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We developed an integrated workflow for circulating tumor cell (CTC) detection and downstream single‐cell analysis based on a novel ChimeraX®‐i120 platform. The platform facilitates negative enrichment, immunofluorescent labeling, and machine learning‐based identification of CTCs. The CTC captured by the platform is also compatible for single‐cell molecular analysis. In this study, potential utility of our workflow was validated in clinical setting. Circulating tumor cell (CTC) analysis holds great potential to be a noninvasive solution for clinical cancer management. A complete workflow that combined CTC detection and single‐cell molecular analysis is required. We developed the ChimeraX®‐i120 platform to facilitate negative enrichment, immunofluorescent labeling, and machine learning‐based identification of CTCs. Analytical performances were evaluated, and a total of 477 participants were enrolled to validate the clinical feasibility of ChimeraX®‐i120 CTC detection. We analyzed copy number alteration profiles of isolated single cells. The ChimeraX®‐i120 platform had high sensitivity, accuracy, and reproducibility for CTC detection. In clinical samples, an average value of > 60% CTC‐positive rate was found for five cancer types (i.e., liver, biliary duct, breast, colorectal, and lung), while CTCs were rarely identified in blood from healthy donors. In hepatocellular carcinoma patients treated with curative resection, CTC status was significantly associated with tumor characteristics, prognosis, and treatment response (all P < 0.05). Single‐cell sequencing analysis revealed that heterogeneous genomic alteration patterns resided in different cells, patients, and cancers. Our results suggest that the use of this ChimeraX®‐i120 platform and the integrated workflow has validity as a tool for CTC detection and downstream genomic profiling in the clinical setting.

To improve the effect of landslide feature extraction, this paper improves the remote sensing image recognition algorithm with the support of a machine learning algorithm. Moreover, this paper combines UAV remote sensing images to extract landslide features, classifies and introduces the evaluation criteria for target detection and several representative target detectors. This paper also constructs the functional structure of the system according to the landslide feature extraction requirements and designs a set of optimization schemes for landslide feature data collection and control measurement suitable for field operations. In addition, this paper analyses the system kernel algorithm process and analyses the system function realization through simulation research. Finally, this paper designs an experiment to evaluate the practicability of the system constructed in this paper. From the results of experimental statistics, we can see that the system constructed in this paper has good practicability.

In order to improve the target recognition effect of small UAV (unmanned aerial vehicle) remote sensing image, this paper proposes a new super-resolution reconstruction method based on the recurrent convolutional network, which can achieve different degrees of super-resolution effect by controlling the number of cycles. Moreover, it can control the number of iterations of small UAVs with different degrees of blur and can be better adapted to the recognition scenarios of UAVs. In addition, this paper studies the target recognition method of small UAV remote sensing image, combines fuzzy clustering method to construct the intelligent remote sensing image target recognition model, combines it with the UAV structure, realizes remote sensing recognition by UAV, and designs experiments to analyze the effect of remote sensing recognition. Further, this paper improves the recognition algorithm and positioning algorithm of remote sensing image, so that recognition and positioning of UAV video remote sensing image can get better results. Finally, this paper verifies the performance of the system through simulation experiments. The research results show that the method proposed in this paper has certain reliability.

Precise real-time obstacle recognition is both vital to vehicle automation and extremely resource intensive. Current deep-learning based recognition techniques generally reach high recognition accuracy, but require extensive processing power. This study proposes a region of interest extraction method based on the maximum difference method and morphology, and a target recognition solution created with a deep convolutional neural network. In the proposed solution, the central processing unit and graphics processing unit work collaboratively. Compared with traditional deep learning solutions, the proposed solution decreases the complexity of algorithm, and improves both calculation efficiency and recognition accuracy. Overall it achieves a good balance between accuracy and computation.

Aiming at the problem of poor effect of object detection with visible images under low light conditions, the decision-level fusion detection method of visible and infrared images is studied. Taking YOLOX as the object detection network based on deep learning, a decision-level fusion detection algorithm of visible and infrared images based on light sensing is proposed. Experiments are carried out on LLVIP dataset, which is a visible-infrared paired dataset for low light vision. Through comparative analysis, it is found that the decision-level fusion algorithm based on Soft-NMS and light sensing obtained the optimal AP value of 69.0%, which is 11.4% higher than the object detection with visible images and 1.1% higher than the object detection with infrared images. The experimental results show that the decision-level fusion algorithm based on Soft-NMS and light sensing can effectively fuse the complementary information of visible and infrared images, and improve the object detection effect under low light conditions.

Denoising enhances image quality by separating noise from observed signals, eliminating extraneous information while preserving essential features and image integrity. However, existing surveys on conventional denoising techniques often focus solely on processing-domain taxonomies, thereby neglecting evolutionary relationships, overlooking recent advances, and lacking multi-modal exploration. Consequently, modern machine learning pipelines have not fully exploited classical techniques. To advance multi-modal denoising and inspire new learning-based algorithms, this paper presents a comprehensive review of traditional denoising methods, quantitatively assesses their cross-modal transferability, and explores their integration into learning frameworks. Specifically, (1) this work proposes a novel taxonomy for conventional denoising techniques, including domain-based and signal-decomposition-based approaches, provides a systematic analysis of their evolutionary relationships, and investigates recent advances. (2) The study evaluates multi-modal denoising performance by applying baseline methods to infrared images and conducting a comparative analysis. (3) This paper surveys the latest research on traditional approaches, retraces their co-evolution with machine learning, and specifically explores the potential for fusing these techniques within learning-based denoising algorithms. In general, this review serves as a valuable reference for researchers in RGB-infrared denoising, image restoration, and related fields. The advancements in these areas significantly impact various domains, including defect detection in industrial production, worker protection safety recognition, and object tracking in smart transportation.

