A Triple-Channel Encrypted Hybrid Fusion Technique to Improve Security of Medical Images

Assuring medical images protection and robustness is a compulsory necessity nowadays.In this paper,a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform(DWT)with the energy compaction of the Discrete Wavelet Transform(DCT).The multi-level Encryption-based Hybrid Fusion Technique(EbhFT)aims to achieve great advances in terms of imperceptibility and security of medical images.A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT transform.Afterwards,a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the watermark.Lastly,a PN-sequence key is formed along with a supplementary key in the third layer of the EbHFT.Thus,the watermarked image is generated by enclosing both keys into DWT and DCT coefficients.The fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT methods.In order to validate the proposed technique,a standard dataset of medical images is used.Simulation results show higher performance of the visual quality(i.e.,57.65)for the watermarked forms of all types of medical images.In addition,EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation(NC).Finally,extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.

Non-probabilistic information fusion technique for structural damage identification based on measured dynamic data with uncertainty

Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification（SMI） and twostep model updating procedure.Due to the insufficiency and uncertainty of information obtained from measurements,the uncertain problem of damage identification is addressed with interval variables in this paper.Based on the first-order Taylor series expansion,the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated,respectively.The possibility of damage existence（PoDE） in elements is proposed as the quantitative measure of structural damage probability,which is more reasonable in the condition of insufficient measurement data.In comparison with the identification method based on a single kind of information,the SMI method will improve the accuracy in damage identification,which reflects the information fusion concept based on the non-probabilistic set.A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.

Structural Stress Identification Using Fuzzy Pattern Recognition and Information Fusion Technique

In order to ensure the service security of space structures under wind load, the stress identification method based on the combination of fuzzy pattern recognition and information fusion technique is proposed, in which the measurements of limited strain sensors arranged on the structure are used. Firstly, the structure is divided into several regions according to the similarity and the most unfavorable region is selected to be the key region for stress identification, while the different numbers of the strain sensors are located on the key region and the normal regions; secondly, the different stress distributions of the key region are obtained based on the measurements of the strain sensors located on the key region and the normal regions separately, in which the fuzzy pattern recognition is used to identify the different stress distributions; thirdly, the stress distributions obtained by the measurements of sensors in normal regions are selected to calculate the synthesized stress distribution of the key region by D-S evidence theory; fourthly, the weighted fusion algorithm is used to assign the different fusion coefficients to the selected stress distributions obtained by the measurements of the normal regions and the key region, while the synthesized stress distribution of the key region can be obtained. Numerical study on a lattice shell model is carried out to validate the reliability of the proposed stress identification method. The simulated results indicate that the method can improve identification accuracy and be effective by different noise disturbing.

Research on the Application of the Light-Energy Spectrum Fusion Technique to Land and Resources Survey

This paper introduces how to use remote sensing images including Landsat (MSS and TM) and airborne radioactivity images to identify the type of rocks in the areas covered by vegetation. The relationship between light spectrum (Landsat MSS and TM) and energy spectrum (U, Th and K) is discussed on the basis of correlation analysis, and it is proven that there are correlations between the Landsat MSS or TM data and the U, Th and K data. By using the fusion technique, new images were generated, which contain both the light spectrum and the energy spectrum information.

Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion

A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion mechanism.The secondorder neighborhood shows the connection between the current pixel and the surroundings of this pixel.This connection is for each RGB component color of the input image.Once the image edges are detected for the three primary colors:red,green,and blue,these colors are merged using the combination rule.Then,the final decision is applied to obtain the segmentation.This process allows different data sources to be combined,which is essential to improve the image information quality and have an optimal image segmentation.Finally,the segmentation results of the proposed model are validated.Moreover,the classification accuracy of the tested data is assessed,and a comparison with other current models is conducted.The comparison results show that the proposed model outperforms the existing models in image segmentation.

Enhancing Surface Soil Moisture Estimation through Integration of Artificial Neural Networks Machine Learning and Fusion of Meteorological, Sentinel-1A and Sentinel-2A Satellite Data

For many environmental and agricultural applications, an accurate estimation of surface soil moisture is essential. This study sought to determine whether combining Sentinel-1A, Sentinel-2A, and meteorological data with artificial neural networks (ANN) could improve soil moisture estimation in various land cover types. To train and evaluate the model’s performance, we used field data (provided by La Tuscia University) on the study area collected during time periods between October 2022, and December 2022. Surface soil moisture was measured at 29 locations. The performance of the model was trained, validated, and tested using input features in a 60:10:30 ratio, using the feed-forward ANN model. It was found that the ANN model exhibited high precision in predicting soil moisture. The model achieved a coefficient of determination (R2) of 0.71 and correlation coefficient (R) of 0.84. Furthermore, the incorporation of Random Forest (RF) algorithms for soil moisture prediction resulted in an improved R2 of 0.89. The unique combination of active microwave, meteorological data and multispectral data provides an opportunity to exploit the complementary nature of the datasets. Through preprocessing, fusion, and ANN modeling, this research contributes to advancing soil moisture estimation techniques and providing valuable insights for water resource management and agricultural planning in the study area.

