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A microstructured fiber with two concentric high-refractive-index rings is proposed to support more orbital angular momentum (OAM) beam propagations and OAM-based mode-division multiplexing (MDM) for improving the channel capacity and spectral efficiency. The two ring cores can be considered as two independent OAM channels without interference for enhancing more OAM channels. A finite element method (FEM) is employed to numerically study the properties of the designed fiber. A large effective refractive index difference between the adjacent OAM modes is achieved, which indicates that the propagated OAM modes can be well separated without multiple input, multiple output in each of the two rings. The calculated results demonstrate that a total of 80 OAM modes with a mode quality above 70% is achieved without any high-order radial modes from 1.5 to 1.6 μm, which contains 18 inner-ring OAM modes and 62 outer-ring OAM modes. Moreover, the designed fiber possesses low confinement loss (−7 dB m−1) and can be potentially utilized for high-capacity communications by OAM-based MDM.

In reality, the quality of an image is generally affected by haze. To obtain a well-quality image, removing haze is a hot issue on theory and application. This paper proposes a new algorithm to remove haze of hazy images. In the algorithm, first, the ambient illumination is estimated by a logarithmic guide filtering that can reserve the characteristics of the bright source areas and improve the dark source areas of the hazy image. Second, to overcome the defect of dark channel prior (DCP) and the over-brightness of the bright channel prior (BCP), two models with two parameters are introduced to improve the DCP and BCP, called multi-channel prior method. At the same time, a self-adaptive method is presented to compute the values of the two parameters. At last, based on the multi-channel prior, a self-adaptive method is proposed to compute the transmission mapping value. Further, four classes hazy images are employed to test the proposed method. The experimental results carried out on the public databases demonstrate that the proposed algorithm can outperform the current state-of-the-arts, including more effective defogging, clearer visibility and richer details.

Captured images are usually influenced by fog or haze. In reality, image dehazing is challenging. This paper proposes a modified artificial multiple-exposure image fusion (AMEF) algorithm to remove the haze from an image. In the algorithm, first, an adaptive gamma -correction transform with the mean and standard deviation values of each component of a hazy image is utilized to verify the intensities. Second, the homomorphic filtering algorithm is introduced into the Gaussian pyramid and Laplacian pyramid to compute the exposed accessible images. Last, a modified Laplacian filter method is presented to calculate the contrast of the exposed accessible images. Further, extensive experimental results demonstrate that the proposed algorithm has superior performance compared with that of some state-of-the-art methods, including higher contrast, richer details and a better visual effect in the dehazed image.

5G Internet of Things (IoT) networks have wideband radio frequency spectrum utility and are therefore of primary importance for sensory data transmission. However, in the 5G era, the big date seriously affects the network’s performance, namely, the high energy- consumption is the focus issue. Compressed sensing (CS) is utilized to promote the performance. In general, sensory data possess spatial and temporal correlation features, so that they are capable of capturing to design sparse basis to relieve transportation cost. In the paper, the spatial-temporal basis is designed and the optimal orthogonal basis selection algorithm based on energy is constructed. Different numerical sparse methods are introduced to appraise sparse performance of the algorithm. Besides, by means of the extensive experiments on real sensory data, we demonstrate that the proposed technique is able to sparest representing original sensory data.

One of the major concerns in 5G IoT networks is that most of the sensor nodes are powered through limited lifetime, which seriously affects the performance of the networks. In this article, Compressive sensing (CS) technique is used to decrease transmission cost in 5G IoT networks. Sparse basis is one of the important steps in the CS. However, most of the existing sparse basis-based method such as DCT (Discrete cosine transform) and DFT (Discrete Fourier Transform) basis do not capture data structure characteristics in the networks. They also do not take into consideration multi-resolution representations. In addition, some of sparse basis-driven methods exploit either spatial or temporal features, resulting in performance degradation of CS-based strategies. To address these challenging problems, we propose a novel spatial–temporal correlation basis algorithm (SCBA). Subsequently, an optimal basis algorithm (OBA) is provided considering greedy scoring criteria. To evaluate the efficiency of OBA, orthogonal wavelet basis algorithm (OWBA) by employing NS (Numerical Sparsity) and GI (Gini Index) sparse metrics is also presented. In addition, we discuss the complexity of the above three algorithms, and prove that OBA has low numerical rank. After experimental evaluation, we found that OBA is capable of the sparsest representing original signal compared to spatial, DCT, haar-1, haar-2, and rbio5.5. Furthermore, OBA has the low recovery error and the highest efficiency.

