MIMO radar for advanced driver-assistance systems and autonomous driving: Advantages and challenges
Important requirements for automotive radar are high resolution, low hardware cost, and
small size. Multiple-input, multiple-output (MIMO) radar technology has been receiving …
small size. Multiple-input, multiple-output (MIMO) radar technology has been receiving …
Machine learning methods that economists should know about
We discuss the relevance of the recent machine learning (ML) literature for economics and
econometrics. First we discuss the differences in goals, methods, and settings between the …
econometrics. First we discuss the differences in goals, methods, and settings between the …
An overview of variable selection methods in multivariate analysis of near-infrared spectra
With the advances in innovative instrumentation and various valuable applications, near-
infrared (NIR) spectroscopy has become a mature analytical technique in various fields …
infrared (NIR) spectroscopy has become a mature analytical technique in various fields …
Advances in surrogate based modeling, feasibility analysis, and optimization: A review
A Bhosekar, M Ierapetritou - Computers & Chemical Engineering, 2018 - Elsevier
The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …
increasing popularity over past three decades. Due to their ability to exploit the black-box …
Feature selection: A data perspective
Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …
efficient in preparing data (especially high-dimensional data) for various data-mining and …
The little engine that could: Regularization by denoising (RED)
Removal of noise from an image is an extensively studied problem in image processing.
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …
A systematic review of compressive sensing: Concepts, implementations and applications
M Rani, SB Dhok, RB Deshmukh - IEEE access, 2018 - ieeexplore.ieee.org
Compressive Sensing (CS) is a new sensing modality, which compresses the signal being
acquired at the time of sensing. Signals can have sparse or compressible representation …
acquired at the time of sensing. Signals can have sparse or compressible representation …
4D automotive radar sensing for autonomous vehicles: A sparsity-oriented approach
We propose a high-resolution imaging radar system to enable high-fidelity four-dimensional
(4D) sensing for autonomous driving, ie, range, Doppler, azimuth, and elevation, through a …
(4D) sensing for autonomous driving, ie, range, Doppler, azimuth, and elevation, through a …
Best subset, forward stepwise or lasso? Analysis and recommendations based on extensive comparisons
In exciting recent work, Bertsimas, King and Mazumder (Ann. Statist. 44 (2016) 813–852)
showed that the classical best subset selection problem in regression modeling can be …
showed that the classical best subset selection problem in regression modeling can be …