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The advancement of smart cities and intelligent industries has accentuated the necessity for reliable indoor communication and precise indoor positioning. In response, this paper presents an innovative integrated system for indoor visible light communication and positioning, designed to facilitate robust communication and pinpoint accuracy in positioning for indoor applications. We incorporate multiband carrierless amplitude and phase (m $m$CAP) modulation to mitigate interference between communication and positioning subcarriers. Unlike the conventional orthogonal frequency division multiplexing‐based subcarrier modulation (OFDM‐SCM), m $m$CAP demonstrates reduced out‐of‐band interference and a diminished peak‐to‐average power ratio (PAPR). Moreover, we introduce the frequency domain optical zero correlation zone (FD‐OZCZ) code, integrated within the m $m$CAP signal to optimize the distribution of positioning subcarriers. Simulation outcomes substantiate that our proposed m $m$CAP, augmented with FD‐OZCZ, outperforms OFDM‐SCM systems in delivering reliable communication and exceptional positioning accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a positioning algorithm based on the combination of K-means clustering and deep neural networks (DNNs) is first presented for multiple light emitting diodes (LEDs) integrated with visible light positioning (VLP) systems. We extracted the maximum value from the collected optical power of LEDs, utilizing the ratio of each optical power to this maximum optical power as the input training data. The experimental results demonstrate that the proposed algorithm outperformed the conventional DNN algorithm in terms of anti-jamming capability and positioning accuracy. In addition, the positioning accuracy of the proposed system reached a millimeter level, which is the highest experimental VLP accuracy, to the best of our knowledge. [ABSTRACT FROM AUTHOR]

AbstractThis article aims to address the issue of low recognition accuracy in existing sorting robots caused by lighting, occlusion, and environmental factors. A fruit recognition method based on a flexible tactile sensor array is described. This method enables the robot to directly perceive the attributes of the objects and identify fruits using a flexible gripper, facilitating intelligent sorting. A novel flexible tactile sensor array is utilized to construct a flexible hand tactile information acquisition platform, which collects tactile time series data for the fruits. Principal component analysis is then employed for dimensionality reduction, followed by the development of an improved particle swarm optimization for the support vector machine model. Through an experimental study, the optimized model is compared with four other models, demonstrating better classification performance. The optimized model achieves an average tactile classification accuracy of up to 98.10% for the five types of fruits. A comparison between the improved optimization algorithm, genetic algorithm, and grid search algorithm reveals the superior optimization performance of the new approach. In the future, this method is expected to be implemented in industrial sorting robots for intelligent sorting on automatic production lines. Furthermore, the algorithm will be further refined to enhance the classification accuracy and efficiency. [ABSTRACT FROM AUTHOR]