What is he best known for?
The fields of study he is best known for:
- Computer network
- Telecommunications
- Artificial intelligence
David Jonathan Julian mostly deals with Computer network, Electronic engineering, Wireless, Power control and Communication channel.
David Jonathan Julian combines subjects such as Telecommunications, Wireless mesh network and Wireless communication systems with his study of Computer network.
David Jonathan Julian has researched Electronic engineering in several fields, including Algorithm, Transmitter power output and Electrical engineering.
His Wireless research incorporates themes from Node and Signal.
His research integrates issues of Mathematical optimization, Constrained optimization and Admission control in his study of Power control.
David Jonathan Julian interconnects Terminal and Information reporting in the investigation of issues within Communication channel.
His most cited work include:
- Power Control By Geometric Programming (735 citations)
- Power control for a wireless communication system utilizing orthogonal multiplexing (216 citations)
- QoS and fairness constrained convex optimization of resource allocation for wireless cellular and ad hoc networks (214 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Wireless, Electronic engineering, Computer network, Signal and Communication channel.
His research in Wireless intersects with topics in Transmission, Electrical engineering, Phase and Real-time computing.
His Electronic engineering research is multidisciplinary, incorporating elements of Acoustics, Pulse, Algorithm, Decoding methods and Communications system.
His Computer network research incorporates elements of Wireless network, Terminal and Distributed computing.
David Jonathan Julian has included themes like Ranging and Control theory in his Signal study.
His work on Control channel as part of general Communication channel study is frequently linked to Mode, therefore connecting diverse disciplines of science.
He most often published in these fields:
- Wireless (29.17%)
- Electronic engineering (25.00%)
- Computer network (25.00%)
What were the highlights of his more recent work (between 2013-2019)?
- Artificial intelligence (10.00%)
- Artificial neural network (4.17%)
- Pattern recognition (3.33%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Artificial neural network, Pattern recognition, Machine learning and Spike.
His study in the field of Deep belief network also crosses realms of Training.
His research integrates issues of Algorithm and Electronic engineering in his study of Artificial neural network.
The study incorporates disciplines such as Bit error rate, Selection and Sensitivity in addition to Algorithm.
His work in the fields of Time delay neural network, such as Physical neural network and Types of artificial neural networks, intersects with other areas such as Process.
His Synapse study integrates concerns from other disciplines, such as Neuron types, Computer hardware and Communication channel.
Between 2013 and 2019, his most popular works were:
- Adaptive selection of artificial neural networks (74 citations)
- Fixed point neural network based on floating point neural network quantization (42 citations)
- Bit width selection for fixed point neural networks (33 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Telecommunications
- Artificial intelligence
His primary scientific interests are in Artificial neural network, Data mining, Time delay neural network, Artificial intelligence and Algorithm.
The various areas that David Jonathan Julian examines in his Artificial neural network study include Electronic engineering and Bit error rate.
David Jonathan Julian interconnects Network architecture, Distributed element model and Information retrieval in the investigation of issues within Data mining.
His research in the fields of Physical neural network and Types of artificial neural networks overlaps with other disciplines such as Process.
His work in the fields of Artificial intelligence, such as Cascading classifiers, Margin classifier and Random subspace method, overlaps with other areas such as Feature model.
His studies in Algorithm integrate themes in fields like Probabilistic neural network and Stochastic neural network.
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