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Multimodality Medical Image Fusion Based on Pixel Significance with Edge-Preserving Processing for Clinical Applications

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Computers, Materials & Continua 2024年第3期78卷 4317-4342页

作者： Bhawna Goyal Ayush Dogra Dawa Chyophel Lepcha Rajesh Singh Hemant Sharma Ahmed Alkhayyat Manob Jyoti Saikia department of ece and UCRD Chandigarh UniversityMohaliPunjab140413India Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India department of ece Uttaranchal Institute of TechnologyUttaranchal UniversityDehradun248007India IES College of Technology IES UniversityBhopal462044India College of Technical Engineering The Islamic UniversityNajaf54001Iraq department of Electrical Engineering University of North FloridaJacksonvilleFL32224USA

Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis.It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases.However,recent image fusion techniques have encountered several challenges,including fusion artifacts,algorithm complexity,and high computing costs.To solve these problems,this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance.First,the method employs a cross-bilateral filter(CBF)that utilizes one image to determine the kernel and the other for filtering,and vice versa,by considering both geometric closeness and the gray-level similarities of neighboring pixels of the images without smoothing edges.The outputs of CBF are then subtracted from the original images to obtain detailed images.It further proposes to use edge-preserving processing that combines linear lowpass filtering with a non-linear technique that enables the selection of relevant regions in detailed images while maintaining structural properties.These regions are selected using morphologically processed linear filter residuals to identify the significant regions with high-amplitude edges and adequate size.The outputs of low-pass filtering are fused with meaningfully restored regions to reconstruct the original shape of the edges.In addition,weight computations are performed using these reconstructed images,and these weights are then fused with the original input images to produce a final fusion result by estimating the strength of horizontal and vertical details.Numerous standard quality evaluation metrics with complementary properties are used for comparison with existing,well-known algorithms objectively to validate the fusion results.Experimental results from the proposed research article exhibit superior per

关键词： Image fusion fractal data analysis biomedical diseases research multiresolution analysis numerical analysis

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Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1341-1364页

作者： Mohamed Zarouan Ibrahim M.Mehedi Shaikh Abdul Latif Md.Masud Rana department of Electrical and Computer Engineering(ece) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia department of Nuclear Engineering King Abdulaziz UniversityJeddah21589Saudi Arabia department of Electrical and Electronic Engineering Rajshahi University of Engineering and TechnologyRajshahiBangladesh

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

关键词： Fault detection Industry 4.0 gradient optimizer algorithm deep learning rotating machineries artificial intelligence

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Design and Implementation of Hand Gesture Detection System Using HM Model for Sign Language Recognition Development

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Journal of Data Analysis and Information Processing 2024年第2期12卷 139-150页

作者： Sharmin Akter Milu Azmath Fathima Tanmay Talukder Inzamamul Islam Md. Ismail Siddiqi Emon department of Computer Science & Telecommunication Engineering (CSTE) Noakhali Science & Technology University Noakhali Bangladesh School of Computer Mathematical & Natural Sciences (SCMNS) Morgan State University Baltimore MD USA department of Physics Morgan State University Baltimore MD USA department of ece Morgan State University Baltimore MD USA department of Computer Science Morgan State University Baltimore MD USA

Gesture detection is the primary and most significant step for sign language detection and sign language is the communication medium for people with speaking and hearing disabilities. This paper presents a novel method for dynamic hand gesture detection using Hidden Markov Models (HMMs) where we detect different English alphabet letters by tracing hand movements. The process involves skin color-based segmentation for hand isolation in video frames, followed by morphological operations to enhance image trajectories. Our system employs hand tracking and trajectory smoothing techniques, such as the Kalman filter, to monitor hand movements and refine gesture paths. Quantized sequences are then analyzed using the Baum-Welch Re-estimation Algorithm, an HMM-based approach. A maximum likelihood classifier is used to identify the most probable letter from the test sequences. Our method demonstrates significant improvements over traditional recognition techniques in real-time, automatic hand gesture recognition, particularly in its ability to distinguish complex gestures. The experimental results confirm the effectiveness of our approach in enhancing gesture-based sign language detection to alleviate the barrier between the deaf and hard-of-hearing community and general people.

