DEVELOPMENT OF MEMS THERMOPILES AND RELATED APPLICATIONS

Ph.DDOCTOR OF PHILOSOPH

CMOS compatible midinfrared wavelength-selective thermopile for high temperature applications

10.1109/JMEMS.2014.2322675Journal of Microelectromechanical Systems241144-15

Self-Supervised Tracking via Target-Aware Data Synthesis

While deep-learning based tracking methods have achieved substantial progress, they entail large-scale and high-quality annotated data for sufficient training. To eliminate expensive and exhaustive annotation, we study self-supervised learning for visual tracking. In this work, we develop the Crop-Transform-Paste operation, which is able to synthesize sufficient training data by simulating various appearance variations during tracking, including appearance variations of objects and background interference. Since the target state is known in all synthesized data, existing deep trackers can be trained in routine ways using the synthesized data without human annotation. The proposed target-aware data-synthesis method adapts existing tracking approaches within a self-supervised learning framework without algorithmic changes. Thus, the proposed self-supervised learning mechanism can be seamlessly integrated into existing tracking frameworks to perform training. Extensive experiments show that our method 1) achieves favorable performance against supervised learning schemes under the cases with limited annotations; 2) helps deal with various tracking challenges such as object deformation, occlusion, or background clutter due to its manipulability; 3) performs favorably against state-of-the-art unsupervised tracking methods; 4) boosts the performance of various state-of-the-art supervised learning frameworks, including SiamRPN++, DiMP, and TransT (based on Transformer).Comment: 11 pages, 7 figure

Online Multiple Support Instance Tracking

Abstract — We propose an online tracking algorithm in which the support instances are selected adaptively within the multiple instance learning framework. The support instances are selected from training 1-norm support vector machines in a feature space, thereby learning large margin classifiers for visual tracking. An algorithm is presented to update the support instances by taking image data obtained previously and recently into account. In addition, a forgetting factor is introduced to weigh the contribution of support instances obtained at different time stamps. Experimental results demonstrate that our tracking algorithm is robust in handling occlusion, abrupt motion and illumination. I

Stem cell-based therapies for ischemic stroke

In recent years, stem cell-based approaches have attracted more attention from scientists and clinicians due to their possible therapeutical effect on stroke. Animal studies have demonstrated that the beneficial effects of stem cells including embryonic stem cells (ESCs), inducible pluripotent stem cells (iPSCs), neural stem cells (NSCs), and mesenchymal stem cell (MSCs) might be due to cell replacement, neuroprotection, endogenous neurogenesis, angiogenesis, and modulation on inflammation and immune response. Although several clinical studies have shown the high efficiency and safety of stem cell in stroke management, mainly MSCs, some issues regarding to cell homing, survival, tracking, safety, and optimal cell transplantation protocol, such as cell dose and time window, should be addressed. Undoubtably, stem cell-based gene therapy represents a novel potential therapeutic strategy for stroke in future

Evolutionary Optimization of Liquid State Machines for Robust Learning

Zhou Y, Jin Y, Ding J. Evolutionary Optimization of Liquid State Machines for Robust Learning. In: Lu H, Tang H, Wang Z, eds. Advances in Neural Networks – ISNN 2019. 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part I. Lecture Notes in Computer Science. Vol 11554. Cham: Springer International Publishing; 2019: 389-398.Liquid State Machines (LSMs) are a computational model of spiking neural networks with recurrent connections in a reservoir. Although they are believed to be biologically more plausible, LSMs have not yet been as successful as other artificial neural networks in solving real world learning problems mainly due to their highly sensitive learning performance to different types of stimuli. To address this issue, a covariance matrix adaptation evolution strategy has been adopted in this paper to optimize the topology and parameters of the LSM, thereby sparing the arduous task of fine tuning the parameters of the LSM for different tasks. The performance of the evolved LSM is demonstrated on three complex real-world pattern classification problems including image recognition and spatio-temporal classification

Hyperbaric oxygen preconditioning ameliorates blood-brain barrier damage induced by hypoxia through modulation of tight junction proteins in an in vitro model

Aim To explore the effects of hyperbaric oxygen preconditioning (HBOP) on the permeability of blood-brain barrier (BBB) and expression of tight junction proteins under hypoxic conditions in vitro. Methods A BBB in vitro model was constructed using the hCMEC/D3 cell line and used when its trans-endothelial electrical resistance (TEER) reached 80-120 Ω · cm2 (tested by Millicell-Electrical Resistance System). The cells were randomly divided into the control group cultured under normal conditions, the group cultured under hypoxic conditions (2%O2) for 24 h (hypoxia group), and the group first subjected to HBOP for 2 h and then to hypoxia (HBOP group). Occludin and ZO-1 expression were analyzed by immunofluorescence assay. Results Normal hCMEC/D3 was spindle-shaped and tightly integrated. TEER was significantly reduced in the hypoxia (P = 0.001) and HBOP group (P = 0.014) compared to control group, with a greater decrease in the hypoxia group. Occludin membranous expression was significantly decreased in the hypoxia group (P = 0.001) compared to the control group, but there was no change in the HBOP group. ZO-1 membranous expression was significantly decreased (P = 0.002) and cytoplasmic expression was significantly increased (P = 0.001) in the hypoxia group compared to the control group, although overall expression levels did not change. In the HBOP group, there was no significant change in ZO-1 expression compared to the control group. Conclusion Hyperbaric oxygen preconditioning protected the integrity of BBB in an in vitro model through modulation of occludin and ZO-1 expression under hypoxic conditions