Showing papers by "Jeng-Shyang Pan published in 2019"

PaDE: An enhanced Differential Evolution algorithm with novel control parameter adaptation schemes for numerical optimization

Abstract: Differential Evolution (DE) variants have been proven to be excellent algorithms in tackling real-parameter single objective numerical optimization because they have secured the front ranks of these competitions for many years. Nevertheless, there are still some weaknesses, e.g. (1) improper control parameter adaptation schemes; and (2) defect in a given mutation strategy., existing in some state-of-the-art DE variants, which may result in slow convergence and worse optimization performance. Therefore, in this paper, a novel Parameter adaptive DE (PaDE) is proposed to tackle the above mentioned weaknesses and the PaDE algorithm has three advantages: (1) A grouping strategy with novel adaptation scheme for C r is proposed to tackle the improper adaptation schemes of C r in some state-of-the-art DE variants; (2) A novel parabolic population size reduction scheme is proposed to tackle the weakness in linear population size reduction scheme; (3) An enhanced time stamp based mutation strategy is proposed to tackle the weakness in a former mutation strategy. The novel PaDE algorithm is verified under 58 benchmarks from two Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single objective numerical optimization, and experiment results show that the proposed PaDE algorithm is competitive with the other state-of-the-art DE variants.

An Improved Flower Pollination Algorithm for Optimizing Layouts of Nodes in Wireless Sensor Network

Abstract: The arrangement of nodes impacts the quality of connectivity and energy consumption in wireless sensor network (WSN) for prolonging the lifetime. This paper presents an improved flower pollination algorithm based on a hybrid of the parallel and compact techniques for global optimizations and a layout of nodes in WSN. The parallel enhances diversity pollinations for exploring in space search and sharing computation load. The compact can save storing variables for computation in the optimization process. In the experimental section, the selected test functions and the network topology issue WSN are used to test the performance of the proposed approach. Compared results with the other methods in the literature show that the proposed algorithm achieves the practical way of reducing the number of its stored memory variables and running times.

Novel Systolization of Subquadratic Space Complexity Multipliers Based on Toeplitz Matrix–Vector Product Approach

Abstract: Systolic finite field multiplier over $GF(2^{m})$ , because of its superior features such as high throughput and regularity, is highly desirable for many demanding cryptosystems On the other side, however, obtaining high-performance systolic multiplier with relatively low hardware cost is still a challenging task due to the fact that the systolic structure usually involves large area complexity Based on this consideration, in this paper, we propose to carry out two novel coherent interdependent efforts First, a new digit-serial multiplication algorithm based on polynomial basis over binary field $(GF(2^{m}))$ is proposed Novel Toeplitz matrix–vector product (TMVP)-based decomposition strategy is employed to derive an efficient subquadratic space complexity Second, The proposed algorithm is then innovatively mapped into a low-complexity systolic multiplier, which involves less area-time complexities than the existing ones A series of resource optimization techniques also has been applied on the multiplier which optimizes further the proposed design (it is the first report on digit-serial systolic multiplier based on TMVP approach covering all irreducible polynomials, to the best of our knowledge) The following complexity analysis and comparison confirm the efficiency of the proposed multiplier, that is, it has lower area-delay product (ADP) than the existing ones The extension of the proposed multiplier for bit-parallel implementation is also considered in this paper

Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power

Abstract: Wind and other renewable energy protects the ecological environment and improves economic efficiency. However, it is difficult to accurately predict wind power because of the randomness and volatility of wind. This paper proposes a new parallel heterogeneous model to predict the wind power. Parallel meta-heuristic saves computation time and improves solution quality. Four communication strategies, which include ranking, combination, dynamic change and hybrid, are introduced to balance exploration and exploitation. The dynamic change strategy is to dynamically increase or decrease the members of subgroup to keep the diversity of the population. The benchmark functions show that the algorithms have excellent performance in exploration and exploitation. In the end, they are applied to successfully realize the prediction for wind power by training the parameters of the neural network.

A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks

Abstract: Everyday, a large number of complex scientific and industrial problems involve finding an optimal solution in a large solution space. A challenging task for several optimizations is not only the combinatorial operation but also the constraints of available devices. This paper proposes a novel optimization algorithm, namely the compact bat algorithm (cBA), to use for the class of optimization problems involving devices which have limited hardware resources. A real-valued prototype vector is used for the probabilistic operations to generate each candidate for the solution of the optimization of the cBA. The proposed cBA is extensively evaluated on several continuous multimodal functions as well as the unequal clustering of wireless sensor network (uWSN) problems. Experimental results demonstrate that the proposed algorithm achieves an effective way to use limited memory devices and provides competitive results.

