Robust zero-watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

In the intricate network environment,the secure transmission of medical images faces challenges such as information leakage and malicious tampering,significantly impacting the accuracy of disease diagnoses by medical professionals.To address this problem,the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi-stage discrete wavelet transform(DWT),Daisy descriptor,and discrete cosine transform(DCT).The algorithm initially encrypts the original medical image through DWT-DCT and Logistic mapping.Subsequently,a 3-stage DWT transformation is applied to the encrypted medical image,with the centre point of the LL3 sub-band within its low-frequency component serving as the sampling point.The Daisy descriptor matrix for this point is then computed.Finally,a DCT transformation is performed on the Daisy descriptor matrix,and the low-frequency portion is processed using the perceptual hashing algorithm to generate a 32-bit binary feature vector for the medical image.This scheme utilises cryptographic knowledge and zero-watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image,which meets the basic re-quirements of medical image watermarking.The embedding and extraction of water-marks are accomplished in a mere 0.160 and 0.411s,respectively,with minimal computational overhead.Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks,with a notable performance in resisting rotation attacks.

Building Facade Point Clouds Segmentation Based on Optimal Dual-Scale Feature Descriptors

To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.

Degree-Based Entropy Descriptors of Graphenylene Using Topological Indices

Graph theory plays a significant role in the applications of chemistry,pharmacy,communication,maps,and aeronautical fields.The molecules of chemical compounds are modelled as a graph to study the properties of the compounds.The geometric structure of the compound relates to a few physical properties such as boiling point,enthalpy,π-electron energy,andmolecular weight.The article aims to determine the practical application of graph theory by solving one of the interdisciplinary problems describing the structures of benzenoid hydrocarbons and graphenylene.The topological index is an invariant of a molecular graph associated with the chemical structure,which shows the correlation of chemical structures using many physical,chemical properties and biological activities.This study aims to introduce some novel degree-based entropy descriptors such as ENTSO,ENTGH,ENTHG,ENTSS,ENTNSO,ENTNReZ1,ENTNReZ2 and ENTNSS using the respective topological indices.Also,the above-mentioned entropy measures and physico-chemical properties of benzenoid hydrocarbons are fitted using linear regression models and calculated for graphenylene structure.

Two-dimensional C_(2)N-based single-atom catalyst with complex microenvironment for enhanced electrochemical nitrogen reduction:A descriptor-based design

The catalytic descriptor with operational feasibility is highly desired towards rational design of high-performance catalyst especially the electrode/electrolyte solution interface working under mild conditions.Herein,we demonstrate that the descriptorΩparameterized by readily accessible intrinsic properties of metal center and coordination is highly operational and efficient in rational design of single-atom catalyst(SAC)for driving electrochemical nitrogen reduction(NRR).Using twodimensional metal(M)-B_(x)P_(y)S_(z)N_m@C_(2)N as prototype SAC models,we reveal that^(*)N_(2)+(H~++e~-)→^(*)N_(2)H acts predominantly as the potential-limiting step(PLS)of NRR on M-B_(2)P_(2)S_(2)@C_(2)N and M-B_(1)P_(1)S_(1)N_(3)@C_(2)N regardless of the distinction in coordination microenvironment.Among the 28 screened M active sites,withΩvalues close to the optimal 4,M-B_(2)P_(2)S_(2)@C_(2)N(M=V(Ω=3.53),Mo(Ω=5.12),and W(Ω=3.92))and M-B_(1)P_(1)S_(1)N_(3)@C_(2)N(M=V(Ω=3.00),Mo(Ω=4.34),and W(Ω=3.32))yield the lowered limiting potential(U_(L))as-0.45,-0.54.-0.36,-0.58,-0.25,and-0.24 V,respectively,thus making them the promising NRR catalysts.More importantly,these SACs are located around the top of volcano-shape plot of U_(L) versusΩ,re-validatingΩas an effective descriptor for accurately predicting the high-activity NRR SACs even with complex coordination.Our study unravels the relationship between active-site structure and NRR performance via the descriptorΩ,which can be applied to other important sustainable electrocatalytic reactions involving activation of small molecules viaσ-donation andπ^(*)-backdonation mechanism.

W-系统矩和Fourier变换下Volume Descriptors不变特征的三维模型检索

针对V-系统矩难以构造快速算法的不足,提出一种W-系统矩及其快速算法,并融合volume descriptors不变特征应用于三维模型检索.首先对模型体素化;其次计算体素模型的W-系统矩和物体运动不变量,得到特征向量;最后进行特征融合及匹配.实验结果表明,该算法的检索效率和性能均优于三维球面调和函数方法和三维Legendre矩方法.

