Jeff Shepard

2 weeks ago | eeworldonline.com | Jeff Shepard

A 5G core (5GC) provides the central control and management for a 5G network. It roughly corresponds with the Evolved Packet Core (EPC) that supports converged voice and data services on a 4G Long-Term Evolution (LTE) network. The 4G EPC is a flat architecture using point-to-point connectivity and is limited in scalability. The 5GC is cloud native and is a service-based architecture (SBA). The use of the cloud supports independent network functions (NFs) in the form of containerized microservices.

2 weeks ago | eeworldonline.com | Jeff Shepard

Maneuverability and guidance systems, not just raw speed, are key differentiators for hypersonic missiles. That requires aerodynamic pressure sensors, optical sensors, inertial sensors, space-based tracking, infrared sensors, and more. Sensor fusion combines them into a single guidance system. This article reviews the sensor types for hypersonic guidance, navigation, and target acquisition systems. Hypersonic guidance systems and associated sensors are an evolving technology.

3 weeks ago | eeworldonline.com | Jeff Shepard

The 5G protocol stack is the architecture of protocols within a 5G network that perform specific functions like managing data transmission, error correction, and resource allocation. It exists as two separate elements: the control plane (CP) and the user plane (UP). It’s more complex than the 4G protocol stack due to the numerous advanced features and technologies added in 5G.

3 weeks ago | eeworldonline.com | Jeff Shepard

The old analogy of missile defense as “hitting a bullet with a bullet” no longer applies. The muzzle velocity of bullets is “only” 1,800 mph. A hypersonic missile travels at about 3,900 mph, twice as fast. A bullet can’t catch a hypersonic missile!To implement a hypersonic missile defense, it takes a comprehensive suite of high-performance sensors in orbit, including optical sensors, radar, and infrared, supported by sensor fusion, ground tracking systems, high-speed signal processing, and more.

1 month ago | microcontrollertips.com | Jeff Shepard

Large data sets are needed to train artificial intelligence (AI) algorithms. Large data sets can be expensive. So, how much data is enough? The complexity of the problem, the model complexity, the quality of the data, and the required level of accuracy primarily determine that. Data augmentation techniques can be used to increase the size of a dataset, and learning curve analysis can be used to determine when training results have been optimized.