High-Order Fiber Mode Beam Parameter Optimization for Transport and Rotation of Single Cells.

Optical tweezers are becoming increasingly important in biomedical applications for the trapping, propelling, binding, and controlled rotation of biological particles. These capabilities enable applications such as cell surgery, microinjections, organelle extraction and modification, and preimplantation genetic diagnosis. In particular, optical fiber-based tweezers are compact, highly flexible, and can be readily integrated into lab-on-a-chip devices. Taking advantage of the beam structure inherent in high-order modes of propagation in optical fiber, LP11, LP21, and LP31 fiber modes can generate structured radial light fields with two or more concentrations in the cross-section of a beam, forming multiple traps for bioparticles with a single optical fiber. In this paper, we report the dynamic modeling and optimization of single cell manipulation with two to six optical traps formed by a single fiber, generated by either spatial light modulation (SLM) or slanted incidence in laser-fiber coupling. In particular, we focus on beam size optimization for arbitrary target cell sizes to enable trapped transport and controlled rotation of a single cell, using a point matching method (PMM) of the T-matrix to compute trapping forces and rotation torque. Finally, we validated these optimized beam sizes experimentally for the LP21 mode. This work provides a new understanding of optimal optical manipulation using high-order fiber modes at the single-cell level.

A unified framework of source camera identification based on features.

Source camera identification, which aims at identifying the source camera of an image, has attracted a wide range of attention in the field of digital image forensics recently. Many approaches to source camera identification have been proposed by extracting some image features. However, most of these methods only focused on extracting features from the single artifact of the camera left on the captured images and ignored other artifacts that may help improve final accuracy. Therefore, in this paper, we propose a feature-based framework for source camera identification, which first captures various pure camera-specific artifacts through preprocessing and residual calculation, then extracts discriminative features through image transform, and finally reduces the algorithm complexity through feature reduction. Based on the framework, a novel source camera identification method is proposed, which can identify different camera brands, models and individuals with high accuracy. A large number of comparative experiments show that the proposed method outperforms the state-of-the-art methods.