Fourier Descriptors with Different Shape Signatures:a Comparative Study for Shape Based Retrieval of Kinematic Constraints

Fourier Descriptors（FD） has been widely used in image analysis and computer vision for shape recognition as they can be made independent of translation,rotation,as well as scaling.They have also been used for developing methods for the analysis and synthesis of four-bar linkages for path generation.This paper focuses on a comparative study of Fourier descriptors derived from various shape signatures of planar closed curves.This includes representations based on Cartesian coordinates,centroid distance,cumulative angle,and curvature.The comparison is conducted not only using commonly used criteria for shape representation and identification but also in the context of shape based retrieval of kinematic constraints for task centered mechanism design.Examples are provided to seek to extract geometric constraints such as circle,circular arc,ellipse and line-segment from a given motion.

Chemical Reactivity Theory (CRT) Study of the Melanoidin M8: Local Conceptual Density Functional Theory Descriptors

This study evaluated a fixed long-range corrected range-separated hybrid (RSH) density functional associated with the Def2TZVP basis set alongside the Solvation Model based on Density (SMD) for the computation of the structure, molecular properties and chemical reactivity of the M8 intermediate melanoidin pigment. The preference of the active sites pertinent to radical, nucleophilic and electrophilic attacks is made through linking them with the electrophilic and nucleophilic Parr functions, Fukui function indices, and condensed Dual Descriptor. This study showed that the MN12SX density functional is the most suitable one for predicting the chemical reactivity of this system.

A Revisited Definition of the Three Solute Descriptors Related to the Van der Waals Forces in Solutions

It is currently admitted that the intermolecular forces implicated in Gas Liquid Chromatography (GLC) can be expressed as a product of parameters (or descriptors) of solutes and of parameters of solvents. The present study is limited to those of solutes, and among them the three ones are involved in the Van der Waals forces, whereas the two ones involved in the hydrogen bonding are left aside at this stage. These three studied parameters, which we call δ, ω and ε, respectively reflect the three types of Van der Waals forces: dispersion, orientation or polarity strictly speaking, and induction-polarizability. These parameters have been experimentally obtained in previous studies for 121 Volatile Organic Compounds (VOC) via an original Multiplicative Matrix Analysis (MMA) applied to a superabundant and accurate GLC data set. Then, also in previous studies, attempts have been made to predict these parameters via a Simplified Molecular Topology procedure (SMT). Because these last published results have been somewhat disappointing, a promising new strategy of prediction is developed and detailed in the present article.

A stochastic reconstruction strategy based on a stratified library of structural descriptors and its application in the molecular reconstruction of naphtha

Molecular reconstruction is a rapid and reliable way to provide molecular detail of petroleum fractions,which is required in the kinetic modeling of petroleum conversation processes at the molecular level.In the typical stochastic reconstruction method,the estimation of properties of pseudo molecules that are generated by Monte Carlo sampling depends on the building of predefined molecular libraries,which is expensive and inaccessible for certain petroleum fractions.In this paper,a novel stochastic reconstruction strategy is proposed,which is based on a stratified library of structural descriptors.Properties of pseudo molecules generated in the novel strategy can be directly estimated by group contribution method in the condition of lacking predefined molecular libraries.In this strategy,the molecular building diagram comprises two steps.First,the ring structure is configured by determining the number of rings.Different from the length of chain adopted in the traditional stochastic reconstruction method,in the second step,number of structural descriptors(SDs)for binding site and chain were determined sequentially for the configuration of binding site and saturated acyclic hydrocarbon chain.These structural descriptors for binding site and chain were selected from group contribution methods.To count the number of partial overlapping sections between structural descriptors for chain,two supplementary structural descriptors were created.All possible saturated structures of hydrocarbon chains can be represented by structural descriptors at the scale of property estimation.This strategy separates the building of a predefined molecule library from the stochastic reconstruction process.The exact structures of pseudo molecules represented by structural descriptors in this work can be determined with sufficient chemical knowledge.Fifty naphtha samples are tested independently to demonstrate the performance of the proposed strategy and the results show that the estimated properties were close enough to the experimental values.This strategy will benefit the molecular management of petrochemical industries and therefore improve economic and environmental efficiencies.

