What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Electrical engineering
- Computer vision
The scientist’s investigation covers issues in Computer vision, Artificial intelligence, Display device, Computer graphics and Optics.
His Computer vision study typically links adjacent topics like Signal.
His study in the fields of Image display, Fixation and Image degradation under the domain of Artificial intelligence overlaps with other disciplines such as Function and Communication.
His Display device research incorporates themes from Controller and Luminance.
His Optics study combines topics from a wide range of disciplines, such as Optoelectronics and OLED.
The various areas that Michael E. Miller examines in his Optoelectronics study include Pixel and Electrical efficiency.
His most cited work include:
- Color oled display with improved power efficiency (300 citations)
- Electronic camera with quick review of last captured image (244 citations)
- Stereoscopic display system with flexible rendering of disparity map according to the stereoscopic fusing capability of the observer (186 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Optics, Pixel and Optoelectronics.
His biological study spans a wide range of topics, including Mode and Signal.
His Computer vision research focuses on subjects like Computer graphics, which are linked to Personalization.
In Optics, Michael E. Miller works on issues like Display device, which are connected to Color image.
His Pixel study combines topics from a wide range of disciplines, such as Electronic circuit, Electronic engineering and Edge.
His work deals with themes such as OLED and Substrate, which intersect with Optoelectronics.
He most often published in these fields:
- Artificial intelligence (31.84%)
- Computer vision (29.60%)
- Optics (21.97%)
What were the highlights of his more recent work (between 2017-2021)?
- Human–computer interaction (9.42%)
- Artificial intelligence (31.84%)
- Workload (5.83%)
In recent papers he was focusing on the following fields of study:
Michael E. Miller spends much of his time researching Human–computer interaction, Artificial intelligence, Workload, Computer vision and Human agent.
His work on Augmented reality as part of general Human–computer interaction study is frequently connected to Operationalization, User engagement and Structure and function, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Augmented reality research includes elements of Representation, Image and Computer graphics, Computer graphics.
The Digital image processing research Michael E. Miller does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Variable, therefore creating a link between diverse domains of science.
His Workload research includes themes of Aeronautics, Response time, Task analysis and Control.
His Computer vision research is multidisciplinary, incorporating perspectives in Illuminance and Reflectivity.
Between 2017 and 2021, his most popular works were:
- Spatialized audio improves call sign recognition during multi-aircraft control. (3 citations)
- Systems modeling language extension to support modeling of human‐agent teams (1 citations)
- Applying Control Abstraction to the Design of Human–Agent Teams (1 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Electrical engineering
- Optics
His primary areas of study are Human–computer interaction, Automation, Workload, Human agent and Task analysis.
While working in this field, he studies both Human–computer interaction and Performance measurement.
Michael E. Miller undertakes interdisciplinary study in the fields of Automation and User engagement through his works.
His Workload research incorporates themes from Systems design and Cockpit.
His Human agent study combines topics in areas such as Cognitive systems engineering and Software engineering.
His work carried out in the field of Task analysis brings together such families of science as Intelligent agent and Human–machine system.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
