Estimation of Conformation Score in Relation to Body Measurements Using 3D Scanner in Swamp Buffaloes

The objective of this study was to develop the appropriate equations to estimate the conformation score both in male and female swamp buffaloes using body part measurements from 3D scanner. The buffaloes' conformation was evaluated using 3D scanning technique in 72 males and 78 females at Surin, Uthaithanee, Bangkok, Nakornpanom and Sakaew provinces of Thailand. Height (A), heart girth (B), shoulder width (C), iliac width (D), ischial tuberosity width (E), the length between shoulder and ileal wing (F, G), the length between ileal wing to ischial tuberosity (H, I), the length between shoulder to ischial tuberosity (J1, J2), tail length (K), knee circumference (L), the width measuring between the tip (M), the middle (N) and the base of horns (O), the horn length (P) and the length measured from the base to the tip of the horn on the same site (Q) were measured. The results found that A B, D, E, FG, J1J2, L and P were significantly higher along with age in both males and females. The scores obtained currently between academics and the philosophers were closely correlated in every categories in both male and females buffaloes over four and three years of age, respectively, except for the reproductive organ in females. The coefficient of determination (R2) for score prediction in male buffaloes under 4 years old was highest when body length and knee circumference were included in the equation: Score = [(0.568 J1J2) + (1.584 L) - 77.89] (R2 = 0.57, n = 19). The prime factor affecting score in male over 4 years of age was heart girth (R2 = 0.70). However, R2 was rise up to 0.85 when girdle width was included into the equation: Score = [(0.485 B) + (1.892 D) - 156.54] (n = 53). In females under 3 years old, the R2 were low in all type of equation (one traits to four traits equation; 0.25-0.42, n = 21). However, in females over 3 years of age the R2 is high (0.66) when girdle width was included in the equation : Score = [2.655 D - 91.52] (n = 57). Therefore, different traits should be used to evaluate the conformation in immature and mature males and females

Deep neural networks for driver identification using accelerometer signals from smartphones

With the evolution of the onboard communications services and the applications of ride-sharing, there is a growing need to identify the driver. This identification, within a given driver set, helps in tasks of antitheft, autonomous driving,fleet management systems or automobile insurance. The object of this paper is to identify a driver in the least invasive way possible, using the smartphone that the driver carries inside the vehicle in a free position, and using the minimum number of sensors, only with the tri-axial accelerometer signals from the smartphone. For this purpose, different Deep Neural Networks have been tested, such as the ResNet-50 model and Recurrent Neural Networks. For the training, temporal signals of the accelerometers have been transformed asimages. The accuracies obtained have been 69.92% and 90.31% at top-1 andtop-5 driver level respectively, for a group of 25 drivers. These results outper-form works in the state of the art, which can even utilize more signals (likeGPS- Global Positioning System- measurement data) or extra-equipment (like the Controller Area-Network of the vehicle)