Adsorption Properties and Quantum Molecular Descriptors of the Folic Acid Drug Adsorbed onto Zigzag and Armchair Single Walled Carbon Nanotubes:DFT Simulations

The adsorption behavior of folic acid onto（5,0） zigzag and（5,5） armchair carbon nanotube（SWCNT） has been investigated using B3 LYP density functional at the 6-31G＊ level. The adsorption energies,molecular orbital analysis and structural changes at the adsorption site are indicative of covalent adsorption on the zigzag SWCNT surface,while the adsorption is physical on the armchair SWCNT surface. The density of states（DOS） Plot and the quantum molecular descriptors（QMD） are witness to the significant changes in the electronic properties of SWCNT systems after the attachment of adsorbed species to the tube surface. According to the calculated results,the single-walled carbon nanotubes are expected to be a potential efficient adsorbent for the adsorption of folic acid drug and also can be used as a suitable drug delivery vehicle within biological systems.

Shape matching and retrieval based on multiple feature descriptors

A lot of 3D shape descriptors for 3D shape retrieval have been presented so far. This paper proposes a new mechanism, which employs several existing global and local 3D shape descriptors as input. With the sparse theory, some descriptors which play the most important role in measuring similarity between query model and the model in the dataset are selected automatically and an affinity matrix is constructed. Spectral clustering method can be implemented to this affinity matrix. Spectral embedding of this affinity matrix can be applied to retrieval, which integrating almost all the advantages of selected descriptors. In order to verify the performance of our approach, we perform experimental comparisons on Princeton Shape Benchmark database. Test results show that our method is a pose-oblivious, efficient and robustness method for either complete or incomplete models.

Recognition of 3-D Aircrafts by Fourier Descriptors with Fast and Efficient Library Search

Fourier descriptors are used as features for 3-D aircraft classification and pose determination from a 2-D image recorded at an arbitrary viewing angle. By the feature ranking of Fourier descriptors, a classification procedure based on the fast nearest neighbour rule is proposed to save the matching time of an unknown aircraft with a partial library search. The testing results of some typical examples indicate this method is generally applicable and efficient in 3-D aircraft recognition.

Prediction of liquid chromatography retention factors for α-branched phenylsulfonyl acetates using quantum chemical descriptors

The logarithms of retention factors normalized to a hypothetical pure water eluent(log k w) were determined on a reversed phase high performance liquid chromatography(RP HPLC) column (Li Chrosorb RP 18 column) for 20 new α\|branched phenylsulfonyl acetates. The atomic charge method was applied to develop quantitative structure retention relationships(QSRRs). Among the available geometric and electronic descriptors, surface area (S), ovality (O), and the charge of carboxyl group(Q OC ) are significant. In the model, the contribution of surface area (S) is the greatest. The molecular mechanism of retention was demonstrated through the model. With the correlation coefficient ( r 2 adj , adjusted for degrees of freedom) of 0.964, the standard error of 0.164 and the F value of 170.39, the model has good predictive capacity.

DFT-Based Prediction of Bioconcentration Factors of Polychlorinated Biphenyls in Fish Species Using Molecular Descriptors

Experimental determination of BCFs is expensive and demanding if performed correctly. Because of this, measuring the BCFs of many thousands of chemical substances that are potential regulatory interest is simply not possible. Hence, prediction of BCFs of the PCBs based on QSAR were made time to time to increase the probability of success and reduce the time and cost in exploring the toxicological and ecological characteristics of molecules. DFT methods are, in general, capable of generating a variety of isolated molecular descriptors as well as local reactivity descriptors quite accurately. In this work, prediction of BCFs of the fifty seven PCBs based on quantum chemical descriptors derived from DFT method using the B88-PW91 GGA energy function with the DZVP basis set have been made. The study concluded that dipole moment and ionization potential are reliable descriptors for correlation of bioconcentration factors of polychlorinated biphenyls with their electronic structures. The resulted QSAR model (r2 = 0.9139, ?= 0.8986, k = 2, SE = 0.2668) can be useful for predicting the BCFs of compounds prior to their synthesis.

Genetic Divergence in Mango and Obtaining Minimum Efficient Descriptors

Mangifera indica (mango) is a typically tropical fruit with considerable economic value. Brazil features a wide variety of cultivars of this fruit, most of which are known under several different names. Indeed, the nomenclature of mango varieties is still quite confusing. Up to now there has been no well-defined scientific principle to differentiate them. The objective of the present work is to compare the different clustering methods in assessing genetic divergence among mango accessions, as well as identify the minimum efficient descriptors for that crop. A total of 20 mango accessions in Cáceres, Mato Grosso state, Brazil were evaluated. When building dissimilarity matrices, the descriptors were divided according to the following groups: leaf, flower/inflorescence, fruit, seed and growth habit/ripening period. With these divisions, combinations were performed among the groups of descriptors. The similarity index was used to obtain the dissimilarity matrices. Later, the accessions were clustered using the methods of Tocher, Ward and UPGMA. The study observed that it was possible to reduce the number of descriptors from 64 to 35, and that the clustering methods were compatible with the study of the genetic diversity of mango.

