Influence of B4C and ZrB2 reinforcements on microstructural,mechanical and wear behaviour of AA 2014 aluminium matrix hybrid composites

Considering their affordability and high strength-to-weight ratio,lightweight aluminium alloys are the subject of intensive research aimed at improving their properties for use in the aerospace industry.This research effort aims to develop novel hybrid composites based on AA 2014 alloy through the use of liquid metallurgy stir casting to reinforce dual ceramic particles of Zirconium Diboride(ZrB_(2))and Boron Carbide(B4C).The weight percentage(wt%)of ZrB_(2) was varied(0,5,10,and 15),while a constant 5 wt%of B4C was maintained during this fabrication.The as-cast samples have been assessed using an Optical Microscope(OM)and a Scanning Electron Microscope(SEM)with Energy Dispersive Spectroscopy(EDS).The properties such as hardness,tensile strength,and wear characteristics of stir cast specimens were assessed to examine the impact of varying weight percentages of reinforcements in AA 2014 alloy.In particular,dry sliding wear behaviour was evaluated considering varied loads using a pin-on-disc tribotester.As the weight%of ZrB_(2) grew and B4C was incorporated,hybrid composites showed higher hardness,tensile strength,and wear resistance.Notably,the incorporation of a cumulative reinforcement consisting of 15 wt%ZrB_(2) and 5 wt%B4C resulted in a significant 31.86%increase in hardness and a 44.1%increase in tensile strength compared to AA 2014 alloy.In addition,it has been detected that wear resistance of hybrid composite pin(containing 20 wt%cumulative reinforcement)is higher than that of other stir cast wear test pins during the whole range of applied loads.Fractured surfaces of tensile specimens showed ductile fracture in the AA 2014 matrix and mixed mode for hybrid composites.Worn surfaces obtained employing higher applied load indicated abrasive wear with little plastic deformation for hybrid composites and dominant adhesive wear for matrix alloy.Hence,the superior mechanical and tribological performance of hybrid composites can be attributed to dual reinforcement particles being dispersed well and the effective transmission of load at this specific composition.

An Ophthalmic Evaluation of Central Serous Chorioretinopathy

Nowadays in the medicalfield,imaging techniques such as Optical Coherence Tomography(OCT)are mainly used to identify retinal diseases.In this paper,the Central Serous Chorio Retinopathy(CSCR)image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application methods.Thefirst approach,which was focused on image quality,improves medical image accuracy.An enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter(BADWUMF).The classifier used here is tofigure out whether the OCT image is a CSCR case or not.150 images are checked for this research work(75 abnormal from Optical Coherence Tomography Image Retinal Database,in-house clinical database,and 75 normal images).This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be minimized.The total precision is 90 percent obtained from the Two Class Support Vector Machine(TCSVM)classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale(SNNWPB)classifier using the MATLAB 2019a program.

Maximizing Solar Potential Using the Differential Grey Wolf Algorithm for PV System Optimization

Maximum Power Point Tracking(MPPT)is crucial for maximizing the energy output of photovoltaic(PV)systems by continuously adjusting the operating point of the panels to track the point of maximum power production under changing environmental conditions.This work proposes the design of an MPPT system for solar PV installations using the Differential Grey Wolf Optimizer(DGWO).It dynamically adjusts the parameters of the MPPT controller,specifically the duty cycle of the SEPIC converter,to efficiently track the Maximum Power Point(MPP).The proposed system aims to enhance the energy harvesting capability of solar PV systems by optimizing their performance under varying solar irradiance,temperature and shading conditions.Simulation results demonstrate the effectiveness of the DGWO-based MPPT system in maximizing the power output of solar PV installations compared to conventional MPPT methods.This research contributes to the development of advanced MPPT techniques for improving the efficiency and reliability of solar energy systems.

