Sine Map Research Articles

An Effective Image Encryption Algorithm Using Bit Reversal Permutation and a New Chaotic Map

In this paper, a different approach to create a new chaotic model and an effective image encryption structure using Bit reversal permutation are proposed. Compared to most frequently used and well known chaotic maps, such as Logistic map, Sine map or Tent map, a new chaotic system based on Logistic map with Sine map is designed and used as an encryption key generator in the proposed algorithm. The new map has increased initial value sensitivity according to the results of Lyapunov analysis and shown better randomness output according to the chaotic trajectory analysis. In cryptography, a good key should be a stochastic and supposed to be sufficiently random and uniformly distributed with equal probability for an effective encryption. The designed chaotic map provides these properties very well. Before the basic encryption process, the Bit reversal method makes all pixel positions of input image rearranged in order to reduce the strong relation of adjacent pixels for higher encryption strength, which will enable strategic information sharing for production planning when this method analyzes a wind energy map in power plants. Other experimental results confirm that the proposed image encryption scheme has sufficient security, an effective encryption capability and can be transferred between power systems, keeping energy planning secret strategically.

Design of a New Dimension-Changeable Hyperchaotic Model Based on Discrete Memristor

The application of a memristor in chaotic circuits is increasingly becoming a popular research topic. The influence of a memristor on the dynamics of chaotic systems is worthy of further exploration. In this paper, a multi-dimensional closed-loop coupling model based on a Logistic map and Sine map (CLS) is proposed. The new chaotic model is constructed by cascade operation in which the output of the Logistic map is used as the input of the Sine map. Additionally, the one-dimensional map is extended to any dimension through the coupling modulation. In order to further increase the complexity and stability of CLS, the discrete memristor model is introduced to construct a discrete memristor-based coupling model with a Logistic map and a Sine map (MCLS). By analyzing the Lyapunov exponents, bifurcation diagram, complexity, and the 0–1 test result, the comparison result between CLS and MCLS is obtained. The dynamics performance analysis shows that the Lyapunov exponents and bifurcation diagrams present symmetrical distribution with variations of some parameters. The MCLS has parameters whose values can be set in a wider range and can generate more complex and more stable chaotic sequences. It proves that the proposed discrete memristor-based closed-loop coupling model can produce any higher dimension hyperchaotic system and the discrete memristor model can effectively improve the performance of discrete chaotic map and make this hyperchaotic system more stable.

Image encryption algorithm based on a 2D-CLSS hyperchaotic map using simultaneous permutation and diffusion

A two-dimensional cross-mode hyperchaotic map based on logistic and sine maps (2D-CLSS) is presented. The hyperchaotic map consists of a logistic map and two sine maps with cross structure. The chaotic behavior of the system is analyzed using bifurcation diagrams, Lyapunov exponential spectra, phase diagrams, etc. The outcomes demonstrate that the system has promising ergodicity and a wide range of hyperchaotic phenomena. Using the proposed 2D-CLSS, an image cryptography algorithm is developed. This image encryption system employs a strategy that simultaneously combines permutation and diffusion to alter the location and the value of the pixels. Experiments and security simulations indicate that the scheme is effective in encrypting images with excellent security against various attacks (differential, noise and data loss, etc.).

A new color image encryption algorithm using multiple chaotic maps with the intersecting planes method

This paper proposes a new and efficient color image encryption algorithm based on multiple chaotic maps (Logistic map, Sine map, and Chebyshev map) and the intersecting planes method inside a cube. All maps were used to fill the three adjacent faces of the cube. These faces represent the three channels of the color image (red, green, and blue). The first phase of our method begins with extracting all the pixels from the original image and looking for the corresponding values of each pixel on the three faces of the cube. Then, we used a circular rotation operation based on the position of each pixel (row, column). This rotation prevents two identical pixels to have the same encrypted value. Afterward, we used the intersecting planes method with the corresponding face to encrypt the pixels. For the confusion part, we chose the 2D transformation Arnold Cat Map to shuffle all the pixels and change their positions according to the parameters calculated from all the pixels of the original image. We evaluated the performance of our algorithm against various differential and statistical attacks and based on multiple factors, such as histogram, entropy, correlation coefficient, UACI, NPCR, and PSNR. We compared the proposed method with several efficient methods in the field. The various results got by our method show its performance and reliability in terms of safety, precision, confidentiality, and robustness.

