Analysis of wavefront coding imaging with cubic phase mask decenter and tilt.

This paper has examined how the decenter and tilt of a cubic phase mask plate influence the imaging of a wavefront coding system. The calculated phase term of pupil function with mask decenter and tilt indicates that both decenter and tilt change the shape of the system modulation transfer function in a predictable way by changing the phase and defocus parameters. Simulation in an on-axis three-mirror Cassegrain system is presented to confirm the calculated formula result. Experimental results for mask decenter are also presented. The results demonstrate that the decenter of a phase mask has less effect on the point spread function in the z direction than the x and y directions.

Prediction of Impulsive Aggression Based on Video Images.

In response to the subjectivity, low accuracy, and high concealment of existing attack behavior prediction methods, a video-based impulsive aggression prediction method that integrates physiological parameters and facial expression information is proposed. This method uses imaging equipment to capture video and facial expression information containing the subject's face and uses imaging photoplethysmography (IPPG) technology to obtain the subject's heart rate variability parameters. Meanwhile, the ResNet-34 expression recognition model was constructed to obtain the subject's facial expression information. Based on the random forest classification model, the physiological parameters and facial expression information obtained are used to predict individual impulsive aggression. Finally, an impulsive aggression induction experiment was designed to verify the method. The experimental results show that the accuracy of this method for predicting the presence or absence of impulsive aggression was 89.39%. This method proves the feasibility of applying physiological parameters and facial expression information to predict impulsive aggression. This article has important theoretical and practical value for exploring new impulsive aggression prediction methods. It also has significance in safety monitoring in special and public places such as prisons and rehabilitation centers.

Multi-Class Wound Classification via High and Low-Frequency Guidance Network.

Wound image classification is a crucial preprocessing step to many intelligent medical systems, e.g., online diagnosis and smart medical. Recently, Convolutional Neural Network (CNN) has been widely applied to the classification of wound images and obtained promising performance to some extent. Unfortunately, it is still challenging to classify multiple wound types due to the complexity and variety of wound images. Existing CNNs usually extract high- and low-frequency features at the same convolutional layer, which inevitably causes information loss and further affects the accuracy of classification. To this end, we propose a novel High and Low-frequency Guidance Network (HLG-Net) for multi-class wound classification. To be specific, HLG-Net contains two branches: High-Frequency Network (HF-Net) and Low-Frequency Network (LF-Net). We employ pre-trained models ResNet and Res2Net as the feature backbone of the HF-Net, which makes the network capture the high-frequency details and texture information of wound images. To extract much low-frequency information, we utilize a Multi-Stream Dilation Convolution Residual Block (MSDCRB) as the backbone of the LF-Net. Moreover, a fusion module is proposed to fully explore informative features at the end of these two separate feature extraction branches, and obtain the final classification result. Extensive experiments demonstrate that HLG-Net can achieve maximum accuracy of 98.00%, 92.11%, and 82.61% in two-class, three-class, and four-class wound image classifications, respectively, which outperforms the previous state-of-the-art methods.

A novel optical readout infrared FPA imaging system with fiber reference channel.

A novel fiber reference optical readout method was proposed in the bi-material micro cantilever infrared imaging system, which consists of an infrared imaging channel, an optical readout channel and a fiber reference channel. The fiber reference channel is used to monitor the intensity fluctuation of the light source, and provide a signal to correct the distortion of the infrared images from the optical readout channel. Comparing with the typical optical readout method without any references, the noise equivalent temperature difference (NETD) of such an infrared imaging system with the fiber reference optical readout method can be reduced by about 33% and edges of the IR images become clearer.

Imaging and image restoration of lens-combined modulated wavefront coding.

In this paper, it presents a new wavefront coding (WFC) technology that we call lens-combined modulated wavefront coding (LM-WFC). Based on the LM-WFC design method, we establish a large depth of field and large aperture system. Simulation results indicate that the modulation transfer function of the system has a defocus invariant characteristic, which all typical WFC systems have, with a large depth of field. Experimental results for comparing the LM-WFC imaging system and a traditional imaging system are presented. The comparative results demonstrate that the LM-WFC system has an imaging performance invariant over a wide depth of field range.