What is she best known for?
The fields of study she is best known for:
- Integrated circuit
- Computer hardware
- Electrical engineering
Jennifer Wong mainly focuses on Programmable logic device, Computer hardware, Multiplexing, Erasable programmable logic device and Programmable logic array.
She usually deals with Programmable logic device and limits it to topics linked to Electronic engineering and Chip and Electrical engineering.
Jennifer Wong studies Digital signal processing which is a part of Computer hardware.
Jennifer Wong has researched Digital signal processing in several fields, including Algorithm, Multiplexer and Barrel shifter.
Her Multiplexing research focuses on subjects like Signal, which are linked to Real-time computing, Sequential logic and Parallel computing.
Her Programmable logic array study combines topics in areas such as Registered memory and Semiconductor memory.
Her most cited work include:
- Method of time multiplexing a programmable logic device (294 citations)
- Time multiplexed programmable logic device (282 citations)
- Configuration modes for a time multiplexed programmable logic device (139 citations)
What are the main themes of her work throughout her whole career to date?
Jennifer Wong mostly deals with Computer hardware, Programmable logic device, Digital signal processing, Electronic engineering and Embedded system.
Her study deals with a combination of Computer hardware and Interconnection.
Her work on Programmable logic array as part of general Programmable logic device research is often related to Erasable programmable logic device and Simple programmable logic device, thus linking different fields of science.
Her Programmable logic array research incorporates elements of Registered memory and Semiconductor memory.
Her Digital signal processing research is multidisciplinary, incorporating perspectives in Electronic circuit, Integrated circuit and Arithmetic.
Her work carried out in the field of Electronic engineering brings together such families of science as AND gate, Synchronous circuit, Clock signal and Electrical engineering, Voltage.
She most often published in these fields:
- Computer hardware (60.47%)
- Programmable logic device (41.86%)
- Digital signal processing (32.56%)
What were the highlights of her more recent work (between 2005-2014)?
- Digital signal processing (32.56%)
- Arithmetic logic unit (16.28%)
- Computer hardware (60.47%)
In recent papers she was focusing on the following fields of study:
Jennifer Wong spends much of her time researching Digital signal processing, Arithmetic logic unit, Computer hardware, Multiplexer and Integrated circuit.
Her research combines Arithmetic and Digital signal processing.
The Computer hardware study combines topics in areas such as Electronic engineering and Adder.
Her Electronic engineering study incorporates themes from Asynchronous circuit and Discrete circuit.
Her Multiplexer study frequently links to other fields, such as Opcode.
Her biological study spans a wide range of topics, including Carry flag and SIMD.
Between 2005 and 2014, her most popular works were:
- Digital signal processing circuit having an adder circuit with carry-outs (63 citations)
- Digital signal processing circuit having a pattern circuit for determining termination conditions (62 citations)
- Digital signal processing circuit having a pattern detector circuit (57 citations)
In her most recent research, the most cited papers focused on:
- Integrated circuit
- Electrical engineering
- Computer hardware
Jennifer Wong mainly investigates Arithmetic logic unit, Digital signal processing, Integrated circuit, Bitwise operation and Multiplexer.
Her research in Arithmetic logic unit intersects with topics in Algorithm, Discrete circuit and Opcode.
Digital signal processing is a subfield of Electronic engineering that Jennifer Wong tackles.
Bitwise operation is integrated with Bit field, Carry-save adder, Adder, Bit array and Serial binary adder in her study.
Jennifer Wong frequently studies issues relating to Computer hardware and Multiplexer.
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.