关键词： Hand Gesture Recognition System

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Cross Layer QoS Aware Scheduling based on Loss-Based Proportional Fairness with Multihop CRN

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Computer Systems Science & Engineering 2022年第9期42卷 1063-1077页

作者： K.Saravanan G.M.Tamilselvan A.Rajendran department of ece KPR Institute of Engineering and TechnologyCoimbatoreIndia department of IT Sri Krishna College of TechnologyCoimbatoreIndia department of ece Karpagam College of EngineeringCoimbatoreIndia

As huge users are involved,there is a difficulty in spectrum allocation and scheduling in Cognitive Radio Networks(CRNs).Collision increases when there is no allocation of spectrum and these results in huge drop rate and network performance degradation.To solve these problems and allocate appropriate spectrum,a novel method is introduced termed as Quality of Service(QoS)Improvement Proper Scheduling(QIPS).The major contribution of the work is to design a new cross layer QoS Aware Scheduling based on Loss-based Proportional Fairness with Multihop(QoSAS-LBPFM).In Medium Access Control(MAC)multi-channel network environment mobile nodes practice concurrent broadcast between several channels.Acquiring the advantage of introduced cross layer design,the real-time channel conditions offered by Cognitive Radio(CR)function allows adaptive sub channel choice for every broadcast.To optimize the resources of network,the LBPFM adaptively plans the radio resources for allocating to diverse services without lessening the quality of service.Results obtained from simulation proved that QoSAS-LBPFM provides enhanced QoS guaranteed performance against other existing QIPS algorithm.

关键词： Network efficiency distributed architecture mobile ad hoc networks Cognitive Radio Networks(CRNs) QoS improvement scheduling

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Evaluation of Codebook Design Using SCMA Scheme Based on A_(n) and D_(n) Lattices

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Intelligent Automation & Soft Computing 2023年第3期35卷 3037-3048页

作者： G.Rajamanickam G.Ravi department of ece Knowledge Institute of TechnologySalem637504India department of ece Sona College of TechnologySalem636001India

The sparse code multiple access(SCMA)scheme is a Non-Orthogonal Multiple Access(NOMA)type of scheme that is used to handle the uplink com-ponent of mobile communication in the current generation.A need of the 5G mobile network is the ability to handle more users.To accommodate this,the SCMA allows each user to deploy a variety of sub-carrier broadcasts,and several consumers may contribute to the same frequency using superposition coding.The SCMA approach,together with codebook design for each user,is used to improve channel efﬁciency through better management of the available spectrum.How-ever,developing a codebook with a greater number of value sets is still another challenge.With enhanced techniques of encoding and decoding for 5G networks,mapping the multidimensional constellations in the SCMA system plays a signif-icant role in improving the system performance and enhancing the overall system performance.The creation of a codebook utilizing the SCMA approach in con-junction with the lattice theory is suggested in this study.The prototype is shaped using a popular lattice,such as A n and D n,as the basis.Afterward,from the primary lattice constellation,the multidimensional complex mother constellation with the most noticeable variance in power is discovered.The lattice-based cod-ing is generated by combining the codebooks with the mother constellation,and the codes in the matrices are mapped by rotating the constellations in this context.The suggested technique,in conjunction with the investigation of novel SCMA codebook sets,provides improved performance in terms of Bit Error Rate(BER)and complexity with regard to Signal to Noise Ratio(SNR).Finally,the bit error rate is reduced for various SNRs during transmission in the channel.

