New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to 7G.Furthermore,it improves the array gain and directivity,increasing the detection range and angular resolution of radar systems.This study proposes two highly efficient SLL reduction techniques.These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,respectively.The convolution process determines the element’s excitations while the GA optimizes the element spacing.For M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,respectively.When the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher SLL.Tomitigate the increased HPBWof the odd and even excitations,the element spacing is optimized using the GA.Thereby,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the SLL.Furthermore,for extreme SLL reduction,the DConv/GA is introduced.In this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation vectors.It provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

Optimization of Cooperative RelayingMolecular Communications for Nanomedical Applications

Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is increased.In this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift velocity.The fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)algorithm.Unlike the previous work,the probability of bit error is expressed in a closed-form expression.It is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano receiver.The simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.

Proposed Privacy Preservation Technique for Color Medical Images

Nowadays,the security of images or information is very important.This paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image security.First,the secret medical image is encrypted using Advanced Encryption Standard(AES)algorithm.Then,the secret report of the patient is embedded into the encrypted secret medical image with the Least Significant Bit(LSB)watermarking algorithm.After that,the encrypted secret medical image with the secret report is concealed in a cover medical image,using Kekre’s Median Codebook Generation(KMCG)algorithm.Afterwards,the stego-image obtained is split into 16 parts.Finally,it is sent to the receiver.We adopt this strategy to send the secret medical image and report over a network securely.The proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio(PSNR),Correlation Coefficient(Cr),Fea-ture Similarity Index Metric(FSIM),and Structural Similarity Index Metric(SSIM).Histogram estimation is used to confirm the matching between the secret medical image before and after transmission.Simulation results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.

An Efficient CNN-Based Automated Diagnosis Framework from COVID-19 CT Images

Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the world.It has dramatically affected daily lives,public health,and the world economy.This paper presents a segmentation and classification framework of COVID-19 images based on deep learning.Firstly,the classification process is employed to discriminate between COVID-19,non-COVID,and pneumonia by Convolutional Neural Network(CNN).Then,the segmentation process is applied for COVID-19 and pneumonia CT images.Finally,the resulting segmented images are used to identify the infected region,whether COVID-19 or pneumonia.The proposed CNN consists of four Convolutional(Conv)layers,four batch normalization layers,and four Rectified Linear Units(ReLUs).The sizes of Conv layer used filters are 8,16,32,and 64.Four maxpooling layers are employed with a stride of 2 and a 2×2 window.The classification layer comprises a Fully-Connected(FC)layer and a soft-max activation function used to take the classification decision.A novel saliencybased region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT images.The acquired findings substantiate the efficacy of the proposed framework for helping the specialists in automated diagnosis applications.

Embedded Coded Relay System for Molecular Communications

With the emergence of the COVID-19 pandemic,the World Health Organization(WHO)has urged scientists and industrialists to exploremodern information and communication technology(ICT)as a means to reduce or even eliminate it.The World Health Organization recently reported that the virus may infect the organism through any organ in the living body,such as the respiratory,the immunity,the nervous,the digestive,or the cardiovascular system.Targeting the abovementioned goal,we envision an implanted nanosystem embedded in the intra living-body network.The main function of the nanosystem is either to perform diagnosis and mitigation of infectious diseases or to implement a targeted drug delivery system(i.e.,delivery of the therapeutic drug to the diseased tissue or targeted cell).The communication among the nanomachines is accomplished via communication-based molecular diffusion.The control/interconnection of the nanosystem is accomplished through the utilization of Internet of bio-nano things(IoBNT).The proposed nanosystem is designed to employ a coded relay nanomachine disciplined by the decode and forward(DF)principle to ensure reliable drug delivery to the targeted cell.Notably,both the sensitivity of the drug dose and the phenomenon of drug molecules loss before delivery to the target cell site in long-distance due to the molecules diffusion process are taken into account.In this paper,a coded relay NM with conventional coding techniques such as RS and Turbo codes is selected to achieve minimum bit error rate(BER)performance and high signal-to-noise ratio(SNR),while the detection process is based on maximum likelihood(ML)probability and minimum error probability(MEP).The performance analysis of the proposed scheme is evaluated in terms of channel capacity and bit error rate by varying system parameters such as relay position,number of released molecules,relay and receiver size.Analysis results are validated through simulation and demonstrate that the proposed scheme can significantly improve delivery performance of the desirable drugs in the molecular communication system.

