Updates & Feature Requests

The Internet of Things (IoT) refers to the network of interconnected devices that communicate and share data over the internet. These devices range from everyday household appliances like refrigerators and thermostats to complex industrial machines. IoT enables devices to collect, transmit, and analyze data in real time, leading to smarter decision-making and automation. By embedding sensors and connectivity features into objects, IoT creates a seamless flow of information that can enhance efficiency, safety, and convenience across various sectors. This interconnected environment transforms traditional systems, making them more responsive and adaptable. As IoT technology advances, it opens up new opportunities for innovation in healthcare, transportation, agriculture, and smart cities. Its ability to connect physical objects to digital systems is revolutionizing how we live, work, and interact with our environment.

How Does IoT Work?

IoT operates through a combination of sensors, connectivity, data processing, and user interface. Devices equipped with sensors collect…

An Automotive Smart Antenna is an advanced in-vehicle communication system designed to enhance signal reception and transmission for GPS, cellular, Wi-Fi, Bluetooth, and V2X technologies.

The GPU version of the model is very inefficent at running 1D (horizontal) transects - there are simply not enough grid points to make good use of 1,000's of GPU cores. However, the GPU is efficient at running many 100's to thousands of transects at once. In this case, effectively, we are running a 2D simulation, where the x-dimension is the same (the distance along the transect), but the y-dimension is the "transect dimension." So, for example, y=1 is the first transect, y=2 the second, and so on. The physics model is reduced to 1D, so there is no hydrodynamic information being passed along the "transect dimension." This should allow for thousands of 1D transects to be run in near real time, the output of which could then be used to mine statistics, understand parameter sensitivity / uncertainity, etc. with a large number of transect samples. For example, lets say I…

You can now run simulations using the fully nonlinear extended Boussinesq equations. The implementation follows the equation and numerical model presented in Kim et al., 2009, minus the horizontal rotational terms. The equations are the conservative form of the Wei et al. model, also presented in Shi et al, 2012. The numerical scheme is a hybrid finite-volume / finite-difference approach, with leading order terms solved in a similar way as the standard Celeris solver, but using a 4th-order MUSCL-TVD scheme and an HLLEM flux solver, with a 4th order predictor-corrector time stepping integration. The implementation is essentially the finite-volume version of COULWAVE - very similiar to FUNWAVE-TVD. Tests show accuracy similar to these models.

The fully nonlinear / high order model should be considered experimental, and is likely not as stable / robust as the standard Celeris solver, particularly when interacting with the simulation (changing depth on the fly, etc.). The…