关键词： 5G NOMA SCMA lattice coding codebook design bit error rate

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Thinned array antenna synthesis using modified binary particle swarm optimization with minimization of sidelobes

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International Journal of Intelligent Computing and Cybernetics 2023年第1期16卷 158-170页

作者： Gayatri Allu M.Surendra Kumar A.M.Prasad Eece GITAMVisakhapatnamIndia department of ece KLR College of Engineering and TechnologyKhammamIndia department of ece University College of Engineering JNTUKKakinadaIndia

Purpose-The purpose of this paper is to propose radiating system by avoiding electromagnetic interference in unwanted directions and to radiate the energy in the required direction with an optimization technique.Design/methodology/approach-Practically,multiple,incompatible variables require concurrent boost on a synthesis of systematic antenna assemblage.The authors have worked out the main statistic penalty function to ensure all the restrictions.Here,MBPSO(Modified Binary Particle Swarm Optimization)is developed and introduced thin planar synthesis restriction.The sigmoid function is used to update the particle position.Different analytical demonstrations have been carried out,and the exhibited methods are predominant than the algorithms.Findings-A 20310 planar antenna array is synthesized using modified BPSO.The authors have suppressed the PSLL in two principal planes and as well as in the entire f plane.Numerical results state that MBPSO outperforms the other binary BPSO,BCSO,ACO,RGA,GAoptimization techniques.MBPSO achieved a51.84 dB PSLL level,whereas BPSO achieved48.57 dB with the same 50%thinning.Originality/value-Planar array antenna formation is one of the most complex syntheses because the array gets filled with more antenna elements.The machine-like complication and implementation of such an antenna arrangement with a broad opening would be expensive.It is not easy to control the required radiation patterns shape by using a uniform feeding network.To get better flexibility for sustaining the sidelobe levelheaded along with consistent amplitude distribution.So as far as prominence has been given to the evolutionary algorithm,find an ideal solution for objective array combinational problems.

关键词： Planar array Thinning PSO Antenna array Sidelobe level Constraints Paper type Research paper

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Detection and Classification of Diabetic Retinopathy Using DCNN and BSN Models

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Computers, Materials & Continua 2022年第7期72卷 597-609页

作者： S.Sudha A.Srinivasan T.Gayathri Devi department of ece SRCSASTRA Deemed UniversityKumbakonamIndia

Diabetes is associated with many complications that could lead to death.Diabetic retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision loss.Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians.Because clinical testing involves complex procedures and is timeconsuming,an automated system would help ophthalmologists to detect DR and administer treatment in a timelymanner so that blindness can be avoided.Previous research works have focused on image processing algorithms,or neural networks,or signal processing techniques alone to detect diabetic retinopathy.Therefore,we aimed to develop a novel integrated approach to increase the accuracy of detection.This approach utilized both convolutional neural networks and signal processing techniques.In this proposed method,the biological electro retinogram(ERG)sensor network(BSN)and deep convolution neural network(DCNN)were developed to detect and classify DR.In the BSN system,electrodes were used to record ERGsignal,which was preprocessed to be noise-free.Processing was performed in the frequency domain by the application of fast Fourier transform(FFT)and mel frequency cepstral coefficients(MFCCs)were extracted.Artificial neural network(ANN)classifier was used to classify the signals of eyes with DR and normal eye.Additionally,fundus images were captured using a fundus camera,and these were used as the input for DCNN-based analysis.The DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images,non-proliferative DR(NPDR)or earlystage DR images,and proliferative DR(PDR)or advanced-stage DR images.Furthermore,it classifiedNPDRaccording tomicroaneurysms,hemorrhages,cotton wool spots,and exudates,and the presence of new blood vessels indicated PDR.The accuracy,sensitivity,and specificity of the ANNclassifier were found to be 94%,95%,and 93%,respectively.Both the accuracy rate and sensitivity rate of theDCN

关键词： Deep convolution neural network artificial neural network nonproliferative diabetic retinopathy biological ERG sensor network

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Enhanced Detection of Cerebral Atherosclerosis Using Hybrid Algorithm of Image Segmentation

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Intelligent Automation & Soft Computing 2023年第4期36卷 733-744页

作者： Shakunthala Masi Helenprabha Kuttiappan department of ece RMD Engineering CollegeChennaiIndia

