We developed IoT & Machine Learning based ALPR
Route-finding is a natural branch of computer science. What we’re really talking about is graph theory, a field that’s been kicking out algorithms since well before electronic computers even existed. The problem is that routing algorithms can be extraordinarily complex for even small numbers of possible paths and small numbers of actors. Traffic, however, deals with a whole lot of routes and a whole lot of cars (and other "agents," such as traffic lights).
Machine-Centric Network With the ongoing transition from a human-centric network to a machine-centric network filled with sensors, bots, robots, drones and smart objects where every object is network attached, we face unprecedented challenges in scaling, securing, managing and optimising networks. Since there is no bound to the number of machines, or the service requirements of individual machine classes, tomorrow’s networks will need to hyper-scale to support billions of nodes while supporting ultra-dynamic and diverse workloads from the vast pool of machines ranging from dumb nodes with minimal requirements to sophisticated endpoints with stringent, real-time demands
Key Points: Data Driven Optimisation Applications based on ML algorithms Self Learning Networks Parking Applications based on IoT solutions
The capacity to simulate induction loop sensors. In the real world, these loops are generally installed under streets to provide nearby traffic lights with information about cars passing above them. In our simulation, place induction loops in each lane before every traffic lights. These induction loops inform their respective traffic lights with the number of cars that have passed over them in the last time step, along with the average speed of these cars and also provide each stoplight with the previous five time steps worth of sensor information.
This can point out that each stop light with the previous five time steps worth of its phases. A phase in this context refers to the specific permutation of lights colours for each lane in the intersection. Each phase is represented by a number in the feature array. Features from Adjacent Traffic Lights: In order for each traffic light agent to learn to coordinate with the other agents, provide each agent with the features given to the adjacent stoplights.. The gradient value is multiplied by the feature value during back propagation, so features with high values get high weights during training. And since they had high values to begin with, their influence is increased quadratically in the final regression value.