HARD-DE: Hierarchical ARchive Based Mutation Strategy With Depth Information of Evolution for the Enhancement of Differential Evolution on Numerical Optimization

Abstract: Differential evolution is a famous and effective branch of evolutionary computation, which aims at tackling complex optimization problems. There are two aspects significantly affecting the overall performance of DE variants, one is trial vector generation strategy and the other is the control parameter adaptation scheme. Here in this paper, a new hierarchical archive-based trial vector generation strategy with depth information of evolution was proposed to get a better perception of landscapes of objective functions as well as to improve the candidate diversity of the trial vectors. Furthermore, novel adaptation schemes both for crossover rate $Cr$ and for population size $ps$ were also advanced in this paper, and consequently, an overall better optimization performance was obtained after these changes. The novel HARD-DE algorithm was verified under many benchmarks of the Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single-objective optimization as well as two benchmarks on real-world optimization from CEC2011 test suite, and the experiment results showed that the proposed HARD-DE algorithm was competitive with the other state-of-the-art DE variants.

New Hybrid Algorithms for Prediction of Daily Load of Power Network

Abstract: Two new hybrid algorithms are proposed to improve the performances of the meta-heuristic optimization algorithms, namely the Grey Wolf Optimizer (GWO) and Shuffled Frog Leaping Algorithm (SFLA). Firstly, it advances the hierarchy and position updating of the mathematical model of GWO, and then the SGWO algorithm is proposed based on the advantages of SFLA and GWO. It not only improves the ability of local search, but also speeds up the global convergence. Secondly, the SGWOD algorithm based on SGWO is proposed by using the benefit of differential evolution strategy. Through the experiments of the 29 benchmark functions, which are composed of the functions of unimodal, multimodal, fixed-dimension and composite multimodal, the performances of the new algorithms are better than that of GWO, SFLA and GWO-DE, and they greatly balances the exploration and exploitation. The proposed SGWO and SGWOD algorithms are also applied to the prediction model based on the neural network. Experimental results show the usefulness for forecasting the power daily load.

Human motion recognition based on SVM in VR art media interaction environment

Abstract: In order to solve the problem of human motion recognition in multimedia interaction scenarios in virtual reality environment, a motion classification and recognition algorithm based on linear decision and support vector machine (SVM) is proposed. Firstly, the kernel function is introduced into the linear discriminant analysis for nonlinear projection to map the training samples into a high-dimensional subspace to obtain the best classification feature vector, which effectively solves the nonlinear problem and expands the sample difference. The genetic algorithm is used to realize the parameter search optimization of SVM, which makes full use of the advantages of genetic algorithm in multi-dimensional space optimization. The test results show that compared with other classification recognition algorithms, the proposed method has a good classification effect on multiple performance indicators of human motion recognition and has higher recognition accuracy and better robustness.

An Adaptation Multi-Group Quasi-Affine Transformation Evolutionary Algorithm for Global Optimization and Its Application in Node Localization in Wireless Sensor Networks.

Abstract: Developing metaheuristic algorithms has been paid more recent attention from researchers and scholars to address the optimization problems in many fields of studies. This paper proposes a novel adaptation of the multi-group quasi-affine transformation evolutionary algorithm for global optimization. Enhanced population diversity for adaptation multi-group quasi-affine transformation evolutionary algorithm is implemented by randomly dividing its population into three groups. Each group adopts a mutation strategy differently for improving the efficiency of the algorithm. The scale factor F of mutations is updated adaptively during the search process with the different policies along with proper parameter to make a better trade-off between exploration and exploitation capability. In the experimental section, the CEC2013 test suite and the node localization in wireless sensor networks were used to verify the performance of the proposed algorithm. The experimental results are compared results with three quasi-affine transformation evolutionary algorithm variants, two different evolution variants, and two particle swarm optimization variants show that the proposed adaptation multi-group quasi-affine transformation evolutionary algorithm outperforms the competition algorithms. Moreover, analyzed results of the applied adaptation multi-group quasi-affine transformation evolutionary for node localization in wireless sensor networks showed that the proposed method produces higher localization accuracy than the other competing algorithms.