Fast stochastic configuration network based on an improved sparrow search algorithm for fire flame recognition

Flame image recognition is of great significance in the fire detection and prevention. In this paper, in order to improve the accuracy of fire recognition, a fast stochastic configuration network (FSCN) method based on an improved sparrow search algorithm (ISSA) is proposed. In the design of fast stochastic configuration network (FSCN), the gradual increase of hidden layer nodes in the original stochastic configuration network (SCN) is canceled, and the number of them is set directly. An improved sparrow search algorithm (ISSA) is used to generate the input weights and biases of hidden layer nodes. At the same time, the supervisory mechanism is retained to judge the weights and biases of all hidden layer nodes, and ISSA is used to regenerate corresponding weights and biases for the nodes that do not meet the constraints in the supervisory mechanism. In the ISSA, sine map, adaptive adjustment of hyper-parameters and mutation strategy are used to improve the optimization ability of the original sparrow search algorithm (SSA). Some parameters in FSCN are optimized by ISSA to make it have better classification performance. Finally, the image processing technology is used to extract features from the flame images and the interference images, and then the feature vectors are obtained to train the ISSA-FSCN. Several simulation experiments have been carried out to verify effectiveness of the proposed ISSA-FSCN method. In the performance verification of ISSA on CEC test suit, ISSA averagely outperforms other algorithms by 33.6% in the average results of 20 functions. In the performance verification of FSCN, the average results of accuracy, precision, recall, F1 and auc are compared. In the experiment 1, ISSA-FSCN averagely outperforms other algorithms by 19.7%, 14.7%, 12.8%, 14.5% and 23.0%. In the experiment 2, ISSA-FSCN averagely outperforms other algorithms by 2.3%, 2.1%, 2.5%, 6.0%, 3.2%. In the experiment 3, ISSA-FSCN averagely outperforms other algorithms by 5.9%, 4.0%, 4.1%, 4.1%, 6.8%.

Balance and Correlation Analysis of Oilfield Injection-Production System Based on Data Mining

Appropriate water injection volume is the most basic parameter required to maintain stable formation pressure and ensure the development effect of water flooding in oilfields. However, the determination of appropriate water injection volume has always been a major problem in oilfield water injection management. Based on the Grey relational algorithm, this paper determines the optimal connected injection-production well group through the study of the dynamic relationship between oil and water wells. To specifically predict water injection, a Sparrow search algorithm optimisation model based on Sine mapping is proposed. A Sine-SSA-BP algorithm was devised to predict water injection volume and both the improved algorithm and the original BP algorithm were applied to real-world data to assess their predictive accuracy. The prediction results of the Sine-SSA-BP algorithm were found to be closer to the true value than the results of the original BP algorithm, and the average error percentage is reduced by 23.86%. Therefore, the new algorithm can predict and calculate the water injection volume more accurately. The research content of this paper can provide a theoretical basis for advising adjustment measures in the block to slow down the rise of water content, maintain stable production, and improve the efficiency of the mechanical mining system.

A selective image encryption algorithm based on a chaotic model using modular sine arithmetic

In this paper, an effective sine modular arithmetic chaotic model is proposed, and applied to image encryption. The new system is a combination of module operation, Sine map and other traditional one-dimensional chaotic maps. The performance analysis demonstrates that the proposed chaotic model has larger chaotic ranges and much better chaotic behaviors than other existing one-dimensional chaotic maps. We apply one of the chaotic maps to the image encryption algorithm. The encryption algorithm first extracts the face part of the image using a face recognition algorithm, and encrypts the information of the face part by combining random cyclic scrambling and forward diffusion techniques. Another encryption algorithm is used to encrypt the whole image. Results of simulations and security analyses show that the proposed encryption algorithm exhibits an excellent security performance and high efficiency in practical applications.

