Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kin...Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.展开更多
Microstructure and tribological properties of copper-based hybrid nanocomposites reinforced with copper coatedmultiwalled carbon nanotubes (MWCNTs) and silicon carbide (SiC) were studied. Carbon nanotube was varied fr...Microstructure and tribological properties of copper-based hybrid nanocomposites reinforced with copper coatedmultiwalled carbon nanotubes (MWCNTs) and silicon carbide (SiC) were studied. Carbon nanotube was varied from 1% to 4% withsilicon carbide content being fixed at 4%. The synthesis of copper hybrid nanocomposites involves ball milling, cold pressing andsintering followed by hot pressing. The developed hybrid nanocomposites were subjected to density, grain size, and hardness tests.The tribological performances of the nanocomposites were assessed by carrying out dry sliding wear tests using pin-on-steel disctribometer at different loads. A significant decrease in grain size was observed for the developed hybrid composites when comparedwith pure copper. An improvement of 80% in the micro-hardness of the hybrid nanocomposite has been recorded for 4% carbonnanotubes reinforced hybrid composites when compared with pure copper. An increase in content of CNTs in the hybridnanocomposites results in lowering of the friction coefficient and wear rates of hybrid nanocomposites.展开更多
Sparrow criterion of resolution is used for assessment of the resolution of two object points of apodized optical systems under incoherent illumination of light. Semicircular arrays of circular aperture with discrete ...Sparrow criterion of resolution is used for assessment of the resolution of two object points of apodized optical systems under incoherent illumination of light. Semicircular arrays of circular aperture with discrete asymmetric apodization have suppressed side-lobes and a narrower central peak in the image plane termed as PSF good side on alternatively the right and left of the strong spectral point facilitates to detect the presence of weak spectral point in the vicinity of bright spectral point. The results of investigations on optimum discrete pupil function with semicircular arrays on the intensity distributions in the composite image of two object points with widely varying in their intensities under various degree of coherence of illumination have been studied. Sparrow resolution limits and the dip in central intensity as function of degree of coherence of the illumination (γ), intensity ratio (α), degree of asymmetric apodization (b) and number of discrete elements in semicircular array (n). The efficiency of aperture functions is discussed in terms of these parameters. Pupil function capabilities in redistribution of energy in composite image of two object points in close vicinity have been verified for different considerations. Current study has found an improvement in two-point resolution characteristics compared to their unapodized counter part. Fourier analytical properties of an optical system are presented for evaluation of this practical problem.展开更多
Titanium tube and stainless steel tube plate were welded by an innovative friction welding of tube to tube plate using an external tool (FWTPET). Copper was used as an interlayer for joining the dissimilar materials a...Titanium tube and stainless steel tube plate were welded by an innovative friction welding of tube to tube plate using an external tool (FWTPET). Copper was used as an interlayer for joining the dissimilar materials and also to minimize the effect of intermetallics formed at the joint interface. The process parameters that govern FWTPET process are plunge rate, rotational speed, plunge depth, axial load and flash trap profile. Among them, the flash trap profile of the tube has a significant influence on the joint integrity. Various flash trap profiles like vertical slots, holes, zig-zag holes, and petals were made on the titanium tube welded to the stainless steel tube plate. Macroscopic and microscopic studies reveal defect-free joints. The presence of copper interlayer and intermetallics was evident from X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) studies. The microhardness survey was presented across and along the interface. A novel test procedure called “plunge shear test” was developed to evaluate the joint properties of the welded joints. The highest shear fracture load of 31.58 kN was observed on the sample having petals as flash trap profile. The sheared surfaces were further characterized using SEM for fractography.展开更多
Human Adaptive Mechatronics(HAM)includes human and computer system in a closed loop.Elderly person with disabilities,normally carry out their daily routines with some assistance to move their limbs.With the short fall...Human Adaptive Mechatronics(HAM)includes human and computer system in a closed loop.Elderly person with disabilities,normally carry out their daily routines with some assistance to move their limbs.With the short fall of human care takers,mechatronics devices are used with the likes of exoskeleton and exosuits to assist them.The rehabilitation and occupational therapy equipments utilize the electromyography(EMG)signals to measure the muscle activity potential.This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the system.Limb characteristics extraction from EMG signal and prediction of optimal controller parameters are modeled.Time and frequency based approach of EMG signal are considered for feature extraction.The models used for estimating motion and muscle parameters from EMG signal for carrying out limb movement predictions are validated.Based on the extracted features,optimal parameters are selected by Modified Lion Optimization(MLO)for controlling the HAM system.Finally,supervised machine learning makes predictions at different points in time for individual sensing using Support Vector Neural Network(SVNN).This model is also evaluated based on optimal parameters of motion estimation and the accuracy level along with different optimization models for various upper limb movements.The proposed model of human adaptive controller predicts the limb movement by 96%accuracy.展开更多
