Optimization of Resource Allocation in Unmanned Aerial Vehicles Based on Swarm Intelligence Algorithms

Due to their adaptability,Unmanned Aerial Vehicles(UAVs)play an essential role in the Internet of Things(IoT).Using wireless power transfer(WPT)techniques,an UAV can be supplied with energy while in flight,thereby extending the lifetime of this energy-constrained device.This paper investigates the optimization of resource allocation in light of the fact that power transfer and data transmission cannot be performed simultaneously.In this paper,we propose an optimization strategy for the resource allocation of UAVs in sensor communication networks.It is a practical solution to the problem of marine sensor networks that are located far from shore and have limited power.A corresponding system model is summarized based on the scenario and existing theoretical works.The minimum throughputmaximizing object is then formulated as an optimization problem.As swarm intelligence algorithms are utilized effectively in numerous fields,this paper chose to solve the formed optimization problem using the Harris Hawks Optimization and Whale Optimization Algorithms.This paper introduces a method for translating multi-decisions into a row vector in order to adapt swarm intelligence algorithms to the problem,as joint time and energy optimization have two sets of variables.The proposed method performs well in terms of stability and duration.Finally,performance is evaluated through numerical experiments.Simulation results demonstrate that the proposed method performs admirably in the given scenario.

A Robust Zero-Watermarking Based on SIFT-DCT for Medical Images in the Encrypted Domain

Remote medical diagnosis can be realized by using the Internet,but when transmitting medical images of patients through the Internet,personal information of patients may be leaked.Aim at the security of medical information system and the protection of medical images,a novel robust zero-watermarking based on SIFT-DCT(Scale Invariant Feature Transform-Discrete Cosine Transform)for medical images in the encrypted domain is proposed.Firstly,the original medical image is encrypted in transform domain based on Logistic chaotic sequence to enhance the concealment of original medical images.Then,the SIFT-DCT is used to extract the feature sequences of encrypted medical images.Next,zero-watermarking technology is used to ensure that the region of interest of medical images are not changed.Finally,the robust of the algorithm is evaluated by the correlation coefficient between the original watermark and the attacked watermark.A series of attack experiments are carried out on this method,and the results show that the algorithm is not only secure,but also robust to both traditional and geometric attacks,especially in clipping attacks.

Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

Medical images are used as a diagnostic tool, so protecting theirconfidentiality has long been a topic of study. From this, we propose aResnet50-DCT-based zero watermarking algorithm for use with medicalimages. To begin, we use Resnet50, a pre-training network, to draw out thedeep features of medical images. Then the deep features are transformedby DCT transform and the perceptual hash function is used to generatethe feature vector. The original watermark is chaotic scrambled to get theencrypted watermark, and the watermark information is embedded into theoriginal medical image by XOR operation, and the logical key vector isobtained and saved at the same time. Similarly, the same feature extractionmethod is used to extract the deep features of the medical image to be testedand generate the feature vector. Later, the XOR operation is carried outbetween the feature vector and the logical key vector, and the encryptedwatermark is extracted and decrypted to get the restored watermark;thenormalized correlation coefficient (NC) of the original watermark and therestored watermark is calculated to determine the ownership and watermarkinformation of the medical image to be tested. After calculation, most ofthe NC values are greater than 0.50. The experimental results demonstratethe algorithm’s robustness, invisibility, and security, as well as its ability toaccurately extract watermark information. The algorithm also shows goodresistance to conventional attacks and geometric attacks.

Interaction between macroalgae and microplastics:Caulerpa lentillifera and Gracilaria tenuistipitata as microplastic bio-elimination vectors

