The dataset exemplifies land vehicle targets, tanks, and comprises 1000 time-frequency representation (TFR) images in jpg format with a resolution of 875x656 pixels. Each image is accompanied by labels containing 14 parameters for geometric parameter prediction.

The dataset is generated specifically to simulate the essential components for driving environments in a virtual campus of Chulalongkorn University, including street blocks, various pavements, lane markings, traffic signs, lamp poles, and pedestrians, among other features. We selected this campus for our simulation due to its distinctive road and pavement environments, which are unique to Thailand and other Asian countries. This choice contrasts with many widely cited datasets that predominantly feature environments from European or other regions.

Experimental results and analysis demonstrate that the proposed scheme is secure, reliable, easy to implement, and capable of handling binary, grayscale, and color secret images as well as cover images.The characteristics and effectiveness of the proposed algorithm are demonstrated through experiments and comparative analysis. Specifically, we illustrate its application by considering four cover images that share one secret image, implemented using Matlab 2018 programming. The experimental images are shown , and each size is 128´128.

Chronic wounds pose an ongoing health concern globally, largely due to the prevalence of conditions such as diabetes and leprosy's disease. The standard method of monitoring these wounds involves visual inspection by healthcare professionals, a practice that could present challenges for patients in remote areas with inadequate transportation and healthcare infrastructure. This has led to the development of algorithms designed for the analysis and follow-up of wound images, which perform image-processing tasks such as classification, detection, and segmentation.