Performance bounds for parameter estimation under misspecified models: Fundamental findings and applications
The objective of this article is to provide an accessible and, the same time, comprehensive
treatment of the fundamental concepts about CRBs and efficient estimators in the presence …
treatment of the fundamental concepts about CRBs and efficient estimators in the presence …
A systematic literature review of software effort prediction using machine learning methods
Abstract Machine learning (ML) techniques have been widely investigated for building
prediction models, able to estimate software development effort as well as to improve the …
prediction models, able to estimate software development effort as well as to improve the …
A comparison of automated classification techniques for image processing in video internet of things
The counts of various types of white blood cells give vital information for identifying a variety
of ailments utilizing the video internet of things (VIoT). This technique needs to be automated …
of ailments utilizing the video internet of things (VIoT). This technique needs to be automated …
Medical image classification using different machine learning algorithms
The different types of white blood cells equips us an important data for diagnosing and
identifying of many diseases. The automation of this task can save time and avoid errors in …
identifying of many diseases. The automation of this task can save time and avoid errors in …
A new class of Bayesian cyclic bounds for periodic parameter estimation
E Nitzan, T Routtenberg… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
Many practical signal processing applications involve estimation of parameters with periodic
nature, such as phase, frequency and direction-of-arrival estimation. The commonly used …
nature, such as phase, frequency and direction-of-arrival estimation. The commonly used …
Bayesian Direction of Arrival Estimation using Atomic Norm Minimization with Prior Knowledge
This paper concerns the direction of arrival (DOA) estimation problem in Bayesian
framework by using the sparse methods that incorporate the prior knowledge within the …
framework by using the sparse methods that incorporate the prior knowledge within the …
Bayesian Direction of Arrival Estimation with Prior Knowledge from Target Tracker
The performance of traditional direction of arrival (DOA) estimation methods always
deteriorates at a low signal-to-noise ratio (SNR) or without sufficient observations. This …
deteriorates at a low signal-to-noise ratio (SNR) or without sufficient observations. This …
Direction finding with L1-norm subspaces
PP Markopoulos, N Tsagkarakis… - Compressive …, 2014 - spiedigitallibrary.org
Conventional subspace-based signal direction-of-arrival estimation methods rely on the
familiar L 2-norm-derived principal components (singular vectors) of the observed sensor …
familiar L 2-norm-derived principal components (singular vectors) of the observed sensor …
Prior mismatch in Bayesian direction of arrival estimation for sparse arrays
JM Kantor, CD Richmond, B Correll… - 2015 IEEE Radar …, 2015 - ieeexplore.ieee.org
We study the mean-squared-error (MSE) performance of Bayesian direction-of-arrival (DOA)
estimation for sparse linear arrays in which prior belief about the target location is …
estimation for sparse linear arrays in which prior belief about the target location is …
Realified L1-PCA for direction-of-arrival estimation: Theory and algorithms
PP Markopoulos, N Tsagkarakis, DA Pados… - EURASIP Journal on …, 2019 - Springer
Subspace-based direction-of-arrival (DoA) estimation commonly relies on the Principal-
Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally …
Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally …