What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Electrical engineering
Tobi Delbruck mainly investigates Artificial intelligence, Computer vision, Neuromorphic engineering, Pixel and Image sensor.
Tobi Delbruck combines subjects such as USB and Asynchronous communication with his study of Artificial intelligence.
His biological study spans a wide range of topics, including Frame, Vision sensor, Computer hardware and Chip.
His Neuromorphic engineering study combines topics in areas such as Frame rate, Very-large-scale integration, Simulation and Neuron.
His Pixel study deals with Latency intersecting with Dynamic range, Bandwidth, Sensor array, Machine vision and Feature extraction.
Tobi Delbruck has researched Image sensor in several fields, including Motion estimation, Telecommunications, Data compression and Spatial frequency.
His most cited work include:
- A 128 $ imes$ 128 120 dB 15 $\mu$ s Latency Asynchronous Temporal Contrast Vision Sensor (1185 citations)
- Neuromorphic Silicon Neuron Circuits (1070 citations)
- Training Deep Spiking Neural Networks Using Backpropagation. (381 citations)
What are the main themes of his work throughout his whole career to date?
Tobi Delbruck mainly focuses on Artificial intelligence, Computer vision, Pixel, Neuromorphic engineering and Image sensor.
Tobi Delbruck frequently studies issues relating to Asynchronous communication and Artificial intelligence.
Tobi Delbruck interconnects Frame and Brightness in the investigation of issues within Computer vision.
Tobi Delbruck has included themes like Dynamic range, Electronic engineering, CMOS, Frame rate and Optical flow in his Pixel study.
His work deals with themes such as Computer architecture, Very-large-scale integration, Computer hardware and Spiking neural network, which intersect with Neuromorphic engineering.
His Image sensor research is multidisciplinary, relying on both Temporal resolution and Machine vision.
He most often published in these fields:
- Artificial intelligence (51.69%)
- Computer vision (40.58%)
- Pixel (24.15%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (51.69%)
- Computer vision (40.58%)
- Neuromorphic engineering (19.32%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Computer vision, Neuromorphic engineering, Spiking neural network and Recurrent neural network.
Artificial intelligence connects with themes related to Latency in his study.
His research investigates the connection with Computer vision and areas like Brightness which intersect with concerns in Image sensor.
His Spiking neural network research includes elements of Dram and Parallel computing.
His research on Recurrent neural network also deals with topics like
- Field-programmable gate array that connect with fields like Asynchronous communication and Corner detection,
- Latency, which have a strong connection to Microphone and Speech recognition,
- Inference which is related to area like Dynamical system, Simulation and PID controller.
His research in Deep learning intersects with topics in Frame and Convolutional neural network.
Between 2018 and 2021, his most popular works were:
- Event-based Vision: A Survey. (162 citations)
- NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps (113 citations)
- EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Electrical engineering
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pixel, Real-time computing and Feature extraction.
Tobi Delbruck works in the field of Artificial intelligence, namely Deep learning.
His Deep learning research includes themes of End-to-end principle, Frame, Neuromorphic engineering, CMOS sensor and Convolutional neural network.
His studies in Computer vision integrate themes in fields like Robotics and Spiking neural network.
His studies deal with areas such as Frame rate, Auxiliary memory, Computer engineering and Neuron as well as Real-time computing.
His Feature extraction research incorporates themes from VHDL, Video tracking, Filter and Asynchronous communication.
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