Impact of COVID-19 on IoT adoption in healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT
COVID-19 has disrupted normal life and has enforced a substantial change in the policies,
priorities and activities of individuals, organisations and governments. These changes are …
priorities and activities of individuals, organisations and governments. These changes are …
Seismic response prediction of structures based on Runge-Kutta recurrent neural network with prior knowledge
In the seismic analysis of structural systems, dynamic response prediction is an essential
problem and is significant in every stage during the structural life cycle. Conventionally …
problem and is significant in every stage during the structural life cycle. Conventionally …
Handling missing data in multivariate time series using a vector autoregressive model-imputation (VAR-IM) algorithm
F Bashir, HL Wei - Neurocomputing, 2018 - Elsevier
Imputing missing data from a multivariate time series dataset remains a challenging
problem. There is an abundance of research on using various techniques to impute missing …
problem. There is an abundance of research on using various techniques to impute missing …
Sampling and sampled-data models: The interface between the continuous world and digital algorithms
Modern signal processing and control algorithms are invariably implemented digitally, yet
most real-world systems evolve in continuous time. Hence, the interaction between sampling …
most real-world systems evolve in continuous time. Hence, the interaction between sampling …
A two-filter approach for state estimation utilizing quantized output data
Filtering and smoothing algorithms are key tools to develop decision-making strategies and
parameter identification techniques in different areas of research, such as economics …
parameter identification techniques in different areas of research, such as economics …
On the uncertainty identification for linear dynamic systems using stochastic embedding approach with gaussian mixture models
In control and monitoring of manufacturing processes, it is key to understand model
uncertainty in order to achieve the required levels of consistency, quality, and economy …
uncertainty in order to achieve the required levels of consistency, quality, and economy …
EM-based identification of continuous-time ARMA models from irregularly sampled data
In this paper we present a novel algorithm for identifying continuous-time autoregressive
moving-average models utilizing irregularly sampled data. The proposed algorithm is based …
moving-average models utilizing irregularly sampled data. The proposed algorithm is based …
Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models
In this paper a Maximum likelihood estimation algorithm for Finite Impulse Response Errors-
in-Variables systems is developed. We consider that the noise-free input signal is Gaussian …
in-Variables systems is developed. We consider that the noise-free input signal is Gaussian …
On filtering methods for state-space systems having binary output measurements
In this paper we develop two filtering algorithms for state-space systems with binary outputs.
We approximate the conditional probability mass function of the output signal given the state …
We approximate the conditional probability mass function of the output signal given the state …
A rank-constrained optimization approach: Application to factor analysis
In this paper, we present a general method for rank-constrained optimization. We use an
iterative convex optimization procedure where it is possible to include any extra convex …
iterative convex optimization procedure where it is possible to include any extra convex …