure

A low-complexity analog linear coding scheme for transmitting asymptotically WSS AR sources

In this letter, we give a low-complexity analog linear coding scheme for transmitting finite-length data blocks of asymptotically wide sense stationary (AWSS) autoregressive (AR) sources through additive white Gaussian noise (AWGN) channels

Distributed clustering algorithm for adaptive pandemic control.

The COVID-19 pandemic has had severe consequences on the global economy, mainly due to indiscriminate geographical lockdowns. Moreover, the digital tracking tools developed to survey the spread of the virus have generated serious privacy concerns. In this paper, we present an algorithm that adaptively groups individuals according to their social contacts and their risk level of severe illness from COVID-19, instead of geographical criteria. The algorithm is fully distributed and therefore, individuals do not know any information about the group they belong to. Thus, we present a distributed clustering algorithm for adaptive pandemic control

Necessary and sufficient conditions for AR vector processes to be stationary: Applications in information theory and in statistical signal processing

As the correlation matrices of stationary vector processes are block Toeplitz, autoregressive (AR) vector processes are non-stationary. However, in the literature, an AR vector process of finite order is said to be stationary if it satisfies the so-called stationarity condition (i.e., if the spectral radius of the associated companion matrix is less than one). Since the term stationary is used for such an AR vector process, its correlation matrices should somehow approach the correlation matrices of a stationary vector process, but the meaning of somehow approach has not been mathematically established in the literature. In the present paper we give necessary and sufficient conditions for AR vector processes to be stationary. These conditions show in which sense the correlation matrices of an AR stationary vector process asymptotically behave like block Toeplitz matrices. Applications in information theory and in statistical signal processing of these necessary and sufficient conditions are also given

Asymptotically equivalent sequences of matrices and capacity of a discrete-time gaussian MIMO channel with memory

Using some recent results on asymptotically equivalent sequences of matrices, we present in this paper, a new derivation of the capacity formula given by Brandenburg and Wyner for a discrete-time Gaussian multiple-input-multiple-output channel with memory. In this paper, we tackle not only the case considered by them, where the number of channel inputs and the number of channel outputs are the same, but also when both numbers are different

Mathematical model for the analysis of jet engine fuel consumption during aircraft climb and descent

This paper proposes a mathematical model that studies the fuel consumption of a jet engine aircraft during the climbing and descent flight phases. Such problem is addressed by providing a closed-form formula of the aircraft's rate-of-climb and rate-of-descent, which then enables obtaining a closed-form formula of the aircraft's fuel consumption that provides the closed-form relationship between the aircraft's fuel consumption and aerodynamic, engine and design parameters. In order to validate our mathematical model, a comparison is made between our results and results provided by Piano-X software and accuracy between both is proven. Furthermore, our mathematical model is applied to the calculation of pollutant gas emissions, specifically, we present a closed-form expression that provides the dependency between the mass of pollutant gas emitted and the aircraft's aerodynamic, engine and design parameters