Faculty

Institution

Unit

Venue

Victor DeBrunner

Professor

Florida State University

Victor Debrunner is currently serving as a Professor at Florida State University.

Honors

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Experiences

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Education

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Victor DeBrunner

Florida State University

Professor

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electrical engineering signal processing computer science digital signal processing machine learning engineering applied mathematics computer vision instrumentation filter design audio signal processing remote sensing power electronics speech processing acoustic signal processing hirschman optimal transform statistical signal processing audio electroacoustics acoustical engineering fir filters adaptive filter discrete hirschman transform hirschman uncertainty wavelet theory digital image processing structural health monitoring electronics bridge engineering image segmentation adaptive control integral transforms noise control information theory beamforming hardware implementation damage detection entropy active control transaction processing nonlinear control timefrequency analysis active noise control image compression spectrum estimation electronic engineering secondary path vibration control automatic control microwave technology field programmable gate array computational imaging circuit analysis eeg signal processing sensitivity analysis system identification radar signal processing frequency control compressive sensing data fusion filtering technique modal analysis engineering noise control direct learning algorithm texture classification finite length sequences impulse response data minimum entropy estimation atmospheric research distance education engineering optimization control optimization information fusion multiscale storm identification optimal transform spr property secondary path identification pattern recognition pattern recognition application statistical pattern recognition soft computing image analysis

Notable Scholarly Works

Notable Federal Grants

Collaborators

An Efficient Implementation for Linear Convolution With Reduced Latency in Fpga. Electronics Letters (2024)

Dingli XueLinda S. DeBrunnerVictor DeBrunnerZhen HuangYing XiaoZhaohang Zhang

linear convolutionelectronic engineeringefficient implementationsemiconductor devicescomputer engineeringprinted electronicsindustrial electronicselectronicslow latencysemiconductorselectrical engineeringmicroelectronicspower electronicsfield programmable gate arrayreduced latencyengineering

Engaging Students in an Introductory Circuits Course. 2022 IEEE International Symposium on Circuits and Systems (ISCAS) (2023)

Linda S. DeBrunnerVictor DeBrunner

engineering educationeducationstudent engagementclassroom instructionintroductory circuits course

Fast Convolution Algorithm for Real-Valued Finite Length Sequences. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2023)

Weiwei WangVictor DeBrunnerLinda S. DeBrunner

computer sciencefast convolution algorithmmachine learning

Hardware Performance of Complex Dot-Product Implementations. 2014 48th Asilomar Conference on Signals, Systems and Computers (2022)

Linda S. DeBrunnerVictor DeBrunner

complex dot-product implementationscomputer engineeringhardware performancecomputer scienceperformance evaluation

A Natively Real-Valued Fft Algorithm. 2014 48th Asilomar Conference on Signals, Systems and Computers (2021)

Rajesh ThomasVictor DeBrunnerLinda DeBrunner

systems engineeringreal-valued fft algorithmmachine learningdigital signal processingsignal processingcomputer scienceradio frequency identification

A Sparse Algorithm for Computing the Dft Using Its Real Eigenvectors. Signals (2021)

Rajesh ThomasVictor DeBrunnerLinda S. DeBrunner

real eigenvectorsgraph theorysparse algorithmsignal processingcomputer science

Iir Filter Sensitivity Predicts Filter Wordlength. 2014 48th Asilomar Conference on Signals, Systems and Computers (2021)

Victor DeBrunnerLinda S. DeBrunner

systems engineeringiir filter sensitivityfilter wordlengthdigital signal processingnatural language processingsignal processingmachine learning

Reduced Complexity Optimal Convolution Based on the Discrete Hirschman Transform. IEEE Transactions on Circuits and Systems I Regular Papers (2021)

Dingli XueLinda S. DeBrunnerVictor DeBrunner

discrete hirschman transformmachine learningelectrical engineeringapplied mathematics

Split-Radix Algorithm for the Discrete Hirschman Transform. IEEE Signal Processing Letters (2021)

Dingli XueLinda DeBrunnerVictor DeBrunnerZhen Huang

discrete hirschman transformsplit-radix algorithmdigital signal processingeeg signal processingelectrical engineeringsignal processingcomputer scienceapplied mathematics

How the Discrete Hirschman Transform Inherits Its Eigenstructure From the Dft. 2014 48th Asilomar Conference on Signals, Systems and Computers (2020)

Rajesh ThomasVictor DeBrunnerLinda DeBrunner

discrete hirschman transformcomputer sciencecomputer vision

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Victor DeBrunner

Professor

Florida State University

Victor Debrunner is currently serving as a Professor at Florida State University.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Victor DeBrunner

Florida State University

Professor

You can click a keyword to filter the displayed items

Notable Scholarly Works

Notable Federal Grants

Collaborators

An Efficient Implementation for Linear Convolution With Reduced Latency in Fpga. Electronics Letters (2024)

Dingli XueLinda S. DeBrunnerVictor DeBrunnerZhen HuangYing XiaoZhaohang Zhang

Engaging Students in an Introductory Circuits Course. 2022 IEEE International Symposium on Circuits and Systems (ISCAS) (2023)

Linda S. DeBrunnerVictor DeBrunner

engineering educationeducationstudent engagementclassroom instructionintroductory circuits course

Fast Convolution Algorithm for Real-Valued Finite Length Sequences. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2023)

Weiwei WangVictor DeBrunnerLinda S. DeBrunner

computer sciencefast convolution algorithmmachine learning

Hardware Performance of Complex Dot-Product Implementations. 2014 48th Asilomar Conference on Signals, Systems and Computers (2022)

Linda S. DeBrunnerVictor DeBrunner

complex dot-product implementationscomputer engineeringhardware performancecomputer scienceperformance evaluation

A Natively Real-Valued Fft Algorithm. 2014 48th Asilomar Conference on Signals, Systems and Computers (2021)

Rajesh ThomasVictor DeBrunnerLinda DeBrunner

systems engineeringreal-valued fft algorithmmachine learningdigital signal processingsignal processingcomputer scienceradio frequency identification

A Sparse Algorithm for Computing the Dft Using Its Real Eigenvectors. Signals (2021)

Rajesh ThomasVictor DeBrunnerLinda S. DeBrunner

real eigenvectorsgraph theorysparse algorithmsignal processingcomputer science

Iir Filter Sensitivity Predicts Filter Wordlength. 2014 48th Asilomar Conference on Signals, Systems and Computers (2021)

Victor DeBrunnerLinda S. DeBrunner

systems engineeringiir filter sensitivityfilter wordlengthdigital signal processingnatural language processingsignal processingmachine learning

Reduced Complexity Optimal Convolution Based on the Discrete Hirschman Transform. IEEE Transactions on Circuits and Systems I Regular Papers (2021)

Dingli XueLinda S. DeBrunnerVictor DeBrunner

discrete hirschman transformmachine learningelectrical engineeringapplied mathematics

Split-Radix Algorithm for the Discrete Hirschman Transform. IEEE Signal Processing Letters (2021)

Dingli XueLinda DeBrunnerVictor DeBrunnerZhen Huang

discrete hirschman transformsplit-radix algorithmdigital signal processingeeg signal processingelectrical engineeringsignal processingcomputer scienceapplied mathematics

How the Discrete Hirschman Transform Inherits Its Eigenstructure From the Dft. 2014 48th Asilomar Conference on Signals, Systems and Computers (2020)

Rajesh ThomasVictor DeBrunnerLinda DeBrunner

discrete hirschman transformcomputer sciencecomputer vision

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University of California, Santa Barbara

adaptive signal processing

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