A bi-population QUasi-Affine TRansformation Evolution algorithm for global optimization and its application to dynamic deployment in wireless sensor networks

Abstract: In this paper, we propose a new Bi-Population QUasi-Affine TRansformation Evolution (BP-QUATRE) algorithm for global optimization. The proposed BP-QUATRE algorithm divides the population into two subpopulations with sort strategy, and each subpopulation adopts a different mutation strategy to keep the balance between the fast convergence and population diversity. What is more, the proposed BP-QUATRE algorithm dynamically adjusts scale factor with a linear decrease strategy to make a good balance between exploration and exploitation capability. We compare the proposed algorithm with two QUATRE variants, PSO-IW, and DE algorithms on the CEC2013 test suite. The experimental results demonstrate that the proposed BP-QUATRE algorithm outperforms the competing algorithms. We also apply the proposed algorithm to dynamic deployment in wireless sensor networks. The simulation results show that the proposed BP-QUATRE algorithm has better coverage rate than the other competing algorithms.

A Novel Improved Bat Algorithm Based on Hybrid Parallel and Compact for Balancing an Energy Consumption Problem

Abstract: This paper proposes an improved Bat algorithm based on hybridizing a parallel and compact method (namely pcBA) for a class of saving variables in optimization problems. The parallel enhances diversity solutions for exploring in space search and sharing computation load. Nevertheless, the compact saves stored variables for computation in the optimization approaches. In the experimental section, the selected benchmark functions, and the energy balance problem in Wireless sensor networks (WSN) are used to evaluate the performance of the proposed method. Results compared with the other methods in the literature demonstrate that the proposed algorithm achieves a practical method of reducing the number of stored memory variables, and the running time consumption.

Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images.

Abstract: Infrared and visible image matching methods have been rising in popularity with the emergence of more kinds of sensors, which provide more applications in visual navigation, precision guidance, image fusion, and medical image analysis. In such applications, image matching is utilized for location, fusion, image analysis, and so on. In this paper, an infrared and visible image matching approach, based on distinct wavelength phase congruency (DWPC) and log-Gabor filters, is proposed. Furthermore, this method is modified for non-linear image matching with different physical wavelengths. Phase congruency (PC) theory is utilized to obtain PC images with intrinsic and affluent image features for images containing complex intensity changes or noise. Then, the maximum and minimum moments of the PC images are computed to obtain the corners in the matched images. In order to obtain the descriptors, log-Gabor filters are utilized and overlapping subregions are extracted in a neighborhood of certain pixels. In order to improve the accuracy of the algorithm, the moments of PCs in the original image and a Gaussian smoothed image are combined to detect the corners. Meanwhile, it is improper that the two matched images have the same PC wavelengths, due to the images having different physical wavelengths. Thus, in the experiment, the wavelength of the PC is changed for different physical wavelengths. For realistic application, BiDimRegression method is proposed to compute the similarity between two points set in infrared and visible images. The proposed approach is evaluated on four data sets with 237 pairs of visible and infrared images, and its performance is compared with state-of-the-art approaches: the edge-oriented histogram descriptor (EHD), phase congruency edge-oriented histogram descriptor (PCEHD), and log-Gabor histogram descriptor (LGHD) algorithms. The experimental results indicate that the accuracy rate of the proposed approach is 50% higher than the traditional approaches in infrared and visible images.

Hyperspectral Image Recognition Using SVM Combined Deep Learning

Abstract: In this paper we present the conbination of deep learning and Support Vector Machine applied on the recognition of hyperspectal images. Hyperspectral image recognition is an essential problem in the practical hyperspectral imagery system. While deep learning is capable of reproducing feature vectors with great dimensions out of original data, it leads to great time cost and the Hugh phoenomenon. Such nonlinear problem is regarded as obstacles and kernel method appears to be a promising way to solve it. The performance of kernel-based learning system is influenced by the choices of kernel function and parameter greatly. We present the kernel learning method termed Support Vector Machine (SVM) applied on feature vectors supplied by deep learning upon hyperspectral image. The learning system is improved by adjusting the parameters and kernel functions to the data structure for improving performance on solving complex tasks. Experimental results validate the feasibility of the proposed methods.

