The existing system used by the customer could only track a few thousand assets and was able to generate only a few standard set of reports. As the number of assets tracked grew exponentially, the system started to break at the seams. The Tracking devices were from different manufacturers following different protocols. There was no proper integration among the devices to send instant alerts. There are thousands of tracking devices spread across multiple geographies, that are moving. The configuration and troubleshooting of these devices incurred heavy costs, which was a logistics challenge. The existing system did not provide sophisticated Analytics, Business Intelligence and Insights from the data

• Difficult environment. Communications equipment on trains must function properly in harsh conditions, such as environment temperatures ranging from -25°C to +85°C, according to the EU standard EN50155.• Railway regulations. All products in a train must adhere to strict standards, relating to working vibration, power consumption, and lifetime.• Lengthy process. Time to market in the railway industry can take years from concept to mass production, so product design requires a solid long term vision.

Microlise is among only a handful of players in the Transport Management Systems space that offer a one stop solution and service capability. Its investment in research and development needs to be underpinned by a robust, global connectivity platform, enabling the reliable transmission of data from vehicles to customers.

The client faces complex material handling due to non-standard load dimensions and weight, increasing safety risks and load handling damages.

Customer experience is a key competitive differentiator for airlines, and increasingly depends on digital channels. How could American Airlines meet its customers’ appetite for instant information and services?

A modern locomotive, for example, has as many as 200 sensors generating more than a billion data points per second. Vibration sensors surround critical components, video cameras scan the track and cab, while other sensors monitor RPM, power, temperature, the fuel mix, exhaust characteristics, and more.Most of today’s locomotives lack sufficient on-board processing power to make full use of all this data. To make matters worse, the data from different subsystems, such as the brakes, fuel system, and engine, remain separate, stored in isolated “boxes” that prevent unified analysis. The data is available, but the technology needed to process it in the most effective manner is not. As new sensors are added to the machine, the problem escalates.

Jooycar sought to provide an end-to-end, cloud-based insurance telematics platform focused on creating value insurers and enhancing policyholders’ experience. Its usage-based insurance (UBI) solution demanded various data inputs and outputs, including automotive data collection, user data collection and output to many possible data bases and applications. To create such a flexible smart car platform, Jooycar enlisted the help of Telit’s IoT technology and expertise.

School bus security is a major concern to all school employees and parents. News accounts appear almost weekly with a story of a child being left alone on a school bus for hours at a time, regardless of how much training takes place in this area, incidences of children being left behind still occur. Besides that, drunk driving is often threats to the security of school bus. In order to eliminate these dangers, Swedish system integrator Trinorth came to Ursalink for an advanced IoT solution with our industrial cellular router.

In this case study, the challenge explored involves LeNet*, one of the prominent image recognition topologies for handwritten digit recognition.   In the case study, we dive into how the training tool can be used to visually set up, tune, and train the Mixed National Institute of Standards and Technology (MNIST) dataset on Caffe* optimized for Intel® architecture. Data scientists are the intended audience.