Vehicle-Type Recognition Method for Images Based on Improved Faster R-CNN Model.

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations.

An image caption model based on attention mechanism and deep reinforcement learning.

Image caption technology aims to convert visual features of images, extracted by computers, into meaningful semantic information. Therefore, the computers can generate text descriptions that resemble human perception, enabling tasks such as image classification, retrieval, and analysis. In recent years, the performance of image caption has been significantly enhanced with the introduction of encoder-decoder architecture in machine translation and the utilization of deep neural networks.

Endoscopic image classification algorithm based on Poolformer.

Image desmoking is a significant aspect of endoscopic image processing, effectively mitigating visual field obstructions without the need for additional surgical interventions. However, current smoke removal techniques tend to apply comprehensive video enhancement to all frames, encompassing both smoke-free and smoke-affected images, which not only escalates computational costs but also introduces potential noise during the enhancement of smoke-free images. In response to this challenge, this paper introduces an approach for classifying images that contain surgical smoke within endoscopic scenes.

A data security scheme based on EEG characteristics for body area networks.

Body area network (BAN) is a body-centered network of wireless wearable devices. As the basic technology of telemedicine service, BAN has aroused an immense interest in academia and the industry and provides a new technical method to solve the problems that exist in the field of medicine. However, guaranteeing full proof security of BAN during practical applications has become a technical issue that hinders the further development of BAN technology.

Quantitative analysis of heart rate variability parameter and mental stress index.

Background: Cardiovascular disease not only occurs in the elderly but also tends to become a common social health problem. Considering the fast pace of modern life, quantified heart rate variability (HRV) indicators combined with the convenience of wearable devices are of great significance for intelligent telemedicine. To quantify the changes in human mental state, this article proposes an improved differential threshold algorithm for R-wave detection and recognition of electrocardiogram (ECG) signals.

A Facile Synthesis of NiFe-Layered Double Hydroxide and Mixed Metal Oxide with Excellent Microwave Absorption Properties.

Microwave-absorbing materials have attracted increased research interest in recent years because of their core roles in the fields of electromagnetic (EM) pollution precaution and information security. In this paper, microwave-absorbing material NiFe-layered double hydroxide (NiFe-LDH) was synthesized by a simple co-precipitation method and calcined for the fabrication of NiFe-mixed metal oxide (NiFe-MMO). The phase structure and micromorphology of the NiFe-LDH and NiFe-MMO were analyzed, and their microwave-absorbing properties were investigated with a vector network analyzer in 2-18 GHz.