The pollution of microplastics(MPs)in the ocean has become a serious matter of concern.The farmed seaweeds(Caulerpa lentillifera and Gracilaria tenuistipitata)were selected to study their ability of adsorption with two typical classes of MPs(polyamides and polystyrene),thereby revealing the interaction between MPs and macroalgae and exploring novel methods of removing MPs from macroalgae.The results demonstrate that polyamides(PA)fibers had no effect on the various physiological parameters of both seaweeds(e.g.,relative growth rate,photosynthetic oxygen evolution rate,the contents of malondialdehyde and extracellular polymeric substances)after 7 days of exposure,except for the chlorophyll-a concentration.However,the effects of polystyrene(PS)particles on the algae were strongly associated with the concentration of MPs exposure.Exposed to the high concentration(100 mg/L)of PS particles,the relative growth rate of C.lentillifera and G.tenuistipitata decreased by 54.56% and 30.62%,respectively,compared to the control,while no significant(P>0.05)harmful effect of PS particles on seaweeds was observed in an environment with a low content of PS particles(25 mg/L).The PS particles in concentration of 100 mg/L significantly(P<0.05)inhibited photosynthetic oxygen evolution rate and extracellular polymeric substances(EPS)contents in both seaweeds,but increased malondialdehyde(MDA)contents.When exposed for 72 h,the MPs adhesion rate of G.tenuistipitata is higher than that of C.lentillifera,which might be due to the higher EPS content of G.tenuistipitata.The MPs desorption experiment indicated that the combination of dewatering and washing had the highest desorption rate of MPs which could reach to 91.45%and 87.23%for C.lentillifera and G.tenuistipitata,respectively.This research demonstrates the potential of macroalgae as a vector for MPs in aquatic environment and provides methodological insights into decontamination procedures for removing the MPs from macroalgae.

Robust Watermarking Algorithm for Medical Images Based on Non-Subsampled Shearlet Transform and Schur Decomposition

With the development of digitalization in healthcare,more and more information is delivered and stored in digital form,facilitating people’s lives significantly.In the meanwhile,privacy leakage and security issues come along with it.Zero watermarking can solve this problem well.To protect the security of medical information and improve the algorithm’s robustness,this paper proposes a robust watermarking algorithm for medical images based on Non-Subsampled Shearlet Transform(NSST)and Schur decomposition.Firstly,the low-frequency subband image of the original medical image is obtained by NSST and chunked.Secondly,the Schur decomposition of low-frequency blocks to get stable values,extracting the maximum absolute value of the diagonal elements of the upper triangle matrix after the Schur decom-position of each low-frequency block and constructing the transition matrix from it.Then,the mean of the matrix is compared to each element’s value,creating a feature matrix by combining perceptual hashing,and selecting 32 bits as the feature sequence.Finally,the feature vector is exclusive OR(XOR)operated with the encrypted watermark information to get the zero watermark and complete registration with a third-party copyright certification center.Experimental data show that the Normalized Correlation(NC)values of watermarks extracted in random carrier medical images are above 0.5,with higher robustness than traditional algorithms,especially against geometric attacks and achieve watermark information invisibility without altering the carrier medical image.

Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

The field of medical images has been rapidly evolving since the advent of the digital medical information era.However,medical data is susceptible to leaks and hacks during transmission.This paper proposed a robust multi-watermarking algorithm for medical images based on GoogLeNet transfer learning to protect the privacy of patient data during transmission and storage,as well as to increase the resistance to geometric attacks and the capacity of embedded watermarks of watermarking algorithms.First,a pre-trained GoogLeNet network is used in this paper,based on which the parameters of several previous layers of the network are fixed and the network is fine-tuned for the constructed medical dataset,so that the pre-trained network can further learn the deep convolutional features in the medical dataset,and then the trained network is used to extract the stable feature vectors of medical images.Then,a two-dimensional Henon chaos encryption technique,which is more sensitive to initial values,is used to encrypt multiple different types of watermarked private information.Finally,the feature vector of the image is logically operated with the encrypted multiple watermark information,and the obtained key is stored in a third party,thus achieving zero watermark embedding and blind extraction.The experimental results confirmthe robustness of the algorithm from the perspective ofmultiple types of watermarks,while also demonstrating the successful embedding ofmultiple watermarks for medical images,and show that the algorithm is more resistant to geometric attacks than some conventional watermarking algorithms.

A Multi-Watermarking Algorithm for Medical Images Using Inception V3 and DCT

Medical images are a critical component of the diagnostic process for clinicians.Although the quality of medical photographs is essential to the accuracy of a physician’s diagnosis,they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images.Traditional watermark embedding algorithm embeds the watermark information into the medical image,which reduces the quality of the medical image and affects the physicians’judgment of patient diagnosis.In addition,watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are destroyed.This paper proposes a novel watermarking algorithm using the convolutional neural networks(CNN)Inception V3 and the discrete cosine transform(DCT)to address above mentioned problems.First,the medical image is input into the Inception V3 network,which has been structured by adjusting parameters,such as the size of the convolution kernels and the typical architecture of the convolution modules.Second,the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical image.At last,the watermarks are encrypted using the logistic map system and hash function,and the keys are stored by a third party.The encrypted watermarks and the original image features are performed logical operations to realize the embedding of zero-watermark.In the experimental section,multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed algorithms.Our NC values for all the images are more than 90%accurate which shows the robustness of the algorithm.Extensive experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.