CT image fusion in the evaluation of radiation treatment planning for non-small cell lung cancer

Objective: We studied the application of CT image fusion in the evaluation of radiation treatment planning for non-small cell lung cancer (NSCLC). Methods: Eleven patients with NSCLC, who were treated with three-dimensional con-formal radiation therapy, were studied. Each patient underwent twice sequential planning CT scan, i.e., at pre-treatment, and at mid-treatment for field reduction planning. Three treatment plans were established in each patient: treatment plan A was based on the pre-treatment planning CT scans for the first course of treatment, plan B on the mid-treatment planning CT scans for the second course of treatment, and treatment plan F on the fused images for the whole treatment. The irradiation doses received by organs at risk in the whole treatment with treatment A and B plans were estimated by the plus of the parameters in treatment plan A and B, assuming that the parameters involve the different tissues (i.e. V20=AV20+BV20), or the same tissues within an organ (i.e. Dmax=ADmax+BDmax). The assessment parameters in the treatment plan F were calculated on the basis of the DVH of the whole treatment. Then the above assessment results were compared. Results: There were marked differ-ences between the assessment results derived from the plus of assessment parameters in treatment plan A and B, and the ones derived from treatment plan F. Conclusion: When a treatment plan is altered during the course of radiation treatment, image fusion technique should be performed in the establishment of a new one. The estimation of the assessment parameters for the whole treatment with treatment plan A and B by simple plus, is inaccurate.

Fusion of Medical Images in Wavelet Domain:A Hybrid Implementation

This paper presents a low intricate,profoundly energy effective MRI Images combination intended for remote visual sensor frameworks which leads to improved understanding and implementation of treatment;especially for radiology.This is done by combining the original picture which leads to a significant reduction in the computation time and frequency.The proposed technique conquers the calculation and energy impediment of low power tools and is examined as far as picture quality and energy is concerned.Reenactments are performed utilizing MATLAB 2018a,to quantify the resultant vitality investment funds and the reproduction results show that the proposed calculation is very quick and devours just around 1%of vitality decomposition by the hybrid combination plans.Likewise,the effortlessness of our proposed strategy makes it increasingly suitable for continuous applications.

Underwater Image Enhancement Using Customized CLAHE and Adaptive Color Correction

Underwater images degraded due to low contrast and visibility issues.Therefore,it is important to enhance the images and videos taken in the underwater environment before processing.Enhancement is a way to improve or increase image quality and to improve the contrast of degraded images.The original image or video which is captured through image processing devices needs to improve as there are various issues such as less light available,low resolution,and blurriness in underwater images caused by the normal camera.Various researchers have proposed different solutions to overcome these problems.Dark channel prior(DCP)is one of the most used techniques which produced a better Peak Signal to Noise Ratio(PSNR)value.However,DCP has some issues such as it tends to darken images,reduce contrast,and produce halo effects.The proposed method solves these issues with the help of contrast-limited adaptive histogram equalization(CLAHE)and the Adaptive Color Correction Method.The proposed method was assessed using Japan Agency for Marine-Earth Science and Technology(JAMSTEC),and some images were collected from the internet.The measure of entropy(MOE),Measure of Enhancement(EME),Mean Square Error(MSE),and PSNR opted as performance measures during experiments.The values of MSE and PSNR achieved by the proposed framework are 0.26 and 32 respectively which shows better results.

Automated Teller Machine Authentication Using Biometric

This paper presents a novel method of a secured card-less AutomatedTeller Machine (ATM) authentication based on the three bio-metrics measures. Itwould help in the identification and authorization of individuals and would provide robust security enhancement. Moreover, it would assist in providing identi-fication in ways that cannot be impersonated. To the best of our knowledge, thismethod of Biometric_ fusion way is the first ATM security algorithm that utilizesa fusion of three biometric features of an individual such as Fingerprint, Face, andRetina simultaneously for recognition and authentication. These biometric imageshave been collected as input data for each module in this system, like a fingerprint, a face, and a retina module. A database is created by converting theseimages to YIQ color space, which is helpful in normalizing the brightness levelsof the image hence mainly (Y component’s) luminance. Then, it attempt toenhance Cellular Automata Segmentation has been carried out to segment the particular regions of interest from these database images. After obtaining segmentation results, the featured extraction method is carried out from these criticalsegments of biometric photos. The Enhanced Discrete Wavelet Transform technique (DWT Mexican Hat Wavelet) was used to extract the features. Fusion ofextracted features of all three biometrics features have been used to bring in themultimodal classification approach to get fusion vectors. Once fusion vectorsware formulated, the feature level fusion technique is incorporated based on theextracted feature vectors. These features have been applied to the machine learning algorithm to identify and authorization of multimodal biometrics for ATMsecurity. In the proposed approach, we attempt at useing an enhanced Deep Convolutional Neural Network (DCNN). A hybrid optimization algorithm has beenselected based on the effectiveness of the features. The proposed approach resultswere compared with existing algorithms based on the classification accuracy toprove the effectiveness of our algorithm. Moreover, comparative results of theproposed method stand as a proof of more promising outcomes by combiningthe three biometric features.

Assessment of Power Quality Based on D-S Evidence Theory

Technological advancement of measurement systems has enhanced the accuracy of power quality assessment by using a combination of measured information. This paper proposes a novel approach for estimating power quality based on information fusion technique of Dempster-Shafer(D-S) evidence theory. First, in order to accurately extract transient features regarding power quality indexes, wavelet packet transform and lifting wavelet transform are proposed to detect various disturbance signals measurement. By using many kinds of transformed transient indexes and steady state indexes, a novel reliability distribution function is constructed,and synthesized assessment index of power quality is drafted based on information fusion technique of D-S evidence theory. Finally,the simulation results prove that D-S evidence theory is a more effective means for evaluating the power quality.