An Unmanned Aerial Vehicle Indoor Low-Computation Navigation Method Based on Vision and Deep Learning.

Recently, unmanned aerial vehicles (UAVs) have found extensive indoor applications. In numerous indoor UAV scenarios, navigation paths remain consistent. While many indoor positioning methods offer excellent precision, they often demand significant costs and computational resources. Furthermore, such high functionality can be superfluous for these applications. To address this issue, we present a cost-effective, computationally efficient solution for path following and obstacle avoidance. The UAV employs a down-looking camera for path following and a front-looking camera for obstacle avoidance. This paper refines the carrot casing algorithm for line tracking and introduces our novel line-fitting path-following algorithm (LFPF). Both algorithms competently manage indoor path-following tasks within a constrained field of view. However, the LFPF is superior at adapting to light variations and maintaining a consistent flight speed, maintaining its error margin within ±40 cm in real flight scenarios. For obstacle avoidance, we utilize depth images and YOLOv4-tiny to detect obstacles, subsequently implementing suitable avoidance strategies based on the type and proximity of these obstacles. Real-world tests indicated minimal computational demands, enabling the Nvidia Jetson Nano, an entry-level computing platform, to operate at 23 FPS.

A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification.

BACKGROUND AND OBJECTIVE: The automatic recognition of myocardial infarction (MI) by artificial intelligence (AI) has been an emerging topic of academic research and an existing classification method that can recognize conventional electrocardiogram (ECG) signals with high accuracy. However, they are employed to classify one-dimensional (1-D) ECG signals rather than three-dimensional (3-D) ECG images, and it is limited to provide physicians with significant recommendations to aid in diagnosis like highlighting abnormal leads. Other studies on 3-D ECG images either did not achieve high accuracy or did not employ an inter-patient classification scheme. By proposing a multi-VGG deep neural network, this study aims to develop an automatic classification method for identifying myocardial infarction with inter-patient high accuracy and proper interpretability using 3-D ECG image and a Grad-CAM++ method. METHODS: We apply a multi-VGG deep convolutional neural network to top-view images of 3-D ECG, which are generated from simply denoised standard 12 leads ECG signals for classification. The multi-network method, which separately classifies QRS areas, ST areas, and whole heartbeats, was applied to improve classification performance. Furthermore, the Grad-CAM++ method was used to provide visually interpretable heatmaps for user's attention to improve network interpretability and assist physicians in MI diagnosis RESULTS: The proposed method achieved 95.65% inter-patient accuracy and exactly perfect inner-patient accuracy in the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database experiment. In the PTB-XL diagnostic ECG database, the proposed method achieved 97.23% inter-patient accuracy. The Grad-CAM++ experiment results also showed that the highlighted areas matched the medical diagnosis criteria for myocardial infarction. CONCLUSIONS: Our method demonstrates that 3-D ECG images with AI classification can be efficiently employed for heart disease diagnosis with both high accuracy and visual interpretability.

Sinugyrosanolide A, an unprecedented C-4 norcembranoid, from the Formosan soft coral Sinularia gyrosa.

Chemical investigations on the acetone extract of the Formosan soft coral Sinularia gyrosa have obtained a novel C-4 norcembranoid possessing an unprecedented tricyclo[9.3.0.0(3,8)]tetradecane skeleton, namely sinugyrosanolide A. The NMR spectroscopic data of the novel norcembranoid were completely assigned by using a combination of 2D NMR experiments including (1)H-(1)H COSY, HSQC, HMBC, and NOESY. The cytotoxicities, anti-HCMV (human cytomegalovirus) endonuclease activities and antibacterial activities were evaluated in vitro. It showed moderate cytotoxicity against P-388 (mouse lymphocytic leukemia) cancer cell line with an EC50 of 11.8µM.

Antiviral and anti-inflammatory diterpenoids from the soft coral Sinularia gyrosa.

Chemical investigation of the soft coral Sinularia gyrosa led to the purification of three new diterpenoids, designated as gyrosanols A-C (1-3). The structures of 1-3 were elucidated through extensive spectroscopic analyses. Compounds 1 and 2 exhibited antiviral activity against HCMV with IC(50)'s of 2.6 and 3.7 microM, respectively. In addition, compounds 1 and 2 showed significant anti-inflammatory activity by reducing the levels of the COX-2 protein (19.6 + or - 3.9% and 29.1 + or - 9.6%, respectively) in RAW 264.7 macrophages.

Bioactive norditerpenoids from the soft coral Sinularia gyrosa.

Chemical investigations of the soft coral Sinularia gyrosa resulted in the isolation of six new norcembranolides, gyrosanolides A-F (1-6), a new norcembrane, gyrosanin A (7), and 11 known norditerpenoids 8-18. The structures of the isolated compounds were elucidated through extensive spectroscopic data and by comparison with reported data in the literature. Compounds 1-3, 7-9, 12, and 13 at concentration of 10microM did not inhibit the COX-2 protein expression, but significantly reduced the levels of the iNOS protein (55.2+/-14.6%, 18.6+/-6.7%, 10.6+/-4.6%, 66.9+/-5.2%, 10.2+/-5.1%, 17.4+/-7.2%, 47.2+/-11.9%, and 56.3+/-5.1%, respectively) by LPS stimulation. Compound 8 showed significant antiviral activity against HCMV (human cytomegalovirus) cells with an IC(50) of 1.9microg/mL.