Demonstration of three‐dimensional indoor visible light positioning with multiple photodiodes and reinforcement learning

To provide high‐quality location‐based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi‐photodiodes (multi‐PDs) three‐dimensional (3D) indoor VLP system enhanced by reinforcement learning (RL), which can realize accurate positioning in the 3D space without any off-line training. The basic 3D positioning model is introduced, where without height information of the receiver, the initial height value is first estimated by exploring its relationship with the received signal strength (RSS), and then, the coordinates of the other two dimensions (i.e., X and Y in the horizontal plane) are calculated via trilateration based on the RSS. Two different RL processes, namely RL1 and RL2, are devised to form two methods that further improve horizontal and vertical positioning accuracy, respectively. A combination of RL1 and RL2 as the third proposed method enhances the overall 3D positioning accuracy. The positioning performance of the four presented 3D positioning methods, including the basic model without RL (i.e., Benchmark) and three RL based methods that run on top of the basic model, is evaluated experimentally. Experimental results verify that obviously higher 3D positioning accuracy is achieved by implementing any proposed RL based methods compared with the benchmark. The best performance is obtained when using the third RL based method that runs RL2 and RL1 sequentially. For the testbed that emulates a typical office environment with a height difference between the receiver and the transmitter ranging from 140 cm to 200 cm, an average 3D positioning error of 2.6 cm is reached by the best RL method, demonstrating at least 20% improvement compared to the basic model without performing RL

Iterative point-wise reinforcement learning for highly accurate indoor visible light positioning

Iterative point-wise reinforcement learning (IPWRL) is proposed for highly accurate indoor visible light positioning (VLP). By properly updating the height information in an iterative fashion, the IPWRL not only effectively mitigates the impact of non-deterministic noise but also exhibits excellent tolerance to deterministic errors caused by the inaccurate a priori height information. The principle of the IPWRL is explained, and the performance of the IPWRL is experimentally evaluated in a received signal strength (RSS) based VLP system and compared with other positioning algorithms, including the conventional RSS algorithm, the k-nearest neighbors (KNN) algorithm and the PWRL algorithm where iterations exclude. Unlike the supervised machine learning method, e.g., the KNN, whose performance is highly dependent on the training process, the proposed IPWRL does not require training and demonstrates robust positioning performance for the entire tested area. Experimental results also show that when a large height information mismatch occurs, the IPWRL is able to first correct the height information and then offers robust positioning results with a rather low positioning error, while the positioning errors caused by the other algorithms are significantly higher

An investigation on the Factors Influencing the dissemination of WeChat Push Based on HSM and the Prediction of its Content Hotspot

With the continuous development of information technology, the carrier of we-media has emerged. The WeChat Subscription Accounts has quickly led the other we-media platforms. During the six years of its emergence, WeChat Subscription Accounts have attracted a lot of traffic and brought huge profit margins. Based on the above background, this study combines the heuristic-systematic model of information processing to classify the heuristic and systematic factors that influence the dissemination of WeChat push. Analyze the factors affecting WeChat push transmission, supplement relevant theories, and provide suggestions for WeChat Subscription Accounts operators

Stretchable hybrid bilayered luminescent composite based on the combination of strain-induced and triboelectrification-induced electroluminescence

High luminescence intensity from materials that are excited by external stimuli is highly desired. In this work, a stretchable hybrid luminescent composite (HLC) that has multiple luminescence modes is reported. The luminescence can be excited either by externally applied mechanical strain or by a moving object that slides against the HLC. When the HLC is deformed, such as being twisted or folded, the ZnS/Cu phosphor experiences mechanical strain that trigger the mechanoluminescence (ML) of the phosphors. Moreover, as the HLC slides against a contact object, the triboelectrification at the contact interface induces the electroluminescence of phosphor. Here, a series of internal and external factors were studied on how they influence the luminescent intensity. It is found that the luminescent intensity from the two modes can be superposed. The HLC material was used to fabricate a fiber-based luminescent device that can be driven by air flow. The overall luminescent intensity is enhanced by over 72% compared to that obtained solely from the ML. The HLC reported in this work has such potential applications as self-powered light sources and sensors as means of detecting dynamic motions and interactio

A Game Theory-Based Obstacle Avoidance Routing Protocol for Wireless Sensor Networks

The obstacle avoidance problem in geographic forwarding is an important issue for location-based routing in wireless sensor networks. The presence of an obstacle leads to several geographic routing problems such as excessive energy consumption and data congestion. Obstacles are hard to avoid in realistic environments. To bypass obstacles, most routing protocols tend to forward packets along the obstacle boundaries. This leads to a situation where the nodes at the boundaries exhaust their energy rapidly and the obstacle area is diffused. In this paper, we introduce a novel routing algorithm to solve the obstacle problem in wireless sensor networks based on a game-theory model. Our algorithm forms a concave region that cannot forward packets to achieve the aim of improving the transmission success rate and decreasing packet transmission delays. We consider the residual energy, out-degree and forwarding angle to determine the forwarding probability and payoff function of forwarding candidates. This achieves the aim of load balance and reduces network energy consumption. Simulation results show that based on the average delivery delay, energy consumption and packet delivery ratio performances our protocol is superior to other traditional schemes

Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE

Recent studies have demonstrated Large Language Models (LLMs) can extend their zero-shot generalization capabilities to multimodal learning through instruction tuning. As more modalities and downstream tasks are introduced, negative conflicts and interference may have a worse impact on performance. While this phenomenon has been overlooked in previous work, we propose a novel and extensible framework, called Octavius, for comprehensive studies and experimentation on multimodal learning with Multimodal Large Language Models (MLLMs). Specifically, we combine the well-known Mixture-of-Experts (MoE) and one of the representative PEFT techniques, i.e., LoRA, designing a novel LLM-based decoder, called LoRA-MoE, for multimodal learning. To the best of our knowledge, we are one of the pioneering efforts to introduce MoE into MLLMs to address this problem. The experimental results (about 20% improvement) have shown the effectiveness and versatility of our design in various 2D and 3D downstream tasks. Code and datasets are available at https://openlamm.github.io/paper_list/Octavius.Comment: 22 pages, 12 figures. Accepted in ICLR 202

Interplay between Lefty and Nodal signaling is essential for the organizer and axial formation in amphioxus embryos

Abstract(#br)The organizer is an essential signaling center required for axial formation during vertebrate embryonic development. In the basal chordate amphioxus, the dorsal blastopore lip of the gastrula has been proposed to be homologous to the vertebrate organizer. Lefty is one of the first genes to be expressed in the organizer. The present results show that Lefty overexpression abolishes the organizer; the embryos were severely ventralized and posteriorized, and failed to develop anterior and dorsal structures. In Lefty knockouts the organizer is enlarged, and anterior and dorsal structures are expanded. Different from Lefty morphants in vertebrates, amphioxus Lefty mutants also exhibited left-right defects. Inhibition of Nodal with SB505124 partially rescued the effects of Lefty loss-of-function on morphology. In addition, while SB505124 treatment blocked Lefty expression in the cleavage stages of amphioxus embryos, activation of Nodal signaling with Activin protein induced ectopic Lefty expression at these stages. These results show that the interplay between Lefty and Nodal signaling plays an essential role in the specification of the amphioxus organizer and axes