Effect of tool tilt angle on strength and microstructural characteristics of friction stir welded lap joints of AA2014-T6 aluminum alloy

Friction stir welding(FSW)has been extensively adopted to fabricate aluminium alloy joints by incorporating various welding parameters that include welding speed,rotational speed,diameters of shoulder and pin and tool tilt angle.FSW parameters significantly affect the weld strength.Tool tilt angle is one of the significant process parameters among the weld parameters.The present study focused on the effect of tool tilt angle on strength of friction stir lap welding of AA2014-T6 aluminium alloy.The tool tilt angle was varied between 0°and 4°with an equal increment of 1°.Other process parameters were kept constant.Macrostructure and microstructure analysis,microhardness measurement,scanning electron micrograph,transmission electron micrograph and energy dispersive spectroscopy analysis were performed to evaluate the lap shear strength of friction stir lap welded joint.Results proved that,defect-free weld joint was obtained while using a tool tilt angle of 1°to 3°.However,sound joints were welded using a tool tilt angle of 2°,which had the maximum lap shear strength of 14.42 kN and microhardness of HV 132.The joints welded using tool tilt angles of 1°and 3°yielded inferior lap shear strength due to unbalanced material flow in the weld region during FSW.

Design of area and power efficient Radix-4 DIT FFT butterfly unit using floating point fused arithmetic

In this work, power efficient butterfly unit based FFT architecture is presented. The butterfly unit is designed using floating-point fused arithmetic units. The fused arithmetic units include two-term dot product unit and add-subtract unit. In these arithmetic units, operations are performed over complex data values. A modified fused floating-point two-term dot product and an enhanced model for the Radix-4 FFT butterfly unit are proposed. The modified fused two-term dot product is designed using Radix-16 booth multiplier. Radix-16 booth multiplier will reduce the switching activities compared to Radix-8 booth multiplier in existing system and also will reduce the area required. The proposed architecture is implemented efficiently for Radix-4 decimation in time(DIT) FFT butterfly with the two floating-point fused arithmetic units. The proposed enhanced architecture is synthesized, implemented, placed and routed on a FPGA device using Xilinx ISE tool. It is observed that the Radix-4 DIT fused floating-point FFT butterfly requires 50.17% less space and 12.16% reduced power compared to the existing methods and the proposed enhanced model requires 49.82% less space on the FPGA device compared to the proposed design. Also, reduced power consumption is addressed by utilizing the reusability technique, which results in 11.42% of power reduction of the enhanced model compared to the proposed design.

Investigation on mechanical properties of aluminium 7075-boron carbide-coconut shell fly ash reinforced hybrid metal matrix composites

The present research work reports the fabrication and evaluation of the mechanical properties of hybrid aluminium matrix composites(HAMC). Aluminium 7075(Al7075) alloy was reinforced with particles of boron carbide(B_4 C) and coconut shell fly ash(CSFA). Al7075 matrix composites were fabricated by stir casting method. The samples of Al7075 HAMC were fabricated with different weight percentages of(0, 3, 6, 9 and 12 wt.%) B_4 C and 3 wt.% of CSFA. The mechanical properties discussed in this work are hardness, tensile strength, and impact strength. Hardness of the composites increased 33% by reinforcements of 12 wt.% B_4 C and 3 wt.% CSFA in aluminium 7075 alloy. The tensile strength of the composites increased 66% by the addition of 9 wt.% B_4 C and 3 wt.% CSFA in aluminium 7075 alloy. Further addition of reinforcements decreased the tensile strength of the composites. Elongation of the composites decreased while increasing B_4 C and CSFA reinforcements in the matrix. The impact energy of the composites increased up to 2.3 J with 9 wt.% B_4 C and 3 wt.% CSFA addition in aluminium alloy. Further addition of reinforcement decreased the impact strength of the composites. The optical micrographs disclosed the homogeneous distribution of reinforcement particles(B_4 C and CSFA) in Al7075 matrix. The homogeneously distributed B_4 C and CSFA particles added as reinforcement in the Al7075 alloy contributed to the improvement of hardness, tensile strength, and impact strength of the composites.

Dry sliding wear characterization of squeeze cast LM13/Fe Cu composite using response surface methodology

