Evaluation of the Application Effect of Short Video Combined with BOPPPS Teaching Mode in Clinical Anesthesia Teaching

Objective:To study the application effect of short video combined with BOPPPS teaching mode in clinical anesthesia practice.Method:48 students assigned to clinical anesthesia in digestive endoscopy of Shanxi Bethune Hospital from July 1,2022 to April 1,2023 were selected as research objects.They were randomly divided into the control group(PowerPoint presentation teaching group)and the observation group(short video combined with BOPPPS teaching group),with 24 students in each group.After the internship,the students’theoretical and technical scores were tested,the effects of the two teaching modes were compared,and the students’satisfaction was investigated.Results:The test scores of students in the observation group were significantly better than those in the control group(P<0.05).The short video combined with BOPPPS teaching mode can significantly improve students’learning interest,operation skills,and memory(P<0.05).The students’satisfaction in the observation group was higher than that in the control group(P<0.05).Conclusion:In clinical practice,the application of short video combined with BOPPPS teaching mode has achieved great effect,which is worth further promotion and research.

Dynamic Global-Principal Component Analysis Sparse Representation for Distributed Compressive Video Sampling

Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.

Data Layout and Scheduling Tasks in a Meteorological Cloud Environmen

With the proliferation of the internet,big data continues to grow exponentially,and video has become the largest source.Video big data intro-duces many technological challenges,including compression,storage,trans-mission,analysis,and recognition.The increase in the number of multimedia resources has brought an urgent need to develop intelligent methods to organize and process them.The integration between Semantic link Networks and multimedia resources provides a new prospect for organizing them with their semantics.The tags and surrounding texts of multimedia resources are used to measure their semantic association.Two evaluation methods including clustering and retrieval are performed to measure the semantic relatedness between images accurately and robustly.A Fuzzy Rule-Based Model for Semantic Content Extraction is designed which performs classification with fuzzy rules.The features extracted are trained with the neural network where each network contains several layers among them each layer of neurons is dedicated to measuring the weight towards different semantic events.Each neuron measures its weight according to different features like shape,size,direction,speed,and other features.The object is identified by subtracting the background features and trained to detect based on the features like size,shape,and direction.The weight measurement is performed according to the fuzzy rules and based on the weight measures.These frameworks enhance the video analytics feature and help in video surveillance systems with better accuracy and precision.

3DV quality model based depth maps for view synthesis in FTV system

Depth maps are used for synthesis virtual view in free-viewpoint television （FTV） systems. When depth maps are derived using existing depth estimation methods, the depth distortions will cause undesirable artifacts in the synthesized views. To solve this problem, a 3D video quality model base depth maps （D-3DV） for virtual view synthesis and depth map coding in the FTV applications is proposed. First, the relationships between distortions in coded depth map and rendered view are derived. Then, a precisely 3DV quality model based depth characteristics is develop for the synthesized virtual views. Finally, based on D-3DV model, a multilateral filtering is applied as a pre-processed filter to reduce rendering artifacts. The experimental results evaluated by objective and subjective methods indicate that the proposed D-3DV model can reduce bit-rate of depth coding and achieve better rendering quality.