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Recently, deep learning-assisted communication systems have achieved many eye-catching results and attracted more and more researchers in this emerging field. Instead of completely replacing the functional blocks of communication systems with neural networks, a hybrid manner of BCJRNet symbol detection is proposed to combine the advantages of the BCJR algorithm and neural networks. However, its separate block design not only degrades the system performance but also results in additional hardware complexity. In this work, we propose a BCJR receiver for joint symbol detection and channel decoding. It can simultaneously utilize the trellis diagram and channel state information for a more accurate calculation of branch probability and thus achieve global optimum with 2.3 dB gain over separate block design. Furthermore, a dedicated neural network model is proposed to replace the channel-model-based computation of the BCJR receiver, which can avoid the requirements of perfect CSI and is more robust under CSI uncertainty with 1.0 dB gain.

 

* Abstract is not available.

 

This paper presents a human body communication (HBC) system design able to transmit the real-time data stream of a wearable electrocardiogram (ECG) device to a wearable Photoplethysmogram (PPG) device. The proposed system used conductive fabric to replace standard electrodes for HBC and build a bridge with smart clothing technologies. The transmission of the ECG from the chest to wrist reached an average accuracy of 97.1% tested for three typical cardiovascular monitoring positions. The fabric used to integrate and demonstrate that HBC can be considered for smart clothing technology was made of 50 % silver-50 %. The effective data transfer rate was 468kbps using on-off-keying (OOK) centered at 30MHz, which corresponds to a transmission time of 51µs per ECG channel.

 

* Abstract is not available.

 

This work develops a hybrid ECG/EMG/PPG acquisition system design that supports baseline wandering cancellation (BWC) technologies and lossless data compression (LDC). A fuzzy prediction algorithm (FPA) with a well-known Huffman encoding is used to implement the proposed LDC algorithm and provides a better data compression ratio (CR) to 2.72 on average according to the evaluation of 48 patterns for MIT-BIH arrhythmia database. Additionally, the proposed moving average filter (MAF) design for BWC greatly saves the total number of additions by 97.7% compared with the traditional method. For practical circuit design, the instrumentation amplifier, right-leg driven circuit, band-pass filter, band-stop filter, Atmega328p, Bluetooth 4.0 into a PCB layout of 5×5 cm2 with a lithium battery (1000mAh) which provides 29.1 hours continuous monitoring. Finally, the hardware cost of the proposed design only takes 35.32 USD and is very suitable for future applications.

 

* Abstract is not available.