Abstract:The static levelling system has significant value in the settling measurement of the structures. A novel pressurized static level based on circular structure is designed by using fiber Bragg grating (FBG) sensing technology in this research. The variation of the level pressure in the connecting vessel can be transmitted through the diaphragm to the FBG of the circular ring. The Ritz method is used to calculate the lateral deformation of the circular ring under the concentrated force, constructing the theoretical sensitivity model of the sensor. Further, finite element simulation and static experiments are applied to modify and verify it. According to the experimental data, two FBGs arranged horizontally along the circular ring, multiplying the sensitivity of the FBG static level to about 4.75 pm/mm, while the resolution can reach 0.02 mm. In addition, the temperature compensation of the sensor can be realized by the synergistic change of the measurement point and the reference point within the system of connecting vessel.

Abstract:In the current study, a monocrystalline Si photovoltaic (PV) cell was modeled using solar cell capacitance simulator (SCAPS) to demonstrate the optoelectronic performance of the cell under harsh environmental conditions. Harsh conditions are simulated in terms of wind speed and temperature fluctuations within the presence of a dust layer. All models are evaluated with respect to a bare model with no dust layer accumulated and operating under standard test conditions (STC). Accordingly, the PV under-test characteristics have been estimated under continuous wind speed and temperature variations. An interesting behavior for the cell operation under relatively high temperatures with an accumulated dust layer was observed. The short circuit current increased by 61.5% with decreasing open-circuit voltage by 47.3%, showing an overall positive trend for the power harvested. Such behavior contradicts the average temperature performance of cells without dust layer accumulation. A detailed justification is illustrated, where the heat transfer rate with dust accumulation highlighted an incremental increase concerning the bare cell by 14.57%.

Abstract:Organic light-emitting devices (OLEDs) have received widespread attention due to their excellent luminescence performance and flexibility. In this work, we demonstrate a novel two-color OLED, in which the forward and reverse units are vertically stacked together using Ag:Mg as an intermediate electrode for charge injection and transfer. Due to the extremely thin Ag:Mg film layer has a relatively smooth surface, it plays a critical role in the reverse unit light emission of the device. Thus, it is realized that the device emits green and red light when forward and reverse voltages are applied respectively. This provides a new possibility for future OLEDs lighting and display developments. The OLEDs were prepared by using such Ag:Mg intermediate electrodes, and a maximum brightness of 451.14 cd/m2 and an external quantum efficiency (EQE) of 3.82% were obtained at a small current density.

Abstract:In order to further study doping-free asymmetric heterojunction (DASH) solar cells, we used AFORS-HET software to optimize the structure of Al/SnO2/a-Si:H (i)/c-Si (p)/a-Si:H (i)/NiOx/Ag. In a certain adjustment range, a series of simulations were carried out on the band gap, electron affinity, thickness and work function (WF) of NiOx, thickness and WF of SnO2, and the thickness of a-Si:H (i). After the above optimization, 21.08% efficiency was obtained at 300 K. This study shows that the solar cells with this structure have good light absorption properties in a very wide spectrum. The present simulation provides instructive suggestions for follow-up experiments of DASH solar cells.

Abstract:Thin films Bi4Ti3O12 (BLT) were deposited using electron beam evaporation on silicon substrate at several times, also on AlN/Si and SiO2/Si substrates. Thin films morphology and thickness were measured via scanning electron microscopy (SEM). The crystallography was studied using X-ray diffraction (XRD) technique for films which have a (0010) preferred orientation in all substrate types. The capacitance values were contingent on frequency value in C-V measurement. The ferroelectric characterization was investigated for BLT film deposited on isolator layer (SiO2 or AlN) for Al/Bi4Ti3O12/SiO2/Si devices. Memory effect value varied from 1 V to 3 V depending on the thin films isolator on substrate.