Interconnected devices and intelligent applications have slashed human intervention in the Internet of Things(IoT),making it possible to accomplish tasks with less human interaction.However,it faces many problems,incl...Interconnected devices and intelligent applications have slashed human intervention in the Internet of Things(IoT),making it possible to accomplish tasks with less human interaction.However,it faces many problems,including lower capacity links,energy utilization,enhancement of resources and limited resources due to its openness,heterogeneity,limited resources and extensiveness.It is challenging to route packets in such a constrained environment.In an IoT network constrained by limited resources,minimal routing control overhead is required without packet loss.Such constrained environments can be improved through the optimal routing protocol.It is challenging to route packets in such a constrained environment.Thus,this work is motivated to present an efficient routing protocol for enhancing the lifetime of the IoT network.Lightweight On-demand Ad hoc Distance-vector Routing Protocol—Next Generation(LOADng)protocol is an extended version of the Ad Hoc On-Demand Distance Vector(AODV)protocol.Unlike AODV,LOADng is a lighter version that forbids the intermediate nodes on the route to send a route reply(RREP)for the route request(RREQ),which originated from the source.A resource-constrained IoT network demands minimal routing control overhead and faster packet delivery.So,in this paper,the parameters of the LOADng routing protocol are optimized using the black widow optimization(BWO)algorithm to reduce the control overhead and delay.Furthermore,the performance of the proposed model is analyzed with the default LOADng in terms of delay,delivery ratio and overhead.Obtained results show that the LOADng-BWO protocol outperforms the conventional LOADng protocol.展开更多
Currently, the 4G network service has caused massive digital growth in high use. Video calling has become the go-to Internet application for many people, downloading even huge files in minutes. Everyone is looking for...Currently, the 4G network service has caused massive digital growth in high use. Video calling has become the go-to Internet application for many people, downloading even huge files in minutes. Everyone is looking for and buying only 4G Subscriber Identity Module (SIM)-capable mobiles. In this case, the expectation of 5G has increased in line with 2G, 3G, and 4G, where the G stands for generation, and it does not indicate Internet or Internet speed. 5G includes next-generation features that are more advanced than those available in 4G network services. The main objective of 5G is uninterrupted telecommunication service in hybrid energy storage system. This paper proposes an intelligent networking model to obtain the maximum energy efficiency and Artificial Intelligence (AI) automation of 5G networks. There is currently an issue where the signal cuts out when crossing an area with one tower and traveling to an area with another tower. The problem of “call drop”, where the call is disconnected while talking, is not present in 5G. The proposed Intelligent Computational Model (ICM) model obtained 96.31% network speed management, 90.63% battery capacity management, 92.27% network device management, 93.57% energy efficiency, and 88.41% AI automation.展开更多
Advanced Metering Infrastructure(AMI)forms an important part in Smart Grids.Routing the data effectively from smart meters to the Edge/Fog node requires an efficient routing protocol.Routing Protocol for Low Power Los...Advanced Metering Infrastructure(AMI)forms an important part in Smart Grids.Routing the data effectively from smart meters to the Edge/Fog node requires an efficient routing protocol.Routing Protocol for Low Power Lossy Area Network(RPL)is a standard routing protocol for IPv6 over Low Power Personal Area Network(6LoWPAN).In a Power Distribution system all the smart meters together form 6LoWPAN network.They communicate with the fog router,which acts as the 6LoWPAN gateway.ContikiRPL was evaluated using Cooja Network simulator for a power distribution network topology.The nodes which were far away from the fog node gave low Packet Delivery Ratio(PDR)and large End to End delay.This paper proposes an aggregation RPL scheme by modifying the existing Contiki RPL.The smart meter nodes communicate to the aggregator,which communicates to the fog node.The results show that the aggregation scheme has 35.6%increase in PDR,lesser hop count and 13.24%decrease in End to End delay on an average compared to existing RPL.展开更多
The Cloud system shows its growing functionalities in various industrial applications.The safety towards data transfer seems to be a threat where Network Intrusion Detection System(NIDS)is measured as an essential ele...The Cloud system shows its growing functionalities in various industrial applications.The safety towards data transfer seems to be a threat where Network Intrusion Detection System(NIDS)is measured as an essential element to fulfill security.Recently,Machine Learning(ML)approaches have been used for the construction of intellectual IDS.Most IDS are based on ML techniques either as unsupervised or supervised.In supervised learning,NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack patterns.Similarly,the unsupervised model fails to provide a satisfactory outcome.Hence,to boost the functionality of unsupervised learning,an effectual auto-encoder is applied for feature selection to select good features.Finally,the Naïve Bayes classifier is used for classification purposes.This approach exposes the finest generalization ability to train the data.The unlabelled data is also used for adoption towards data analysis.Here,redundant and noisy samples over the dataset are eliminated.To validate the robustness and efficiency of NIDS,the anticipated model is tested over the NSL-KDD dataset.The experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%,which is higher compared to J48,AB tree,Random Forest(RF),Regression Tree(RT),Multi-Layer Perceptrons(MLP),Support Vector Machine(SVM),and Fuzzy.Similarly,False Alarm Rate(FAR)and True Positive Rate(TPR)of Naive Bayes(NB)is 0.3 and 0.99,respectively.When compared to prevailing techniques,the anticipated approach also delivers promising outcomes.展开更多
