No abstract

Currently, most agricultural products in developing countries are exported to many countries around the world. Therefore, the classification of these products according to different standards is necessary. In Vietnam, dragon fruit is considered as the fruit with the highest export rate. Currently, the classification of dragon fruit is carried manually, lead to low-quality classification high labor costs. Therefore, this study describes an automatic dragon fruit classifying system using non-destructive measurements, based on a convolutional neural network (CNN). This classifying system uses a combination of a model of machine learning and image processing using a convolutional neural network to identify the external features of dragon fruits; the fruits are then classified and evaluated by groups. The dragon fruit is recognized by the system, which extracts the objects combined with the signal obtained from the loadcell to calculate and determine dragon fruit in each group. The training data are collected from the dragon fruit processing system, with a dataset of images obtained from more than 1287 dragon fruits, to train the model. In this system, the classification of the processing speed and accuracy are the two most important factors. The results show that the classification system achieves high efficiency. The system is effective with existing dragon fruit types. In Vietnamese factories, the processing speed of the system increases the sorting capacity of export packing facilities to six times higher than that of the manual method, with an accuracy of more than 96%.

Facial anthropometrics are measurements of human faces and are important figures that are used in many different fields, such as cosmetic surgery, protective gear design, reconstruction, etc. Therefore, the first procedure is to extract facial landmarks, then measurements are carried out by professional devices or based on experience. The aim of this review is to provide an update and review of 3D facial measurements, facial landmarks, and nasal reconstruction literature. The novel methods to detect facial landmarks including non-deep and deep learning are also introduced in this paper. Moreover, the nose is the most attractive part of the face, so nasal reconstruction or rhinoplasty is a matter of concern, and this is a significant challenge. The documents on the use of 3D printing technology as an aid in clinical diagnosis and during rhinoplasty surgery are also surveyed. Although scientific technology development with many algorithms for facial landmarks extraction have been proposed, their application in the medical field is still scarce. Connectivity between studies in different fields is a major challenge today; it opens up opportunities for the development of technology in healthcare. This review consists of the recent literature on 3D measurements, identification of landmarks, particularly in the medical field, and finally, nasal reconstruction technology. It is a helpful reference for researchers in these fields.

The development of robotics is undeniable in recent years. Many developing contries face the growth of the elderly population, it is the premise and impetus for the development of research on humanoid robots to serve humans. Many studies on various aspects of robotics are carried out in different parts of the world. Many novel methods were introduced for the design of the robot’s external appearance, internal mechanisms, and gestures. Recent research on humanoid robots is designed to appear to be copies of the anthropometric indicators of real people, which may affect the security of other people’s identities. Besides, these robots cause a feeling of horror in the user if their appearance is in the position of the uncanny valley. Therefore, these designs need to be carefully considered before fabrication. Artificial skin is studied for various purposes such as ensuring collision safety for industrial users and helping robots to perceive basic tactile sensations. This review consists of the recent literature on the interaction of appearance and behavior of robot interaction, artificial skin, and especially humanoid robots, including appearance, such as android and Geminoid robots. This work can provide a reference for humanoid robot research, including uncanny valley hypotheses, artificial skin, and humanoid robots.

Measuring and labeling human face landmarks are time-consuming jobs that are conducted by experts. Currently, the applications of the Convolutional Neural Network (CNN) for image segmentation and classification have made great progress. The nose is arguably one of the most attractive parts of the human face. Rhinoplasty surgery is increasingly performed in females and also in males since surgery can help to enhance patient satisfaction with the resulting perceived beautiful ratio following the neoclassical proportions. In this study, the CNN model is introduced to extract facial landmarks based on medical theories: it learns the landmarks and recognizes them based on feature extraction during training. The comparison between experiments has proved that the CNN model can detect landmarks depending on desired requirements. Anthropometric measurements are carried out by automatic measurement divided into three images with frontal, lateral, and mental views. Measurements are performed including 12 linear distances and 10 angles. The results of the study were evaluated as satisfactory with a normalized mean error (NME) of 1.05, an average error for linear measurements of 0.508 mm, and 0.498° for angle measurements. Through its results, this study proposed a low-cost automatic anthropometric measurement system with high accuracy and stability.