Using three-dimensional discrete spherical Fourier descriptors based on surface curvature voxels for pollen particle recognition

This paper presents a new method for extract three-dimensional （3D） discrete spherical Fourier descriptors based on surface curvature voxels for pollen particle recognition. In order to reduce the high amount of pollen information and noise disturbance, the geometric normalized curvature voxels with the principal curvedness are first extracted to represent the intrinsic pollen volumetric data. Then the curvature voxels are decomposed into radial and angular components with spherical harmonic transform in spherical coordinates. Finally the 3D discrete Fourier transform is applied to the decomposed curvature voxels to obtain the 3D spherical Fourier descriptors for pollen recognition. Experimental results show that the presented descriptors are invariant to different pollen particle geometric transformations, such as pose change and spatial rotation, and can obtain high recognition accuracy and speed simultaneously.

Extracting and smoothing contours in mammograms using Fourier descriptors

Contour is an important pattern descriptor in image processing and particularly in region description, registration and length estimation. In many applications where contour is used, a good segmentation and an efficient smoothing method are needed. In X-ray images, such as mammograms, where object edge is not clearly discernible, estimating the object’s contour may yield substantial shift along the boundary due to noise or segmentation drawbacks. An appropriate smoothing is therefore required to reduce these effects. In this paper, an approach based on local adaptive threshold segmentation to extract contour and a new smoothing approach founded on Fourier descriptors are introduced. The experimental results of extraction obtained from a set of mammograms and compared with the breast regions delineated by radiologists yielded a percent overlap area of 98.7% ± 0.9% with false positive and negative rates of 0.36 ± 0.74 and 0.93 ± 0.44 respectively. The proposed method was tested on a set of images and improved the accuracy, leading to an average error of less than one pixel.

The Use of Fourier Descriptors for the Classification and Analysis of Peripheral Blood Smears Image

The article discusses the use of Fourier descriptors for the analysis and classification of blood cells. A model describing the contour boundaries in the form of two-dimensional numerical sequence Fourier descriptors. The influence of the shape and orientation of the figures on the parameters of the Fourier descriptors. Explore ways to ensure the invariance of the Fourier descriptors with respect to geometric transformations. A model of the graphical representation of the Fourier descriptors of computer graphics tools. A method of forming a space of informative features based on Fourier descriptors for the neural network, classifying the contours of borders image segments.

Relationships between morphological descriptors and RAPD markers for assessing genetic variability in Hippophae rhamnoides L.

Knowledge of genetic relatedness among accessions of germplasm is necessary for the development of breeding strategies to produce improved cultivars. The present investigation on Hippophae rhamnoides was carried out to assess its genetic variability in Himachal Pradesh, India, by employing morphological and RAPD markers. Different areas of Himachal Pradesh were surveyed and eight sites were finally selected Twenty four genotypes were selected for further studies, i.e., three genotypes from each site. On the basis of morphological studies, the genotypes of the Ropa site （Kinnaur） were considered elite genotypes. To assess the variability at the molecular level, RAPD patterns were studied by random primers. The total number of bands amplified was 607, out of which 487 bands were identified as polymorphic, depicting 80.23 per cent variability. Six unique bands were produced from three primers （OPA- 05, OPA-10 and OPD-08） specific for five genotypes, including three genotypes of the Ropa site, Kinnaur District.

Improving Protein Sequence Classification Performance Using Adjacent and Overlapped Segments on Existing Protein Descriptors

In protein sequence classification research, it is popular to convert a variable length sequence of protein into a fixed length numerical vector by using various descriptors, for instance, composition of k-mer composition. Such position-independent descriptors are useful since they are applicable to any length of sequence;however, positional information of subsequence is discarded even though it might have high contribution to classification performance. To solve this problem, we divided the original sequence into some segments, and then calculated the numerical features for them. It enables us to partially introduce positional information (for instance, compositions of serine in anterior and posterior segments of a sequence). Through comprehensive experiments on the number of segments and length of overlapping region, we found our classification approach with sequence segmentation and feature selection is effective to improve the performance. We evaluated our approach on three protein classification problems and achieved significant improvement in all cases which have a dataset with sufficient amino acid in each sequence. This result has shown the great potential of using additional segments in protein sequence classification to solve other sequence problems in bioinformatics.