M-of-N threshold gates with hysteresis form a class of circuit elements that have important application in NULL Convention Logic ", a novel asynchronous logic design methodology. General design guidelines for these M-of-N gates are presented using CMOS technology. Three types of circuit implementations are discussed: static, semi-static and dynamic. In addition, initialization techniques are presented for use in establishing a known initial state.

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Intrabody communication has been of great research interest in recent years. This paper proposes a novel, compact but accurate body transmission channel model based on RC distribution networks and transmission line theory. The comparison between simulation and measurement results indicates that the proposed approach accurately models the body channel characteristics. In addition, the impedance-matching networks at the transmitter output and the receiver input further maximize the power transferred to the receiver, relax the receiver complexity, and increase the transmission performance. Based on the simulation results, the power gain can be increased by up to 16 dB after matching. A binary phase-shift keying modulation scheme is also used to evaluate the bit-error-rate improvement.

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Hybrid analog/digital processing is crucial for millimeter-wave (mmWave) MIMO systems, due to its ability to balance the gain and cost. Despite fruitful recent studies, the optimal beamforming/combining method remains unknown for a practical multiuser, broadband mmWave MIMO equipped with low-resolution phase shifters and low-resolution analog-to-digital converters (ADCs).In this paper, we leverage artificial intelligence techniques to tackle this problem. Particularly, we propose a neural hybrid beamforming/combining (NHB) MIMO system, where the various types of hybrid analog/digital mmWave MIMO systems are transformed into a corresponding autoencoder (AE) based neural networks. Consequently, the digital and analog beamformers/combiners are obtained by training the AE based new model in an unsupervised learning manner, regardless of particular configurations. Using this approach, we can apply amachine learning-based design methodology that is compatible with a range of different beamforming/combing architectures. We also propose an iterative training strategy for the neural network parameter updating, which can effective guarantee fast convergence of the established NHB model. According to simulation results, the proposed NHB can offer a significant performance gain over existing methods in term of bit

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