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Performance analysis of antenna with different substrate materials at 60 GHz
With a tremendous demand on data rate and bandwidth utilization in RF communication systems, current research studies leads to explore the millimeter-wave unlicensed 57-64 GHz bandwidth of electromagnetic spectrum. This high frequency band is not channelized for long distance communication. However, it could be efficiently utilized for indoor and short range digital communication transmission systems for enhanced data rate and bandwidth utilization. This paper focused on designing antenna that utilizes 60 GHz frequency which falls under this spectrum by using different substrate materials such as like RT Duroid, FR4 Epoxy, Rogers RO3010 and GaAs for Rectangular Microstrip Antenna (RMSA). This research paper presents a detailed analysis on performance parameters comparison like gain, VSWR, return loss & radiation pattern analysis for the 60 GHz antenna designed with substrate materials having different properties. 2017 IEEE. -
Performance analysis of Clustering algorithms for dyslexia detection
Clustering algorithms plays vital role in analysing and evaluating vast number of high dimensional health care data ranging from medical data repositories, clinical data, electronic health records, body sensor networks, IoT devices, and so on. Dyslexia, a learning disorder is a common problem that is found in children during the initial stages of formal education, which is detected as mild to severe. It can also be one of the reasons of failure in the school. According to the literature this difficulty is commonly seen among Special Education Need children. There are few studies focussed on the application of classification algorithms for detecting the presence of dyslexia. This paper focusses one of SDG, goal 4:Quality Education, as dyslexic students can be given equal and quality education. Analyses of an online gamified test-based dataset is done by applying various clustering techniques such as K-means, Fuzzy c-means, and Bat K-means to assess their effectiveness in detecting the problem dyslexia. As the dataset is large, it is observed that usage of clustering methods gives us gain insight into the distribution of data to observe characteristics of each cluster. The clustering results are evaluated using root mean squared error (RMSE), mean absolute error (MAE), Xie-Beni index and it is found K Means outperforms FCM, Bat K Means algorithm for analysing different levels of the learning disorder. The Electrochemical Society -
Performance Analysis of Deep Learning Algorithms for Intrusion Detection in IoT
Due to the wide availability of IoT devices at affordable cost and the ease of use has increased IoT devices increased usage. Due to the enormous usage of the Internet of Things (IoT) devices, the security aspects related to the data are also a significant concern in this data-driven world. Negligence of security measures from users can result in severe data falsification or data thefts. In this scenario, the Intrusion Detection System has a pivotal role in IoT security. Incorporating the deep learning techniques is an effective way to predict various attacks, either known or unknown. This paper highlights the various security threats associated with IoT, the importance of deep learning in IoT intrusion detection, and various IoT intrusion detection systems using deep learning. Comparative analysis of the different deep learning techniques was performed. The results have shown Convolution Neural Networks gave high accuracy in prediction based on various evaluation metrics. 2021 IEEE. -
Performance Analysis of Deep Learning Pretrained Image Classifiation Models
Convolutional Neural Networks (CNNs) is revolutionized in the field of computer vision, with the high accuracy and capability to learn features from raw data. In this research work focused on a comparative analysis of two popular CNN architectures, VGG16 and VGG19. The CIFAR dataset consists of 60,000 images, each with a resolution of 32x32 and it's belong to one of the 10 classes. Experimental results are compared with VGG16 and VGG19 in terms of their accuracy and training time, and to identify any differences in their ability to learn features from the CIFAR-10 dataset. The results of this research can aid in directing the choice of appropriate architectures for image classification tasks as well as the advantages of optimisation strategies for enhancing the efficiency of deep learning models. In order to enhance the performance of these structures, more optimisation methods and datasets may be investigated in subsequent research. 2023 IEEE. -
Performance analysis of Flyash with Bentonite in grounding pit
In modern era of power systems, generation, transmission and distribution is well operated and maintained to satisfy the demand and supply management. Various problems are faced every day in all the power system areas. One among the problems faced in all the high voltage electrical equipment's is grounding. Natural grounding is done using charcoal and salt to maintain the less resistance in the grounding pit but the less resistance grounding pit becomes high resistive area due to various reasons like improper maintenance, charcoal and salt getting dissolved in soil after some period of time. In this paper a new method of grounding which uses Flyash and Bentonite is proposed and the performance is been analyzed by various standard methods. 2016 IEEE. -
Performance Analysis of Logical Structures Using Ternary Quantum Dot Cellular Automata (TQCA)-Based Nanotechnology
