International Journal of Electrical and Electronics Research最新文献

{"title":"An Efficient Hybrid Analysis to Improve Data Rate Signal Transmission in Cognitive Radio Networks Using Multi- Hop","authors":"Bhaveshkumar Kathiriya, Divyesh R. Keraliya","doi":"10.37391/ijeer.110307","DOIUrl":"https://doi.org/10.37391/ijeer.110307","url":null,"abstract":"Spectrum scarcity problems can be resolved with the emerging communiqué technologies known as cognitive radio (CR). Cognitive radio networks (CRNs) will give mobile users greater bandwidth via wirelessly heterogeneity design and dynamic spectrum acquisition methods. The Cognitive Radio Mobile Ad-Hoc Network (CR-MANET) idea of Adaptive Routing a new network paradigm may be realized by using the functions of spectrum management to overcome such difficulties. Secondary users (SUs) have the freedom to opportunistically explore and make use of the open spaces on licensed channels. When a primary user (PU) interferes with a licensed channel, this forces the SU to leave it and switch to an open channel. Because of the constant channel switching those results, SUs degrades as a result. In this result recommends a number of channels, number of hop CRN that uses a fuzzy decision-making system that is genetically optimized for channel selection, channel switching, and spectrum allocation. According to study, the suggested architecture achieves higher PDR, throughput, latency, and transmission time than fuzzy and genetic algorithms. Through simulations the result demonstrates significant improvements in data rate performance, making it a promising solution for enhancing communication efficiency in cognitive radio networks.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132274364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of Turbo Trellis Coding Modulation Scheme for Fading Channel","authors":"R.K. Goswami, K. S. Rao, Swathi Nambari","doi":"10.37391/ijeer.110305","DOIUrl":"https://doi.org/10.37391/ijeer.110305","url":null,"abstract":"In the context of data communication, encountering fading channels can lead to errors occurring at the receiving end due to multipath propagation. To address this challenge, researchers have persistently worked towards developing Error Correction Schemes that effectively manage these errors and guarantee error-free data reception for the receiver. One area of focus lies in the implementation of Forward Error Correction Schemes directly at the transmitter end. Nonetheless, integrating error correction coding using these schemes comes with the drawback of increased bandwidth requirements since additional bits must be included to facilitate error correction. Fortunately, there exists a coding scheme known as Trellis Coded Modulation (TCM), which specifically tackles this concern. In the case of TCM, the modulation scheme has been chosen based on the rate of the convolutional coding scheme. Nevertheless, TCM has certain limitations when it comes to correcting a high number of errors, which prompted the emergence of Turbo Coding. Turbo Coding employs two coders at the transmitter, arranged either in a serial or parallel configuration, along with an appropriate decoder at the receiver. This paper introduces a Turbo Coding scheme design utilizing convolutional coders with a rate of 2/3, arranged in a serially concatenated configuration, resulting in an effective rate of 4/9. For preserving bandwidth, the Turbo Coding is applied to TCM scheme. Consequently, when employing the convolutional coding scheme with a rate of 2/3, the modulation scheme has to be 8-QAM. However, to maintain bandwidth after coding, when utilizing the Turbo coding scheme with a rate of 4/9, the modulation scheme is upgraded to 512-QAM. MATLAB simulations were conducted to evaluate the error correcting capabilities of the designed scheme compared to the convolutional coding scheme that uses the constituent convolutional encoder. The comparison has also been made with the uncoded data communication utilizing simple QPSK modulation scheme. The results indicate that under Rician fading channel conditions, the Turbo Trellis Coding Modulation Scheme provides an approximate gain of 5 dB compared to the convolutional coding scheme and approximately 8 dB gain compared to uncoded one.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127536671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Second Harmonic Frequency Adjustment Strategy for Class-E Amplifier Design","authors":"Dongho Lee","doi":"10.37391/ijeer.110303","DOIUrl":"https://doi.org/10.37391/ijeer.110303","url":null,"abstract":"Class-E amplifiers are a type of switching amplifiers with an efficiency that approaches 100%. The harmonic frequency is very important in the design of Class-E amplifiers. In this study, the second harmonic frequency is considered in the design of a Class-E amplifier. The Class-E amplifier has been fabricated on FR4 and has demonstrated a power-added efficiency (PAE) of 74.5% at 1.01 GHz. This result shows that the termination of the second-harmonic output is essential for switching amplifiers.