In order to solve the problem that the emotion recognition rate of single-mode physiological signals is not high in the physiological signals based emotion recognition, in this paper, we propose a convolutional recurrent neural network based method for multi-modal physiological signal emotion recognition task. The method used convolutional neural network to learn the spatial representations of multi-channel EEG signals and the Long Short-term Memory network to learn the temporal representations of peripheral physiological signals (EOG, EMG, GSR, RSP, BVP, and TMP). The two representations are combined for emotion recognition and classification. In the two emotion dimensions of Arousal and Valence, our experiments conducted on the open source dataset DEAP shows that, this method achieve 89.68% and 89.19% average accuracy in the EEG emotion classification, 63.06% and 62.41% average accuracy in the peripheral physiological signal emotion classification, 93.06% and 91.95% average accuracy in the combined feature emotion classification. The experimental results show that the convolutional recurrent neural network based method that we proposed efficiently extract multi-modal physiological signal feature to improve the emotion recognition performance.

The pointer instrument has the advantages of being simple, reliable, stable, easy to maintain, having strong anti-interference properties, and so on, which has long occupied the main position of electrical and electric instruments. Though the pointer instrument structure is simple, it is not convenient for real-time reading of measurements. In this paper, a RK3399 microcomputer was used for real-time intelligent reading of a pointer instrument using a camera. Firstly, a histogram normalization transform algorithm was used to optimize the brightness and enhance the contrast of images; then, the feature recognition algorithm You Only Look Once 3rd (YOLOv3) was used to detect and capture the panel area in images; and Convolutional Neural Networks were used to read and predict the characteristic images. Finally, predicted results were uploaded to a server. The system realized automatic identification, numerical reading, an intelligent online reading of pointer data, which has high feasibility and practical value. The experimental results show that the recognition rate of this system was 98.71% and the reading accuracy was 97.42%. What is more, the system can accurately locate the pointer-type instrument area and read corresponding values with simple operating conditions. This achievement meets the demand of real-time readings for analog instruments.

In recent years, contactless sleep breath detection using WiFi signals has gained significant attention. In this paper, we present a position-free breath detection system that utilizes the channel state information (CSI) from a pair of WiFi devices. To address the “blind spot” issue, we propose a robust detection method that takes advantage of both the amplitude and phase of the CSI ratio, ensuring comprehensive coverage for breath detection. We also utilize the periodicity and variability features to select the most suitable breath pattern from the resulting signals. To mitigate the noise interference in non-ideal sleep positions, we propose a novel PCA-VMD fusion method to fully exploit the complementary advantage of sensing over different TX-RX pairs. In this way, we can extract the fine breath frequency component for the breath rate estimation. Extensive experiments are conducted to demonstrate the superiority of the proposed scheme to the existing state-of-the-arts.

In brain-computer-interface (BCI) devices, signal acquisition via reducing the electrode channels can reduce the computational complexity of models and filter out the irrelevant noise. Differential entropy (DE) plays an important role in emotional components of signals, which can reflect the area activity differences. Therefore, to extract distinctive feature signals and improve the recognition accuracy based on feature signals, a method of DE feature signal recognition based on a Convolutional Gated Recurrent Unit network was proposed in this paper. Firstly, the DE and power spectral density (PSD) of each original signal were mapped to two topographic maps, and the activated channels could be selected in activation modes. Secondly, according to the position of original electrodes, 1D feature signal sequences with four bands were reconstructed into a 3D feature signal matrix, and a radial basis function interpolation was used to fill in zero values. Then, the 3D feature signal matrices were fed into a 2D Convolutional Neural Network (2DCNN) for spatial feature extraction, and the 1D feature signal sequences were fed into a bidirectional Gated Recurrent Unit (BiGRU) network for temporal feature extraction. Finally, the spatial-temporal features were fused by a fully connected layer, and recognition experiments based on DE feature signals at the different time scales were carried out on a DEAP dataset. The experimental results showed that there were different activation modes at different time scales, and the reduction of the electrode channel could achieve a similar accuracy with all channels. The proposed method achieved 87.89% on arousal and 88.69% on valence.

In recent years, the analysis of macro- and micro-expression has drawn the attention of researchers. These expressions provide visual cues to an individual’s emotions, which can be used in a broad range of potential applications such as lie detection and policing. In this paper, we address the challenge of spotting facial macro- and micro-expression from videos and present compelling results by using a deep learning approach to analyze the optical flow features. Unlike other deep learning approaches that are mainly based on Convolutional Neural Networks (CNNs), we propose a Transformer-based deep learning approach that predicts a score indicating the probability of a frame being within an expression interval. In contrast to other Transformer-based models that achieve high performance by being pre-trained on large datasets, our deep learning model, called SL-Swin, which incorporates Shifted Patch Tokenization and Locality Self-Attention into the backbone Swin Transformer network, effectively spots macro- and micro-expressions by being trained from scratch on small-size expression datasets. Our evaluation outcomes surpass the MEGC 2022 spotting baseline result, obtaining an overall F1-score of 0.1366. Additionally, our approach performs well on the MEGC 2021 spotting task, with an overall F1-score of 0.1824 and 0.1357 on the CAS(ME)${}^2$ and SAMM Long Videos, respectively. The code is publicly available on GitHub.

The Internet of Things requires greater attention to the security and privacy of the network. Compared to other public-key cryptosystems, elliptic curve cryptography can provide better security and lower latency with shorter keys, rendering it more suitable for IoT security. This paper presents a high-efficiency and low-delay elliptic curve cryptographic architecture based on the NIST-$p_{256}$ prime field for IoT security applications. A modular square unit utilizes a fast partial Montgomery reduction algorithm, demanding just a mere four clock cycles to complete a modular square operation. The modular square unit can be computed simultaneously with the modular multiplication unit, consequently improving the speed of point multiplication operations. Synthesized on the Xilinx Virtex-7 FPGA platform, the proposed architecture completes one PM operation in 0.08 ms using 23.1 k LUTs at 105.3 MHz. These results show significantly better performance compared to that in previous works.