Wireless sensor networks(WSN)comprise a set of numerous cheap sensors placed in the target region.A primary function of the WSN is to avail the location details of the event occurrences or the node.A major challenge i...Wireless sensor networks(WSN)comprise a set of numerous cheap sensors placed in the target region.A primary function of the WSN is to avail the location details of the event occurrences or the node.A major challenge in WSN is node localization which plays an important role in data gathering applications.Since GPS is expensive and inaccurate in indoor regions,effective node localization techniques are needed.The major intention of localization is for determining the place of node in short period with minimum computation.To achieve this,bio-inspired algorithms are used and node localization is assumed as an optimization problem in a multidimensional space.This paper introduces a new Sparrow Search Algorithm with Doppler Effect(SSA-DE)for Node Localization in Wireless Networks.The SSA is generally stimulated by the group wisdom,foraging,and anti-predation behaviors of sparrows.Besides,the Doppler Effect is incorporated into the SSA to further improve the node localization performance.In addition,the SSA-DE model defines the position of node in an iterative manner using Euclidian distance as the fitness function.The presented SSA-DE model is implanted in MATLAB R2014.An extensive set of experimentation is carried out and the results are examined under a varying number of anchor nodes and ranging error.The attained experimental outcome ensured the superior efficiency of the SSA-DE technique over the existing techniques.展开更多
Security is the one of the major challenges for routing the data between the source and destination in an Internet of Things(IoT)network.To overcome this challenge,a secure Lightweight On-demand Ad hoc Distancevector...Security is the one of the major challenges for routing the data between the source and destination in an Internet of Things(IoT)network.To overcome this challenge,a secure Lightweight On-demand Ad hoc Distancevector—Next Generation(LOADng)Routing Protocol is proposed in this paper.As the LOADng protocol is the second version of Ad Hoc On-Demand Distance Vector(AODV)protocol,it retains most of the basic functionality and characteristics of AODV.During the route discovery process,the cyclic shift transposition algorithm(CSTA)is used to encrypt the control packets of the LOADng protocol to improve its security.CSTA approach only derives transposition and substitution without product cipher with respect to input data.Besides this,for choosing the best probable path between the source and destination,routing metrics such as link quality Indicator(LQI),hop count(HC)and queue length(QL)are included in the control packets.The data is then securely sent using CSTA using the optimal secure path selected.Experimental Results depict that the proposed secure and optimal LOADng(SO-LOADng)using CSTA encryption obtains better throughput,delivery ratio encryption time and decryption time than the existing state-ofart approaches.展开更多
Internet of Things (IoT) is transforming the technical setting ofconventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to theI...Internet of Things (IoT) is transforming the technical setting ofconventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to theIoT enabled models are resource-limited and necessitate crisp responses, lowlatencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentionedchallenges. But the intrinsic high latency of CC makes it nonviable. The longerlatency degrades the outcome of IoT based smart systems. CC is an emergentdispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes theenergy utilization of the cloud infrastructure and rises the income of serviceproviders by the minimization of the processing time of the user job. Withthis motivation, this paper presents an intelligent Chaotic Artificial ImmuneOptimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabledcloud environment. The proposed CAIOA-RS algorithm solves the issue ofresource allocation in the IoT enabled cloud environment. It also satisfiesthe makespan by carrying out the optimum task scheduling process with thedistinct strategies of incoming tasks. The design of CAIOA-RS techniqueincorporates the concept of chaotic maps into the conventional AIOA toenhance its performance. A series of experiments were carried out on theCloudSim platform. The simulation results demonstrate that the CAIOA-RStechnique indicates that the proposed model outperforms the original version,as well as other heuristics and metaheuristics.展开更多