Complex illumination condition is one of the most critical challenging problems for practical face recognition. However, numerous studies have had no effective solutions reported for full illumination variation of face images in the facial recognition research field. In order to effectively solve full illumination variation problem, we propose a novel approach for illumination normalization for facial images based on the enhanced contrast method of histogram equalization (HE) and fusion of illumination estimations (FOIE). Then, feature extraction is applied with consideration of both Gabor wavelet and principal component analysis methods to process illumination normalization. Next, a support vector machine classifier (SVM) is used for face classification. Experimental results show that superior performance can be obtained in the developed approach by comparisons with some state-of-the-arts.

The captan residues in apple juice were detected by fluorescence spectrometry, and the content level of captan was predicted based on a genetic algorithm and support vector machines (GA-SVMs). According to the captan concentration in apple juice, the experimental samples were divided into four levels, including no excess, slight excess, moderate excess, and severe excess. A GA was used to select the characteristic wavelength and optimize SVM parameters, and SVM was applied to train the classification model. 50 characteristic wavelength points were selected from the original fluorescence spectra, which contained 401 wavelength points, and the classification accuracy of the training set and test set is 99.02% and 100%, respectively, which is higher than the traditional PLS method. The results show that a GA can effectively select the feature wavelengths, and an SVM model can accurately predict the content level of captan residues. A fast and non-destructive analysis method, combined with a GA and SVM based on fluorescence spectroscopy, was realized.

Complex illumination condition is one of the most critical challenging problems for practical face recognition. In this study, the authors propose a novel method to improve the illumination invariants for solving this challenge. Firstly, a new method based on the Lambert reflectance model is proposed to extract illumination invariant, which is insensitive to complex illumination variations. Secondly, in order to repair the defects caused by the process of illumination invariants extraction, the fast mean filter is utilised to smooth and remove noise. Lastly, for raising the richness of information in the output image, a nonlinear normalisation transformation is proposed. Compared with the state-of-the-arts, experimental results show that the proposed method can extract more robust illumination invariants. Apart from it, the richness of information in the processed image is greater and the performance of the face recognition rate is superior.

A concept of noncoaxially propagating discrete data by coding/decoding orbital angular momentum (OAM) of vortex beam through multiple channels is proposed. Four independent channels along different directions (diffraction orders/angles) are established by a series of specially designed holograms. Sixteen vortex beams are used to code each sequence with the length of 4 bits into an OAM mode in each channel. The theoretical analysis (simulation) demonstrates that the proposed scheme is reliable. In addition, an experiment is also designed and performed for practically certifying the feasibility of the proposed scheme. A 16-bits-length sequence, which is coded into four OAM modes in four channels (one channel with one OAM mode), is successfully received/decoded by observing the intensity-profile arrays (OAM-mode spectrum) generated by a specially designed Dammann vortex grating. The measured results show that the proposed concept is also viable in practice. Moreover, the received/recovered images indicate that the performance of the system decreases as the atmosphere turbulence strength increases when a color image and a gray-scale image are simultaneously propagated. Besides, the measured bit error rate (BER) gradually decreases with the increase of bit rate or propagation distance. The acceptable BER under the threshold of forward error correct can be achieved for a short-distance communication network.

The Retinex model explains how the human visual system regulates the color and brightness of objects perceived by the human eyes. Based on this theory, an image enhancement algorithm based on image derived graph for weak illumination image by deep network is proposed in this paper. Firstly, in view of low contrast, low overall brightness and blurred details of dark areas in images captured under weak illumination, traditional image enhancement methods are used to process images to obtain derivative maps: adaptive contrast-limited histogram equalization, logarithmic histogram equalization, adaptive gamma correction with weighted distribution. Then, the image illumination component is obtained by deep decomposition network. The end-to-end mapping relationship between low illumination image and normal illumination image is trained, and the final output image is enhanced by deep enhancement network. The experimental results show that the proposed algorithm has better robustness as well as the output image has richer details, higher contrast, better visual effect and image quality compared with some state-of-the-arts.

