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In this manuscript, spherical fuzzy set (SFS) and T-spherical fuzzy set (TSFS) are discussed, which are two generalizations of fuzzy set (FS), intuitionistic fuzzy set (IFS), Pythagorean fuzzy set (PFS) and picture fuzzy set (PFS). As TSFS is more capable of processing and expressing unknown information in unknown environment, it is widely used in various areas. However, how to accurately measure the distance between TSFSs is still an unsolved problem. This manuscript discusses some limitations of the existing divergence measures and the problems that the existing divergence measures cannot be applied to the information provided in the TSFSs environment by some numerical examples. Therefore, a new divergence measure under TSFSs structure is proposed by utilizing the advantages of Jensen-Shannon divergence, which is called TSFSJS distance. This TSFSJS distance not only satisfies the distance measurement axiom, but also can better distinguish the difference between TSFSs than other distance measures. More importantly, this TSFSJS distance can avoid counter-intuitive results through the argument of some numerical results in the paper. The proposed approach can deal with more types of uncertain information as demonstrated by establishing a comparative study.

In this manuscript, nine similarity measures of T-spherical fuzzy set (TSFS) considering the membership degree, the hesitancy degree, the non-membership degree and the refusal degree are developed according to the cosine function. Besides, the generalizations of existing similarity measures are the similarity measures of TSFS proposed in this paper, which indicates the breadth and novelty of the proposed similarity measures. More importantly, the nine similarity measures of TSFSs are applied to pattern recognition. Then, we make a comparative study, that is, we apply the nine similarity measures of TSFSs developed in this manuscript to picture fuzzy environment, and the results obtained are consistent with the previous results. This application make the problem of building material recognition better solved in the real world. Finally, two numerical examples show the validity of the proposed similarity measure between TSFSs.

Fourier-domain optical coherence tomography (FD-OCT), including spectral-domain OCT (SD-OCT) and swept-source OCT (SS-OCT), allows the volumetric imaging of the tissue architecture with a faster speed and higher detection sensitivity than does time-domain OCT. Although the hardware implementations of SD-OCT and SS-OCT are different, these technologies share very similar signal processing steps for image reconstruction. In this study, we developed hardware implementations and software architectures to design a generic framework for FD-OCT. For SD-OCT systems, an external synchronization approach was used to realize a data acquisition schematic similar to that used in SS-OCT by carefully managing the timing clocks in the detection unit and for the waveform generation. In addition, by utilizing modules and factory concepts, a software engine can be developed that supports various acquisition devices and software operations or image processing functions with high operation flexibility while maintaining its robustness. Data processing and data saving were optimized using the parallel computing method with the OpenMP library and by leveraging the parallelism within the acquired data, respectively.