non-type template parameter74 Conclusions Zero-cost abstractions Encapsulation Inheritance Dynamic Polymorphism Static Polymorphism Negative-cost abstractions More C++ for the embedded world! Architecture

to Robotics) ● AI/ML/CV Software for ROS 2.0 ○ Object detection/segmentation/tracking/velocity estimation & etc. ○ A ROS service to support Intel® OpenVINO™ - the Open Visual Inference & Neural Network com/performance. •Cost reduction scenarios described are intended as examples of how a given Intel-based product, in the specified circumstances and configurations, may affect future costs and provide cost savings savings. Circumstances will vary. Intel does not guarantee any costs or cost reduction. •This document contains information on products, services and/or processes in development. All information provided

call_command', 'clearsessions', schedule_type='H') Groups A group example with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s Group example with Parzen-window estimation import numpy from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

call_command', 'clearsessions', schedule_type='H') Groups A group example with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s Group example with Parzen-window estimation import numpy from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'