This paper puts forward a novel image enhancement method via Mean and Variance based Subimage Histogram Equalization (MVSIHE), which effectively increases the contrast of the input image with brightness and details well preserved compared with some other methods based on histogram equalization (HE). Firstly, the histogram of input image is divided into four segments based on the mean and variance of luminance component, and the histogram bins of each segment are modified and equalized, respectively. Secondly, the result is obtained via the concatenation of the processed subhistograms. Lastly, the normalization method is deployed on intensity levels, and the integration of the processed image with the input image is performed. 100 benchmark images from a public image database named CVG-UGR-Database are used for comparison with other state-of-the-art methods. The experiment results show that the algorithm can not only enhance image information effectively but also well preserve brightness and details of the original image.

An effective method to enhance the contrast of digital images is proposed in this paper. A histogram function is developed to make the histogram curve smoother, which can be used to avoid the loss of information in the processed image. Besides the histogram function, an adaptive gamma correction for the histogram is proposed to stretch the brightness contrast. Moreover, the log-exp transformation strategy is presented to progressively increase the low intensity while suppressing the decrement of the high intensity. In order to further widen the dynamic range of the image, the nonlinear normalization transformation is put forward to make the output image more natural and clearer. In the experiment on non-uniform illumination images, the average contrast per pixel (CPP), root mean square (RMS), and discrete entropy (DE) metrics of the developed approach are shown to be superior to selected state-of-the-art methods.

Complex illumination condition is one of the most critical challenging problems for practical face recognition. In this paper, we propose a novel method based on deep learning to solve the adverse impact imposed by illumination variation in the face recognition process. Firstly, illumination preprocessing is applied to improve the adverse effects of intense illumination changes on face images. Secondly, the Log-Gabor filter is used to obtain the Log-Gabor feature images of different scales and directions, then, LBP (Local Binary Pattern) features of images subblock is extracted. Lastly, texture feature histograms are formed and input into the deep belief network (DBN) visual layer, then face classification and recognition are completed through deep learning in DBN. Experimental results show that superior performance can be obtained in the developed approach by comparisons with some state-of-the-arts.

A novel coding/decoding concept based on the orbital angular momentum (OAM) mode and radial mode of a vortex beam is proposed for short-haul free-space optical communication. Sixty-four kinds of vortex beams, which are comprised of sixteen OAM modes and four radial modes, are employed and utilized for coding each of 6-bits-length sequences into one of the 64-ary symbols. A specially-designed Dammam vortex grating (DVG) is utilized to generate an intensity-array profile, where the received vortex beam is demodulated into Gaussian beam in the related coordinate (order). In one of the coordinates, the received vortex beam can be converted into a bright spot (Gaussian beam) with a certain number of intensity rings. According to the coordinate and number of intensity rings, the received vortex beam can be identified and then decoded into the initially-propagated information sequence. The corresponding theory analysis/simulations and experiments demonstrate that the proposed concept is valid and feasible. In addition, the measured results also demonstrate that the performance of the proposed concept declines as the transmission rate/ atmosphere turbulence strength/propagation distance increases. By adopting advanced devices and approaches, the performance of the proposed scenario can be significantly improved, and then the proposed concept can be potentially utilized for short-haul free-space optical communication in the future.

A novel concept of coding/decoding data is proposed by multiplexing vortex beams for short-haul-free-space light communication. Eight vortex beams carrying different orbital angular momentum (OAM) modes (-5, 3, -2, 1, 5, -3, 2, -1) are employed to code the initial information sequences into each 256-ary symbols by different multiplexing approaches. A specially-designed Dammam vortex grating is utilized to decode the received vortex beams into the initial information sequences. In order to certifying the validity and feasibility of the proposed concept in practice, a proof-of-concept experiment is also proposed and established. The measured results demonstrate that a 128 × 128 gray-scale image (“Lena”) can be perfectly propagated over a 10 m free space by the proposed concept. In order to further evaluate the performance of the proposed concept, bit error rate (BER) against the received optical signal-to-noise ratio (OSNR) is also employed to analyzing the influences of some parameters on the system performances. The measured BER distributions demonstrate that the BER performance decreases as the data rate/propagation distance/atmosphere turbulence strength increases, respectively.