Chaotic Cuckoo Search Algorithm for Solving Unmanned Combat Aerial Vehicle Path Planning Problems

Abstract: This paper applies an improved Cuckoo Search algorithm, named Chaotic CS algorithm, to solve Unmanned Combat Aerial Vehicle (UCAV) path planning problems. A circle-type chaotic map for generating chaotic sequences is used to specify the scaling factor (() of step size and fraction probability (pa) of abandonment for host nests formulated in the Original CS algorithm. The advantage of using Chaotic CS algorithm can dynamically change the parameters of ( and pa by using the chaotic sequences over the course of iterations, resulting in an improvement for searching performance to find out the global best solution. The enhanced CS algorithm shows flexible and robust capabilities to optimize complex and multimodal objective functions by evaluating standard benchmark functions. Furthermore, the Chaotic CS algorithm is applied to solve complex design problem. Two scenarios of UCAV path planning problems are carried out for the practical applications. The simulation results indicate that the Chaotic CS algorithm can efficiently be used for computing optimal flight path of UCAV.

Distributed fault estimation design of interconnected systems with external disturbances

Abstract: In this study, a new distributed fault estimation observer (DFEO) design method is proposed based on unknown input observer (UIO) for interconnected systems with external disturbances. The coupling terms among interconnected subsystems are utilised to construct DFEO. At the same time, according to the characteristics of UIO, external interference is completely decoupled to improve the robustness of fault estimation. Furthermore, the authors propose a multi-constraint design with robust H ∞ and regional pole configurations to analyse and design gain matrices of DFEO, which can be calculated using linear matrix inequalities. Furthermore, asymptotic estimation of sensor faults is discussed with the help of DFEO. Finally, a simulation of the interconnected system is provided to illustrate the effectiveness of the proposed technique.

An Improved Integer Transform Combining with an Irregular Block Partition

Abstract: After conducting deep research on all existing reversible data hiding (RDH) methods based on Alattar’s integer transform, we discover that the frequently-used method in obtaining the difference value list of an image block may lead to high embedding distortion. To this end, we propose an improved Alattar’s transform-based-RDH-method. Firstly, the irregular block partition method which makes full use of high correlation between two neighboring pixels is proposed to increase the embedding performance. Specifically, each image block is composed of a center pixel and several pixels surrounding this center pixel. Thus, the difference value list is created by using the center pixel to predict each pixel surrounding it. Since the center pixel is highly related to each pixel surrounding it, a sharp difference value histogram is generated. Secondly, the mean value of an image block in Alattar’s integer transform has embedding invariance property, and therefore, it can be used for increasing the estimation performance of a block’s local complexity. Finally, two-layer embedding is combined into our scheme in order to optimize the embedding performance. Experimental results show that our method is effective.

A new finite-time cooperative control algorithm for uncertain multi-agent systems

Abstract: In this paper, the problem of fixed-time consensus tracking control is investigated for multi-agent networks with input uncertain dynamics under the undirected graph. First, a nonsingular terminal ...

Node Coverage Optimization Strategy Based on Ions Motion Optimization.

Abstract: This paper proposes a novel coverage strategy approach based on Ions Motion Optimization (IMO) for optimal coverage problem in the wireless sensor networks (WSN). In specific arear, how to reasonably arrange the sensor nodes to achieve the best coverage is the key to improving whole network performance. The node-coverage of the monitored area is modeled for objective function by the probability of each node to pixel and the joint coverage of each pixel point into the region for a whole network coverage optimization. Simulations of the coverage strategy are implemented in different scenario densities for optimal coverage. The results compared with the other approaches in the literature shows that the proposed approach can provide the effectively improving convergence speed and coverage rate of nodes, so leading to whole network coverage effect and prolonging network lifetime.

Security Analysis and Improvement on an Image Encryption Algorithm Using Chebyshev Generator

Abstract: Chaotic map including Chebyshev’s polynomial have been studied and used in many cryptographic areas recently due to its low cost of computation and high level of security. Some research works have been proposed to use Chebyshev’s polynomial in image encryption by setting up two-stage encryption algorithms. Pixels of a plain image are first permuted by a Permutation process. Then each pixel values are changed by a Diffusion process. A two-stage image encryption algorithm is generally believed to be more secure than a single stage image encryption algorithm. In this paper, however, we demonstrate a recent two-stage image encryption algorithm proposed by Wang et al., is insecure against chosen plaintext attack. An attacker may be able to decrypt a cipher image after knowing some ciphers of images which are chosen by the attacker. We present an subtle but efficient improvement over Wang et al.’s algorithm so that it is not only immune to the attack we presented but also statistically improved when experiment is conducted to measure pixels’ correlation, NPCR and UACI.