Abstract:The plasmonic-based graphite- and graphene-nanopores have been investigated by employing the hybrid quantum/classical scheme (HQCS). Transverse, longitudinal, and total absorption spectra obtained from HQCS are analyzed for the graphite and graphene nanopores. Analyses were examined for each structure in the presence of deoxyribonucleic acid (DNA) nucleobases. The simple excitation of the total mode in graphene nanopore shows the best selectivity for DNA sequencing. A novel method based on the transverse and longitudinal modes in a time-series approach has been proposed for selectivity improvements. In the proposed time-series method, the outstanding results show that graphite nanopore is more sensitive than and as selective as graphene nanopore. This paper suggests that time-step analysis of the plasmonic absorption in the graphite nanopore is a promising method for DNA sequencing.

Abstract:In order to prepare CdxZn1-xS films with lower cadmium content and better performance as a buffer layer for copper indium gallium selenide (CIGS) solar cells, the performance of CdxZn1-xS films deposited in a mixture of solutions containing extremely low cadmium sources was systematically investigated by chemical bath deposition (CBD) with the synergy of chemical experiments and numerical simulations. The experimental results show that the films have the best overall performance at a cadmium source condition of 0.007 M.

Abstract:In order to reduce the high complexity of the successive cancellation list (SCL) algorithm for polar codes, a simplified SCL decoding algorithm based on critical sets (CS-SCL decoding algorithm) is proposed. The algorithm firstly constructs the critical sets according to the channel characteristics of the polar codes as well as comprehensively considering both the minimum Hamming weight (MHW) of the information bits and the channel reliability. The information bits within the critical sets and the path splitting are still performed by the SCL decoding algorithm while the information bits outside the critical sets are directly performed by the hard decision. Thus, the number of path ordering, copying, and deleting can be reduced during decoding. Furthermore, the computational complexity of the SCL decoding can also be reduced. Simulation results demonstrate that the decoding complexity of the proposed CS-SCL decoding algorithm, compared with the conventional SCL decoding algorithm, is reduced by at least 70%, while compared with the simplified SCL (PS-SS-SCL) algorithm which constructs the critical set with the first and second information bits of the Rate-1 nodes, its decoding complexity can also be reduced. Moreover, the loss of the error correction performance for the proposed CS-SCL decoding algorithm is minor. Therefore, the proposed CS-SCL algorithm is effective and can provide a reasonable tradeoff between the decoding performance and complexity for the decoding algorithm of polar codes.

Abstract:Clinical information about a variety of disorders is available through blood cell counting, which is usually done by manual methods. However, manual methods are complex, time-consuming and susceptible to the subjective experience of inspectors. Although many efforts have been made to develop automated blood cell counting algorithms, the complexity of blood cell distribution and the highly overlapping nature of some red blood cells (RBCs) remain significant challenges that limit the improvement of analytical accuracy. Here, we proposed an end-to-end method for blood cell counting based on deep learning. Firstly, U-Net++ was used to segment the whole blood cell image into several regions of interest (ROI), and each ROI contains only one single cell or multiple overlapping cells. Subsequently, YOLOv5 was used to detect blood cells in each ROI. Specifically, we proposed several strategies, including fine classification of RBCs, adaptive adjustment for non-maximal suppression (NMS) threshold and blood cell morphology constraints to improve the accuracy of detection. Finally, the detection outcomes for each ROI were combined and superimposed. The results show that our method can effectively address the issue of high overlap and precisely segment and detect blood cells, with a 98.18% accuracy rate for blood cell counting.

Abstract:With the rapid development of computer vision, point clouds technique was widely used in practical applications, such as obstacle detection, roadside detection, smart city construction, etc. However, how to efficiently identify the large scale point clouds is still an open challenge. For relieving the large computation consumption and low accuracy problem in point cloud classification, a large scale point cloud classification framework based on light bottle transformer (light-BotNet) is proposed. Firstly, the two-dimensional (2D) and three-dimensional (3D) feature values of large scale point cloud were extracted for constructing point cloud feature images, which employed the prior knowledge to normalize the point cloud features. Then, the feature images are input to the classification network, and the light-BotNet network is applied for point cloud classification. It is an interesting attempt to combine the traditional image features with the transformer network. For proving the performance of the proposed method, the large scale point cloud benchmark Oakland 3D is utilized. In the experiments, the proposed method achieved 98.1% accuracy on the Oakland 3D dataset. Compared with the other methods, it can both reduce the memory consumption and improve the classification accuracy in large scale point cloud classification.