In order to improve the performance of a communication system, a novel concept of low-crosstalk demodulating vortex beams by a combination of partial-receiving apertures (PRAs) is proposed. The corresponding theory analysis and simulations are performed for certifying the valid of the proposed concept in theory. A proof-of-concept experiment is also proposed and established to verity the feasibility of the proposed concept in practice. Based on the captured intensity-array profiles in experiments, the total crosstalk, which is induced by partial receiving apertures, can be evenly distributed among the parasitic orbital angular momentum (OAM) modes, then the maximum ratio of intensity between the induced parasitic OAM modes and the propagated OAM modes can be notably decreased when two unsymmetrical PRAs are utilized. The measured bit error rate curves (with respect to the differently-placed PRAs) also show that the performance of system can be improved in sequence when three ways of one PRA, two symmetric PRAs and two asymmetric PRAs are orderly utilized for demodulating OAM beams, respectively. Besides, the measured results also demonstrate that the proposed system performances are declined with the increases of data rates or propagation distances. Consequently, our proposed concept in this paper is valid and reliable in both theory and practice, and then can be used to demodulate the vortex beams with low crosstalk for free-space optical communication in future.

For the problems and deficiencies of the current farmland solid remote monitoring system, this paper introduces a kind of farmland solid remote monitoring system based on embedded technology and BS mode, the system achieves data acquisition and A / D converting, sending monitoring data to CDMA through serial port, converting the data to TCP/IP data packets, sending the data by using CDMA DTU, connecting to the remote web server, providing real-time data acquisition and remote transmission, also, the system realizes the monitoring data web management, the system is proved to be effective according to the result of running.

In order to ensure the belt conveyor operation reliable and safe, centralized monitoring and control is very necessary. This paper takes the belt conveyor for coal mine as the background, designs the monitoring system based on PLC technology. The system is composed of PLC and touch screen. It is used to control the machine and display running information real-time. The hardware and software of the system station is designed by S7-300 PLC, including the module setting of PLC and its interface wires. After introducing the system design, simulation is made in the software PLCSIM. The simulation results illustrate the effectiveness and correctness of the proposed method.

At present, the multilevel FPGA scheme is used widely in system for people life. In order to satisfy the need of product update, it is very urgent to find a way to solve the problem of Multilevel FPGA upgrade. The traditional method of single FPGA upgrade contains two steps, the first one is storing the configuration file to external NOR_FLASH; the second step is reading the file from external NOR_FLASH and configuring the FPGA. According to the traditional method, it would need more NORFLASH for each FPGA, which would not only increase the cost of product but also increase complexity of the circuit. So the traditional method was not suitable for multilevel FPGA upgrade. In order to solve the problem of multilevel FPGA upgrade online, it raised a scheme to implicate multilevel FPGA upgrade online. The design has been proved correct in SSD system. It also can be used in other system with the structure of multilevel FPGA.

According the universal serial cyclic redundancy check (CRC) technology, one of the new CRC algorithm based on matrix is refered, which descibe an new parallel CRC coding circuit structure with r matrix transformation and pipeline technology. According to the method of parallel CRC coding in high-speed data transmiting, it requires a lot of artificial calculation. Due to the large amount of calculation, it is easy to produce some calculation error. According to the traditional thought of the serial CRC, the algorithm of parallel CRC based on the thought of matrix transformation and iterative has been deduced and expressed. The improved algorithm by pipeline technology has been applied in other systems which require high timing requirements of problem, The desin has been inplemented through Verilog hardware description language in FPGA device, which has achieved a good validation. It has becomed a very good method for high-speed CRC coding and decoding.

In view of present mine safety monitoring system's insufficiency, this article introduces the mine safety monitoring system based on Zig Bee and web, the system has several unique advantages, which not only enhance the timeliness and validity of the monitoring system, but also realize the monitoring data long-distance management, the system is proved to be effective according to the result of running.

The static var generator (SVG) with appropriate control method is a good solution to solve the problem of reactive power impact and harmonic pollution which is generated by the nonlinear load. In this paper, a control strategy using active current separation and the current indirect control is used for SVG. The active current separation is a method for detecting the harmonic and reactive current, and then the sum of the harmonic and reactive current is used as the value of compensation. By studying on the method, we set up the simulation model and obtained the detection results of this algorithm, and we also get the reactive power compensation results. The theoretical analysis and simulation results illustrate the effectiveness and correctness of the proposed method.