The vending industry is undergoing a sea change, taking advantage of new technologies to go beyond just delivering snacks to creating a new retail location. Intelligent vending machines can be found in many public locations as well as company facilities, selling different types of goods and services, including even computer accessories, gold bars, tickets, and office supplies. With increasing sophistication, they may also provide time- and location-based data pertaining to sales, inventory, and customer preferences. But at the end of the day, vending machine operators know greater profitability is driven by higher sales and lower operating costs.

Of the 70 million corporate washrooms, 20 percent are equipped with environmentally unfriendly, inefficient, and unhygienic hand-dryers and around 80 percent offer expensive paper towels that create three million tons of waste. As such, reducing building management costs, boosting facility efficiencies, and cutting energy use is very lucrative. SAVORTEX has built the world's first IoT-enabled messaging hand-dryer to disrupt this inefficient sector and transform corporate washrooms into smart spaces. Built on Intel® technology, the SAVORTEX AdDryer* uses the Internet of Things (IoT) to deliver waste savings, alert cleaners to washroom needs based on usage, and engage consumers with tailored, high-definition video messages, allowing enterprises to transform costly commercial washrooms into connected, hygienic, and sustainable revenue-producing assets.ChallengesIngrained issues: High costs, inefficient maintenance, and energy consumption in washrooms make them difficult to manage. SAVORTEX wanted to solve multiple problems at once.Excessive waste: The prevalent drying choice of expensive paper towels significantly impacts the environment in both manufacture and disposal.Status quo: Conventional brushed-motor hand-dryers are energy-hungry and have a short life-expectancy so are costly to run, but finding budget to deploy new solutions on the necessary scale can be difficult.

Coor wanted an IoT solution to simplify office manager's job. The solution must require the following features: boost productivity, apply technology innovation, and consolidate management.

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.

As a pioneering IT enabler and technology driver, Intel’s objective was to demonstrate to clients that the modern data center operating environment does not require the level of cooling traditionally thought necessary to guarantee reliable performance. Given the significant energy cost and capital expense associated with cooling technology, there would be considerable potential for dramatically reducing TCO by designing and building data center facilities that run at a higher ambient temperature.Demonstrating the validity of this approach required an accurate and straightforward way to scientifically prove how TCO could be reduced without business risk or compromising IT performance. Intel needed a dependable solution that would rapidly allow a detailed assessment of the entire data center environment under varying workloads and conditions, holistically and at an individual component level.