SVS-JOIN : efficient spatial visual similarity join for geo-multimedia

In the big data era, massive amount of multimedia data with geo-tags has been generated and collected by smart devices equipped with mobile communications module and position sensor module. This trend has put forward higher request on large-scale geo-multimedia retrieval. Spatial similarity join is one of the significant problems in the area of spatial database. Previous works focused on spatial textual document search problem, rather than geo-multimedia retrieval. In this paper, we investigate a novel geo-multimedia retrieval paradigm named spatial visual similarity join (SVS-JOIN for short), which aims to search similar geo-image pairs in both aspects of geo-location and visual content. Firstly, the definition of SVS-JOIN is proposed and then we present the geographical similarity and visual similarity measurement. Inspired by the approach for textual similarity join, we develop an algorithm named SVS-JOIN B by combining the PPJOIN algorithm and visual similarity. Besides, an extension of it named SVS-JOIN G is developed, which utilizes spatial grid strategy to improve the search efficiency. To further speed up the search, a novel approach called SVS-JOIN Q is carefully designed, in which a quadtree and a global inverted index are employed. Comprehensive experiments are conducted on two geo-image datasets and the results demonstrate that our solution can address the SVS-JOIN problem effectively and efficiently

Average AoI Minimization for Energy Harvesting Relay-aided Status Update Network Using Deep Reinforcement Learning

A dual-hop status update system aided by energy harvesting (EH) relays with finite data and energy buffers is studied in this work. To achieve timely status updates, the best relays should be selected to minimize the average age of information (AoI), which is a recently proposed metric to evaluate information freshness. The average AoI minimization can be formulated as a Markov decision process (MDP), but the state space for capturing channel and buffer evolution grows exponentially with the number of relays, leading to high solution complexity. We propose a relay selection (RS) scheme based on deep reinforcement learning (DRL) according to the instantaneous channel packet freshness and buffer information of each relay. Simulation results show a significant improvement of the proposed DRL-based RS scheme over state-of-art approaches.Comment: This article has been accepted for publication in IEEE Wireless Communications Letters. Citation information: DOI 10.1109/LWC.2023.327886

High-Mobility Pentacene-Based Thin-Film Transistors With a Solution-Processed Barium Titanate Insulator

Abstract—Pentacene-based organic thin-film transistors (OTFTs) with solution-processed barium titanate (Ba1.2Ti0.8O3) as a gate insulator are demonstrated. The electrical properties of pentacene-based TFTs show a high field-effect mobility of 8.85 cm2 · V−1 · s−1, a low threshold voltage of −1.89 V, and a low subthreshold slope swing of 310 mV/decade. The chemical composition and binding energy of solution-processed barium titanate thin films are analyzed through X-ray photoelectron spectroscopy. The matching surface energy on the surface of the barium titanate thin film is 43.12 mJ · m−2, which leads to Stranski–Krastanov mode growth, and thus, high mobility is exhibited in pentacene-based TFTs. Index Terms—Barium titanate, high field-effect mobility, high permittivity, organic thin-filmtransistor (OTFT), solution process