Related papers: Deep Learning for Optical Tweezers
We presented an optical system to perform imaging interested objects in complex scenes, like the creature easy see the interested prey in the hunt for complex environments. It utilized Deep-learning network to learn the interested objects's…
Modern deep learning relies nearly exclusively on dedicated electronic hardware accelerators. Photonic approaches, with low consumption and high operation speed, are increasingly considered for inference but, to date, remain mostly limited…
For several decades optical tweezers have proven to be an invaluable tool in the study and analysis of a myriad biological responses and applications. However, as every tool, it can have undesirable or damaging effects upon the very sample…
Optical tweezer arrays have emerged as a key experimental platform for quantum computation, quantum simulation, and quantum metrology, enabling unprecedented levels of control over single atoms and molecules. However, existing tweezer…
The recent advent of deep artificial neural networks has resulted in a dramatic increase in performance for object classification and detection. While pre-trained with everyday objects, we find that a state-of-the-art object detection…
Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field-of-view, and phototoxicity. To overcome these limitations,…
In this paper, we highlight recent advances in the use of machine learning for implementing equalizers for optical communications. We highlight both algorithmic advances as well as implementation aspects using conventional and neuromorphic…
Optical tweezers have revolutionized particle manipulation at the micro- and nanoscale, playing a critical role in fields such as plasmonics, biophysics, and nanotechnology. While traditional optical trapping methods primarily rely on…
Deep-learning algorithms enable precise image recognition based on high-dimensional hierarchical image features. Here, we report the development and implementation of a deep-learning-based image segmentation algorithm in an autonomous…
Deep Learning (DL) has become a crucial technology for Artificial Intelligence (AI). It is a powerful technique to automatically extract high-level features from complex data which can be exploited for applications such as computer vision,…
In the last two decades, optical vortices carried by twisted light wavefronts have attracted a great deal of interest, providing not only new physical insights into light-matter interactions, but also a transformative platform for boosting…
Object detection is the identification of an object in the image along with its localisation and classification. It has wide spread applications and is a critical component for vision based software systems. This paper seeks to perform a…
We have theoretically investigated optical tweezing of gain-functionalized microspheres using a highly focused single beam in the nonparaxial regime. We employ the Mie-Debye theory of optical tweezers to calculate the optical force acting…
Deep neural networks (DNNs) are reshaping the field of information processing. With their exponential growth challenging existing electronic hardware, optical neural networks (ONNs) are emerging to process DNN tasks in the optical domain…
Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase…
Plasmonic optical tweezers based on Double Nanohole (DNH) structures are an emerging tool for label-free single-molecule manipulation. However, their current performance is hindered by low signal-to-noise ratios for small proteins,…
The assembly of arbitrary 3D structures using nano- to micron-scale colloidal building blocks has broad applications in photonics, electronics, and biology. Combining optical tweezers (OT) with two-photon polymerization (TPP) enables 3D…
Recognition of objects using Deep Neural Networks is an active area of research and many breakthroughs have been made in the last few years. The paper attempts to indicate how far this field has progressed. The paper briefly describes the…
Non-invasive optical manipulation of particles has emerged as a powerful and versatile tool for biological study and nanotechnology. In particular, trapping and rotation of cells, cell nuclei and sub-micron particles enables unique…
We present a novel approach to determine the optical properties of materials in the nanoscale range using optical tweezers (OT). Fluorescent polymer-based nanostructures (pdots) are optically trapped in a Gaussian beam OT and the trap…