Internet of things (IoT) has been significantly raised owing to thedevelopment of broadband access network, machine learning (ML), big dataanalytics (BDA), cloud computing (CC), and so on. The development of IoTtechno...Internet of things (IoT) has been significantly raised owing to thedevelopment of broadband access network, machine learning (ML), big dataanalytics (BDA), cloud computing (CC), and so on. The development of IoTtechnologies has resulted in a massive quantity of data due to the existenceof several people linking through distinct physical components, indicatingthe status of the CC environment. In the IoT, load scheduling is realistictechnique in distinct data center to guarantee the network suitability by fallingthe computer hardware and software catastrophe and with right utilize ofresource. The ideal load balancer improves many factors of Quality of Service(QoS) like resource performance, scalability, response time, error tolerance,and efficiency. The scholar is assumed as load scheduling a vital problem inIoT environment. There are many techniques accessible to load scheduling inIoT environments. With this motivation, this paper presents an improved deerhunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) modelfor load scheduling in IoT environment. The goal of the IDHOA-T2F is todiminish the energy utilization of integrated circuit of IoT node and enhancethe load scheduling in IoT environments. The IDHOA technique is derivedby integrating the concepts of Nelder Mead (NM) with the DHOA. Theproposed model also synthesized the T2L based on fuzzy logic (FL) systemsto counterbalance the load distribution. The proposed model finds usefulto improve the efficiency of IoT system. For validating the enhanced loadscheduling performance of the IDHOA-T2F technique, a series of simulationstake place to highlight the improved performance. The experimental outcomesdemonstrate the capable outcome of the IDHOA-T2F technique over therecent techniques.展开更多
In wireless body sensor network(WBSN),the set of electrocardiogram(ECG)data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient.While tra...In wireless body sensor network(WBSN),the set of electrocardiogram(ECG)data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient.While transmit-ting these collected data some adversaries may capture and misuse it due to the compromise of security.So,the major aim of this work is to enhance secure trans-mission of ECG signal in WBSN.To attain this goal,we present Pity Beetle Swarm Optimization Algorithm(PBOA)based Elliptic Galois Cryptography(EGC)with Chaotic Neural Network.To optimize the key generation process in Elliptic Curve Cryptography(ECC)over Galoisfield or EGC,private key is chosen optimally using PBOA algorithm.Then the encryption process is enhanced by presenting chaotic neural network which is used to generate chaotic sequences or cipher data.Results of this work show that the proposed cryptogra-phy algorithm attains better encryption time,decryption time,throughput and SNR than the conventional cryptography algorithms.展开更多
The implementation of Peak Average to Power Ratio(PAPR)reduction technologies will play an important role in the regularization of Fifth Generation(5G)radio communication.PAPR reduction in the advanced waveform will b...The implementation of Peak Average to Power Ratio(PAPR)reduction technologies will play an important role in the regularization of Fifth Generation(5G)radio communication.PAPR reduction in the advanced waveform will be the key part of designing a 5G network for different applications.This work introduces the simulation of an Advanced Partial Transmission Sequence(A-PTS)reduction techniques for Orthogonal Frequency Division Multiplexing(OFDM)and Filter Bank Multi-Carrier(FBMC)transmission schemes.In the projected A-PTS,the FBMC signals are mapped into the number of sub-blocks and Inverse Fast Fourier transform(IFFT)is performed to estimate the high peak power in the time domain.The FBMC sub-blocks are multiplied with the phase elements to achieve an optimal PAPR value.A MATLAB 2014v simulation is used to estimate the PAPR,Bit Error Rate(BER),Error Vector Magnitude(EVM),and Modulation Error Rate(MER)performance of the proposed reduction schemes.The simulated result reveals that the performance of the projected algorithm is better than the conventional algorithms.展开更多
The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network t...The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network transformation have received maximum attention.An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling.The dynamic electrical energy stored model using Electric Vehicles(EVs)is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids.This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder(HBFOA-SAE)model for IoT Enabled energy systems.The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge(SOC)values in the IoT based energy system.To accomplish this,the SAE technique was executed to proper determination of the SOC values in the energy systems.Next,for improving the performance of the SOC estimation process,the HBFOA is employed.In addition,the HBFOA technique is derived by the integration of the hill climbing(HC)concepts with the BFOA to improve the overall efficiency.For ensuring better outcomes for the HBFOA-SAE model,a comprehensive set of simulations were performed and the outcomes are inspected under several aspects.The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.展开更多
In recent years,numerous investigations have explored the use of biochar for the removal of organic and inorganic pollutants in single component systems.Biochar is a carbonaceous material produced from waste biomass,m...In recent years,numerous investigations have explored the use of biochar for the removal of organic and inorganic pollutants in single component systems.Biochar is a carbonaceous material produced from waste biomass,mainly by thermochemical conversion methods.This material was used as a biosorbent in various removal processes of pollutants,and its efficiency was strongly influenced by the characteristics of the biomass feedstock.This review integrates the recent works of literature to understand the biosorption behaviour of dyes onto biochar-based biosorbents.The factors influencing the biosorption process and the mechanisms describing the biosorption behaviours of the biochar have been broadly reviewed.Furthermore,the biosorption models can be used to comprehend the competence of the biochar as biosorbent for dye removal techniques.展开更多
文摘Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.