Dry sliding wear is one of the predominant factors to be considered while selecting material for automotive and aerospace applications. Researchers have been exploring novel aluminium matrix composites(AMC), which offer minimum wear rate for various tribological applications. In this present work, an attempt has been made to reinforce LM13 aluminium alloy with copper coated steel fibers(10 wt.%) using squeeze casting process and to perform dry sliding wear test using pin-on-disc tribometer. Microstructure of cast samples was examined using image analysis system to investigate the dispersion of reinforcement in matrix. Dry sliding wear test was performed by considering factors such as load(10–50 N), sliding velocity(1–5 m·s(-1)) and sliding distance(500–2,500 m). Wear test was performed according to the experimental design at room temperature. Three factors and five levels central composite design were used to design the experiments using response surface methodology. Based on the results of the experiments, a regression model was developed to predict the wear rate of composites and checked for its adequacy using significance tests, analyses of variance and confirmation tests. Worn surface of samples was investigated using field emission scanning electron microscope and reported with its mechanisms. Microstructure of cast samples revealed uniform dispersion of reinforcement throughout the matrix. Response surface plots revealed that wear rate of composites increases with increasing load up to 50 N with the velocity 1–5 m·s(-1) and a sliding distance up to 2,500 m. However wear rate decreasesd with increasing velocity at lower loads(up to 20 N) and increased after reaching transition velocity of 2 m·s(-1). Dry sliding wear process parameters were optimised for obtaining minimum wear rate and they were found to be a load of 18.46 N, velocity of 4.11 m·s(-1), sliding distance of 923 m. Worn surface of samples revealed a mild wear at lower loads(up to 30 N), and severe wear was observed at high loads(40–50 N) due to higher level of deformation on the surface.

Ameliorate Security by Introducing Security Server in Software Defined Network

Software Defined Network(SDN)deals with huge data processing units which possess network management.However,due to centralization behavior ensuring security in SDN is the major concern.In this work to ensure security,a security server has been at its aid to check the vulnerability of the networks and to keep an eye on the packet according to the screening policies.A Secure Shell Connection(SSH)is established by the security server which does a frequent inspection of the network’s logs.Malware detection and the Intrusion Detection System policies are also incorporated in the server for the effective scanning of the packets.In response to a suspicious log or the packets in the SDN network there is a change in the security norms.Hence the proposed work updates the security policies in accordance with the attacker mentality.

Design of Hybrid True Random Number Generator for Cryptographic Applications

In real-time applications,unpredictable random numbers play a major role in providing cryptographic and encryption processes.Most of the existing random number generators are embedded with the complex nature of an amplifier,ring oscillators,or comparators.Hence,this research focused more on implementing a Hybrid Nature of a New Random Number Generator.The key objective of the proposed methodology relies on the utilization of True random number generators.The randomness is unpredictable.The additions of programmable delay lines will reduce the processing time and maintain the quality of randomizing.The performance comparisons are carried out with power,delay,and lookup table.The proposed architecture was executed and verified using Xilinx.The Hybrid TRNG is evaluated under simulation and the obtained results outperform the results of the conventional random generators based on Slices,area and Lookup Tables.The experimental observations show that the proposed Hybrid True Random Number Generator(HTRNG)offers high operating speed and low power consumption.

5G Data Offloading Using Fuzzification with Grasshopper Optimization Technique

Data offloading at the network with less time and reduced energy con-sumption are highly important for every technology.Smart applications process the data very quickly with less power consumption.As technology grows towards 5G communication architecture,identifying a solution for QoS in 5G through energy-efficient computing is important.In this proposed model,we perform data offloading at 5G using the fuzzification concept.Mobile IoT devices create tasks in the network and are offloaded in the cloud or mobile edge nodes based on energy consumption.Two base stations,small(SB)and macro(MB)stations,are initialized and thefirst tasks randomly computed.Then,the tasks are pro-cessed using a fuzzification algorithm to select SB or MB in the central server.The optimization is performed using a grasshopper algorithm for improving the QoS of the 5G network.The result is compared with existing algorithms and indi-cates that the proposed system improves the performance of the system with a cost of 44.64 J for computing 250 benchmark tasks.

Study on Enhanced Antibacterial and Cytotoxicity of Pure and Cadmium Doped Cerium Oxide against Gram-Positive and Gram-Negative Bacteria

Pure and Cadmium (Cd) doped Cerium oxide nanoparticles (CeNPs) have been synthesised by the simple chemical co-precipitation technique. Cadmium ions of concentrations 1, 3 and 5 mol% were doped to investigate their influence on the structural and optical properties of CeO2. The synthesised samples have been subjected to X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray (EDX) analysis and high-resolution transmission electron microscopy (HRTEM). The XRD and Raman patterns have witnessed the cubic structure of the cerium oxide nanoparticles. The average particle size of CeO2 was found to be around 10 nm. SEM image has also ascertained that the grain size of pure CeO2 appeared is bigger than that of the Cd-doped, which intern indicates the grain growth upon doping. Besides, the antibacterial activity of the cadmium doped cerium oxide nanoparticles against some human pathogens revealed that they have exhibited the maximum zone of inhibition against gram-positive bacteria than the gram-negative species. Further, the cytotoxic effect of Cd-doped CeO2 sample is examined in cultured (MCF-7, A549 and Hep-2) cell.

