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OJSP Volume 2 | 2021

Forecasting Video QoE With Deep Learning From Multivariate Time-Series

The end users’ satisfactory Quality of Experience (QoE) is a fundamental criterion for networked video service providers such as video-on-demand providers (Netflix, YouTube, etc.), cloud gaming providers (Google Stadia, PlayStation Now, etc.) and videoconferencing providers (Zoom, Microsoft Teams, etc.). To know the QoE, providers today typically predict it from the Quality of Service (QoS) parameters or the client-side's actual QoE metrics measured at the current time-step.

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Data Selection in Neural Networks

In the era of big data, profitable opportunities are becoming available for many applications. As the amount of data keeps increasing, machine learning becomes an attractive tool to analyze the information acquired. However, harnessing meaningful data remains a challenge. The machine learning tools employed in many applications apply all training data without taking into consideration how relevant are some of them. In this paper, we propose a data selection strategy for the training step of Neural Networks to obtain the most significant data information and improve algorithm performance during training. 

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Effect of Generalized Improper Gaussian Noise and In-Phase/Quadrature-Phase Imbalance on Quadrature Spatial Modulation

Quadrature spatial modulation (QSM) isa recently proposed multiple-input multiple-output (MIMO) wireless transmission paradigm that has garnered considerable research interest owing to its relatively high spectral efficiency. QSM essentially enhances the spatial multiplexing gain while maintaining all the inherent advantages of spatial modulation (SM).

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A JPEG Forensic Detector for Color Bitmap Images

Identification of decompressed JPEG images, especially those compressed with high JPEG quality factors, is a challenging issue in image forensics. Furthermore, the applicability of the existing JPEG forensic detectors in forgery localization is limited by their inability to cope with spatial misalignment in the 8×8 JPEG grid.

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Fast Sequential Clustering in Riemannian Manifolds for Dynamic and Time-Series-Annotated Multilayer Networks

This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking. 

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Designing Sequence Set With Minimal Peak Side-Lobe Level for Applications in High Resolution RADAR Imaging

Constant-modulus sequence set with low peak side-lobe level is a necessity for enhancing the performance of modern active sensing systems like Multiple Input Multiple Output (MIMO) RADARs. In this paper, we consider the problem of designing a constant-modulus sequence set by minimizing the peak side-lobe level, which can be cast as a non-convex minimax problem, and propose a Majorization-Minimization technique based iterative monotonic algorithm named as the PSL minimizer.

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