In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques.Major objective of this work is to detect of cerebral atherosclerosis for image segmentation applica-tion.Detection of some abnormal structures in human body has become a difﬁcult task to complete with some simple images.For expounding and distinguishing neural architecture of human brain in an effective manner,MRI(Magnetic Reso-nance Imaging)is one of the most suitable and signiﬁcant technique.Here we work on detection of Cerebral Atherosclerosis from MRI images of patients.Cer-ebral Atherosclerosis is a cerebral vascular disease causes narrowing of the arteries due to buildup of fatty plaque inside the blood vessels of the brain.It leads to Ischemic stroke if not diagnosed early.Stroke affects majorly old age people and percentage of affected women is more compared to men.Results:Preproces-sing is done by using alpha trimmed meanﬁlter which is used to remove noise and also it enhances the image.Segmentation of cerebral atherosclerosis is done by using K-means clustering,Contextual clustering,and proposed Hybrid algo-rithm.Various parameters like Correlation,Pixel density,energy is determined and from the analysis of parameters it is determined that proposed Hybrid algo-rithm is efﬁcient.

关键词： Atherosclerosis Ischemic stroke Alpha trimmed meanﬁlter K-means Contextual clustering Hybrid algorithm

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An efficient approach for anti-jamming in IRNSS receivers using improved PSO based parametric wavelet packet thresholding

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Satellite Navigation 2022年第3期3卷 51-70,I0003页

作者： Jacob Silva Lorraine Kambham Madhu Ramarakula department of ece University College of EngineeringJNTUKKakinadaAndhra Pradesh533003India

The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India,extending up to 1500 km.However,when a receiver encounters a Continuous Wave Interference,its positioning accuracy degrades,or sometimes it even fails to work.Wavelet Packet Transform(WPT)is the most widely used technique for anti-jamming in Global Navigation Satellite System receivers.But the conventional method suffers from threshold drifting and employs inflexible thresholding functions.So,to address these issues,an efficient approach using Improved Particle Swarm Optimization based Parametric Wavelet Packet Thresholding(IPSO-PWPT)is proposed.Firstly,a new parameter adaptive thresholding function is constructed.Then,a new form of inertia weight is presented to enhance the performance of PSO.Later,IPSO is used to optimize the key parameters of WPT.Finally,the implementation of the IPSO-PWPT anti-jamming algorithm is discussed.The performance of the proposed technique is evaluated for various performance metrics in four jamming environments.The evaluation results manifest the proposed method’s efficacy compared to the conventional WPT in terms of anti-jamming capability.Also,the results show the ability of the new thresholding function to process various signals effectively.Furthermore,the findings reveal that the improved PSO outperforms the variants of PSO.

关键词： Continuous wave interference(CWI) IRNSS Particle swarm optimization(PSO) Wavelet packet transform(WPT)

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Artificial Neural Network-Based Development of an Efficient Energy Management Strategy for Office Building

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Intelligent Automation & Soft Computing 2023年第7期37卷 1225-1242页

作者： Payal Soni J.Subhashini department of ece SRMISTKattankulathurChennai603203India

In the current context,a smart grid has replaced the conventional grid through intelligent energy management,integration of renewable energy sources(RES)and two-way communication infrastructures from power gen-eration to distribution.Energy management from the distribution side is a critical problem for balancing load demand.A unique energy manage-ment strategy(EMS)is being developed for office building equipment.That includes renewable energy integration,automation,and control based on the Artificial Neural Network(ANN)system using Matlab Simulink.This strategy reduces electric power consumption and balances the load demand of the traditional grid.This strategy is developed by taking inputs from an office building electricity consumption behavior study,a power generation study of a solar photovoltaic system,and the supply pattern of a grid in peak and non-peak hours.All this is done in consideration of the Indian scenario,where real-time data of month-wise ANN-based intelligent switching has been established for intermittent renewable sources and peak load reduction,as well as average load reduction,has been demonstrated along with the power control loop without the battery system.

关键词： HVAC ANN demand response power consumption smart grid Weather and temperature occupancy EMS

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