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. However, several challenges still persist in this domain. Therefore, this paper proposes a novel method to address the issue of visual information loss and non-dynamic adjustment of input images during decoding. We introduce a guided decoding network that establishes a connection between the encoding and decoding parts. Through this connection, encoding information can provide guidance to the decoding process, facilitating automatic adjustment of the decoding information. In addition, Dense Convolutional Network (DenseNet) and Multiple Instance Learning (MIL) are adopted in the image encoder, and Nested Long Short-Term Memory (NLSTM) is utilized as the decoder to enhance the extraction and parsing capability of image information during the encoding and decoding process. In order to further improve the performance of our image caption model, this study incorporates an attention mechanism to focus details and constructs a double-layer decoding structure, which facilitates the enhancement of the model in terms of providing more detailed descriptions and enriched semantic information. Furthermore, the Deep Reinforcement Learning (DRL) method is employed to train the model by directly optimizing the identical set of evaluation indexes, which solves the problem of inconsistent training and evaluation standards. Finally, the model is trained and tested on MS COCO and Flickr 30 k datasets, and the results show that the model has improved compared with commonly used models in the evaluation indicators such as BLEU, METEOR and CIDEr.

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. This classification method provides crucial target frame information for enhancing surgical smoke removal, improving the scientific robustness, and enhancing the real-time processing capabilities of image-based smoke removal method. The proposed endoscopic smoke image classification algorithm based on the improved Poolformer model, augments the model's capacity for endoscopic image feature extraction. This enhancement is achieved by transforming the Token Mixer within the encoder into a multi-branch structure akin to ConvNeXt, a pure convolutional neural network. Moreover, the conversion to a single-path topology during the prediction phase elevates processing speed. Experiments use the endoscopic dataset sourced from the Hamlyn Centre Laparoscopic/Endoscopic Video Dataset, augmented by Blender software rendering. The dataset comprises 3,800 training images and 1,200 test images, distributed in a 4:1 ratio of smoke-free to smoke-containing images. The outcomes affirm the superior performance of this paper's approach across multiple parameters. Comparative assessments against existing models, such as mobilenet_v3, efficientnet_b7, and ViT-B/16, substantiate that the proposed method excels in accuracy, sensitivity, and inference speed. Notably, when contrasted with the Poolformer_s12 network, the proposed method achieves a 2.3% enhancement in accuracy, an 8.2% boost in sensitivity, while incurring a mere 6.4 frames per second reduction in processing speed, maintaining 87 frames per second. The results authenticate the improved performance of the refined Poolformer model in endoscopic smoke image classification tasks. This advancement presents a lightweight yet effective solution for the automatic detection of smoke-containing images in endoscopy. This approach strikes a balance between the accuracy and real-time processing requirements of endoscopic image analysis, offering valuable insights for targeted desmoking process.

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. In this article, we propose a data encryption method based on electroencephalogram (EEG) characteristic values and linear feedback shift register (LFSR) to solve the problem of data security in BAN. First, the characteristics of human EEG signals were extracted based on the wavelet packet transform method and as the MD5 input data to ensure its randomness. Then, an LFSR stream key generator was adopted. The 128-bit initial key obtained through the message-digest algorithm 5 (MD5) was used to generate the stream key for BAN data encryption. Finally, the effectiveness of the proposed security scheme was verified by various experimental evaluations. The experimental results showed that the correlation coefficient of data before and after encryption was very low, and it was difficult for the attacker to obtain the statistical features of the plaintext. Therefore, the EEG-based security scheme proposed in this article presents the advantages of high randomness and low computational complexity for BAN systems.

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. Methods: HRV is a specific quantitative indicator of autonomic nerve regulation of the heart. The recognition rate is increased by improving the starting position of R wave and the time-window function of the traditional differential threshold method. The experimental platform is a wearable sign monitoring system constructed based on body area networks (BAN) technology. Analytic hierarchy process (AHP) is used to construct the mental stress assessment model, the weight judgment matrix is constructed according to the influence degree of HRV analysis parameters on mental stress, and the consistency check is carried out to obtain the weight value of the corresponding HRV analysis parameters. Results: Experimental results show that the recognition rate of R wave of real-time 5 min ECG data collected by this algorithm is >99%. The comprehensive index of HRV based on weight matrix can greatly reduce the deviation caused by the measurement error of each parameter. Compared with traditional characteristic wave recognition algorithms, the proposed algorithm simplifies the process, has high real-time performance, and is suitable for wearable analysis devices with low-configuration requirements. Conclusion: Our algorithm can describe the mental stress of the body quantitatively and meet the requirements of application demonstration.

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. Both NiFe-LDH and NiFe-MMO possessed abundant interfaces and a low dielectric constant, which were beneficial to electromagnetic wave absorption, owing to the synergistic effect of multi-relaxation and impedance matching. The optimum reflection loss (RL) of NiFe-LDH and NiFe-MMO was -58.8 dB and -64.4 dB, respectively, with the thickness of 4.0 mm in the C band. This work demonstrates that LDH-based materials have a potential application in electromagnetic wave absorption.