IEFHAC: Image encryption framework based on hessenberg transform and chaotic theory for smart health.

Smart cities aim to improve the quality of life by utilizing technological advancements. One of the main areas of innovation includes the design, implementation, and management of data-intensive medical systems also known as big-data Smart Healthcare systems. Smart health systems need to be supported by highly efficient and resilient security frameworks. One of the important aspects that smart health systems need to provide, is timely access to high-resolution medical images, that form about 80% of the medical data. These images contain sensitive information about the patient and as such need to be secured completely. To prevent unauthorized access to medical images, the process of image encryption has become an imperative task for researchers all over the world. Chaos-based encryption has paved the way for the protection of sensitive data from being altered, modified, or hacked. In this paper, we present an Image Encryption Framework based on Hessenberg transform and Chaotic encryption (IEFHAC), for improving security and reducing computational time while encrypting patient data. IEFHAC uses two 1D-chaotic maps: Logistic map and Sine map for the confusion of data, while diffusion has been achieved by applying the Hessenberg household transform. The Sin and Logistic maps are used to regeneratively affect each other’s output, as such dynamically changing the key parameters. The experimental analysis demonstrates that IEFHAC shows better results like NPCR ranging from 99.66 to 100%, UACI of 37.39%, lesser computational time of 0.36 s, and is more robust to statistical attacks.

Improved Sine-Tangent chaotic map with application in medical images encryption

The attractive properties of ergodicity, unpredictability, and initial state sensitivity have made chaotic maps the go-to tools in many applications, including cryptography and cyber–physical systems. Despite this, two challenges arise when using chaos systems in cryptography: (i) some one-dimensional (1D) chaotic maps do not satisfy the unpredictability property, and (ii) although they exhibit a complex and chaotic behavior, high-dimensional (HD) chaotic maps incur higher computational complexity. To address these issues, this paper proposes a new 1D chaotic map dubbed the improved Sine-Tangent map (IST map) that is derived from of the Sine map, a tangent function, and a Chebyshev polynomial of the first kind. Relative to chaotic maps, the proposed IST map provides better unpredictability and ergodicity, a vast chaotic range, enhanced complex behavior, and competitive computational complexity. Based on the IST map, we also introduced an encryption scheme for securing medical images in telemedicine. It consists of two diffusion phases, i.e., a bitwise XOR operation and a bitwise expanded XOR (eXOR) operation, with an intermediated confusion one, i.e., random circular-shift. An overriding step is foremost first completed before performing these cryptography phases, i.e., key generation. The secret key of the IST map is updated using the sine and cosine values of the sum of pixels of the input image. This leads to a unique secret key for each image. That is, one-time chaotic sequences are produced for each input image. The cyclic pattern of the sine and cosine values of the sum of pixels provides a prominent sensibility to small changes in the input image. Thus, the proposed algorithm is capable of resisting any chosen/known plaintext attacks. A performance analysis shows that the proposed algorithm outperforms a set of state-of-the-art comparison algorithms and its variants based on Sine, SE, and ST maps since it allows the best performance/complexity trade-off.

Optimal dispatching of microgrid based on improved moth-flame optimization algorithm based on sine mapping and Gaussian mutation

Because the traditional power generation method has caused certain damage to the environment, the microgrid system composed of renewable energy has been widely developed and applied. This paper studies distributed power sources including photovoltaics, wind turbines, energy storage systems, gas turbines, and fuel cells. Under the conditions of microgrid islands and grid-connected operation, the fuel cost, operation and maintenance cost, and the electricity transaction cost between the microgrid and the distribution network, establish the optimal objective function for the operating cost of the microgrid. At the same time, due to the standard moth-flame optimization algorithm having low optimization accuracy and are easy to fall into local optimal solution, an improved moth-flame optimization algorithm based on Sine mapping and Gaussian mutation is proposed. This algorithm is used to obtain the output of each distributed power source and total operating cost in a dispatch period. Finally, an example is used to verify the effectiveness and economy of the proposed model and the improved algorithm.

