What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
Ahmed H. Tewfik spends much of his time researching Artificial intelligence, Digital watermarking, Computer vision, Watermark and Algorithm.
Ahmed H. Tewfik has researched Artificial intelligence in several fields, including Speech recognition, Distortion, Frequency domain and Pattern recognition.
His Digital watermarking study combines topics in areas such as Digital audio, Audio signal, Multimedia, Sequence and Robustness.
Many of his studies on Computer vision involve topics that are commonly interrelated, such as Perceptual Masking.
The concepts of his Watermark study are interwoven with issues in Image processing, Image segmentation, Filter and Host.
His Algorithm research is multidisciplinary, relying on both Mathematical optimization, Wavelet, Wavelet transform and Signal processing.
His most cited work include:
- Multimedia data-embedding and watermarking technologies (859 citations)
- Digital watermarks for audio signals (571 citations)
- Robust audio watermarking using perceptual masking (449 citations)
What are the main themes of his work throughout his whole career to date?
Ahmed H. Tewfik mainly investigates Artificial intelligence, Algorithm, Computer vision, Pattern recognition and Speech recognition.
Artificial intelligence and Machine learning are frequently intertwined in his study.
His Algorithm research includes themes of Mathematical optimization, Theoretical computer science, Wavelet and Radar.
His study in Iterative reconstruction, Data compression, Image resolution, Image processing and Frequency domain is carried out as part of his studies in Computer vision.
Ahmed H. Tewfik interconnects Brain–computer interface and Cluster analysis in the investigation of issues within Pattern recognition.
His Watermark research extends to Digital watermarking, which is thematically connected.
He most often published in these fields:
- Artificial intelligence (38.65%)
- Algorithm (21.91%)
- Computer vision (17.93%)
What were the highlights of his more recent work (between 2012-2021)?
- Artificial intelligence (38.65%)
- Speech recognition (12.35%)
- Electroencephalography (6.18%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Speech recognition, Electroencephalography, Deep learning and Pattern recognition.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Algorithm, Machine learning and Computer vision.
His Algorithm research integrates issues from State and Lattice reduction.
His work on Object detection as part of general Computer vision study is frequently connected to Data acquisition, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
He usually deals with Speech recognition and limits it to topics linked to Decoding methods and Local field potential.
His work deals with themes such as Image and Generative grammar, which intersect with Pattern recognition.
Between 2012 and 2021, his most popular works were:
- Speech Recognition with No Speech or with Noisy Speech (35 citations)
- Advancing Speech Recognition With No Speech Or With Noisy Speech (25 citations)
- State-of-the-art Speech Recognition using EEG and Towards Decoding of Speech Spectrum From EEG (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Algorithm
His primary scientific interests are in Artificial intelligence, Speech recognition, Electroencephalography, Electronic engineering and Deep learning.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Brain–computer interface, Computer vision, Algorithm, Machine learning and Pattern recognition.
In the subject of general Computer vision, his work in Filter, Template matching and Artifact is often linked to ECG Measurement, thereby combining diverse domains of study.
His research integrates issues of Speech spectrum, Noise, Connectionism, Decoding methods and Speech synthesis in his study of Electroencephalography.
His work on Bandwidth as part of general Electronic engineering research is often related to Successive approximation ADC, thus linking different fields of science.
His work carried out in the field of Deep learning brings together such families of science as Signal and Channel use.
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