SPL Volume 28 | 2021

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2021

SPL Volume 28 | 2021

Point Clouds (PCs) have recently been adopted as the preferred data structure for representing 3D visual contents. Examples of Point Cloud (PC) applications range from 3D representations of small objects up to large scenes, both still or dynamic in time. PC adoption triggered the development of new coding, transmission, and display methodologies that culminated in new international standards for PC compression. 

Video inpainting aims to fill missing regions with plausible content in a video sequence. Deep learning-based video inpainting methods have made promising progress over the past few years. However, these methods tend to generate degraded completion content, such as missing textural details.

Previous works about linguistic steganography such as synonym substitution and sampling-based methods usually manipulate observed symbols explicitly to conceal secret information, which may give rise to security risks. In this letter, in order to preclude straightforward operation on observed symbols, we explored generation-based linguistic steganography in latent space by means of encoding secret messages in the selection of implicit attributes (semanteme) of natural language.

Traffic flow prediction is a challenging task while most existing works are faced with two main problems in extracting complicated intrinsic and extrinsic features. In terms of intrinsic features, current methods don't fully exploit different functions of short-term neighboring and long-term periodic temporal patterns.

Consider a robust multiple-input single-output downlink beamforming optimization problem in a frequency division duplexing system. The base station (BS) sends training signals to the users, and every user estimates the channel coefficients, quantizes the gain and the direction of the estimated channel and sends them back to the BS. 

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