Symmetric Image Encryption Algorithm Based on a New Product Trigonometric Chaotic Map

In the present work, a neotype chaotic product trigonometric map (PTM) system is proposed. We demonstrate the chaotic characteristics of a PTM system by using a series of complexity criteria, such as bifurcation diagrams, Lyapunov exponents, approximate entropy, permutation entropy, time-series diagrams, cobweb graphs, and NIST tests. It is proved that the PTM system has a wider chaotic parameter interval and more complex chaotic performance than the existing sine map system. In addition, a novel PTM based symmetric image encryption scheme is proposed, in which the key is related to the hash value of the image. The algorithm realizes the encryption strategy of one-graph-one-key, which can resist plaintext attack. A two-dimensional coordinate traversal matrix for image scrambling and a one-dimensional integer traversal sequence for image pixel value transformation encryption are generated by the pseudo-random integer generator (PRING). Security analysis and various simulation test results show that the proposed image encryption scheme has good cryptographic performance and high time efficiency.

Recognition of a linear source contamination based on a mixed-integer stacked chaos gate recurrent unit neural network-hybrid sparrow search algorithm.

Groundwater contamination source recognition involves the recovery of contamination source time series release histories from observation data. In the present study, a linear source contamination recognition task was addressed. When using a simulation-optimization inverse framework to solve the recognition task, high calculated expense and high dimensional search space always hinder the task efficiency. Moreover, traditional surrogate methods face obstacle of handling with time-sequence data. Therefore, a novel stacked chaos gate recurrent unit (SCGRU) neural network was proposed as a surrogate model to precisely emulate the sequence to sequence mapping relationship of a high computational running simulation model. To address the challenge of high dimensional search, a mixed-integer programming strategy was employed to reduce the dimension of unknown variables. Furthermore, a hybrid sparrow search algorithm (HSSA) was implemented to alleviate being trapped into local optimum. In particular, the proposed SCGRU-HSSA framework was utilized to determine the length and release intensities during the stress period of a linear source. Based on the results obtained, the following conclusions were derived: (1) SCGRU can replace the origin simulation model with high accuracy and fast running speed; (2) when using chaos sine mapping and a Cauchy mutation strategy, the SSA escaped from the local optimum, improving the search efficiency of the recognition task; and (3) SCGRU-HSSA methodology is stable and reliable in recognizing features of linear source contamination.

An Optimized Multisource Bilinear Convolutional Neural Network Model for Flame Image Identification of Coal Mine

Underground fire monitoring is an important tool to improve coal mine production safety. In this paper, a multi-source information identification method based on bilinear convolutional neural network (B-CNN) is proposed, which consists of construction of multi-source image acquisition system, B-CNN and integrated decision making based on multi-source B-CNN. Aiming at the problem that Softmax loss function based on the gradient descent in B-CNN is easy falling into the local optimum, an improved Grasshopper Optimization Algorithm (GOA) is proposed to optimally selected two parameters of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {W}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {\theta } $ </tex-math></inline-formula> ; the method of initial solution generation based on sine mapping and the method of accepting bad solution with certain probability are respectively adopted. In order to solve high computational complexity in the stage of model training and integrated recognition by multi-source B-CNN, an image feature preprocessing method is proposed in this paper. Several feature vectors of color feature, shape feature and texture feature of the collected image are extracted and used as input vectors of B-CNN to complete model training and integrated recognition. In simulation experiments, firstly, four Benchmark functions are used to verify the performance of the improved GOA; then, by scaling, expanding and rotating the image to simulate the results of image acquisition at multiple positions and angles, different information sources can be formed to complete the integrated recognition by B-CNN. Three performance indexes of Accuracy, Precision and Recall are used to evaluate the simulation result of different comparative models, which show that the proposed method has better recognition effects.