In ancient times, there was no system to record or document music. A basic notation system to write European music was formulated around 14th century in the Baroque period which slowly evolved into the standard notation system that we have today. Later, the musical pieces from the classical and post-classical period of European music were documented as scores using this standard European staff notations. These notations are used by most of the modern genres of music due to their versatility. Hence, it is very important to develop a method that can store such music sheets containing handwritten music scores digitally. Optical music recognition (OMR) is a system that automatically interprets the scanned handwritten music scores. In this work, we have proposed a classifier ensemble of deep transfer learning models with support vector machine (SVM) as the aggregator for handwritten music symbol recognition. We have applied three pre-trained deep learning models, namely ResNet50, GoogleNet and DenseNet161 (each trained on ImageNet), and fine-tuned on our target datasets i.e., music symbol image datasets. The proposed ensemble technique can capture a more complex association of the base classifiers, thus improving the overall performance. We have evaluated the proposed model on five publicly available standard datasets, namely Handwritten Online Music Symbols (HOMUS), Capitan_Score_Uniform, Capitan_Score_Non-uniform, Rebelo_real and Fornés, and achieved state-of-the-art results for all these datasets. Additionally, we have evaluated our model on publicly available two non-music symbols datasets, namely CMATERdb 2.1.2 containing 120 handwritten Bangla city names and CMATERdb 3.1.1 dataset containing handwritten Bangla numerals to validate its effectiveness on diversified datasets. The source code of this present work is available at https://github.com/ashis0013/Music-Symbol-Recognition .

Object detection is one of the essential tasks in computer vision, with most detection methods relying on a limited number of sizes for anchor boxes. However, the boundaries of particular composite objects, such as ports, highways, and golf courses, are ambiguous in remote sensing images, and therefore, it is challenging for the anchor-based method to accommodate the substantial size variation of the objects. In addition, the dense placement of anchor boxes imbalances the positive and negative samples, which affects the end-to-end architecture of deep learning methods. Hence, this paper proposes a single-stage object detection model named Xnet to address this issue. The proposed method designs a deformable convolution backbone network used in the feature extraction stage. Compared to the standard convolution, it adds learnable parameters for dynamically analyzing the boundary and offset of the receptive field, rendering the model more adaptable to size variations within the same class. Moreover, this paper presents a novel anchor-free detector that classifies objects in feature images point-by-point, without relying on anchor boxes. Several experiments on the large remote sensing dataset DIOR challenging Xnet against other popular methods demonstrate that our method attains the best performance, surpassing by 4.7% on the mAP (mean average precision) metric.

We propose a progressive hierarchical analysis model to perceive the collective activities. Compared with previous activity recognition works, it not only recognizes the collective activities, but also perceives the location and the action category of each individual. At first, we perform the person temporal consistency detection procedure for each individual of the collective activities. A person detection network and conditional random field are used to receive the bounding box sequences of the activity participators. Then, we recognize the individual actions using the learned spatial features and the motion features based on LSTM. At last, the combination of the recognized person-level action category vector, the scene context features and the interaction Context features are used to recognize the collective activities. We evaluate the proposed approach on benchmark collective activity datasets. Extensive experiments demonstrate the effects of the progressive hierarchical analysis model.

Convolutional neural networks have pushed forward image analysis research and computer vision over the last decade, constituting a state-of-the-art approach in object detection today. The design of increasingly deeper and wider architectures has made it possible to achieve unprecedented levels of detection accuracy, albeit at the cost of both a dramatic computational burden and a large memory footprint. In such a context, cloud systems have become a mainstream technological solution due to their tremendous scalability, providing researchers and practitioners with virtually unlimited resources. However, these resources are typically made available as remote services, requiring communication over the network to be accessed, thus compromising the speed of response, availability, and security of the implemented solution. In view of these limitations, the on-device paradigm has emerged as a recent yet widely explored alternative, pursuing more compact and efficient networks to ultimately enable the execution of the derived models directly on resource-constrained client devices. This study provides an up-to-date review of the more relevant scientific research carried out in this vein, circumscribed to the object detection problem. In particular, the paper contributes to the field with a comprehensive architectural overview of both the existing lightweight object detection frameworks targeted to mobile and embedded devices, and the underlying convolutional neural networks that make up their internal structure. More specifically, it addresses the main structural-level strategies used for conceiving the various components of a detection pipeline (i.e., backbone, neck, and head), as well as the most salient techniques proposed for adapting such structures and the resulting architectures to more austere deployment environments. Finally, the study concludes with a discussion of the specific challenges and next steps to be taken to move toward a more convenient accuracy–speed trade-off.