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In recent years, with the continuous derivative of the Internet of Things (IoT) business, the use of various types of information sensing equipment needs to continue to increase. However, power supply conditions are getting tougher. On the other hand, as the function of the devices itself increases, the power consumption also increases, and the lack of energy demand is more obvious. To solve these problems, a dual-frequency RF energy harvesting system is designed in this paper. First of all, in order to expand the effective receiving range of RF energy, this paper designed a 920 MHz hexagonal windmill circular-polarized antenna, and added an energy receiving circuit in the 2.45 GHz band on this basis, which fully alleviated the problem of insufficient energy collection for small devices in the Internet of Things while ensuring a small volume wavelength ratio. In addition, in order to improve the energy conversion efficiency, this paper also designed a dual-frequency rectifier circuit based on conjugate separation matching, using the harmonic signal of 920 MHz signal to broaden the bandwidth of 2.45 GHz. The experimental results show that the peak conversion rates of 920 MHz and 2.45 GHz channels are 72.8%@0dBm and 38.7%@-2dBm, respectively. Overall testing in a Wireless Fidelity (Wi-Fi) room with a commercial Radio Frequency Identification (RFID) reader. The experimental results show that the system can steadily output 3.29 V–3.3 V voltage within 6 m charging distance, which is 20% higher than the output voltage of the single-channel receiving system, and the receiving angle is 36.84% larger. [ABSTRACT FROM AUTHOR]

The accurate monitoring and management of dairy cattle behavior are critical for improving farm productivity as well as animal welfare and health status. In this paper, we present a self-powered dairy-cattle-behavior monitoring system that harnesses 915 MHz radio-frequency (RF) energy harvesting and bidirectional long short-term memory (Bi-LSTM) networks. The system aims to enable continuous and real-time monitoring of cattle behaviors while eliminating the need for battery replacements. By harvesting RF energy from the surrounding electromagnetic radiation, our system achieves long-term, self-sustainable operation, reducing maintenance efforts and costs. The Bi-LSTM network effectively captures the temporal dependencies and patterns in the collected sensor data, enabling accurate behavior recognition and prediction. Experimental results demonstrate the effectiveness of the proposed system in accurately classifying cattle behaviors, with an overall accuracy of 96.79%. Compared with traditional manual observation methods and battery-dependent systems, our self-powered monitoring system offers enhanced automation, improved welfare monitoring, and increased operational efficiency. The combination of RF energy harvesting, and Bi-LSTM networks affords a promising approach for self-powered and intelligent dairy-cattle-behavior monitoring, facilitating optimized management practices in the dairy industry.