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In this paper, we investigate a multitone simultaneous wireless information and power transfer (SWIPT) based modulation scheme for battery-less Internet of Things (IoT) nodes that works in the ultra high frequency (UHF) region. The conventional SWIPT system is assumed to have power-consuming communication modules. Having such modules on to the IoT nodes whose power is harvested from radio frequency (RF) power sources is too unrealistic. In addition, waveform design from the aspect of power harvesting through SWIPT still has room for consideration. Recent studies have explored multitone based SWIPT to increase the power conversion efficiency (PCE). In these schemes, information is modulated by changing each tone's nature or varying the number of tones. Among these methods, we focused on modulation schemes known as frequency-shift multitone based SWIPT, which shifts frequencies among the tones for information encoding. Unlike previously proposed methods where demodulation requires some power-consuming fast Fourier transfers (FFTs), especially under small communication bandwidths, we applied a signal detection method by measuring output peak to average power ratios (PAPRs) for frequency-shift multitone based SWIPT to reduce power consumption. Based on our analysis, different tone configurations in the frequency domain would yield varieties of nonlinear outputs during the rectification process. In addition, these specific nonlinear output patterns depend on the tone configurations. With this feature, it is possible to demodulate in the time domain at the receiver side using PAPR based measurements, which could eliminate FFT operation. This paper describes how measuring PAPRs enables the detection of signals in theory and validates this through simulations and experiments. We also estimate the communication rates. Based on our results, we achieved 0.46 bits/s/Hz when the number of tones was 6 and estimated that there were $(N-1)(N-2)/2 + 1$ different PAPRs from a given multitone waveform whose number of tones was $N$ .

Causal discovery is a methodology for learning causal graphs from data, and LiNGAM is a well-known model for causal discovery. This paper describes an open-source Python package for causal discovery based on LiNGAM. The package implements various LiNGAM methods under different settings like time series cases, multiple-group cases, mixed data cases, and hidden common cause cases, in addition to evaluation of statistical reliability and model assumptions. The source code is freely available under the MIT license at https://github.com/cdt15/lingam. [ABSTRACT FROM AUTHOR]