A 2D Chaotic Oscillator for Analog IC

In this paper, we have proposed the design of an analog two-dimensional (2D) discrete-time chaotic oscillator. 2D chaotic systems are studied because of their more complex chaotic behavior compared to one-dimensional (1D) chaotic systems. The already published works on 2D chaotic systems are mainly focused either on the complex analytical combinations of familiar 1D chaotic maps such as Sine map, Logistic map, Tent map, and so on, or off-the-shelf component-based analog circuits. Due to complex hardware requirements, neither of them is feasible for hardware-efficient integrated circuit (IC) implementations. To the best of our knowledge, this proposed work is the first-ever report of an analog 2D discrete-time chaotic oscillator design that is suitable for hardware-constrained IC implementations. The chaotic performance of the proposed design is analyzed with bifurcation plots, the transient response, 2D Lyapunov exponent, and correlation coefficient measurements. It is demonstrated that the proposed design exhibits promising chaotic behavior with low hardware cost. The real-world application of the proposed 2D chaotic oscillator is presented in a random number generator (RNG) design. The applicability of the RNG in cryptography is verified by passing the generated random sequence through four standard statistical tests namely, NIST, FIPS, TestU01, and Diehard.

Improved Hybrid Particle Swarm Optimizer with Sine-Cosine Acceleration Coefficients for Transient Electromagnetic Inversion

Background: Recently, Particle Swarm Optimization (PSO) has been increasingly used in geophysics due to its simple operation and fast convergence. Objective: However, PSO lacks population diversity and may fall to local optima. Hence, an Improved Hybrid Particle Swarm Optimizer with Sine-Cosine Acceleration Coefficients (IH-PSO-SCAC) is proposed and successfully applied to test functions in Transient Electromagnetic (TEM) nonlinear inversion. Method: A reverse learning strategy is applied to optimize population initialization. The sine-cosine acceleration coefficients are utilized for global convergence. Sine mapping is adopted to enhance population diversity during the search process. In addition, the mutation method is used to reduce the probability of premature convergence. Results: The application of IH-PSO-SCAC in the test functions and several simple layered models are demonstrated with satisfactory results in terms of data fit. Two inversions have been carried out to test our algorithm. The first model contains an underground low-resistivity anomaly body and the second model utilized measured data from a profile of the Xishan landslide in Sichuan Province. In both cases, resistivity profiles are obtained, and the inverse problem is solved for verification. Conclusion: The results show that the IH-PSO-SCAC algorithm is practical, can be effectively applied in TEM inversion and is superior to other representative algorithms in terms of stability and accuracy.

Estimation for Model Parameters and Maximum Power Points of Photovoltaic Modules Using Stochastic Fractal Search Algorithms

The performance of a photovoltaic (PV) power generation system could be improved through the optimal control and operation of a PV module which is one of the fundamental components of this system. Thus, an appropriate PV module model along with precise knowledge of its parameters is necessary. This paper proposes a novel technique to estimate the source current, the saturation current of diodes, the shunt resistance, the series resistance, the ideality coefficient of diodes and the maximum power points (MPPs) of PV modules at the same time. This estimation problem can be described by the minimization of the root mean squared error (RMSE) of the powers obtained from the PV module through estimation and experiment. The improved stochastic fractal search (ISFS) algorithm is proposed to solve this minimization with two modifications. The first replaces the logarithmic function with the exponential function in the standard deviation of the diffusion technique to improve the exploration ability efficiently in the search space. The second utilizes the sine map instead of the uniform distribution in both the diffusion and update techniques for improving the performance of the ISFS algorithm. Numerical results demonstrate the remarkable ability of the ISFS algorithm in obtaining both the model parameters and MPPs of the PV module with high accuracy. The comparison shows that the ISFS algorithm outperforms other meta-heuristic algorithms such as a stochastic fractal search (SFS) algorithm, a particle swarm optimization (PSO) algorithm, and an improved particle swarm optimization (IPSO) algorithm in the proposed parameter estimation application.

