Environment Animals Wildlife Endangered Species World's largest wildlife crossing nears completion in California The 210-foot-long Wallis Annenberg Wildlife Crossing will help animals safely cross California's famed 101 freeway. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. One of the project's most recent milestones was completion of the concrete deck on the bridge that spans across Agoura Road. This phase of work was the culmination of a complex and multi-agency effort that moved multiple high-voltage power lines and telecommunication lines underground and rerouted and upgraded a regional waterline, all while protecting two natural creeks and preserving dozens of heritage oak trees. To build the foundation of that structure, crews also had to drill and construct dozens of 75-foot-deep concrete pile structures along Agoura Road. California is known for its giant, multi-laned highways bustling under bright blue skies.

Deep learning training training algorithms are a huge success in recent years in many fields including speech, text,image video etc. Deeper and deeper layers are proposed with huge success with resnet structures having around 152 layers. Shallow convolution neural networks(CNN's) are still an active research, where some phenomena are still unexplained. Activation functions used in the network are of utmost importance, as they provide non linearity to the networks. Relu's are the most commonly used activation function.We show a complex piece-wise linear(PWL) activation in the hidden layer. We show that these PWL activations work much better than relu activations in our networks for convolution neural networks and multilayer perceptrons. Result comparison in PyTorch for shallow and deep CNNs are given to further strengthen our case.

With the steady advances in autonomous driving, advanced safety features using one or more sensors are highly desirable. In order to avoid collisions and unintended breaking maneuvers, it is crucial to detect potential road obstacles accurately. Although camera and LiDAR-based object detection have been studied in the literature [1, 2], it's only recently that interest in radar-based object detection using ML methods has begun, primarily because of its low cost, long-range detection capability, and robustness to poor weather conditions. Traditionally, automotive radar-based object detection is performed through peak detection using simple local thresholding methods such as the Constant False-Alarm Rate (CFAR) algorithm [3]. With the breakthroughs of ML in numerous applications [4, 5, 6, 7], radar-based object perception using ML has attracted attention [8, 9, 10, 11, 12, 13].