Computer Vision-Based Traffic Sign Detection and Extraction: A Hybrid Approach Using GIS And Machine Learning

Traffic sign detection and positioning have drawn considerable attention because of the recent development of autonomous driving and intelligent transportation systems. In order to detect and pinpoint traffic signs accurately, this research proposes two methods. In the first method, geo-tagged Google Street View images and road networks were utilized to locate traffic signs. In the second method, both traffic signs categories and locations were identified and extracted from the location-based GoPro video. TensorFlow is the machine learning framework used to implement these two methods. To that end, 363 stop signs were detected and mapped accurately using the first method (Google Street View image-based approach). Then 32 traffic signs were recognized and pinpointed using the second method (GoPro video-based approach) for better location accuracy, within 10 meters. The average distance from the observation points to the 32 ground truth references was 7.78 meters. The advantages of these methods were discussed. GoPro video-based approach has higher location accuracy, while Google Street View image-based approach is more accessible in most major cities around the world. The proposed traffic sign detection workflow can thus extract and locate traffic signs in other cities. For further consideration and development of this research, IMU (Inertial Measurement Unit) and SLAM (Simultaneous Localization and Mapping) methods could be integrated to incorporate more data and improve location prediction accuracy

Evaluating master integrals in non-factorizable corrections to tt-channel single-top production at NNLO QCD

We studied the two-loop non-factorizable Feynman diagrams for the $t$-channel single-top production process in quantum chromodynamics. We present a systematic computation of master integrals of the two-loop Feynman diagrams with one internal massive propagator in which a complete uniform transcendental basis can be built. The master integrals are derived by means of canonical differential equations and uniform transcendental integrals. The results are expressed in the form of Goncharov polylogarithm functions, whose variables are the scalar products of external momenta, as well as the masses of the top quark and the $W$ boson. We also gave a discussion on the diagrams with potential elliptic sectors.Comment: solving the differential equations and perform numerical checks in physical regio

Evaluating master integrals in non-factorizable corrections to t-channel single-top production at NNLO QCD

We studied the two-loop non-factorizable Feynman diagrams for the t-channel single-top production process in quantum chromodynamics. We present a systematic computation of master integrals of the two-loop Feynman diagrams with one internal massive propagator in which a complete uniform transcendental basis can be built. The master integrals are derived by means of canonical differential equations and uniform transcendental integrals. The results are expressed in the form of Goncharov polylogarithm functions, whose variables are the scalar products of external momenta, as well as the masses of the top quark and the W boson. We also gave a discussion on the diagrams with potential elliptic sectors

Exploration of eco-environment and urbanization changes in coastal zones: A case study in China over the past 20 years

Abstract With the rapid development of urbanization and population migration, since the 20th century, the natural and eco-environment of coastal areas have been under tremendous pressure due to the strong interference of human response. To objectively evaluate the coastal eco-environment condition and explore the impact from the urbanization process, this paper, by integrating daytime remote sensing and nighttime remote sensing, carried out a quantitative assessment of the coastal zone of China in 2000–2019 based on Remote Sensing Ecological Index (RSEI) and Comprehensive Nighttime Light Index (CNLI) respectively. The results showed that: 1) the overall eco-environmental conditions in China's coastal zone have shown a trend of improvement, but regional differences still exist; 2) during the study period, the urbanization process of cities continued to advance, especially in seaside cities and prefecture-level cities in Jiangsu and Shandong, which were much higher than the average growth rate; 3) the Coupling Coordination Degree (CCD) between the urbanization and eco-environment in coastal cities is constantly increasing, but the main contribution of environmental improvement comes from non-urbanized areas, and the eco-environment pressure in urbanized areas is still not optimistic. As a large-scale, long-term series of eco-environment and urbanization process change analysis, this study can provide theoretical support for mesoscale development planning, eco-environment condition monitoring and environmental protection policies from decision-makers

Induction of Maternal Immune Activation in Mice at Mid-gestation Stage with Viral Mimic Poly(I:C)

Maternal immune activation (MIA) model is increasingly well appreciated as a rodent model for the environmental risk factor of various psychiatric disorders. Numerous studies have demonstrated that MIA model is able to show face, construct, and predictive validity that are relevant to autism and schizophrenia. To model MIA, investigators often use viral mimic polyinosinic:polycytidylic acid (poly(I:C)) to activate the immune system in pregnant rodents. Generally, the offspring from immune activated dam exhibit behavioral abnormalities and physiological alterations that are associated with autism and schizophrenia. However, poly(I:C) injection with different dosages and at different time points could lead to different outcomes by perturbing brain development at different stages. Here we provide a detailed method of inducing MIA by intraperitoneal (i.p.) injection of 20 mg/kg poly(I:C) at mid-gestational embryonic 12.5 days (E12.5). This method has been shown to induce acute inflammatory response in the maternal-placental-fetal axis, which ultimately results in the brain perturbations and behavioral phenotypes that are associated with autism and schizophrenia