Search

A tone mapping operator (TMO) is a module in the image signal processing pipeline that is used to convert high dynamic range images to low dynamic range images for display. Currently, state-of-the-art TMOs typically take complex algorithms and are implemented on graphics processing units, making it difficult to run with low latency on edge devices, and TMOs implemented in hardware circuits often lack additional noise suppression because of latency and hardware resource constraints. To address these issues, we proposed a low-latency noise-aware TMO for hardware implementation. Firstly, a locally weighted guided filter is proposed to decompose the luminance image into a base layer and a detail layer, with the weight function symmetric concerning the central pixel value of a window. Secondly, the mean and standard deviation of the basic layer and the detail layer are used to estimate the noise visibility according to the human visual characteristics. Finally, the gain for the detail layer is calculated to achieve adaptive noise suppression. In this process, luminance is first processed by the log2 function before being filtered and then symmetrically converted back to the linear domain by the exp2 function after compression. Meanwhile, the algorithms within the proposed TMO were optimized for hardware implementation to minimize latency and cache, achieving a low latency of 60.32 μ s under video specification of 1080 P at 60 frames per second and objective metric smoothness in dark flat regions could be improved by more than 10% compared to similar methods. [ABSTRACT FROM AUTHOR]

Weak bearing fault feature extraction (FFE) research has previously focused on bearing fault signal of transient impulse extraction whereas higher harmonic feature extraction of fault feature frequency aspects are relatively less. While traditional envelope demodulation method has certain limitations to capture the rolling bearing fault characteristic frequency of higher harmonic. To this end, combining adaptive chirp mode decomposition (ACMD), improved maximum correlation kurtosis deconvolution (IMCKD), and 1.5-dimensional Teager energy cyclo-stationary spectrum (1.5-DTECS), in the current work we propose a three-stage defect detection system. The performance of the proposed method is evaluated by analyzing the three-stage FFE (STFFE) of multiple kinds of rolling bearing fault data. The findings reveal that the three-stage fault detection approach successfully suppresses noise, highlights the fault impact, and extracts the higher order harmonics of the bearing defect characteristic frequency more effectively. The research contributes to the field of bearing fault high harmonic component extraction and provides guidance on techniques related to the extraction of bearing impulse characteristic. [ABSTRACT FROM AUTHOR]

Raman spectroscopic imaging (RSI) can simultaneously measure the spectra and spatial information of samples. The measured data include two-dimensional spatial images and one-dimensional Raman spectra. Traditionally, RSI can be obtained by point scanning, line scanning, and wide-field illumination schemes. The point scanning scheme provides the best resolution but a lower imaging speed. On the contrary, wide-field illumination can image fast but provides a lower spatial resolution. To integrate the advantages of the two schemes, a complementary scheme for wide-field RSI was proposed, which uses frequency modulation based spatially encoded (FMSE) light as the excitation. In this scheme, all the beams of wide-field illumination are modulated at different frequencies. Thus, each excitation beam has its own modulation frequency, and the excited Raman signal will carry this modulation information. For the detection module, a single point detector was used to collect time series Raman signals carrying the unique modulation information. Using a sparse reconstruction-based demodulation strategy, the Raman image can be recovered effectively. The feasibility of the method was verified with numerical simulations. The reconstruction results showed that it is feasible to conduct Raman spectroscopic imaging with high-resolution and high speed under the illumination of FMSE light and detection of a single-point detector. [ABSTRACT FROM AUTHOR]

