What is he best known for?
The fields of study he is best known for:
- Electrical engineering
- Artificial intelligence
- Microsoft Windows
Jeffrey Jacobsen spends much of his time researching Display device, Computer graphics, Liquid-crystal display, Computer vision and Artificial intelligence.
His research in Display device intersects with topics in Wireless and Computer hardware.
His study in Computer hardware focuses on Host in particular.
In his research on the topic of Computer graphics, Multi-function display and Display resolution is strongly related with Optical head-mounted display.
Jeffrey Jacobsen combines subjects such as Electronic circuit, Active matrix, Electroluminescent display and Liquid crystal with his study of Liquid-crystal display.
In general Computer vision, his work in Monocular is often linked to Virtual retinal display linking many areas of study.
His most cited work include:
- Head mounted display system (537 citations)
- Single crystal silicon arrayed devices for display panels (307 citations)
- Monocular display device (243 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Computer hardware, Headset, Display device, Computer vision and Artificial intelligence.
His work carried out in the field of Computer hardware brings together such families of science as Wireless, Pager, Gesture and Communications system.
He has included themes like Liquid-crystal display, Active matrix, Computer graphics and Wireless network interface controller in his Display device study.
His biological study spans a wide range of topics, including Digital camera, Electronic circuit and Liquid crystal.
His Computer graphics study combines topics from a wide range of disciplines, such as Optical head-mounted display and Multi-function display.
The various areas that he examines in his Computer vision study include Head and Mobile phone.
He most often published in these fields:
- Computer hardware (32.26%)
- Headset (27.96%)
- Display device (27.96%)
What were the highlights of his more recent work (between 2011-2019)?
- Headset (27.96%)
- Computer vision (24.73%)
- Artificial intelligence (24.73%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Headset, Computer vision, Artificial intelligence, Computer hardware and Optical head-mounted display.
The Headset study combines topics in areas such as Voice command device, Speech recognition, Head and Mode.
His Voice command device study integrates concerns from other disciplines, such as Host and Display device.
His study in the fields of Gesture, Scene analysis and Image under the domain of Computer vision overlaps with other disciplines such as Accelerometer.
Jeffrey Jacobsen interconnects Remote control vehicle and PATH in the investigation of issues within Artificial intelligence.
As part of one scientific family, Jeffrey Jacobsen deals mainly with the area of Computer hardware, narrowing it down to issues related to the Embedded system, and often Docking station.
Between 2011 and 2019, his most popular works were:
- Headset computer that uses motion and voice commands to control information display and remote devices (103 citations)
- Computer headset with detachable 4G radio (81 citations)
- Instant translation system (60 citations)
In his most recent research, the most cited papers focused on:
- Electrical engineering
- Microsoft Windows
- The Internet
His primary scientific interests are in Headset, Computer hardware, Embedded system, Set and Head.
His Headset study combines topics in areas such as Wireless, Multimedia, Artificial intelligence, Computer vision and Voice command device.
His studies link Mobile phone with Artificial intelligence.
His study ties his expertise on Blocking together with the subject of Computer vision.
His Voice command device research includes themes of Motion, Peripheral vision, Host and Gesture.
His Embedded system research incorporates themes from Network communication, Docking station and Display device.
This overview was generated by a machine learning system which analysed the scientist’s
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