Study on Hazardous Scenario Analysis of High-Tech Facilities and Emergency Response Mechanism of Science and Technology Parks Based on IoT

Abstract: A High-tech plant also uses a large amount of electrical energy and mechanical energy, heat, radiant energy, chemical energy, and human factors cause by the process requirements. The ability of each unit to improve the maintenance and rescue capacity is most important works especially based on big data and IoT. This study high-tech plant hazard types and possible situational analysis includes fire, explosion, and toxic gas leakage for analysis. However, this study uses the risk assessment method and simulates for disaster prevention and prevention operations. The sensing layer is mainly carried out in three parts include fire, explosion and toxic gas leakage. Finally, the rationality of this study is verified.

Diversity Enhanced Ion Motion Optimization for Localization in Wireless Sensor Network.

Abstract: A new optimization algorithm (named DIMO) based on diversity enhanced Ion Motion Optimization (IMO) is proposed. Diversity learning strategy and random perturbations are applied to improve IMO by modifying its individual evolutionary information. A set of selected benchmark functions and an estimation localization in Wireless sensor network (WSN) are used to test the performance of the proposed algorithm. The experimental results compared with the others algorithms in the literature shows that the proposed approach are superior to the other algorithms in optimization accuracy, convergence speed, and robustness.

Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction

Abstract: Visual secret sharing is a secret sharing scheme where the decryption requires no computation. It has found many applications in online transaction security, privacy protection, and bar code security, etc. Recently, researches have indicated that combining visual secret sharing with the widely used Quick Response code may provide additional security mechanism to online transaction. However, current methods are either pixel-based, which requires high computational complexity or module-based, which sacrifices error correction capability of the original Quick Response code. Designing module-based visual secret sharing for the Quick Response code without sacrificing error correction capability is a challenging problem. To solve this problem, this paper proposes a (3, 3)-threshold visual secret sharing for Quick Response code scheme that fully explores the extra freedom provided by color visual secret sharing and color stacking. The binary secret Quick Response code is encoded into color shares. By stacking all the three shares, a binary color Quick Response code can be reconstructed. After the inherent pre-processing steps in a standard Quick Response code decoder, the original binary secret Quick Response code can be completely reconstructed. Thus, the original error correction capability of the Quick Response code is fully preserved. Theoretical analysis shows that the visual secret sharing for Quick Response code is secure under the condition that the computational device available to the attacker is limited to a decoder for standard Quick Response code. Experimental results verify that the secret Quick Response code cannot be reconstructed from just one share or any two shares. However, it can be 100% reconstructed once the three shares are stacked. The proposed visual secret sharing for Quick Response code is module-based, and it does not sacrifice the error correction capability. Furthermore, No extra pre-processing steps other than the standard Quick Response code decoder are required.

Beautified QR code with high storage capacity using sequential module modulation

Abstract: For the issue of beautification and capacity expansion of the Quick Response(QR) code, we proposed an algorithm based on sequential module modulation. First, the modules for the padding codewords are modulated by the module-based binarized background image. Then, to increase the storage capacity, low-pass textured patterns are designed for both the black modules and the white modules. The modules of the plain QR code are modulated by the second-level message. Finally, these modulated modules are further modulated by the L-channel of the background image in Lab color space. The module elimination parameter in the second modulation is optimized to maximize an objective function that accounts for both the visual quality and the decoding error. Experimental results demonstrate that, the proposed algorithm is superior to the reference method in terms of visual quality and capacity.

A Novel Rough Fuzzy Clustering Algorithm with A New Similarity Measurement

Abstract: With the emergence of exponential growth of datasets in various fields, fuzzy theory-based approaches are widely used to improve or optimize the data clustering algorithms. These improved algorithms can achieve better results than the original counterparts in practical applications. However, the fuzzy clustering algorithms including the traditional improved algorithms normally ignore the clustering boundary uncertainty, inter-class compactness and complex data problems, thereby result in the unsatisfactory clustering results. To address this issue, in this paper, a novel rough fuzzy clustering algorithm based on a new similarity measure is proposed by utilizing the upper approximation and lower approximation of rough set. We also develop the method of transforming fuzzy clustering model into rough set model. Our experiment results show that the improved algorithm can get better clustering effect.

