What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Statistics
His scientific interests lie mostly in Fourier transform, Fractional Fourier transform, Algorithm, Non-uniform discrete Fourier transform and Mathematical analysis.
His studies deal with areas such as Object detection, Optics and Time–frequency analysis as well as Fourier transform.
His Fractional Fourier transform research is multidisciplinary, incorporating perspectives in Calculus, Short-time Fourier transform and Applied mathematics.
His work on Discrete sine transform as part of general Short-time Fourier transform research is frequently linked to Harmonic wavelet transform, thereby connecting diverse disciplines of science.
His Algorithm study combines topics from a wide range of disciplines, such as Decimation, Interpolation, Encryption and Signal processing.
His studies in Non-uniform discrete Fourier transform integrate themes in fields like Mathematical optimization and Discrete Fourier transform.
His most cited work include:
- Double image encryption based on random phase encoding in the fractional Fourier domain (218 citations)
- Detection and parameter estimation of multicomponent LFM signal based on the fractional Fourier transform (205 citations)
- Short-Time Fractional Fourier Transform and Its Applications (178 citations)
What are the main themes of his work throughout his whole career to date?
Ran Tao mainly focuses on Algorithm, Fractional Fourier transform, Artificial intelligence, Fourier transform and Pattern recognition.
Ran Tao has researched Algorithm in several fields, including Spectral density estimation, Time–frequency analysis, Signal and Signal processing.
The concepts of his Fractional Fourier transform study are interwoven with issues in Short-time Fourier transform and Discrete Fourier transform.
His Artificial intelligence course of study focuses on Computer vision and Radar imaging.
His biological study spans a wide range of topics, including Radar and Optics.
His Non-uniform discrete Fourier transform research includes themes of Mathematical optimization and Multidimensional signal processing.
He most often published in these fields:
- Algorithm (40.20%)
- Fractional Fourier transform (34.55%)
- Artificial intelligence (28.57%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (28.57%)
- Pattern recognition (15.95%)
- Hyperspectral imaging (10.63%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Hyperspectral imaging, Algorithm and Fractional Fourier transform.
His research integrates issues of Lidar and Detector in his study of Pattern recognition.
His research on Hyperspectral imaging also deals with topics like
- Discriminative model and related Constraint,
- Rank that connect with fields like Image quality,
- Spatial analysis that connect with fields like Artificial neural network and Sliding window protocol.
His Algorithm research incorporates elements of Radar, Newton's rings, Curvature and Robustness.
His Fractional Fourier transform study is related to the wider topic of Fourier transform.
His studies examine the connections between Fourier transform and genetics, as well as such issues in Optics, with regards to Estimation theory.
Between 2018 and 2021, his most popular works were:
- ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features (54 citations)
- Hyperspectral Anomaly Detection by Fractional Fourier Entropy (20 citations)
- Reality-Preserving Multiple Parameter Discrete Fractional Angular Transform and Its Application to Color Image Encryption (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Quantum mechanics
Artificial intelligence, Pattern recognition, Hyperspectral imaging, Feature extraction and Convolutional neural network are his primary areas of study.
His Artificial intelligence study integrates concerns from other disciplines, such as Process and Fractional Fourier transform.
His Fractional Fourier transform study is concerned with Fourier analysis in general.
His research integrates issues of Representation, Signal, Fourier transform and Detector in his study of Pattern recognition.
Ran Tao focuses mostly in the field of Fourier transform, narrowing it down to topics relating to Optical imaging and, in certain cases, Algorithm.
His studies deal with areas such as Contextual image classification, Object detection, Deep learning and Probability distribution as well as Feature extraction.
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