The Spatial Dimming Scheme for the MU-MIMO-OFDM VLC System

Multiuser visible light communication (MU-VLC) systems utilizing multiple-input multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) are gaining increased attentions recently. Visible light communication (VLC) links are expected to work under different illumination conditions and, thus, the need for dimming control mechanisms. However, the traditional analog- and digital-based dimming schemes have adverse effects on the data communications performance, such as clipping distortion and the variation of the duty cycle. In this paper, spatial dimming schemes based on the zero-forcing and the minimum mean-squared error precoding schemes are proposed for direct-current biased optical OFDM based indoor MU-MIMO VLC system, and the bipolar optical OFDM signal is biased by a fixed dc level. Transmit antenna selection algorithms are designed for the optimum working light emitting diodes (LEDs) subset at each dimming level. Owing to the simultaneously exploration of the selection diversity of LEDs-based lights and the channel state information, the proposed spatial dimming schemes outperform the traditional dimming schemes, which is also verified by simulation results. Thus, the proposed schemes are shown to have a great potential to be applied in practical MU-MIMO-OFDM VLC systems

Exploiting Visual Semantic Reasoning for Video-Text Retrieval

Video retrieval is a challenging research topic bridging the vision and language areas and has attracted broad attention in recent years. Previous works have been devoted to representing videos by directly encoding from frame-level features. In fact, videos consist of various and abundant semantic relations to which existing methods pay less attention. To address this issue, we propose a Visual Semantic Enhanced Reasoning Network (ViSERN) to exploit reasoning between frame regions. Specifically, we consider frame regions as vertices and construct a fully-connected semantic correlation graph. Then, we perform reasoning by novel random walk rule-based graph convolutional networks to generate region features involved with semantic relations. With the benefit of reasoning, semantic interactions between regions are considered, while the impact of redundancy is suppressed. Finally, the region features are aggregated to form frame-level features for further encoding to measure video-text similarity. Extensive experiments on two public benchmark datasets validate the effectiveness of our method by achieving state-of-the-art performance due to the powerful semantic reasoning.Comment: Accepted by IJCAI 2020. SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserved. http://static.ijcai.org/2020-accepted_papers.htm

A Novel Received Signal Strength Assisted Perspective-three-Point Algorithm for Indoor Visible Light Positioning

In this paper, a received signal strength assisted Perspective-three-Point positioning algorithm (R-P3P) is proposed for visible light positioning (VLP) systems. The basic idea of R-P3P is to joint visual and strength information to estimate the receiver position using 3 LEDs regardless of the LEDs' orientations. R-P3P first utilizes visual information captured by the camera to estimate the incidence angles of visible lights. Then, R-P3P calculates the candidate distances between the LEDs and the receiver based on the law of cosines and the Wu-Ritt's zero decomposition method. Based on the incidence angles, the candidate distances and the physical characteristics of the LEDs, R-P3P can select the exact distances from all the candidate distances. Finally, the linear least square (LLS) method is employed to estimate the position of the receiver. Due to the combination of visual and strength information of visible light signals, R-P3P can achieve high accuracy using 3 LEDs regardless of the LEDs' orientations. Simulation results show that R-P3P can achieve positioning accuracy within 10 cm over 70% indoor area with low complexity regardless of LEDs orientations.Comment: arXiv admin note: substantial text overlap with arXiv:2004.0629