Road traffic sign detection and recognition play an important role in advanced driver assistance systems (ADAS) by providing real-time road sign perception information. In this paper, we propose an improved (Single Shot Detector) SSD algorithm via multi-feature fusion and enhancement, named MF-SSD, for traffic sign recognition. First, low-level features are fused into high-level features to improve the detection performance of small targets in the SSD. We then enhance the features in different channels to detect the target by enhancing effective channel features and suppressing invalid channel features. Our algorithm gets good results in domestic real-time traffic signs. The proposed MF-SSD algorithm is evaluated with the German Traffic Sign Recognition Benchmark (GTSRB) dataset. The experimental results show that the MF-SSD algorithm has advantages in detecting small traffic signs. Compared with existing methods, it achieves higher detection accuracy, better efficiency, and better robustness in complex traffic environment.

Currently, cardiovascular diseases become one of the major threat to human's life. The early prevention of cardiovascular diseases plays a critical role in the healthcare engineering. Point of care monitoring the blood lipid level is capable of making the positive contribution to the prevention of cardiovascular disease. Ubiquitous smartphones paved the way as the flexible and widespread platform for the interaction of various health information. In this manuscript, we report the world's first medical smartphone as an electrochemical analyzer for blood lipid monitoring. Integrating an electrochemical analyzer into a smartphone allows us to measure the current generated by the enzymatic reaction with the total cholesterol test strip. The disposable test strip is used to convert the biochemical signal to electrical signal through the electrochemical reaction. The proposed medical smartphone can provide accurate evaluation of patient's blood lipid level as compared to the clinical biochemical analyzer. The proposed medical smartphone system is a promising platform as a point-of-care device for blood total cholesterol (TC) monitoring, which can be applied for long-term prevention of cardiovascular disease due to its portability, reliability, lower cost, convenience, and internet-based medical data interaction.

Wearable biosensors have attracted considerable attention because of their potential use in real-time monitoring and personalized disease diagnosis. The good deformability and stretchability of flexible electronics enable the elimination of expensive and bulky detection instruments, combination of diagnosis and treatment strategies, modification of traditional clinical practice involving invasive detection methods, and creation of a new platform for personalized wearable medical devices. Using a combination of flexible devices, new sensing mechanisms, and structural design methodologies, continuous physiological health checks can be realized under the premise of noninvasivess; moreover, physiological information obtained from the human body can be transformed into intuitive data and medical staff can realize predictive and objective disease diagnosis. Most of the present reviews have focused on introducing the material composition of flexible sensors. In this study, we initially focus on electronic sensing and subsequently focus on the electronic technology itself. Then, the latest developments associated with flexible and wearable sensing devices are discussed and the sensing of continuous monitoring Is summarized. The mechanism, system structure, typical applications, current challenges, and possible future development associated with the usage of biosensors are presented.

Webshell is a backdoor web page-based program. Malicious attackers obtain some privileges through the Webshell so as to realize the operation and control of the website. However, due to confusion coding technology, Webshell detection becomes difficult. This paper presents a Webshell detection model based on a word attention mechanism. In the model, we mainly focus on intra-line word association. After using Word2vec to vectorize the words, we use GRU (Gated Recursive Unit) and attention mechanism to train and detect the samples. The experimental results show that the model has a high detection rate and low loss function.