Robust Watermarking Algorithm for Medical Volume Data Based on Polar Cosine Transform and 3D-DCT

The amount of 3D data stored and transmitted in the Internet of Medical Things(IoMT)is increasing,making protecting these medical data increasingly prominent.However,there are relatively few researches on 3D data watermarking.Moreover,due to the particularity of medical data,strict data quality should be considered while protecting data security.To solve the problem,in the field of medical volume data,we proposed a robust watermarking algorithm based on Polar Cosine Transform and 3D-Discrete Cosine Transform(PCT and 3D-DCT).Each slice of the volume data was transformed by PCT to obtain feature row vector,and then the reshaped three-dimensional feature matrix was transformed by 3D-DCT.Based on the contour information of the volume data and the detail information of the inner slice,the visual feature vector was obtained by applying the per-ceptual hash.In addition,the watermark was encrypted by a multi-sensitive initial value Sine and Piecewise linear chaotic Mapping(SPM)system,and embedded as a zero watermark.The key was stored in a third party.Under the same experimental conditions,when the volume data is rotated by 80 degrees,cut 25%along the Z axis,and the JPEG compression quality is 1%,the Normalized Correlation Coefficient(NC)of the extracted watermark is 0.80,0.89,and 1.00 respectively,which are significantly higher than the comparison algorithm.

Metabolomic and proteomic responses of Phaeodactylum tricornutum to hypoxia

Diatoms are important contributors to global net primary productivity,and play a crucial role in the biogeochemical cycles of carbon,phosphorus,nitrogen,iron,and silicon.Currently in some regions in the ocean,there’s a trend that carbon content is high while oxygen concentration is low,and the underlying mechanisms of diatoms’response to low oxygen environments are worth investigating.Phaeodactylum tricornutum is a model diatom whose genome has been sequenced;it provides a universal molecular toolbox and a stable transgenic expression system.Therefore,the study of the responses of P.tricornutum to low oxygen has not only fundamental research significance but also important ecologic al significance.In this study,growth rates were determined and proteomic analysis and metabolomic analysis were performed to examine P.tricornutum responses under different oxygen concentrations(2%oxygen concentration for hypoxic condition and 21%oxygen concentration for the normal condition(sterilized air)).Results show that the hypoxic environment inhibited the growth of P.tricornutum.In the hypoxic conditions,P.tricornutum could reset its metabolism pathways,including enhancement in lipid utilization,replenishment of tricarboxylic acid(TCA)cycle through the glyoxylic acid cycle,and down-regulation of photorespiration to reduce energy waste.Additionally,the stress resistance mechanism was activated to facilitate the adaptation to low oxygen conditions.This study helps to reveal the different metabolic changes to hypoxia of diatom from that of higher plants,which might be ascribed to their different habitats and needs further exploration in the future.

Mixed Attention Densely Residual Network for Single Image Super-Resolution

Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance further.The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information.Specifically,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep network.This structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature learning.The qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.

Coupling interface constructions of FeOOH/NiCo_(2)S_(4)by microwave-assisted method for efficient oxygen evolution reaction

The oxygen evolution reaction(OER) with slow kinetics is the rate-limiting step of electrochemical water splitting.A reasonable construction of interface nanostructures is the key to improving the OER efficiency and durability of non-noble metal electrocatalysts.In this study,a FeOOH/NiCo_(2)S_(4) core-shell nanorod array with abundant heterogeneous interfaces and high density of active sites was successfully prepared by a microwave-as sis ted method.Experimental research and theoretical calculations show that the abundant strong coupling Ni/Co-S-Fe interface helps in adjusting the electronic structure of the material surface,optimizing the adsorption energy of the intermediate,and realizing an efficient catalytic process.The as-synthesized FeOOH/NiCo_(2)S_(4)/NF composite electrode exhibited lower overpotential(198 mV) and Tafel slope(62 mV·dec^(-1)) at a current density of 10 mA·cm^(-2)and excellent stability(approximately 100% retention after100 h) than the NiCo_(2)S_(4)/nickel foam(NF).In conclusion,constructing heterojunctions with complementary active materials is an effective strategy to design efficient and robust OER electrocatalysts.