Information security is a fundamental and urgent issue in the digital transformation era. Cryptographic techniques and digital signatures have been applied to protect and authenticate relevant information. However, with the advent of quantum computers and quantum algorithms, classical cryptographic techniques have been in danger of collapsing because quantum computers can solve complex problems in polynomial time. Stemming from that risk, researchers worldwide have stepped up research on post-quantum algorithms to resist attack by quantum computers. In this review paper, we survey studies in recent years on post-quantum cryptography (PQC) and provide statistics on the number and content of publications, including a literature overview, detailed explanations of the most common methods so far, current implementation status, implementation comparisons, and discussion on future work. These studies focused on essential public cryptography techniques and digital signature schemes, and the US National Institute of Standards and Technology (NIST) launched a competition to select the best candidate for the expected standard. Recent studies have practically implemented the public key encryption/key encapsulation mechanism (PKE/KEM) and digital signature schemes on different hardware platforms and applied various optimization measures based on other criteria. Along with the increasing number of scientific publications, the recent trend of PQC research is increasingly evident and is the general trend in the cryptography industry. The movement opens up a promising avenue for researchers in public key cryptography and digital signatures, especially on algorithms selected by NIST.

In this letter, a multi-task deep convolutional neural network, namely MoDANet, is proposed to perform modulation classification and DOA estimation simultaneously. In particular, the network architecture is designed with multiple residual modules, which tackle the vanishing gradient problem. The multi-task learning (MTL) efficiency of MoDANet was evaluated with different variants of Y-shaped connection and fine-tuning some hyper-parameters of the deep network. As a result, MoDANet with one shared residual module using more filters, larger filter size, and longer signal length can achieve better performance of modulation classification and DOA estimation, but those might result in higher computational complexity. Therefore, choosing these parameters to attain a good trade-off between accuracy and computational cost is important, especially for resource-constrained devices. The network is investigated with two typical propagation channel models, including Pedestrian A and Vehicular A, to show the effect of those channels on the efficiency of the network. Remarkably, our work is the first DL-based MTL model to handle two unrelated tasks of modulation classification and DOA estimation.

In this paper, we present a new approximation method for non-linear activation functions including tanh and sigmoid functions using stochastic computing (SC) logic based on the piecewise-linear approximation (PWL) for the full range of [-1, 1]. SC implementations with PWL approximation expansions for non-linear functions are based on a 90nm CMOS process. The implementation results shown that the proposed SC circuits can provide better performance compared with the previous methods such as the well-known Maclaurin expansions based, Bernstein polynomial based and finite-state-machine (FSM) based implementations. The implementation results are also presented and discussed.

Detection of vehicles on the road can contribute to the establishment of an intelligent transportation management system to allow smooth transportation and the reduction of road accidents. Thus far, an efficient and low-cost polymer flexible pressure sensor for vehicle detection is lacking. This paper presents a flexible sensor for vehicle sensing and demonstrates a wireless system for monitoring vehicles on the road. A vehicle sensor was fabricated by sandwiching a polyurethane material between aluminum top/bottom electrodes. The sensing mechanism was based on changes in capacitance due to variation in the distance between the two electrodes at an applied external pressure. A clear response against a pressure load of 0.65 Mpa was observed, which is the same pressure as that of the car tire area in contact with the road. Significantly, the sensor was easy to embed on the road line due to its mechanical flexibility and large size. A field test was carried out by embedding the sensor on the road and crossing the sensor with a car. Moreover, the signal displayed on the tablet indicated that the sensing system can be used for wireless detection of the axle, speed, or weight of the vehicle on the road. The findings suggest that the flexible pressure sensor is a promising tool for use as a low-cost vehicle detector in future intelligent transportation management.

Hand gesture recognition is an efficient and practical solution for the non-contact human–machine interaction in smart devices. To date, vision-based methods are widely used in this research area, but they are susceptible to light conditions. To address this issue, radar-based gesture recognition using micro-Doppler signatures can be applied as an alternative. Accordingly, the use of a novel densely convolutional neural network model, Dop-DenseNet, is proposed in this paper for improving hand gesture recognition in terms of classification accuracy and latency. The model was designed with cross or skip connections in a dense architecture so that the former features, which can be lost in the forward-propagation process, can be reused. We evaluated our model with different numbers of filter channels and experimented with it using the Dop-Net dataset, with different time lengths of input data. As a result, it was found that the model with 64 3 × 3 filters and 200 time bins of micro-Doppler spectrogram data could achieve the best performance trade-off, with 99.87% classification accuracy and 3.1 ms latency. In comparison, our model remarkably outperformed the selected state-of-the-art neural networks (GoogLeNet, Res- Net-50, NasNet-Mobile, and MobileNet-V2) using the same Dop-Net dataset.