Accelerating the
Industrial Internet of Things
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3 case studies
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Building Smart IoT-Connected Railways
Building Smart IoT-Connected Railways
Building Smart IoT-Connected Railways
Intel
• 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.


Industries: Automotive
Functions: Product Development
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Smart Support for Vulnerable Adults from Cascade3d
Smart Support for Vulnerable Adults from Cascade3d
Smart Support for Vulnerable Adults from Cascade3d
Intel
•Smart healthcare. Cascade3d wanted to integrate its analytics into an IoT platform so that data from sensors could be gathered, filtered, and analyzed to transform and optimize professional healthcare.• Education. IoT solutions are seen to be quite complex and can be daunting for less technical people, so adoption in the healthcare industry requires education as well as low barriers to use.• Data privacy. Especially when dealing with vulnerable individuals, the security of all data collected is paramount, as is sensitively dealing with this data.


Industries: Equipment & Machinery
Functions: Product Development
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Employing Intel Deep Learning SDK Toward Bettering Image Recognition Models
Employing Intel Deep Learning SDK Toward Bettering Image Recognition Models
Employing Intel Deep Learning SDK Toward Bettering Image Recognition Models
Intel
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.


Industries: Construction & Buildings
Functions: Product Development
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