Accurate drone corner position estimation in complex backgrounds with boundary classification.

This study develops an efficient approach for precise channel frame detection in complex backgrounds, addressing the critical need for accurate drone navigation. Leveraging YOLACT and group regression, our method outperforms conventional techniques that rely solely on color information. We conducted extensive experiments involving channel frames placed at various angles and within intricate backgrounds, training the algorithm to effectively recognize them. The process involves initial edge image detection, noise reduction through binarization and erosion, segmentation of channel frame line segments using the Hough Transform algorithm, and subsequent classification via the K-means algorithm. Ultimately, we obtain the regression line segment through linear regression, enabling precise positioning by identifying intersection points. Experimental validations validate the robustness of our approach across diverse angles and challenging backgrounds, making significant advancements in UAV applications.

A Convolutional Neural-Network-Based Training Model to Estimate Actual Distance of Persons in Continuous Images.

Distance and depth detection plays a crucial role in intelligent robotics. It enables drones to understand their working environment to avoid collisions and accidents immediately and is very important in various AI applications. Image-based distance detection usually relies on the correctness of geometric information. However, the geometric features will be lost when the object is rotated or the camera lens image is distorted. This study proposes a training model based on a convolutional neural network, which uses a single-lens camera to estimate humans' distance in continuous images. We can partially restore depth information loss using built-in camera parameters that do not require additional correction. The normalized skeleton feature unit vector has the same characteristics as time series data and can be classified very well using a 1D convolutional neural network. According to our results, the accuracy for the occluded leg image is over 90% at 2 to 3 m, 80% to 90% at 4 m, and 70% at 5 to 6 m.

Electromagnetically-driven integrated microfluidic platform using reverse transcription loop-mediated isothermal amplification for detection of severe acute respiratory syndrome coronavirus 2.

Rapid, sensitive and accurate diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of great need for effective quarantining and treatment. Real-time reverse-transcription polymerase chain reaction requiring thermocyling has been commonly used for diagnosis of SARS-CoV-2 though it may take two to 4 h before lengthy sample pretreatment process and require bulky apparatus and well-trained personnel. Since multiple reverse transcription loop-mediated isothermal amplification (multiple RT-LAMP) process without thermocycling is sensitive, specific and fast, an electromagnetically-driven microfluidic chip (EMC) was developed herein to lyse SARS-CoV-2 viruses, extract their RNAs, and perform qualitative analysis of three marker genes by on-chip multiple RT-LAMP in an automatic format within 82 min at a limit of detection of only ∼5000 copies per reaction (i.e. 200 virus/ µL). This compact EMC may be especially promising for SARS-CoV-2 diagnostics in resource-limited countries.

EpiRank: Modeling Bidirectional Disease Spread in Asymmetric Commuting Networks.

Commuting network flows are generally asymmetrical, with commuting behaviors bi-directionally balanced between home and work locations, and with weekday commutes providing many opportunities for the spread of infectious diseases via direct and indirect physical contact. The authors use a Markov chain model and PageRank-like algorithm to construct a novel algorithm called EpiRank to measure infection risk in a spatially confined commuting network on Taiwan island. Data from the country's 2000 census were used to map epidemic risk distribution as a commuting network function. A daytime parameter was used to integrate forward and backward movement in order to analyze daily commuting patterns. EpiRank algorithm results were tested by comparing calculations with actual disease distributions for the 2009 H1N1 influenza outbreak and enterovirus cases between 2000 and 2008. Results suggest that the bidirectional movement model outperformed models that considered forward or backward direction only in terms of capturing spatial epidemic risk distribution. EpiRank also outperformed models based on network indexes such as PageRank and HITS. According to a sensitivity analysis of the daytime parameter, the backward movement effect is more important than the forward movement effect for understanding a commuting network's disease diffusion structure. Our evidence supports the use of EpiRank as an alternative network measure for analyzing disease diffusion in a commuting network.