Spectrum prediction is challenging due to its multi-dimension, complex inherent
dependency, and heterogeneity among the spectrum data. In this paper, we first propose a …

This paper addresses the problem of stochastic modeling of atomic ensembles under multi-
source noise and makes the model interpretable. First, based on Itô's lemma and Allan …

Classifying individuals with neurological disorders and healthy subjects using EEG is a
crucial area of research. The current feature extraction approach focuses on the frequency …

DFT-based complex harmonic wavelet (CHW) is being used to directly compute frequency
content with respect to time by employing discrete Fourier transform and inverse discrete …

In this paper, we propose a deep learning (DL)-based task-driven spectrum prediction
framework, named DeepSPred. The DeepSPred comprises a feature encoder and a task …

The past two decades have witnessed dramatic advancement in computer‐aided design
(CAD). However, development of human–computer interfaces (HCI) for CAD have not kept …

The powertrain is a core component of military heavy tracked vehicles, and the accurate
evaluation of its reliability and health is gradually becoming a key technology to improve our …

Selecting features with strong discriminatory capabilities is crucial for data classification
challenges. Recurrence Quantification Analysis (RQA) is a promising technique for detecting …

Time series have broad usage in the wireless Internet of Things. This article proposes a
nonlinear time series prediction algorithm based on the Small-World Scale-Free Network …

A large-scale dynamic network is a fundamental data source in many big-data-related
applications, which can be seamlessly described by an HDI tensor. Yet such an HDI tensor …