Efficient FPGA implementation of AES 128 bit for IEEE 802.16e mobile WiMax standards

In an advancement of communication field, wireless technology plays a predominant role in data transmission. In the timeline of wireless domain, Wi-Fi, Bluetooth, zigbee etc are some of the standards, which are being used in today’s wireless medium. In addition, the WiMax is introduced by IEEE in IEEE 802.16 for long distance communication, specifically 802.16e standard for mobile WiMax. It is an acronym of Worldwide Interoperability for Microwave Access. It is to be deliver wireless transmission with high quality of service in a secured environment. Since, security becomes dominant design aspect of every communication, a new technique has been proposed in wireless environment. Privacy across the network and access control management is the goal in the predominant aspects in the WiMax protocol. Especially, MAC sub layer should be evaluated in the security architecture. It has been proposed on cryptography algorithm AES that require high cost. Under this scenario, we present the optimized AES 128 bit counter mode security algorithm for MAC layer of 802.16e standards. To design a efficient MAC layer, we adopt the modification of security layers data handling process. As per the efficient design strategy, the power and speed are the dominant factors in mobile device. Since we concentrate mobile WiMax, efficient design is needed for MAC Security layer. Our proposed model incorporates the modification of AES algorithm. The design has been implemented in Xilinx virtex5 device and power has been analyzed using XPower analyzer. This proposed system consumes 41% less power compare to existing system.

Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services

Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.

Copy-Move Geometric Tampering Estimation Through Enhanced SIFT Detector Method

Digital picture forgery detection has recently become a popular and sig-nificant topic in image processing.Due to advancements in image processing and the availability of sophisticated software,picture fabrication may hide evidence and hinder the detection of such criminal cases.The practice of modifying origi-nal photographic images to generate a forged image is known as digital image forging.A section of an image is copied and pasted into another part of the same image to hide an item or duplicate particular image elements in copy-move forgery.In order to make the forgeries real and inconspicuous,geometric or post-processing techniques are frequently performed on tampered regions during the tampering process.In Copy-Move forgery detection,the high similarity between the tampered regions and the source regions has become crucial evidence.The most frequent way for detecting copy-move forgeries is to partition the images into overlapping square blocks and utilize Discrete cosine transform(DCT)com-ponents as block representations.Due to the high dimensionality of the feature space,Gaussian Radial basis function(RBF)kernel based Principal component analysis(PCA)is used to minimize the dimensionality of the feature vector repre-sentation,which improves feature matching efficiency.In this paper,we propose to use a novel enhanced Scale-invariant feature transform(SIFT)detector method called as RootSIFT,combined with the similarity measures to mark the tampered areas in the image.The proposed method outperforms existing state-of-the-art methods in terms of matching time complexity,detection reliability,and forgery location accuracy,according to the experimental results.The F1 score of the proposed method is 92.3%while the literature methods are around 90%on an average.

Graph-Based Replication and Two Factor Authentication in Cloud Computing

Many cutting-edge methods are now possible in real-time commercial settings and are growing in popularity on cloud platforms.By incorporating new,cutting-edge technologies to a larger extent without using more infrastructures,the information technology platform is anticipating a completely new level of devel-opment.The following concepts are proposed in this research paper:1)A reliable authentication method Data replication that is optimised;graph-based data encryp-tion and packing colouring in Redundant Array of Independent Disks(RAID)sto-rage.At the data centre,data is encrypted using crypto keys called Key Streams.These keys are produced using the packing colouring method in the web graph’s jump graph.In order to achieve space efficiency,the replication is carried out on optimised many servers employing packing colours.It would be thought that more connections would provide better authentication.This study provides an innovative architecture with robust security,enhanced authentication,and low cost.