文摘Microstructure and tribological properties of copper-based hybrid nanocomposites reinforced with copper coatedmultiwalled carbon nanotubes (MWCNTs) and silicon carbide (SiC) were studied. Carbon nanotube was varied from 1% to 4% withsilicon carbide content being fixed at 4%. The synthesis of copper hybrid nanocomposites involves ball milling, cold pressing andsintering followed by hot pressing. The developed hybrid nanocomposites were subjected to density, grain size, and hardness tests.The tribological performances of the nanocomposites were assessed by carrying out dry sliding wear tests using pin-on-steel disctribometer at different loads. A significant decrease in grain size was observed for the developed hybrid composites when comparedwith pure copper. An improvement of 80% in the micro-hardness of the hybrid nanocomposite has been recorded for 4% carbonnanotubes reinforced hybrid composites when compared with pure copper. An increase in content of CNTs in the hybridnanocomposites results in lowering of the friction coefficient and wear rates of hybrid nanocomposites.
文摘Sparrow criterion of resolution is used for assessment of the resolution of two object points of apodized optical systems under incoherent illumination of light. Semicircular arrays of circular aperture with discrete asymmetric apodization have suppressed side-lobes and a narrower central peak in the image plane termed as PSF good side on alternatively the right and left of the strong spectral point facilitates to detect the presence of weak spectral point in the vicinity of bright spectral point. The results of investigations on optimum discrete pupil function with semicircular arrays on the intensity distributions in the composite image of two object points with widely varying in their intensities under various degree of coherence of illumination have been studied. Sparrow resolution limits and the dip in central intensity as function of degree of coherence of the illumination (γ), intensity ratio (α), degree of asymmetric apodization (b) and number of discrete elements in semicircular array (n). The efficiency of aperture functions is discussed in terms of these parameters. Pupil function capabilities in redistribution of energy in composite image of two object points in close vicinity have been verified for different considerations. Current study has found an improvement in two-point resolution characteristics compared to their unapodized counter part. Fourier analytical properties of an optical system are presented for evaluation of this practical problem.
基金financial support provided by UGC-DAE-CSR (CSR-KN/CRS-04/201213/738) through fellowship
文摘Titanium tube and stainless steel tube plate were welded by an innovative friction welding of tube to tube plate using an external tool (FWTPET). Copper was used as an interlayer for joining the dissimilar materials and also to minimize the effect of intermetallics formed at the joint interface. The process parameters that govern FWTPET process are plunge rate, rotational speed, plunge depth, axial load and flash trap profile. Among them, the flash trap profile of the tube has a significant influence on the joint integrity. Various flash trap profiles like vertical slots, holes, zig-zag holes, and petals were made on the titanium tube welded to the stainless steel tube plate. Macroscopic and microscopic studies reveal defect-free joints. The presence of copper interlayer and intermetallics was evident from X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) studies. The microhardness survey was presented across and along the interface. A novel test procedure called “plunge shear test” was developed to evaluate the joint properties of the welded joints. The highest shear fracture load of 31.58 kN was observed on the sample having petals as flash trap profile. The sheared surfaces were further characterized using SEM for fractography.
基金This work was supported by the Deanship of Scientific Research,King Khalid University,Kingdom of Saudi Arabia under research Grant Number(R.G.P.2/100/41).
