Bin Zhang¹, En-Cheng Liou^2,*, Yi-Chih Tung³, Muhammad Usman^2,4, Chiung-An Chen^2,4, Chao-Shun Yang^2,4

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2551-2571, 2025, DOI:10.32604/cmes.2025.072471 - 26 November 2025

Abstract Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication (ISAC) systems, enabling precise navigation in environments where Global Positioning System (GPS) signals are unavailable. Existing methods, such as map-based navigation or site-specific fingerprinting, often require intensive data collection and lack generalization capability across different buildings, thereby limiting scalability. This study proposes a cross-site, map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge. The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes, facilitating accurate… More >

Wei Zhuang^1,2, Yixian Shen¹, Chunming Gao³, Lu Li¹, Haoran Sang⁴, Fei Qian^5,*

Computer Systems Science and Engineering, Vol.41, No.1, pp. 255-269, 2022, DOI:10.32604/csse.2022.020590 - 08 October 2021

Abstract Since the outbreak of the world-wide novel coronavirus pandemic, crowd counting in public areas, such as in shopping centers and in commercial streets, has gained popularity among public health administrations for preventing the crowds from gathering. In this paper, we propose a novel adaptive method for crowd counting based on Wi-Fi channel state information (CSI) by using common commercial wireless routers. Compared with previous researches on device-free crowd counting, our proposed method is more adaptive to the change of environment and can achieve high accuracy of crowd count estimation. Because the distance between access point More >

Sung-Jung Hsiao¹, Wen-Tsai Sung^2,*

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 729-748, 2022, DOI:10.32604/iasc.2022.020332 - 22 September 2021

Abstract In this study, a multisensory physiological measurement system was built with wireless transmission technology, using a DSPIC30F4011 as the master control center and equipped with physiological signal acquisition modules such as an electrocardiogram module, blood pressure module, blood oxygen concentration module, and respiratory rate module. The physiological data were transmitted wirelessly to Android-based mobile applications via the TCP/IP or Bluetooth serial ports of Wi-Fi. The Android applications displayed the acquired physiological signals in real time and performed a preliminary abnormity diagnosis based on the measured physiological data and built-in index diagnostic data provided by doctors,… More >

Xi Liu, Xinjun Dong, Yang Wang^*, Jacob Dodson, Bryan Joyce

Sound & Vibration, Vol.52, No.3, pp. 6-11, 2018, DOI:10.32604/sv.2018.03857

Abstract This article reports the latest development of a wireless sensing system, named Martlet, on high-g shock acceleration measurement. The Martlet sensing node design is based on a Texas Instruments Piccolo microcontroller, with clock frequency programmable up to 90 MHz. The high clock frequency of the microcontroller enables Martlet to support high-frequency data acquisition and high-speed onboard computation. In addition, the extensible design of the Martlet node conveniently allows incorporation of multiple sensor boards. In this study, a high-g accelerometer interface board is developed to allow Martlet to work with the selected microelectromechanical system (MEMS) high-g accelerometers. Besides low-pass More >