Study of Inherently Safer Design Strategy Application for IC Process Power Supply System

Abstract: This study took 11 inherently safer design (ISD) strategies as the main thread. A 12″ furnace process tool power supply system ISD was reviewed and studied and the system elements were analyzed according to FMEA. The detailed process of ISD was established. Improvements were made during the annual maintenance period. After the ISD improvement of the actual processing of three batches of 69 wafers of High-K/Poly-Si thin film was validated by SiH 4 . The yield results were higher than 90%. Finally, the engineering improvements were implemented at the Hsinchu Science and Industrial Park 12″ FAB and facility in Taiwan for safety performance calculation.

An Improved Bat Algorithm Based on Hybrid with Ant Lion Optimizer

Abstract: Bat Algorithm (BA) is one of the fundamental algorithms for solving optimization problems. However, the BA still exists weaknesses in terms of exploitation and exploration. In this paper, an enhancing capability of exploration and exploitation for BA by hybridizing BA with Ant Lion Optimizer (ALO) is proposed for the global optimization problems. In the experimental section, several benchmark functions are used to test the performance of the proposed approach. Compared results with other algorithms literature show that the proposed method provides a new competitive algorithm.

Study on Health Protection Behavior Based on the Big Data of High-Tech Factory Production Line

Abstract: The concept of occupational health becomes more and more popular in 2019. This study choose workers on the PCB production lines of A Factory and B Factory in Taoyuan are taken as the target population in 2014 first generation of health promotion management database system, and a total of 340 active workers aged between 20 and 65, and with more than a year of work experience, are selected as the research subjects to facilitate the effectiveness of this study. However, in the electronic heath database test project of this study, when the various departments use the health promotion management database system as a management decision-making action, the time is greatly shortened, which is also because the employees of various departments change from the behaviors and cognition, attitude, and self-efficacy, which is also very important positive results.

Pigeon-Inspired Optimization for Node Location in Wireless Sensor Network

Abstract: Wireless Sensor Network (WSN) refers to a network of devices that can communicate the information gathered from a monitored field through wireless links. As a critical technology of WSN, the localization algorithm plays a vital role in improving node location accuracy and network efficiency. A hybrid Pigeon Inspired Optimization (PIO) with a typical localization model is proposed to solve the problem of node localization in WSN. The self-learning idea of PIO and speed formula are combined to improve exploring and exploiting agents of PIO. Fitness function for optimization is mathematically modeled based on analysis Pareto distances. The simulation results compared with the other approaches in the literature, e.g., the improved particle swarm optimization (PSO) and the cuckoo search (CS) show that the proposed method effectively improves the location accuracy of nodes and reduces the cumulative error caused by success positioning nodes.

Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity

Abstract: In recent years, reversible data hiding (RDH) has become a research hotspot in the field of multimedia security that has aroused more and more researchers’ attention. Most of the existing RDH algorithms are aiming at continuous-tone images. For RDH in encrypted halftone images (RDH-EH), the original cover image cannot be recovered losslessly after the watermark is extracted. For some application scenarios such as medical or military images sharing, reversibility is critical. In this paper, a reversible data hiding scheme in encrypted color halftone images (RDH-ECH) is proposed. In the watermark embedding procedure, the cover image is copied into two identical images to increase redundancy. We use wet paper code to embed the watermark into the image blocks. Thus, the receiver only needs to process the image blocks by the check matrices in order to extract the watermarks. To increase embedding capacity, we embed three layers in the embedding procedure and combine the resulting images into one image for convenience of transmission. From the experimental results, it can be concluded that the original image can be restored entirely after the watermarks are extracted. Besides, for marked color halftone images, our algorithm can implement high embedding capacity and moderate visual quality.

A Review of the Development and Application of Natural Language Processing

Abstract: With the development of convolutional neural networks and deep learning and a series of very significant breakthroughs in computer speech, many new models and methods have been provided for the field of Natural language processing. Natural language processing is a very important branch of artificial intelligence, and its application requirements and relevant fields are also becoming wider and wider. This paper first summarizes the related concepts of Natural language processing; then introduces in detail the development process of Natural language processing; then elaborates on the research progress of the application field of Natural language processing, including lexical analysis, syntactic analysis, machine translation and other fields; finally, the semantic understanding, the problem of low resources and the development direction of other fields are summarized and forecasted.