Ternary Quantum-Dot Cellular Automata (TQCA) is a developing nanotechnology that guarantees lower power utilization and littler size, with quicker speed contrasted with innovative transistor. In this article, we are going to propose a novel architecture of level-sensitive scan design (LSSD) in TQCA. These circuits are helpful for the structure of numerous legitimate and useful circuits. Recreation consequences of proposed TQCA circuits are developed by utilizing such QCA designer tool. In realization to particular specification, we need to find the parameter values by using Schrodinger equation. Here, we have optimized the different parameter in the equation of Schrodinger. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Performance Analysis of Nonlinear Companding Techniques for PAPR Mitigation in 5G GFDM Systems
Generalized Frequency Division Multiplexing (GFDM) is a 5G waveform contender that offers asynchronous and non-orthogonal data transmission, featuring several advantages, some of them being low latency, reduced out-of-band (OOB) radiation and low adjacent channel leakage ratio. GFDM is a non-orthogonal multicarrier waveform which enables data transmission on a time frequency grid. However, like orthogonal frequency division multiplexing and many other multicarrier systems, high peak-to-average power ratio (PAPR) is one of the main problems in GFDM, which degrades the high-power amplifier (HPA) efficiency and distorts the transmitted signal, thereby affecting the bit error rate (BER) performance of the system. Hence, PAPR reduction is essential for improved system performance and enhanced efficiency. Nonlinear companding techniques are known to be one of the effective low complexity PAPR reduction techniques for multicarrier systems. In this paper, a GFDM system is evaluated using mu law companding, root companding and exponential companding techniques for efficient PAPR reduction. The PAPR and BER graphs are used to evaluate the proposed methods in the presence of an HPA. Simulations show that, out of these three techniques, exponential companding was found to provide a trade-off between the PAPR reduction and BER performance. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Performance analysis of OFF-GRID solar photo voltaic system
Day by day the demand for electrical energy is increasing. We can't rely on conventional energy sources for meeting this increasing demand as they are depleting. So it is necessary to find an alternative method to harness the energy that we are lacking. Solar energy generation seems to be a promising technology for this dilemma. It is environmental friendly and infinite source of energy. Photovoltaic systems can be broadly classified into two-an on-grid system or an off-grid system. The energy generated from a solar PV system is based on several factors like irradiance, types of solar PV used and temperature. Analyzing the existing system efficiency is of prime importance for the characterization of the problems and for the improvements. This study deals with the performance analysis of an on-grid and off-grid system. The analysis is carried out by modeling an existing system in MATLAB/SIMULINK which is already in operation. It can be extended to analyze the grid stability. This study aims the quantification of various performance parameters like power output, losses in the system, system efficiency and the total energy transfer. 2015 IEEE. -
Performance analysis of optimized corporate-fed microstrip array for ISM band applications
This paper presents a low cost high gain corporate feed rectangular microstrip patch antenna array of two elements having cuttings at the corners, with detailed steps of design process, operates in Industrial Scientific Medical (ISM) band (2.4 GHz). The proposed antenna structures are designed using FR4 dielectric substrate having permittivity ?r= 44 and substrate thickness of 1.6 mm. The gain of these simulated antennas are obtained as 2.4819 dB with return loss of -17.779 dB for a single element patch and 6.3128 dB with return loss of -15.8320 dB for an array of two elements. The simulations have been carried out by using Antenna simulator HFSS version 15.0.0 to obtain the VSWR, return loss and radiation pattern. 2017 IEEE. -
Performance Analysis of Several CNN Based Models for Brain MRI in Tumor Classification
Classification is one of the primary tasks in data mining and machine learning which is used for categorizing data into classes. In this paper, brain MRI images are used for classification of tumors into three categories namely, Meningioma, Glioma, and Pituitary Tumor. These methodologies used are spatial based, depth based, feature map based and depth based CNN showcasing the power of deep learning in automating the tumor detection process. To evaluate the performance of several deep learning models, data is divided into training and testing data where a generalization method is used for comparison. The experimental results demonstrate promising accuracy, showing that a few techniques are valuable tools for radiologists and physicians, along with further analysis. The best accuracy obtained is 96% using MobileNet and ResNet50 in comparison to other CNN methodologies used in this paper. 2024 IEEE. -
Performance analysis of training algorithms of multilayer perceptrons in diabetes prediction