[Display omitted] • This work overcomes the trade-off challenge between particle size and quantum efficiency of nitride phosphors encountered in traditional preparation methods. • The underlying mechanism is clarified to be the increased Eu2+ percentage and the unaltered chemical stiffness after the mechanochemical processing. • Small-sized nitride phosphors with superhigh brightness and ultralong durability. High brightness mini-LED backlit displays attract extensive advanced applications. However, it is a challenge to develop small-sized phosphors concurrently satisfying high brightness and long-term durability to accommodate mini-LED chips. Herein, a two-step facile approach combining high-temperature sintering and mechanochemical milling is proposed to obtain small-sized β-Sialon:Eu nitride phosphors (D50 = 3.13 μm) with a five-fold increase in quantum efficiency (QE, 16% → 79%) and a superb on chip durability with 100% maintenance of QE for 1400 h. The high performance is attributed to the increased Eu2+ percentage (67% → 77%) and the unaltered chemical stiffness after the mechanochemical processing. Crucially, β-Sialon:Eu film made from small-sized powders shows higher packaging density and better light uniformity, concomitantly with an improved QE of 83%. Moreover, this approach is generally applicable to other robust nitride phosphors. By using small-sized β-Sialon:Eu and (Sr,Ca)AlSiN 3 :Eu films, as-fabricated mini-LED backlights simultaneously achieve a superhigh brightness of 302,150 nits and an ultralong durability exceeding 2,000 h, opening up an avenue for mini-LED backlit displays being a viable technology for high-end applications. [ABSTRACT FROM AUTHOR]

• Dual-scale structures are important for the stable Cassie state. • Small-scale structures can prevent the transition of Cassie-Wenzel. • Small-scale structures can facilitate the transition of Wenzel-Cassie. In this work, we first create three-dimensional molecular-dynamics (MD) model to study the effect of structure parameters including pillar size, pillar height, pillar groove, and intrinsic contact angle (θ e , or Young's angle) on wetting behavior of drops on the one-scale nanopillared surface. We find that drops tend to exhibit the Cassie state on the nanopillared surface with small pillar size and large θ e. Considering comprehensively the stability of the Cassie state, excellent hydrophobicity, and mechanical durability of microstructures, the pillar height and the pillar groove should be moderate. Then, only considering the wettability, we could see the dual-scale structure as the one-scale structure with large θ e * that results from the small-scale structure for increasing the θ e and cannot be achieved on the smooth surface in experiments. Importantly, we observe that the dual-scale structure, corresponding to large θ e * resulting from the small-scale structure for increasing the θ e , are important for the formation and the stability of the Cassie state. Moreover, the dual-scale structure can prevent the transition of drops from the Cassie state to the Wenzel state. Interestingly, fabricating the small-scale structure on top of the large-scale structure (corresponding to large θ e * of the one-scale structure) can facilitate the transformation of the Wenzel state of drops on the large-scale structure to the Cassie state, and the Cassie state is stable. This study provides an inspiration for the fabrication of stable artificial superhydrophobic surfaces. [ABSTRACT FROM AUTHOR]

Compared with static optical imaging, dynamic optical imaging technology can obtain quantitative pharmacokinetics information, such as the probe metabolism curve, removal rate, and binding potential of the receptor. Accurate segmentation of the region of interest (ROI) is an important step in dynamic optical imaging. Generally, the ROI is manually labeled by researchers based on experience. This will lead to two unavoidable problems. First, manual segmentation of the ROI is very time consuming, especially when there are many sequential dynamic optical images. Second, manual segmentation cannot ensure accuracy when the optical signal gradually decays to a point at which it is difficult to distinguish by using the naked eyes. These problems will inevitably lead to inaccuracy of quantitative results of dynamic optical imaging. Here, we presented a machine learning-based automatic segmentation method to avoid these time-consuming and inaccuracy problems caused by manual segmentation. The K-means clustering algorithm and fuzzy c-means clustering algorithm were implemented to separate the ROI from the background of sequential dynamic optical images. Automatic selection of clustering results was completed by mathematical methods. The accuracy and feasibility of machine learning-based methods were verified by comparing their results with the manual segmentation results. The preliminary results demonstrated that the machine learning-based automatic segmentation has coherent performance with the manual one. [ABSTRACT FROM AUTHOR]