文摘Human Adaptive Mechatronics(HAM)includes human and computer system in a closed loop.Elderly person with disabilities,normally carry out their daily routines with some assistance to move their limbs.With the short fall of human care takers,mechatronics devices are used with the likes of exoskeleton and exosuits to assist them.The rehabilitation and occupational therapy equipments utilize the electromyography(EMG)signals to measure the muscle activity potential.This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the system.Limb characteristics extraction from EMG signal and prediction of optimal controller parameters are modeled.Time and frequency based approach of EMG signal are considered for feature extraction.The models used for estimating motion and muscle parameters from EMG signal for carrying out limb movement predictions are validated.Based on the extracted features,optimal parameters are selected by Modified Lion Optimization(MLO)for controlling the HAM system.Finally,supervised machine learning makes predictions at different points in time for individual sensing using Support Vector Neural Network(SVNN).This model is also evaluated based on optimal parameters of motion estimation and the accuracy level along with different optimization models for various upper limb movements.The proposed model of human adaptive controller predicts the limb movement by 96%accuracy.
文摘Interconnected devices and intelligent applications have slashed human intervention in the Internet of Things(IoT),making it possible to accomplish tasks with less human interaction.However,it faces many problems,including lower capacity links,energy utilization,enhancement of resources and limited resources due to its openness,heterogeneity,limited resources and extensiveness.It is challenging to route packets in such a constrained environment.In an IoT network constrained by limited resources,minimal routing control overhead is required without packet loss.Such constrained environments can be improved through the optimal routing protocol.It is challenging to route packets in such a constrained environment.Thus,this work is motivated to present an efficient routing protocol for enhancing the lifetime of the IoT network.Lightweight On-demand Ad hoc Distance-vector Routing Protocol—Next Generation(LOADng)protocol is an extended version of the Ad Hoc On-Demand Distance Vector(AODV)protocol.Unlike AODV,LOADng is a lighter version that forbids the intermediate nodes on the route to send a route reply(RREP)for the route request(RREQ),which originated from the source.A resource-constrained IoT network demands minimal routing control overhead and faster packet delivery.So,in this paper,the parameters of the LOADng routing protocol are optimized using the black widow optimization(BWO)algorithm to reduce the control overhead and delay.Furthermore,the performance of the proposed model is analyzed with the default LOADng in terms of delay,delivery ratio and overhead.Obtained results show that the LOADng-BWO protocol outperforms the conventional LOADng protocol.
基金supported by the Advanced and Innovative Research Laboratory(AAIR Labs-www.aairlab.com)India(No.AAIRL-IN-2023-47).
文摘Currently, the 4G network service has caused massive digital growth in high use. Video calling has become the go-to Internet application for many people, downloading even huge files in minutes. Everyone is looking for and buying only 4G Subscriber Identity Module (SIM)-capable mobiles. In this case, the expectation of 5G has increased in line with 2G, 3G, and 4G, where the G stands for generation, and it does not indicate Internet or Internet speed. 5G includes next-generation features that are more advanced than those available in 4G network services. The main objective of 5G is uninterrupted telecommunication service in hybrid energy storage system. This paper proposes an intelligent networking model to obtain the maximum energy efficiency and Artificial Intelligence (AI) automation of 5G networks. There is currently an issue where the signal cuts out when crossing an area with one tower and traveling to an area with another tower. The problem of “call drop”, where the call is disconnected while talking, is not present in 5G. The proposed Intelligent Computational Model (ICM) model obtained 96.31% network speed management, 90.63% battery capacity management, 92.27% network device management, 93.57% energy efficiency, and 88.41% AI automation.
基金National Funding from the FCT- Fundacao Para a Ciencia e a Tecnologia through the UID/ EEA/50008/2019 Project by Brazilian National Council for Scientific and Technological Development via Grant No. 309335/2017-5
文摘Advanced Metering Infrastructure(AMI)forms an important part in Smart Grids.Routing the data effectively from smart meters to the Edge/Fog node requires an efficient routing protocol.Routing Protocol for Low Power Lossy Area Network(RPL)is a standard routing protocol for IPv6 over Low Power Personal Area Network(6LoWPAN).In a Power Distribution system all the smart meters together form 6LoWPAN network.They communicate with the fog router,which acts as the 6LoWPAN gateway.ContikiRPL was evaluated using Cooja Network simulator for a power distribution network topology.The nodes which were far away from the fog node gave low Packet Delivery Ratio(PDR)and large End to End delay.This paper proposes an aggregation RPL scheme by modifying the existing Contiki RPL.The smart meter nodes communicate to the aggregator,which communicates to the fog node.The results show that the aggregation scheme has 35.6%increase in PDR,lesser hop count and 13.24%decrease in End to End delay on an average compared to existing RPL.