Encrypting Multiple Images With an Enhanced Chaotic Map

A multiple-image encryption scheme based on an enhanced chaotic map is proposed. This scheme combines multiple grayscale images into three planes. An amplified sine map is used to generate a dynamic permutation table and a chaotic sequence. The initial parameters of the amplified sine map are obtained from an elliptic curve coordinate, computing using elliptic curve point multiplication between the input image hash value and the seed value of an elliptic curve. The dynamic permutation table performs cyclic shift transformation on the input image, diffusing it horizontally and vertically. The diffused image is converted to a cipher image through an XOR operation with the chaotic sequence generated by the amplified sine map. Using an amplified sine map provides larger key space, more extensive chaotic range, more extensive control parameters, better sensitive initial values and enhanced security against cryptanalysis. The experiment results indicate that the proposed scheme is fast, secure, efficient and can resist certain cryptographic attacks. Compared to other recent schemes, the proposed scheme is shown to be secure and efficient.

Secured information sharing in SCM: Parametric Analysis on Improved Beetle Swarm Optimization

Today, with the progression in ICT, information sharing (IS) has turned outto be more possible. IS in supply chains (SC) has grown up more proficient by the global preamble of long-term coordination and cooperation that leads to enhancement of aggressive advantages of companies. However, lack of IS in companies results in incompetence of coordinating activities in organizations. Here, this work aims to enhance the security and privacy of the proposed block-chain-aided SCM, wherein the modified “Data Sanitization and Data Restoration” is done with an optimal key creation procedure to protect the receptive data in every block. Here, the optimal key is chosen via Sine Map estimation-based BSO (SME-BSO) algorithm. Finally, parametric analysis varies ra2 from 0-1, 0.2, 0.4, 0.6 and 0.8.

Bifurcations and Dynamical Behavior of 2D Coupled Chaotic Sine Maps

The main characteristics of a dynamical system are determined by the bifurcation theory. In particular, in this paper we examined the properties of the discrete dynamical system of a two coupled maps, i.e. the maps with an invariant unidimensional submanifold. The study of coupled chaotic systems shows rich and complex dynamic behaviors, particularly through structures of bifurcations or chaotic synchronization. A bifurcation is a qualitative change of the system behavior under the influence of control parameters. This change may correspond to the disppearance or appearance of new singularities or a change in the nature of singularities. We can define different kinds of bifurcations for fixed points and period two cycles as, saddle-node, period doubling, transcritical or pitchfork bifurcations. The study of the sequence of bifurcations permits to understand the mechanisms that lead to chaos. The phenomena of synchronization and antisynchronization in coupled chaotic systems is very important because its applications in several areas, such as secure communication or biology. In this paper, we study bifurcation properties of a two-dimensional coupled map T with three parameters. The first objective is to locate the bifurcation curves and their evolution in the parametric plane (<i>a,b</i>), when a third parameter <i>c</i> varies. The equations of some bifurcation curves are given analytically; cusp points and co-dimension two points on these bifurcation curves are determined. The second is related to the study of the chaotic synchronization and antisynchronization in the phase space (<i>x,y</i>).

Opto-Video Encryption Based on Logistic Adjusted Sine map in FrFT

In the last few years, videos became the most common form of information transmitted over the internet, and a lot of the traffic is confidential and must be protected and delivered safely to its intended users. This introduces the challenges of presenting encryption systems that can encode videos securely and efficiently at the same time. This paper presents an efficient opto-video encryption system using Logistic Adjusted Sine map (LASM) in the Fractional Fourier Transform (FrFT). In the presented opto-video LASM-based FrFT scheme, the encoded video is split into distinct frames and transformed into optical signals utilizing an optical supply. Each of the developed optical video frames is ciphered by utilizing the LASM in optical FrFT system using two-phase modulation forms on the video frame, the first in the time-domain and the second in the FrFT domain. In the end, the ciphervideo frame is spotted utilizing a CCD digital camera and transformed into a digital structure that can be managed using a computer. We test the proposed opto-video LASM-based FrFT scheme using various security tools. The outcomes demonstrate that the presented scheme can effectively encrypt and decrypt video signals. In addition, it encrypts videos with a high level of encryption quality without sacrificing its resistance to noise immunity. Finally, the test outcomes demonstrate that the presented scheme is immune to known attacks.