A Hybrid Security Framework for Medical Image Communication

Authentication of the digital image has much attention for the digital revolution.Digital image authentication can be verified with image watermarking and image encryption schemes.These schemes are widely used to protect images against forgery attacks,and they are useful for protecting copyright and rightful ownership.Depending on the desirable applications,several image encryption and watermarking schemes have been proposed to moderate this attention.This framework presents a new scheme that combines a Walsh Hadamard Transform(WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding(DRPE).First,on the sender side,the secret medical image is encrypted using DRPE.Then the encrypted image is watermarking based on WHT.The combination between watermarking and encryption increases the security and robustness of transmitting an image.The performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index(SSIM),Peak Signal-to-Noise Ratio(PSNR),Normalized cross-correlation(NC),and Feature Similarity Index(FSIM).

Cancellable Multi-Biometric Template Generation Based on Arnold Cat Map and Aliasing

The cancellable biometric transformations are designed to be computationally difficult to obtain the original biometric data.This paper presents a cancellable multi-biometric identification scheme that includes four stages:biometric data collection and processing,Arnold’s Cat Map encryption,decimation process to reduce the size,and finalmerging of the four biometrics in a single generated template.First,a 2D matrix of size 128×128 is created based on Arnold’s Cat Map(ACM).The purpose of this rearrangement is to break the correlation between pixels to hide the biometric patterns and merge these patterns together for more security.The decimation is performed to keep the dimensions of the overall cancellable template similar to those of a single template to avoid data redundancy.Moreover,some sort of aliasing occurs due to decimation,contributing to the intended distortion of biometric templates.The hybrid structure that comprises encryption,decimation,andmerging generates encrypted and distorted cancellable templates.The simulation results obtained for performance evaluation show that the system is safe,reliable,and feasible as it achieves high security in the presence of noise.

Secure Cancelable Template Based on Double Random Phase Encoding and Entropy Segmentation

In this paper,a proposed cancellable biometric scheme is based on multiple biometric image identifiers,Arnold’s cat map and double random phase encoding(DRPE)to obtain cancellable biometric templates.The proposed segmentation scheme that is used to select the region of interest for generating cancelable templates is based on chaos entropy low correlation statistical metrics.The objective of segmentation is to reduce the computational cost and reliability of template creation.The left and right biometric(iris,fingerprint,palm print and face)are divided into non-overlapping blocks of the same dimensions.To define the region of interest(ROI),we select the block with the highest entropy.To shorten the registration process time and achieve a high level of security,we select 25%of the image volume of the biometric data.In addition,the low-cost security requirement lies in the use of selective encryption(SE)technology.The step of selecting the maximum entropy is executed on all biometric blocks.The maximum right and left multi-biometric blocks are arranged in descending order from the entropy perspective and select 50%of each biometric couple and store the single matrix.The obtained matrix is scrambled with a certain number of iterations using Arnold’s Cat Map(ACM).The obtained scrambled matrix is encrypted with the DRPE to generate the cancellable biometric templates,which are further concatenated.The simulation results display better performance of the suggested cancellable biometric system in noise scenarios using the area under the receiver operating characteristic(AROC).The strength of the suggested technique is examined with correlation,irregular deviation,maximum difference and maximum deviation.The recommended proposed approach shows that the ability to distinguish the authentic and imposter biometrics of user seven in different levels of the noise environment.

High-Sensitivity Early Detection Biomedical Sensor for Tuberculosis With Low Losses in the Terahertz Regime Based on Photonic Crystal Fiber Technology

Tuberculosis is one of the most contagious and lethal illnesses in the world,according to the World Health Organization.Tuberculosis had the leading mortality rate as a result of a single infection,ranking above HIV/AIDS.Early detection is an essential factor in patient treatment and can improve the survival rate.Detection methods should have high mobility,high accuracy,fast detection,and low losses.This work presents a novel biomedical photonic crystal fiber sensor,which can accurately detect and distinguish between the different types of tuberculosis bacteria.The designed sensor detects these types with high relative sensitivity and negligible losses compared to other photonic crystal fiber-based biomedical sensors.The proposed sensor exhibits a relative sensitivity of 90.6%,an effective area of 4.342×10^(-8)m^(2),with a negligible confinement loss of 3.13×10^(-9)cm^(-1),a remarkably low effective material loss of 0.0132cm-f,and a numerical aperture of 0.3462.The proposed sensor is capable of operating in the terahertz regimes over a wide range(1 THz-2.4 THz).An abbreviated review of non-optical detection techniques is also presented.An in-depth comparison between this work and recent related photonic crystal fiber-based literature is drawn to validate the efficacy and authenticity of the proposed design.