For the biomedical transceiver, the data transmission is often asymmetric. At the downlink, the transceiver only needs to receive a simple command to control the operation of the external device, and the receiving data rate is low, about hundreds of Kb/s. However, data collected by external devices such as temperature sensors, pressure sensors, or cameras are often very large, which results in a transmitting data rate of several Mb/s. Therefore, a high energy-efficient modulator is needed. Compared with conventional digital modulator, analog modulator circuits have demonstrated superior energy efficiency at high data rates. This article presents a quasi-digital quadrature phase-shift keying (QPSK) modulator design realized by pure analog circuits which follows a logic design flow. The simulation results show that the system can generate a stable carrier of 64 MHz that meets intra-body communications (IBCs) requirements with a data transmission rate of 10 Mb/s. When the signal-to-noise ratios (SNRs) of the Gaussian channel is 14 dB, it can still maintain a bit error rate (BER) below 10 4 .

Localization of origins of premature ventricular contraction (PVC) is significant in the treatment of ventricular arrhythmia. Existing localization methods usually adopt a heartbeat localization algorithm in which R-peak localization and wavelet transform processes are rather time-consuming. Considering the fact that ECG is usually long and the overall quantity of ECG is large, efficient localization of origins of PVC is desired. In this paper, we propose to use random signal segmentation to process ECG so that the R-peak localization and wavelet transform processes are discarded with improved efficiency. The study included the dataset from 843 patients with spontaneous PVCs, which were clustered and labeled corresponding to 4 regions in the entire ventricle. The dataset contains a total number of 76221 groups, each incorporating 250 sampling points in 12 leads. To obtain the optimal classification model, 75% (sample group = 57166) of the whole dataset is randomly selected for training, while the remaining 25% (sample group = 19055) are selected for testing. The deep residual network (ResNet) is used to estimate the performance of classification methods. Experimental results show that the proposed scheme achieves a speedup of 2.86x while with limited accuracy loss.

In this study, an irregular low-density parity-check (iLDPC) coded rate compatible modulation (iLDPC-RCM) is proposed. A particularly designed interleaver is inserted between LDPC and RCM such that a fast convergence and a low bit-error rate (BER) can be achieved. The combination of strong error correction capability of irregular LDPC code and the seamless adaptation of RCM enables the proposed scheme to achieve a robust and spectrally efficient transmission over time-varying channels. In addition, a joint belief propagation algorithm is proposed to lower the BER of iLDPC-RCM and speed up the convergence of iterative decoding. Simulation results show that the proposed iLDPC-RCM improves the BER and throughput performance while maintaining an acceptable computational complexity at the same time, validating its advantages over the regular LDPC coded RCM with the same coding rate.