文摘The Cloud system shows its growing functionalities in various industrial applications.The safety towards data transfer seems to be a threat where Network Intrusion Detection System(NIDS)is measured as an essential element to fulfill security.Recently,Machine Learning(ML)approaches have been used for the construction of intellectual IDS.Most IDS are based on ML techniques either as unsupervised or supervised.In supervised learning,NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack patterns.Similarly,the unsupervised model fails to provide a satisfactory outcome.Hence,to boost the functionality of unsupervised learning,an effectual auto-encoder is applied for feature selection to select good features.Finally,the Naïve Bayes classifier is used for classification purposes.This approach exposes the finest generalization ability to train the data.The unlabelled data is also used for adoption towards data analysis.Here,redundant and noisy samples over the dataset are eliminated.To validate the robustness and efficiency of NIDS,the anticipated model is tested over the NSL-KDD dataset.The experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%,which is higher compared to J48,AB tree,Random Forest(RF),Regression Tree(RT),Multi-Layer Perceptrons(MLP),Support Vector Machine(SVM),and Fuzzy.Similarly,False Alarm Rate(FAR)and True Positive Rate(TPR)of Naive Bayes(NB)is 0.3 and 0.99,respectively.When compared to prevailing techniques,the anticipated approach also delivers promising outcomes.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘Wireless sensor networks(WSN)comprise a set of numerous cheap sensors placed in the target region.A primary function of the WSN is to avail the location details of the event occurrences or the node.A major challenge in WSN is node localization which plays an important role in data gathering applications.Since GPS is expensive and inaccurate in indoor regions,effective node localization techniques are needed.The major intention of localization is for determining the place of node in short period with minimum computation.To achieve this,bio-inspired algorithms are used and node localization is assumed as an optimization problem in a multidimensional space.This paper introduces a new Sparrow Search Algorithm with Doppler Effect(SSA-DE)for Node Localization in Wireless Networks.The SSA is generally stimulated by the group wisdom,foraging,and anti-predation behaviors of sparrows.Besides,the Doppler Effect is incorporated into the SSA to further improve the node localization performance.In addition,the SSA-DE model defines the position of node in an iterative manner using Euclidian distance as the fitness function.The presented SSA-DE model is implanted in MATLAB R2014.An extensive set of experimentation is carried out and the results are examined under a varying number of anchor nodes and ranging error.The attained experimental outcome ensured the superior efficiency of the SSA-DE technique over the existing techniques.
文摘Security is the one of the major challenges for routing the data between the source and destination in an Internet of Things(IoT)network.To overcome this challenge,a secure Lightweight On-demand Ad hoc Distancevector—Next Generation(LOADng)Routing Protocol is proposed in this paper.As the LOADng protocol is the second version of Ad Hoc On-Demand Distance Vector(AODV)protocol,it retains most of the basic functionality and characteristics of AODV.During the route discovery process,the cyclic shift transposition algorithm(CSTA)is used to encrypt the control packets of the LOADng protocol to improve its security.CSTA approach only derives transposition and substitution without product cipher with respect to input data.Besides this,for choosing the best probable path between the source and destination,routing metrics such as link quality Indicator(LQI),hop count(HC)and queue length(QL)are included in the control packets.The data is then securely sent using CSTA using the optimal secure path selected.Experimental Results depict that the proposed secure and optimal LOADng(SO-LOADng)using CSTA encryption obtains better throughput,delivery ratio encryption time and decryption time than the existing state-ofart approaches.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘Internet of Things (IoT) is transforming the technical setting ofconventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to theIoT enabled models are resource-limited and necessitate crisp responses, lowlatencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentionedchallenges. But the intrinsic high latency of CC makes it nonviable. The longerlatency degrades the outcome of IoT based smart systems. CC is an emergentdispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes theenergy utilization of the cloud infrastructure and rises the income of serviceproviders by the minimization of the processing time of the user job. Withthis motivation, this paper presents an intelligent Chaotic Artificial ImmuneOptimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabledcloud environment. The proposed CAIOA-RS algorithm solves the issue ofresource allocation in the IoT enabled cloud environment. It also satisfiesthe makespan by carrying out the optimum task scheduling process with thedistinct strategies of incoming tasks. The design of CAIOA-RS techniqueincorporates the concept of chaotic maps into the conventional AIOA toenhance its performance. A series of experiments were carried out on theCloudSim platform. The simulation results demonstrate that the CAIOA-RStechnique indicates that the proposed model outperforms the original version,as well as other heuristics and metaheuristics.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/209/42)This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-Track Path of Research Funding Program.
