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Nowadays, classifying human activities is applied in many essential fields, such as healthcare, security monitoring, and search and rescue missions. Radar sensor-based human activity classification is regarded as a superior approach in comparison to other techniques, such as visual perception-based methodologies and wearable gadgets. However, noise usually exists throughout the process of extracting raw radar signals, decreasing the quality and reliability of the extracted features. This paper presents a novel method for removing white Gaussian noise from raw radar signals using a denoising algorithm before classifying human activities using a deep convolutional neural network (DCNN). Specifically, the denoising algorithm is used as a preprocessing step to remove white Gaussian noise from the input raw radar signal. After that, a lightweight Cross-Residual Convolutional Neural Network (CRCNN) with adaptable cross-residual connections is suggested for classification. The analysis results show that the denoising algorithm with a range-bin interval of 3 and a cut-threshold value of 3 achieves the best denoising effect. When the denoising algorithm was applied to the dataset, CRCNN improved the right classification rate by up to 10% compared to the recognition results achieved with the original noise-added dataset. Additionally, a comparison of the CRCNN with the denoising algorithm solution with six cutting-edge DCNNs was conducted. The experimental results reveal that the proposed model greatly outperforms the others. [ABSTRACT FROM AUTHOR]

For decades, the spatial resolution of conventional far‐field optical imaging has been constrained due to the diffraction limit. The emergence of optical super‐resolution imaging has facilitated biological research in the nanoscale regime. However, the existing super‐resolution modalities are not feasible in many biological applications due to weaknesses, like complex implementation and high cost. Recently, various newly proposed techniques are advantageous in the enhancement of the system resolution, background suppression, and improvement of the hardware complexity so that the above‐mentioned issues could be addressed. Most of these techniques entail the modification of factors, like hardware, light path, fluorescent probe, and algorithm, based on conventional imaging systems. Particularly, subtraction technique is an easily implemented, cost‐effective, and flexible imaging tool which has been applied in widespread utilizations. In this review, the principles, characteristics, advances, and biological applications of these techniques are highlighted in optical super‐resolution modalities. [ABSTRACT FROM AUTHOR]