M
Muhammed Veli
Researcher at University of California, Los Angeles
Publications - 25
Citations - 2073
Muhammed Veli is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Artificial neural network & Deep learning. The author has an hindex of 10, co-authored 23 publications receiving 1210 citations.
Papers
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Journal ArticleDOI
All-optical machine learning using diffractive deep neural networks
TL;DR: 3D-printed D2NNs are created that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum.
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All-Optical Machine Learning Using Diffractive Deep Neural Networks
TL;DR: In this paper, an all-optical Diffractive Deep Neural Network (D2NN) architecture is proposed to learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively.
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Design of task-specific optical systems using broadband diffractive neural networks
TL;DR: A broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally incoherent broadband source to all-optically perform a specific task learned using deep learning is reported.
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Terahertz pulse shaping using diffractive surfaces.
Muhammed Veli,Deniz Mengu,Nezih T. Yardimci,Yi Luo,Jingxi Li,Yair Rivenson,Mona Jarrahi,Aydogan Ozcan +7 more
TL;DR: In this paper, a diffractive network is used to shape an arbitrary broadband pulse into a desired optical waveform, forming a compact and passive pulse engineering system, which can find broad applications in e.g., communications, ultra-fast imaging and spectroscopy.
Journal ArticleDOI
High-Throughput and Label-Free Single Nanoparticle Sizing Based on Time-Resolved On-Chip Microscopy
Euan McLeod,T. Dincer,Muhammed Veli,Yavuz Nuri Ertas,Chau Chau Nguyen,Wei Luo,Alon Greenbaum,Alborz Feizi,Aydogan Ozcan +8 more
TL;DR: This work combines holographic on-chip microscopy with vapor-condensed nanolens self-assembly inside a cost-effective hand-held device and captures time-resolved in situ images of the particles, resulting in significant signal enhancement for the label-free detection and sizing of individual deeply subwavelength particles.