文摘Internet of things (IoT) has been significantly raised owing to thedevelopment of broadband access network, machine learning (ML), big dataanalytics (BDA), cloud computing (CC), and so on. The development of IoTtechnologies has resulted in a massive quantity of data due to the existenceof several people linking through distinct physical components, indicatingthe status of the CC environment. In the IoT, load scheduling is realistictechnique in distinct data center to guarantee the network suitability by fallingthe computer hardware and software catastrophe and with right utilize ofresource. The ideal load balancer improves many factors of Quality of Service(QoS) like resource performance, scalability, response time, error tolerance,and efficiency. The scholar is assumed as load scheduling a vital problem inIoT environment. There are many techniques accessible to load scheduling inIoT environments. With this motivation, this paper presents an improved deerhunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) modelfor load scheduling in IoT environment. The goal of the IDHOA-T2F is todiminish the energy utilization of integrated circuit of IoT node and enhancethe load scheduling in IoT environments. The IDHOA technique is derivedby integrating the concepts of Nelder Mead (NM) with the DHOA. Theproposed model also synthesized the T2L based on fuzzy logic (FL) systemsto counterbalance the load distribution. The proposed model finds usefulto improve the efficiency of IoT system. For validating the enhanced loadscheduling performance of the IDHOA-T2F technique, a series of simulationstake place to highlight the improved performance. The experimental outcomesdemonstrate the capable outcome of the IDHOA-T2F technique over therecent techniques.
文摘In wireless body sensor network(WBSN),the set of electrocardiogram(ECG)data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient.While transmit-ting these collected data some adversaries may capture and misuse it due to the compromise of security.So,the major aim of this work is to enhance secure trans-mission of ECG signal in WBSN.To attain this goal,we present Pity Beetle Swarm Optimization Algorithm(PBOA)based Elliptic Galois Cryptography(EGC)with Chaotic Neural Network.To optimize the key generation process in Elliptic Curve Cryptography(ECC)over Galoisfield or EGC,private key is chosen optimally using PBOA algorithm.Then the encryption process is enhanced by presenting chaotic neural network which is used to generate chaotic sequences or cipher data.Results of this work show that the proposed cryptogra-phy algorithm attains better encryption time,decryption time,throughput and SNR than the conventional cryptography algorithms.
基金supported by Taif University Researchers Supporting Projects(TURSP).Under number(TURSP-2020/73),Taif University,Taif,Saudi Arabia.
文摘The implementation of Peak Average to Power Ratio(PAPR)reduction technologies will play an important role in the regularization of Fifth Generation(5G)radio communication.PAPR reduction in the advanced waveform will be the key part of designing a 5G network for different applications.This work introduces the simulation of an Advanced Partial Transmission Sequence(A-PTS)reduction techniques for Orthogonal Frequency Division Multiplexing(OFDM)and Filter Bank Multi-Carrier(FBMC)transmission schemes.In the projected A-PTS,the FBMC signals are mapped into the number of sub-blocks and Inverse Fast Fourier transform(IFFT)is performed to estimate the high peak power in the time domain.The FBMC sub-blocks are multiplied with the phase elements to achieve an optimal PAPR value.A MATLAB 2014v simulation is used to estimate the PAPR,Bit Error Rate(BER),Error Vector Magnitude(EVM),and Modulation Error Rate(MER)performance of the proposed reduction schemes.The simulated result reveals that the performance of the projected algorithm is better than the conventional algorithms.
文摘The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network transformation have received maximum attention.An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling.The dynamic electrical energy stored model using Electric Vehicles(EVs)is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids.This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder(HBFOA-SAE)model for IoT Enabled energy systems.The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge(SOC)values in the IoT based energy system.To accomplish this,the SAE technique was executed to proper determination of the SOC values in the energy systems.Next,for improving the performance of the SOC estimation process,the HBFOA is employed.In addition,the HBFOA technique is derived by the integration of the hill climbing(HC)concepts with the BFOA to improve the overall efficiency.For ensuring better outcomes for the HBFOA-SAE model,a comprehensive set of simulations were performed and the outcomes are inspected under several aspects.The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.
文摘In recent years,numerous investigations have explored the use of biochar for the removal of organic and inorganic pollutants in single component systems.Biochar is a carbonaceous material produced from waste biomass,mainly by thermochemical conversion methods.This material was used as a biosorbent in various removal processes of pollutants,and its efficiency was strongly influenced by the characteristics of the biomass feedstock.This review integrates the recent works of literature to understand the biosorption behaviour of dyes onto biochar-based biosorbents.The factors influencing the biosorption process and the mechanisms describing the biosorption behaviours of the biochar have been broadly reviewed.Furthermore,the biosorption models can be used to comprehend the competence of the biochar as biosorbent for dye removal techniques.