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"5a 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128200021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Study of the CNN Based Models Used for Remote Sensing Image Classification","authors":"Supritha N, Narasimha Murthy M S","doi":"10.37391/ijeer.110301","DOIUrl":"https://doi.org/10.37391/ijeer.110301","url":null,"abstract":"Remotely sensed images, their classification and accuracy play a vital role in measuring a country’s scientific growth and technological development. Remote Sensing (RS) can be interpreted as a way of assessing the characteristics of a surface or an entity from a distance. This task of identifying and classifying datasets of RS images can be done using Convolutional Neural Network (CNN). For classifying images of large-scale areas, the traditional CNN approach produces coarse maps. For addressing this issue, Object based CNN method can be used. Classifying images with high spatial resolution can be done effectively using Object based image analysis. Deep learning methods offer the strength of auto learning the spatial features of an image. Object scale based adaptive CNN is a novel technique that can improve the accuracy of image classification of high spatial resolution images. For efficient RS image classification, a novel Deep learning approach called distributed CNN can be used which leads to enhanced accuracy of RS image classification. In this paper, three CNN models have been compared while considering the training time and efficiency to classify RS images as parameters of measure to assess the CNN models.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"30 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120836043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image Forgery Detection Using Integrated Convolution-LSTM (2D) and Convolution (2D)","authors":"Yogita Shelar, Dr. Prashant Sharma, Dr. Chandan Singh. D. Rawat","doi":"10.37391/ijeer.110253","DOIUrl":"https://doi.org/10.37391/ijeer.110253","url":null,"abstract":"Digital forensics and computer vision must explore image forgery detection and their related technologies. Image fraud detection is expanding as sophisticated image editing software becomes more accessible. This makes changing photos easier than with the older methods. Convolution LSTM (1D) and Convolution LSTM (2D) + Convolution (2D) are popular deep learning models. We tested them using the public CASIA.2.0 image forgery database. ConvLSTM (2D) and its combination outperformed ConvLSTM (1D) in accuracy, precision, recall, and F1-score. We also provided a related work on image forgery detection models and methods. We also reviewed publicly available datasets used in picture forgery detection research, highlighting their merits and drawbacks. Our investigation revealed the state of picture fraud detection and the deep learning models that worked well. Our work greatly impacts fraudulent photo detection. First, it highlights how important deep learning models are for picture forgery detection. Second, ConvLSTM (2D) + Conv (2D) detect image forgeries better than ConvLSTM (1D). Finally, our dataset analysis and proposed integrated approach help research construct more effective and accurate picture forgery detection systems.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122465458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frequency Diversity Using Random Time Delay in Amplify-And-Forward Relay","authors":"Yu-Ra Heo, Min-A Lee, Eui-Rim Jeong","doi":"10.37391/ijeer.110237","DOIUrl":"https://doi.org/10.37391/ijeer.110237","url":null,"abstract":"In a tactical wireless communication environment, it is common for device-to-device communication to occur without a base station, but this can be problematic when the distance between the source and destination is too far. Relays are often used to improve transmission distance and reliability, but amplification-based relaying can result in lower communication performance compared to other methods. This paper proposes a method for obtaining diversity gain through the application of time delay during relaying. The proposed method is compared to a conventional method that uses phase rotation to obtain diversity gain. Simulation results show that the proposed method has slightly lower or similar performance improvement compared to the conventional relay method.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129043749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Closed Loop Constant v/f Control of Induction Motor with Torque Control Based on DPWM","authors":"E. Nandhini, A. Sivaprakasam","doi":"10.37391/ijeer.110239","DOIUrl":"https://doi.org/10.37391/ijeer.110239","url":null,"abstract":"An easy sensor-less scalar control algorithm is described in this article as a method for controlling the speed of an induction motor. For developing a closed-loop v/f control of the induction motor drive, a torque controller with PI is implemented. The torque command was estimated by utilizing the voltage command, the feedback current, and a torque estimator. Additionally, a torque reference was provided for the Torque PI controller. Considering this, the purpose of this work is to investigate the closed-loop PI-based torque control of an induction motor drive that applies DPWM. In addition to this, it provides a description of a PI-based closed-loop torque control that works in conjunction with v/f control and tries to discover the most effective method for driving an induction motor based on an examination of Total Harmonic Distortion (THD) and torque ripple. To evaluate the performance studies of open- and closed-loop strategies with DPWM techniques for induction motor driving, the MATLAB/Simulink environment has been deployed. Experimental results have been validated.