Artificial Intelligence plays a vital role in developing machines or software that can create intelligence. Artificial Neural Networks is a field of neuroscience which contributes tremendous developments in Artificial Intelligence. This paper focuses on the study of performance of various training algorithms of Multilayer Perceptrons in Diabetes Prediction. In this study, we have used Pima Indian Diabetes data set from UCI Machine Learning Repository as input dataset. The system is implemented in MatlabR2013. The Pima Indian Diabetes dataset consists of about 768 instances. The input data is the patient history and the target output is the prediction result as tested positive or tested negative. From the performance analysis, it was observed that out of all the training algorithms, Levenberg-Marquardt Algorithm has given optimal training results. 2015 IEEE. -
Performance Analysis of User Behavior Pattern Mining Using Web Log Database for User Identification
User behavior analytics is a progressive research domain. Understanding the users behavior patterns and identifying their behavior patterns will provide solutions to many issues like identity theft and user authentication. So many research works are done in analyzing the frequent access patterns of the users by pre-processing access logs and applying various algorithms to understand the frequent access behavior of the user. From the literature, it founds that the frequent user access pattern identification needs improvement on prediction accuracy and the minimal false positives. To accomplish these, three different approaches were proposed to overcome the existing issues and intended to reduce false positives and improve the frequent pattern mining accuracy based on web access logs. Proposed methods were found to be good while compared with the existing works. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Performance Analysis of Various Machine Learning Classification Models Using Twitter Data: National Education Policy
With the exponential growth of social networking sites, people are using these platforms to express their sentiments on everyday issues. Collection and analysis of people's reactions to purchases of products, public services, etc. are important from a marketing and innovation perspective. Sentiment analysis also called opinion mining or emotion extraction is the classification of emotions in text. This technique has been widely used over the years to determine sentiment within given text data. Twitter is a social media platform primarily used by people to express their feelings about specific events. In this paper, collected tweets about National Education Policy which has been a hot topic for a while; and analyzed them using various machine learning algorithms such as Random Forest classifier, Logistic Regression, SVM, Decision Tree, XGBoost, Naive Bayes. This study shows that the Decision tree algorithm is performing best, compare to all the other algorithms. 2023 IEEE. -
Performance Analysis of YOLOv7 and YOLOv8 Models for Drone Detection
Drone detection techniques are used to detect unmanned aerial systems (UAS) also commonly known as drones. A rapid increase in these drones has limited the airspace safety and so the research for drone detection has emerged. This study compares between the two widely used deep-learning models, previously used YOLOv7 and the latest YOLOv8. The overall finding of this study suggests that the YOLOv8 deep-learning model appears to be more promising and may make valuable contributions on their own. We got the result that for 10 epochs YOLOv8 gave 50.16% accuracy while YOLOv7 gave 48.16% accuracy making YOLOv8 more promising for the task. As a practical application for future work, we intend to deploy YOLOv8 on edge devices to achieve real-time drone detection in critical security applications. 2023 IEEE. -
Performance and Steady State Heat Transfer Analysis of Functionally Graded Thermal Barrier Coatings Systems
Thermal barrier coatings (TBCfs), typically 8 wt.% Yttria Stabilized Zirconia (8YSZ), in single layered configuration have been traditionally used in aerospace components to protect them from degradation at high temperatures and to improve the thermal efficiency of the system. This paper compares the performance of two types of TBC configurations: Single layered and multilayered functionally graded materials (FGM). Aerospace alloy, Inconel 718 substrates, NiCrAlY bond coat (BC) and 8YPSZ top coat (TC) were the materials used. FGM configuration was used to improve the durability and life of the conventional TBC system by reducing the coefficient of thermal expansion (CTE) mismatch. The TBCs were subjected to thermal fatigue (thermal shock and thermal barrier test) in laboratory scale burner rig test and oxidation stability test in high temperature furnace upto 1000. The as-sprayed and thermal fatigue tested specimen were characterized by X-ray diffraction (XRD) analysis and Scanning Electron Microscope (micro-structure). Results are discussed in the light of suitability of coating configuration, thermal fatigue and spalling characteristics with reference to aerospace applications at temperatures in the 9000C to 15000C range. Computational work was carried out comprising a simulation model involving the developed TBCs. 2018 Elsevier Ltd. -
Performance Evaluation and Comparison of Various Personal Cloud Storage Services for Healthcare Images
In recent times, usage of personal cloud storage services for storing e-health records in on a rise. This is due to the constant accessibility, easy sharing, and safe storage of the data at a nominal cost. In this paper, we have analyzed the performance of four personal cloud storage services: Google Drive, Dropbox, Sync.com, and Icedrive using medical image data files of various sizes. The parameters checked were number of packets transmitted during file upload and duration of time to upload, download, and delete the files. The results show us a comparative analysis of the personal cloud storage services based on the parameters and also help us identify certain gaps for the future. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Performance Evaluation of Area-Based Segmentation Technique on Ambient Sensor Data for Smart Home Assisted Living
