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采用动态谐波分析方法实现任意组合用电器检测。
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2022年11月10日至13日,第八届中国国际“互联网+”大学生创新创业大赛总决赛举行。我院“和光同乘——智能无线光通信与定位技术领航者”项目团队(本人为核心成员)经过层层角逐,最终成功斩获高教主赛道本科生创意组国赛银奖。本次获奖是取得了苏州大学电子信息学院历届“互联网+大赛”参赛的最好成绩。
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本发明涉及一种自适应调光的可见光通信系统及调制解调方法,包括信源;自适应M‑PAM调制器将信源发出的信号分别调制成第一电信号和第二电信号;双路脉冲产生器,其交替产生第一脉冲控制信号和第二脉冲控制信号,第一脉冲控制信号和第二脉冲控制信号皆为周期性信号;当第一脉冲控制信号的高电平的剩余工作时间等于第二脉冲控制信号的上升沿的时间时,第二脉冲控制信号开始产生;当第二脉冲控制信号的高电平的剩余工作时间等于第一脉冲控制信号的上升沿的时间时,第一脉冲控制信号开始产生。其实现了可见光和红外双路信号的无间隔、无延迟切换,随时可以加载自适应M‑PAM调制信号。
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本实用新型涉及一种基于FPGA的可见光红外交替通信发射装置,包括输入接口、FPGA单元、数模转换单元和光发射单元;所述FPGA单元与输入接口连接;FPGA单元发出第一脉冲控制信号和第二脉冲控制信号,所述第一脉冲控制信号与第二脉冲控制信号交替产生;所述光发射单元包括可见光发射电路和红外光发射电路;所述第一脉冲控制信号与可见光发射电路通信连接,所述第二脉冲控制信号与所述红外光发射电路通信连接;所述数模转换单元向可见光发射电路和红外光发射电路中输入调制信号。其构建了与可见光互补的红外通信发射装置,可以在一个PWM调光周期内实现可见光和红外的交替传输,提升现有可见光通信系统的时间利用效率。
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本发明公开了一种无线光通信数据传输装置及方法,包括发射模块和接收模块;所述发射模块包括传输门,所述发射模块用于将串行数据转换为多路控制信号控制传输门输出电信号,并将电信号转化为光信号发射至接收模块;所述接收模块包括峰值检测器和比较器组,所述接收模块用于将接收的光信号转换为电信号并经过比较器组的阈值判断后输出串行数据,其中,所述峰值检测器根据接收到的电信号为比较器组提供参考电压,比较器组根据参考电压进行分压确定阈值。本发明能够不受距离传输环境限制,增强无线光通信的稳定性和移动性,并能够通过高阶PAM提高带宽利用率.
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Published in Optics Express, OSA, 2021
Cognitive visible light communication (VLC) has attracted increasing attention. By sharing underutilized VLC spectrum resources of primary users (PUs) with secondary users (SUs) opportunistically, improved spectrum utilization can be achieved without interfering with PUs. As an essential component in cognitive VLC, reliable spectrum sensing is crucial to ensure accurate cognition of PU’s signal. However, due to limiting factors such as low signal-to-noise ratio (SNR) and link blocking in VLC systems, it would be difficult for a single SU to identify the status of PUs accurately and rapidly. To tackle this issue, we propose a new collaborative sensing (CS) scheme which can enhance sensing accuracy effectively by coordinating multiple SUs to participate in spectrum sensing.
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Published in Optics Letters, OSA, 2022
In this Letter, we propose a flexible bandwidth compression scheme for visible light communication (VLC) systems employing multi-band carrierless amplitude and phase (CAP) modulation.
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Published in Preprint, 2024
Temporal misalignment (time offset) between sensors is common in low cost visual-inertial odometry (VIO) systems. Such temporal misalignment introduces inconsistent constraints for state estimation, leading to a significant positioning drift especially in high dynamic motion scenarios. In this article, we focus on online temporal calibration to reduce the positioning drift caused by the time offset for high dynamic motion VIO. For the time offset observation model, most existing methods rely on accurate state estimation or stable visual tracking. For the prediction model, current methods oversimplify the time offset as a constant value with white Gaussian noise. However, these ideal conditions are seldom satisfied in real high dynamic scenarios, resulting in the poor performance. In this paper, we introduce online time offset modeling networks (TON) to enhance real-time temporal calibration. TON improves the accuracy of time offset observation and prediction modeling. Specifically, for observation modeling, we propose feature velocity observation networks to enhance velocity computation for features in unstable visual tracking conditions. For prediction modeling, we present time offset prediction networks to learn its evolution pattern. To highlight the effectiveness of our method, we integrate the proposed TON into both optimization-based and filter-based VIO systems. Simulation and real-world experiments are conducted to demonstrate the enhanced performance of our approach. Additionally, to contribute to the VIO community, we will open-source the code of our method on: https://github.com/Franky-X/FVON-TPN.
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