In the field of stock farming, body dimensions and weight of a yak may reasonably reveal its growing characters, productivity and genetic characteristics. However, it is arduous for the herdsman to manually measure the body and dimension of yaks. Fortunately, with the development of mobile computing and edge devices, it is preferable and possible for the herdsman to estimate the yaks weight and size with handheld devices (e.g., mobile phones). This paper aims at providing machine visual-based yak body height and weight estimate method for edge devices. In our method, to begin with a foreground image of the yak is extracted; and measuring point identification is carried out to identify measuring points of the yak. Then, a ratio of its body dimensions is acquired. Both body dimensions and weight of the yak are acquired through comparison with relevant data. 25 yaks in different age groups were randomly selected from a herd to perform experiments. As indicated by corresponding experimental results, the foreground extraction approach has the potential to generate split images with good boundaries. As for the measuring point identification method selected for the yak, it features preferable accuracy and stability. For example, estimated values of its standing height, body length, chest depth, hipcross height and body weight, average errors between the measured values and them are proven to be 1.95%, 3.11%, 4.91%,3.35% and 7.79% respectively. By contrast to the traditional measuring approaches, the proposed method may improve measurement efficiency and reduce stimulation caused by manual measurement to the yak.

The diagnosis of papillary thyroid carcinoma has always been a concerned and challenging issue and it is very important and meaningful to have a definite diagnosis before the operation. In this study, we tried to use an artificial intelligence algorithm instead of medical statistics to analyze the genetic fingerprint from gene chip results to identify papillary thyroid carcinoma. We trained 20 artificial neural network models with differential genes and other important genes related to the cell metabolic cycle as the list of input features, and apply them to the diagnosis of papillary thyroid cancer in the independent validation data set. The results showed that when we used the DEGs and all genes lists as input features the models got the best diagnostic performance with AUC=98.97% and 99.37% and the accuracy were both 96%. This study revealed that the proposed artificial neural network models constructed with genetic fingerprints could achieve a prediction of papillary thyroid carcinoma. Such models can support clinicians to make more accurate clinical diagnoses. At the same time, it provides a novel idea for the application of artificial intelligence in clinical medicine.

The link alignment in underwater wireless optical communication (UWOC) systems is a knotty problem. The diffractive deep neural network (D 2 NN) has shown great potential in accomplishing tasks all optically these years. In this paper, a 6-layer D 2 NN is proposed to alleviate the link alignment difficulties in UWOC systems. Simulation results demonstrate that the proposed method can focus incident light with tilt angles from 0°to 60°into a 6.25% area of the detection plane with an average focusing efficiency of 93.15%. Extra simulations further reveal that more layers lead to a sustained performance improvement before reaching a bottleneck, and the D 2 NN can achieve large field angle focusing within a certain focusing area. The proposed receiver design, which can be highly integrated with detectors, holds promise to realize reliable link establishment in UWOC systems in the future.

No abstract