Image-based water level measurement is a visual-sensing technique which automatically inspects the reading of the water line via image processing instead of the human eye. It can be realized easily on an existing video surveillance system and has advantages like low cost, non-contact, as well as results that are verifiable. It has the potential to be widely used in flood and waterlogging monitoring, while facing the challenge that water-line detection under complex natural or artificial illumination conditions is quite difficult in field applications. To handle this problem, a method is proposed assuming that the water line is generally located on the row with the largest local change of gray or edge features in the image of the water gauge. The water line is determined by coarse-to-fine detection of the position of the maximum mean difference (MMD) of the horizontal projections of gray and edge images. Image-based flow-level measurement systems were developed at two measurement sites. In situ comparative experiments were conducted with the float-type stage gauge and other image-based methods. The results show that the fusion of gray and edge features can overcome the shortcomings of single feature methods under complex illumination conditions such as dim light, glares, shadows and artificial night lighting. A coarse-to-fine strategy utilizes the periodicity of the surface pattern distribution of the standard bicolor water gauge, which improves the reliability of water-line detection. The resolution and accuracy of water-level measurement are 1 mm and 1 cm, respectively. In particular, the MMD value is efficient at identifying extremely unfavorable conditions and reducing gross errors.

Hyperspectral remote sensing images (HSIs) have great research and application value. At present, deep learning has become an important method for studying image processing. The Generative Adversarial Network (GAN) model is a typical network of deep learning developed in recent years and the GAN model can also be used to classify HSIs. However, there are still some problems in the classification of HSIs. On the one hand, due to the existence of different objects with the same spectrum phenomenon, if only according to the original GAN model to generate samples from spectral samples, it will produce the wrong detailed characteristic information. On the other hand, the gradient disappears in the original GAN model and the scoring ability of a single discriminator limits the quality of the generated samples. In order to solve the above problems, we introduce the scoring mechanism of multi-discriminator collaboration and complete semi-supervised classification on three hyperspectral data sets. Compared with the original GAN model with a single discriminator, the adjusted criterion is more rigorous and accurate and the generated samples can show more accurate characteristics. Aiming at the pattern collapse and diversity deficiency of the original GAN generated by single discriminator, this paper proposes a multi-discriminator generative adversarial networks (MDGANs) and studies the influence of the number of discriminators on the classification results. The experimental results show that the introduction of multi-discriminator improves the judgment ability of the model, ensures the effect of generating samples, solves the problem of noise in generating spectral samples and can improve the classification effect of HSIs. At the same time, the number of discriminators has different effects on different data sets.

In this paper, a test structure for simultaneously determining thermal conductivity and the coefficient of thermal expansion (CTE) of polysilicon thin film is proposed. The test structure consists of two double-clamped beams with different lengths. A theoretical model for extracting thermal conductivity and CTE based on electrothermal analysis and resonance frequency approach is developed. Both flat and buckled beams are considered in the theoretical model. The model is confirmed by finite element software ANSYS. The test structures are fabricated by surface micromachined fabrication process. Experiments are carried out in our atmosphere. Thermal conductivity and CTE of polysilicon thin film are obtained to be (29.96  ±  0.92) W · m · K−1 and (2.65  ±  0.03)  ×  10−6 K−1, respectively, with temperature ranging from 300–400 K.

Geometrical parameters, such as the thickness and the sidewall etch angle of microelectromechanical systems (MEMS) thin films, are important information for device design and simulation, material property extraction, and quality control in a fabrication process line. This paper presents an in-line test microstructure for measuring geometrical parameters of surface micromachined thin films. The structure consists of four-probe bridges with continuous step structures and deposited at three different angles. The extraction method takes advantage of the resistances of the step structures to determine the thickness and the sidewall etch angle of the phosphosilicate glass (PSG) layer and the thickness of the polysilicon layer. The sheet resistance and the width of the test structure are required for the extraction method and can also be measured by using the test structure. Thicknesses of (2.080 ± 0.011) µm, (2.142 ± 0.012) µm, (1.614 ± 0.014) µm and (2.892 ± 0.012) µm are obtained for the Poly 1 layer, the Oxide 1 layer, the Poly 2 layer and the stacked layer of Oxide 1-Oxide 2, respectively. The sidewall etch angles for the Oxide 1 layer and the stacked layer of Oxide 1-Oxide 2 are obtained as (77.51 ± 0.61)° and (76.17 ± 0.91)°, respectively. In comparison to previously reported thickness measurement approaches, the proposed method is nondestructive, and makes use of four-point probe technique which is featured with electrical input and output configuration, simple operation, low cost, fast response, good repeatability and ease of integration. Therefore, this method is more suited to in-line monitoring the MEMS fabrication process.

This paper presents a simple method for the in situ determination of Young’s moduli of surface-micromachined bilayer thin films. The test structure consists of a cantilever, a bottom drive electrode located near the anchor, and a bottom contact electrode placed below the free end of the cantilever. The cantilever is driven by applying a voltage sweep between the cantilever and the drive electrode, and bends due to the electrostatic force. A novel theoretical model is derived to relate Young’s modulus with the applied voltage and structure dimensions. The theoretical model is validated by finite element simulation. Test structures for Au/polysilicon thin films are fabricated by the PolyMUMPsand tested with the current–voltage measurement system. The measured Young modulus of polysilicon ranges from 152.344 GPa to 154.752 GPa, and the measured Young modulus of Au ranges from 71.794 GPa to 74.880 GPa. Compared with existing extraction methods, the proposed method is featured with simple operation, good repeatability, relatively high precision, and low requirements for equipment. It can be used alongside the application of a process control monitor (PCM) in surface-micromachining process lines.