An Optimized Novel Trust-Based Security Mechanism Using Elephant Herd Optimization

Routing strategies and security issues are the greatest challenges in Wireless Sensor Network(WSN).Cluster-based routing Low Energy adaptive Clustering Hierarchy(LEACH)decreases power consumption and increases net-work lifetime considerably.Securing WSN is a challenging issue faced by researchers.Trust systems are very helpful in detecting interfering nodes in WSN.Researchers have successfully applied Nature-inspired Metaheuristics Optimization Algorithms as a decision-making factor to derive an improved and effective solution for a real-time optimization problem.The metaheuristic Elephant Herding Optimizations(EHO)algorithm is formulated based on ele-phant herding in their clans.EHO considers two herding behaviors to solve and enhance optimization problem.Based on Elephant Herd Optimization,a trust-based security method is built in this work.The proposed routing selects routes to destination based on the trust values,thus,finding optimal secure routes for transmitting data.Experimental results have demonstrated the effectiveness of the proposed EHO based routing.The Average Packet Loss Rate of the proposed Trust Elephant Herd Optimization performs better by 35.42%,by 1.45%,and by 31.94%than LEACH,Elephant Herd Optimization,and Trust LEACH,respec-tively at Number of Nodes 3000.As the proposed routing is efficient in selecting secure routes,the average packet loss rate is significantly reduced,improving the network’s performance.It is also observed that the lifetime of the network is enhanced with the proposed Trust Elephant Herd Optimization.

Segmentation Based Real Time Anomaly Detection and Tracking Model for Pedestrian Walkways

Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so on.Besides,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety.The recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,etc.In this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian walkways.The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,etc.The proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition process.For object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized objects.For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)models.The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.

A Novel Segment White Matter Hyperintensities Approach for Detecting Alzheimer

Segmentation has been an effective step that needs to be done before the classification or detection of an anomaly like Alzheimer’s on a brain scan.Segmentation helps detect pixels of the same intensity or volume and group them together as one class or region,where in that particular region of interest(ROI)can be concentrated on,rather than focusing on the entire image.In this paper White Matter Hyperintensities(WMH)is taken as a strong biomarker that supports and determines the presence of Alzheimer’s.As thefirst step a proper segmentation of the lesions has to be carried out.As pointed out in various other research papers,when the WMH area is very small or in places like the Septum Pellucidum the detection of the lesion is hard tofind.To overcome such problem areas a very optimized and accurate Threshold would be required to have a precise segmentation to detect the area of localization.This would help in proper detection and classification of the Anomaly.In this paper an elaborate comparison of various thresholding techniques has been done for segmentation.A novel idea for detection of Alzheimer’s has been presented in this paper,which encompasses the effectiveness of an optimized and adaptive technique.The Unet architecture has been taken as the baseline model with an adaptive kernel model embedded within the architecture.Various state-of-the-art technologies have been used with the dataset and a comparative study has been presented with the current architecture used in the paper.The lesion segmentation in narrow areas has accurately been detected compared to the other models and the number of false positives has been reduced to a great extent.

Energy-Efficient Clustering Using Optimization with Locust Game Theory

Wireless sensor networks(WSNs)are made up of several sensors located in a specific area and powered by a finite amount of energy to gather environmental data.WSNs use sensor nodes(SNs)to collect and transmit data.However,the power supplied by the sensor network is restricted.Thus,SNs must store energy as often as to extend the lifespan of the network.In the proposed study,effective clustering and longer network lifetimes are achieved using mul-ti-swarm optimization(MSO)and game theory based on locust search(LS-II).In this research,MSO is used to improve the optimum routing,while the LS-II approach is employed to specify the number of cluster heads(CHs)and select the best ones.After the CHs are identified,the other sensor components are allo-cated to the closest CHs to them.A game theory-based energy-efficient clustering approach is applied to WSNs.Here each SN is considered a player in the game.The SN can implement beneficial methods for itself depending on the length of the idle listening time in the active phase and then determine to choose whether or not to rest.The proposed multi-swarm with energy-efficient game theory on locust search(MSGE-LS)efficiently selects CHs,minimizes energy consumption,and improves the lifetime of networks.The findings of this study indicate that the proposed MSGE-LS is an effective method because its result proves that it increases the number of clusters,average energy consumption,lifespan extension,reduction in average packet loss,and end-to-end delay.

PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

Data transmission through a wireless network has faced various signal problems in the past decades.The orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among others.The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network.This transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various techniques.This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication.Partial transmit sequence(PTS)helps in the fast transfer of data in wireless LTE.PTS is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G networks.Result indicates that the proposed system outperforms other existing techniques.Therefore,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization.Hence,the specified design supports in improving the proposed PAPR reduction architecture.