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125251952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bi-Usage Energy Efficient Techniques (BUEET) for Energizing the Vehicle and Broadcasting the Information in Highway Scenario","authors":"Chinju R. Nair, J. A. Samath","doi":"10.37391/ijeer.110236","DOIUrl":"https://doi.org/10.37391/ijeer.110236","url":null,"abstract":"The proposed technique BUEET is introduced mainly for two major reasons such as (i) RSU broadcast the emergency messages and the energy status to the vehicles without any interruption. It helps to shake hands with the neighboring node for energy sharing and (ii) RSU boost up the energy efficiency level with the help of energy sharing by the adjacent vehicles. Most of the self-organizing protocols in wireless sensor networks considers only initial energy consumption phase and neglects the maintenance phase of topology. The vehicles are cooperatively interacted to form a reliable network structure. RSU’s are placed in the roadway infrastructure and On-Board Units (OBU’s) are placed in the vehicles, then the communication takes place with the help of these devices. Therefore, this provides the clear roadmap structure by receiving the information from various sources of vehicles. If the information received from the RSU is not relevant to the vehicles then it automatically transmits the information to the adjacent vehicles travelling in the highway. The adjacent vehicle checks the information and transmits to another vehicle if it is relevant. Therefore, the proposed method considers two parameters such as packet transmission and energy efficiency on NS3 simulation. Packet transmission among the vehicles plays a vital role in the proposed technique and the nodes without any route breaks and the loss of connection leads to a strong network. Energy efficiency is analyzed and compared with other existing schemes; in result it is proved that BUEET’s energy efficient is higher in all the nodes. It helps to communicate among the nodes without energy loss. When the energy among the nodes in the network is high, then the performance of the nodes also good. The maximum number of Hop’s helps to transmit the information without any delay.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131777929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Low-Pass Filter Structures Using Spur-Lines to Generate Additional Attenuation Poles","authors":"In-seon Kim, Beom-Jun Park, Cheol-Soo Lee, Joo-Rae Park, Ghiback Kim","doi":"10.37391/ijeer.110230","DOIUrl":"https://doi.org/10.37391/ijeer.110230","url":null,"abstract":"We propose two novel low-pass filter (LPF) structures that have generated additional attenuation poles from applying spur-lines to typical open-stub LPFs. The first structure has the spur-lines added to the serial lines, and the second structure has the spur-lines added to the parallel open stub. From the characteristic analyses of the filters with the proposed design, it was confirmed that the length of the spur-line was an important variable for controlling the stopband bandwidth and attenuation depth. The two types of LPFs were fabricated and then validated by the agreement between the theoretical and measured results. From the measured results, the stopband bandwidths of the serial- and parallel-configuration spur-line LPFs were improved by approximately 4.7 times and 1.4 times of the defined reference level, respectively, compared to that of the typical LPF.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125139703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Approach to Cervical Cancer Detection Using Hybrid Stacked Ensemble Models and Feature Selection","authors":"Pratiksha D. Nandanwar, Dr. Somnath B. Dhonde","doi":"10.37391/ijeer.110246","DOIUrl":"https://doi.org/10.37391/ijeer.110246","url":null,"abstract":"Around the world, millions of women are diagnosed with cervical cancer each year. Early detection is very important to produce a better overall quality of life for those diagnosed with the disease and reduce the burden on the healthcare system. In recent years, the field of machine learning (ML) has been developing methods that can improve the accuracy of detecting cervical cancer. This paper presents a new approach to this problem by using a combination of image segmentation and feature extraction techniques. The proposed approach is divided into three phases. The first stage involves image segmentation, which is performed to extract the regions of interest from the input image. The second stage is comprised of extracting the features from the ROI with the help of the Histogram and Hu Moments techniques. The techniques used in this approach, namely the Hu Moments and Histogram techniques, respectively, can capture the shape information in the ROI. In the third stage of the project, we use a hybrid approach to classify the image. The proposed model is composed of several base classifiers, which are trained on varying subsets of the features that were extracted. These resulting classifiers then make a classification decision. We tested the proposed model against a large dataset of images for cervical cancer. The results of the experiments revealed that it performed better than the existing methods in detecting the disease. It was able to achieve an accuracy of 96.5%, an F1 score of 96.9%, and a recall of 96.7%. The proposed model was successful in accomplishing a remarkable accuracy of 96.5%, making it an ideal candidate for use in the detection of cervical cancer. It was also able to perform feature extraction using the Histogram techniques and image segmentation. The proposed method could help medical professionals improve the diagnosis and reduce the burden of this disease on women worldwide.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115159800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}