No abstract

Photodiode-based (PD-based) visible light positioning (VLP) has become a research focus of indoor positioning technology, while the existing VLP models rarely consider the anti-interference and positioning time of that. In this paper, indoor real-time three-dimensional visible light positioning system using fingerprinting and extreme learning machine (ELM) is proposed to make the system achieve not only high positioning accuracy and elevated anti-interference but also well-behaved real-time ability. In contrast to the positioning system based on K-Nearest Neighbor or Support Vector Machine, the proposed system achieves the highest positioning accuracy and the state-of-the-art positioning speed. Furthermore, the visible light positioning kernel is proposed as a method to reduce the size of the fingerprint database and thus reduce the training time exponentially. Both the simulation and the experiment results show that the proposed system achieves real-time 3-D positioning with high anti-interference. Therefore, this scheme can be considered as one of the effective methods for indoor 3-D positioning. INDEX TERMS Extreme learning machine (ELM), photodiode (PD), positioning fingerprint, real-time positioning, visible light positioning (VLP).

No abstract

<p>We demonstrate an internet access application based on optical camera communication and bluetooth. The app will access the website while the camera in the phone receives the optical signal. © 2022 The Author(s)</p>

<p>We demonstrate an internet access application based on optical camera communication and bluetooth. The app will access the website while the camera in the phone receives the optical signal. © 2022 The Author(s)</p>

<p>Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers' personalities and emotions after cognitive processing have an important influence on conversation. However, most existing datasets for conversational AI ignore human personalities and emotions, or only consider part of them. It's difficult for dialogue systems to understand speakers' personalities and emotions although large-scale pre-training language models have been widely used. In order to consider both personalities and emotions in the process of conversation generation, we propose CPED, a large-scale Chinese personalized and emotional dialogue dataset, which consists of multi-source knowledge related to empathy and personal characteristic. These knowledge covers gender, Big Five personality traits, 13 emotions, 19 dialogue acts and 10 scenes. CPED contains more than 12K dialogues of 392 speakers from 40 TV shows. We release the textual dataset with audio features and video features according to the copyright claims, privacy issues, terms of service of video platforms. We provide detailed description of the CPED construction process and introduce three tasks for conversational AI, including personality recognition, emotion recognition in conversations as well as personalized and emotional conversation generation. Finally, we provide baseline systems for these tasks and consider the function of speakers' personalities and emotions on conversation.</p> <p>Our motivation is to propose a dataset to be widely adopted by the NLP community as a new open benchmark for conversational AI research. The full dataset is available at https://github.com/scutcyr/CPED.</p>

<p>Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers' personalities and emotions after cognitive processing have an important influence on conversation. However, most existing datasets for conversational AI ignore human personalities and emotions, or only consider part of them. It's difficult for dialogue systems to understand speakers' personalities and emotions although large-scale pre-training language models have been widely used. In order to consider both personalities and emotions in the process of conversation generation, we propose CPED, a large-scale Chinese personalized and emotional dialogue dataset, which consists of multi-source knowledge related to empathy and personal characteristic. These knowledge covers gender, Big Five personality traits, 13 emotions, 19 dialogue acts and 10 scenes. CPED contains more than 12K dialogues of 392 speakers from 40 TV shows. We release the textual dataset with audio features and video features according to the copyright claims, privacy issues, terms of service of video platforms. We provide detailed description of the CPED construction process and introduce three tasks for conversational AI, including personality recognition, emotion recognition in conversations as well as personalized and emotional conversation generation. Finally, we provide baseline systems for these tasks and consider the function of speakers' personalities and emotions on conversation.</p> <p>Our motivation is to propose a dataset to be widely adopted by the NLP community as a new open benchmark for conversational AI research. The full dataset is available at https://github.com/scutcyr/CPED.</p>