Activity recognition(AR) is a popular subject of research in the recent past. Recognition of activities performed by human beings, enables the addressing of challenges posed by many real-world applications such as health monitoring, providing security etc. Segmentation plays a vital role in AR. This paper evaluates the efficiency of Area-Based Segmentation using different performance measures. Area-Based segmentation was proposed in our earlier research work. The evaluation of the Area-Based segmentation technique is conducted on four real world datasets viz. Aruba17, Shib010, HH102, and HH113 comprising of data pertaining to an individual, living in the test bed home. Machine learning classifiers, SVM-R, SVM-P, NB and KNN are adopted to validate the performance of Area-Based segmentation. Amongst the four chosen classification algorithms SVM-R exhibits better in all the four datasets. Area-Based segmentation recognise the four test bed activities with accuracies of 0.74, 0.98, 0.66, and 0.99 respectively. The results reveal that Area based segmentation can efficiently segment sensor data stream which aids in accurate recognition of smart home activities. 2019 Procedia Computer Science. All rights reserved. -
Performance Evaluation of Convolutional Neural Networks for Stellar Image Classification: A Comparative Study
This study analyzes three distinct convolutional neural network (CNN) models, ResNet, Parallel CNN, and VGG16, for object classification using the Star-Galaxy Classification dataset. The dataset comprises a vast collection of celestial object images, including galaxies, stars, and quasars. The effectiveness of each CNN model is evaluated based on accuracy, a commonly used performance metric. The results reveal that the Parallel CNN model achieved the highest accuracy of 90.08% in classifying celestial objects, followed by VGG16 with an accuracy of 86%, and ResNet with an accuracy of 83%. Specifically, the Parallel CNN model demonstrates superior performance in classifying galaxies and stars. These findings provide valuable insights into the strengths and weaknesses of each model for this specific classification task, guiding the development of more effective CNN models for similar applications in cosmology and other fields. This research contributes to the growing literature on CNN models' application in astronomy and underscores the importance of selecting appropriate models to achieve high accuracy in object classification tasks. The study's insights can be utilized to inform the development of more effective CNN models for similar tasks and facilitate advancements in astronomical research. 2023 IEEE. -
Performance Evaluation of OTFS Under Different Channel Conditions for LEO Satellite Downlink
Orthogonal Time Frequency Space (OTFS) modulation scheme is being actively pursued as a viable alternative to Orthogonal Frequency Division Multiplexing (OFDM) modulation scheme in future wireless standards due to the inherent ability of OTFS to mitigate the Doppler effects in high mobility scenarios. The inclusion of Non Terrestrial Network (NTN) in Release 17 of 3GPP (3rd generation partnership project) New Radio (NR) standard signifies the vital role of Satellite Communications to achieve coverage extension, capability augmentation and seamless global connectivity. In this context, it becomes important to study the suitability of OTFS modulation scheme with respect to satellite channel scenarios. In this paper, we consider the downlink channel scenarios defined by 3GPP NR NTN for Low Earth Orbit (LEO) satellites at sub-6 GHz and millimetre wave frequencies for evaluating the performance of OTFS modulation schemes. Simulation results using LMMSE (Linear Minimum Mean Square Error) and MRC (Maximum Ratio Combining) detection algorithms confirm that OTFS modulation is highly robust against Doppler effects and performs consistently across all channel conditions. From simulation results, it is observed that the performance of iterative MRC detection is better than LMMSE for 16QAM and 64QAM modulation schemes by achieving respective gains of around 5 dB and 10 dB for corresponding Bit Error Rate (BER) values of 0.01 and 0.1. 2023 IEEE. -
Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique
The Collaborative Filtering (CF) technique is the most common neighbourhood-based recommendation strategy, that provides personalized recommendation to a user for the items using a similarity measure. Hence, the selection of the appropriate similarity measure becomes crucial in the CF based recommendation system. The traditional similarity measures merely focus only on the historical ratings provided by the users to compute the similarity, completely ignoring the fact that preferences change over a period of time. Considering this, the paper aims to develop an effective Recommendation System that uses temporal information to capture the changes in the preferences over a period of time. For this, the existing exponential and power time decay functions are integrated with Cosine, Pearson Correlation, and Gower's similarity measures to compute similarity. The similarity is computed at the similarity computation and prediction levels of recommendation processes. Experimental findings in terms of MAE and RMSE on the MovieLens-100k demonstrate that performance of Gower's coefficient is better when applied with the exponential function at the similarity computation level of the recommendation process. 2022 Elsevier B.V.. All rights reserved.