Optical Fiber Probe-Based Manipulation of Cells

In a liquid environment, optical trapping and multifunctional manipulation of biological cells, in a noncontact, noninvasive, and high-precision way, have become one of the research focuses in the field of integrated optics, biophotonics, and clinical medicine. However, it still faces great challenges to perform multifunctional manipulation in very narrow spaces with high flexibility, including stable retaining, controllable deformation, and precise regulation of a cell chain. Therefore, in this chapter, we introduce the multifunctional manipulation for biological cells based on the elaborately designed fiber probes. With the probes, the sequential organization, precise regulation, and bidirectional transportation of the cell chain were performed. We also discuss the potential applications of fiber probes on the endocytosis and exocytosis purpose, which will play an important role in the detection and treatment of complex disease

Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images

Recovering the 3D representation of an object from single-view or multi-view RGB images by deep neural networks has attracted increasing attention in the past few years. Several mainstream works (e.g., 3D-R2N2) use recurrent neural networks (RNNs) to fuse multiple feature maps extracted from input images sequentially. However, when given the same set of input images with different orders, RNN-based approaches are unable to produce consistent reconstruction results. Moreover, due to long-term memory loss, RNNs cannot fully exploit input images to refine reconstruction results. To solve these problems, we propose a novel framework for single-view and multi-view 3D reconstruction, named Pix2Vox. By using a well-designed encoder-decoder, it generates a coarse 3D volume from each input image. Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e.g., table legs) from different coarse 3D volumes to obtain a fused 3D volume. Finally, a refiner further refines the fused 3D volume to generate the final output. Experimental results on the ShapeNet and Pix3D benchmarks indicate that the proposed Pix2Vox outperforms state-of-the-arts by a large margin. Furthermore, the proposed method is 24 times faster than 3D-R2N2 in terms of backward inference time. The experiments on ShapeNet unseen 3D categories have shown the superior generalization abilities of our method.Comment: ICCV 201

Enhanced Security and Privacy for Blockchain-enabled Electronic Medical Records in eHealth.

PhD Theses.Electronic medical records (EMRs) as part of an eHealth system are vital assets centrally managed by medical institutions and used to maintain up to date patients' medical histories. Such centralised management of EMRs may result in an increased risk of EMR damage or loss to medical institutions. In addition, it is di cult to monitor and control who can access their EMRs and for what reasons as eHealth may increasingly involve the use of IoT devices such as eHealth wearables and distributed networks. Blockchain is proposed as a promising method applied to support distributed data storage to maintain and share EMRs using its inherent immutability (forgery resistance). However, the original blockchain design cannot restrict unauthenticated or unauthorised data access for use as part of EMR management. Therefore, two novel authorisation schemes to enhance the security and privacy of blockchain use for EMRs are proposed in this work. The rst one can omit the agent layer (gateway) to authorise users' access to blockchain-enabled EMRs with block level gran- ularity, whilst maintaining compatibility with the underlying Blockchain data structure. Then, an improved scheme is proposed to implement multiple levels of granularity autho- risation, whilst supporting exible data queries. This scheme dispenses with the need to use a public key infrastructure (PKI) in authorisation and hence reduces the resource cost of computation and communication. Furthermore, to realise privacy preservation during authorisation, a challenge-response anonymous authorisation is proposed that avoids the disclosure of users' credentials when authorising data access requests. Compared with the baseline schemes, the proposed authorisation schemes can decrease the time consumption of computation and data transmission and reduce the transmitted data size so that they can be used in low-resource IoT devices applied to blockchain- enabled EMRs as demonstrated in performance experiments. In addition, theoretical i validations of correctness demonstrate that the proposed authorisation schemes work correctly