As a general and rigid mathematical tool, wavelet theory has found many applications and is constantly developing. This article reviews the development history of wavelet theory, from the construction method to the discussion of wavelet properties. Then it focuses on the design and expansion of wavelet transform. The main models and algorithms of wavelet transform are discussed. The construction of rational wavelet transform (RWT) is provided by examples emphasizing the advantages of RWT over traditional wavelet transform through a review of the literature. The combination of wavelet theory and neural networks is one of the key points of the review. The review covers the evolution of Wavelet Neural Network (WNN), the system architecture and algorithm implementation. The review of the literature indicates the advantages and a clear trend of fast development in WNN that can be combined with existing neural network algorithms. This article also introduces the categories of wavelet-based applications. The advantages of wavelet analysis are summarized in terms of application scenarios with a comparison of results. Through the review, new research challenges and gaps have been clarified, which will serve as a guide for potential wavelet-based applications and new system designs.

With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.

Accurate prediction of a patient's length of stay in the hospital is important to enable efficient and effective management of hospital beds. This paper studies length-of-stay prediction for pediatric patients with respiratory diseases using three decision tree methods: bagging, Adaboost, and random forest. A data set of 11,206 records retrieved from the hospital information system is used for analysis after preprocessing and transformation into a numerical data type using a computation method and an expansion method. Two testing methods, a bisection test and a periodic test, are designed to assess performance. Bagging shows the best results on the bisection test (0.296 RMSE, 0.831 ${R^{2}}$ , and ${0.723 \pe 1}$ ) on the testing set of the whole data test. The performances of the three methods are similar on the periodic test, whereas Adaboost performs slightly better than the other two methods. This study also investigates the importance of various data fields to the prediction of length of stay. The result of the study indicates that decision tree methods can be used for LOS prediction.

The Caenorhabditis elegans (C. elegans) is an ideal model organism for studying human diseases and genetics due to its transparency and suitability for optical imaging. However, manually sorting a large population of C. elegans for experiments is tedious and inefficient. The microfluidic-assisted C. elegans sorting chip is considered a promising platform to address this issue due to its automation and ease of operation. Nevertheless, automated C. elegans sorting with multiple parameters requires efficient identification technology due to the different research demands for worm phenotypes. To improve the efficiency and accuracy of multi-parameter sorting, we developed a deep learning model using You Only Look Once (YOLO)v7 to detect and recognize C. elegans automatically. We used a dataset of 3931 annotated worms in microfluidic chips from various studies. Our model showed higher precision in automated C. elegans identification than YOLOv5 and Faster R-CNN, achieving a mean average precision (mAP) at a 0.5 intersection over a union (mAP@0.5) threshold of 99.56%. Additionally, our model demonstrated good generalization ability, achieving an mAP@0.5 of 94.21% on an external validation set. Our model can efficiently and accurately identify and calculate multiple phenotypes of worms, including size, movement speed, and fluorescence. The multi-parameter identification model can improve sorting efficiency and potentially promote the development of automated and integrated microfluidic platforms.

Human chromosome classification is essential to the clinical diagnosis of cytogenetical diseases such as genetic disorders and cancer. This process, however, is time-consuming and requires specialist knowledge. Considerable efforts have been made to automat the process. Recently, methods based on Convolutional Neural Networks achieved state-of-the-art results on the chromosome classification task. Many studies used karyotype images in performance evaluation, few studies have reported the results of human chromosome classification on microscopical images. This paper proposes a novel method to classify single chromosome images into one of 24 types. In the proposed method an extended ResNet was first devised to extract features of single chromosome images. A label feature vector was then extracted for each of 24 chromosome types based on a validation dataset. Hausdorff distance between feature vector of an input image and each of 24 label feature vectors were calculated, and the label feature vector that has minimum hausdorff distance to the feature vector of the input image was selected as the potential label of the input image. To finally allocate the single chromosomes from a same microscopical image into one of 24 types, a Label Redistribution strategy was used to shrink the label space and to increase the efficiency of chromosome classification. Experiments were implemented with 90,624 single chromosome images, 644 of which were randomly picked to form a testing set in advance. The classification accuracy on microscopical images using our proposed method achieved an accuracy of 94.72%.

Radar-based human activity recognition (HAR) is a popular area of research. Despite claims of high accuracy on self-collected datasets, the robustness of these models under data variations has been overlooked. This article focuses on corruption robustness analysis of radar micro-Doppler spectrogram classification for radar HAR. First, a taxonomy is proposed to classify corruptions into temporal, Doppler, and intensity domains, accompanied by strategies to effectively manage their severity for a balanced evaluation. Second, an analysis framework is presented to assess the robustness of corruption in radar sensing, providing insight into what factors to consider and how to evaluate using a dedicated corruption fmetric. Finally, a benchmarking study evaluates different model architectures and training methods to improve corruption robustness in two radar-based HAR tasks. The results indicate that higher capacity convolutional neural networks (CNNs) show improved classification accuracy, albeit with a risk of overfitting. In particular, adversarial training and data augmentation are identified as effective techniques to improve corruption robustness. However, corruption robustness is not a solved problem for the radar HAR task. Robustness to different types of corruption robustness could be dataset and model-dependent. In essence, our study contributes to a deeper understanding of the complex interplay between model architecture, training methods, and corruption robustness.