Using Finite-Time Lyapunov Exponents (FTLE) method, Lagrangian coherent structures (LCSs) in a fully developed flat-plate turbulent boundary layer are successfully identified from a two-dimensional (2D) velocity field obtained by time-resolved 2D PIV measurement. The typical LCSs in the turbulent boundary layer are hairpin-like structures, which are characterized as legs of quasi-streamwise vortices extending deep into the near wall region with an inclination angle θ to the wall, and heads of the transverse vortex tube located in the outer region. Statistical analysis on the characteristic shape of typical LCS reveals that the probability density distribution of θ accords well with t-distribution in the near wall region, but presents a bimodal distribution with two peaks in the outer region, corresponding to the hairpin head and the hairpin neck, respectively. Spatial correlation analysis of FTLE field is implemented to get the ensemble-averaged inclination angle θ R of typical LCS. θ R first increases and then decreases along the wall-normal direction, similar to that of the mean value of θ. Moreover, the most probable value of θ saturates at y + =100 with the maximum value of about 24°, suggesting that the most likely position where hairpins transit from the neck to the head is located around y + =100. The ensemble-averaged convection velocity U c of typical LCS is finally calculated from temporal-spatial correlation analysis of FTLE field. It is found that the wall-normal profile of the convection velocity U c (y) accords well with the local mean velocity profile U(y) beyond the buffer layer, evidencing that the downstream convection of hairpins determines the transportation properties of the turbulent boundary layer in the log-region and beyond. turbulent boundary layer, coherent structures, vortex identification scheme, finite-time Lyapunov exponents, hairpin-like vortices Turbulence, existing universally in nature and engineering, has been regarded as one of the most complicated problems in fluid dynamics. Since Kline et al. [1] revealed the existence of streaky structures and burst events in the turbulent boundary layer in the 1960s, numerous efforts have been devoted to such coherent structures/motions. Now, it is well accepted that coherent structures/motions with different shape and scale constitute the self-sustaining mechanisms of wall turbulence. Moreover, vortices are regarded as the dominant structures, which play important roles in the generation of other coherent structures/motions, e.g., streaks, burst and bulges [2][3][4] . Hairpin-like vortices, large-scale transverse vortices and streamwise vortices have all been observed in wall turbulence, among which hairpin-like vortices are regarded as the fundamental elements, whose reproduction and evolution are the prerequisite for the self-sustaining of other coherent structures/motions [5] .Hairpin-like vortices in wall turbulence are immersed in chaotic background fluctuations with small scale and significant intensity, and their symmetry and integ...

A correlated imaging system with flexible frame rate was proposed and fabricated on the Field Programmable Gate Array (FPGA) for acquiring temperature images. Real-time images of temperatures are reconstructed on chip at a frame rate of 10 Hz, and higher rates up to 1280 frames per second are also achieved in case of resolution sacrifice. Compressive sensing method enables the flexible frame rate for dynamic temperature images. An incandescent filament was imaged and compared by the proposed imaging system and a commercial Charge-Coupled Device (CCD) camera. Reconstructed images of temperature distributions agreed well with those from the camera in the range from 1500 K to 2500 K. Moreover, the proposed system captured the excitation frequency of an acoustically excited flame by varying the frame rate. The simple structure and flexible frame rate provide an alternative to dynamical temperature imaging, especially in case the common CCD camera fails to work for its slow response.

and the recent success in Crete in Greece (2016).IST 2017 aimed to explore and generate new knowledge in the multidisciplinary areas of imaging systems and medical diagnostic devices by reporting relevant novel scientific results, and technological and clinical advances. Especially, focus was given to the latest developments in sensors, metrology, image analysis, pattern recognition, machine learning, deep learning, informatics, big data and data mining, and cybersecurity methodologies.The papers included in this special issue are substantially extended versions of the conference proceeding papers. We hope that this special issue can inspire and stimulate researchers towards the advancement of science and technology in all aspects of imaging systems and techniques.