December 2019 - Journal of Electronics and Informatics
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TOTAL DOCUMENTS

59
(FIVE YEARS 59)

H-INDEX

3
(FIVE YEARS 3)

Published By Inventive Research Organization

2582-3825
Updated Friday, 22 October 2021

2021 ◽  
Vol 3 (3) ◽  
pp. 194-208
Author(s):  
P. Karthigaikumar

Transistor sizing is one the developing field in VLSI. Many researches have been conducted to achieve automatic transistor sizing which is a complex task due to its large design area and communication gap between different node and topology. In this paper, automatic transistor sizing is implemented using a combinational methods of Graph Convolutional Neural Network (GCN) and Reinforcement Learning (RL). In the graphical structure the transistor are represented as apexes and the wires are represented as boundaries. Reinforcement learning techniques acts a communication bridge between every node and topology of all circuit. This brings proper communication and understanding among the circuit design. Thus the Figure of Merit (FOM) is increased and the experimental results are compared with different topologies. It is proved that the circuit with prior knowledge about the system, performs well.


2021 ◽  
Vol 3 (3) ◽  
pp. 178-193
Author(s):  
B. Vivekanandam

The invention of the first vaccine has also raised several anti-vaccination views among people. Vaccine reluctance may be exacerbated by the growing reliance on social media, which is considered as a source of health information. During this COVID'19 scenario, the verification of non-vaccinators via the use of biometric characteristics has received greater attention, especially in areas such as vaccination monitoring and other emergency medical services, among other things. The traditional digital camera utilizes the middle-resolution images for commercial applications in a regulated or contact-based environment with user participation, while the latter uses high-resolution latent palmprints. This research study attempts to utilize convolutional neural networks (CNN) for the first time to perform contactless recognition. To identify the COVID '19 vaccine using the CNN technique, this research work has used the contactless palmprint method. Further, this research study utilizes the PalmNet structure of convolutional neural network to resolve the issue. First, the ROI region of the palmprint was extracted from the input picture based on the geometric form of the print. After image registration, the ROI region is sent into a convolutional neural network as an input. The softmax activation function is then used to train the network so that it can choose the optimal learning rate and super parameters for the given learning scenario. The neural networks of the deep learning platform were then compared and summarized.


2021 ◽  
Vol 3 (3) ◽  
pp. 167-177
Author(s):  
R. Kanthavel ◽  
R. Dhaya

The most common orthopedic illness in the worldwide, osteoarthritis (OA), affects mainly hand, hip, and knee joints. OA invariably leads to surgical intervention, which is a huge burden on both the individual and the society. There are numerous risk factors that contribute to OA, although the pathogenesis of OA and the molecular basis of through such are unknown at this time. OA is presently identified with an analyses were used to examine and, if required, corroborated through imaging - a radiography study. These traditional methods, on the other hand, are not susceptible to sense the beginning phases of OA, making the creation of precautionary interventions for specific disease problematic. As a result, other approaches which might permit for the timely identification of OA are needed. As a result, computerized perception algorithms give measurable indicators that may be used to determine the severity of OA from photographs in an automated and systematic manner. The study of Knee radiography and its quantitative analysis is analyzed in this paper.


2021 ◽  
Vol 3 (3) ◽  
pp. 150-166
Author(s):  
P Karuppusamy

In the recent research studies, adaptive control has been used to monitor a reference control system that has been selected by the researcher. The acceptable reference system can be executed with the presence of nonlinearity and undesirable switches, as they have been disabled. This research study examines the challenges in adapting the state and time-varying parameters, when noise and state disturbances are present in a nonlinear control system. Additionally, this adaptive controller points out the best or most accurate option based on recursive least squares calculations by using the sensor data. This convergence has a rate of arrival with a set time of origin that serves as an estimate for error estimation. In addition, this research work has evaluated the amount of variance in the estimate, which has been caused by external disturbances using adaptive estimators from the reference value present in the noisy domain. The findings obtained via simulation demonstrate the performance of the adaptive estimator through the difference obtained between the reference value and observed value.


2021 ◽  
Vol 3 (2) ◽  
pp. 138-149
Author(s):  
B Vivekanandam

One of the most crucial roles of the cognitive radio (CR) is detection of spectrum ‘holes’. The ‘no a-priori knowledge required’ prospective of blind detection techniques has attracted the attention of researchers and industries, using simple Eigen values. Over the years, a number of study and research has been carried out to determine the impact of thermal noise in the performance of the detector. However, there has not been much work on the impact of man-made noise, which also hinders the performance of the detector. As a result, both man-made impulse noise and thermal Gaussian noise are examined in this proposed study to determine the performance of blind Eigen value-based spectrum sensing. Many studies have been conducted over long sample length by oversampling or increasing the duration of sensing. As a result, a research progress has been made on shorter sample lengths by using a novel algorithm. The proposed system utilizes three algorithms; they are contra-harmonic-mean minimum Eigen value, contra-harmonic mean Maximum Eigen value and maximum Eigenvalue harmonic mean. For smaller sample lengths, there is a substantial rise in the number of cooperative secondary users, as well as a low signal-to-noise ratio when employing the maximum Eigen value Harmonic mean. The experimental analysis of the proposed work with respect to impulse noise and Gaussian signal using Nakagami-m fading channel is observed and the results identified are tabulated.


2021 ◽  
Vol 3 (2) ◽  
pp. 114-125
Author(s):  
Subarna Shakya

By contributing to the system enhancement, the integration of Nano systems for nanosensors with biomaterials proves to be a unique element in the development of novel innovative systems. The techniques by which manipulation, handling, and preparation of the device are accomplished with respect to industrial use are a critical component that must be considered before the system is developed. The approach must be able to be used in a scanning electron microscope (SEM), resistant to environmental changes, and designed to be automated. Based on this deduction, the main objective of this research work is to develop a novel design of Nano electronic parts, which address the issue of packaging at a nanoscale. The proposed research work has used wood fibres and DNA as the bio material to develop nanoscale packaging. The use of a certain atomic force microscope (ATM) for handling DNA in dry circumstances is demonstrated with SCM wood fibrils/fibers manipulation in a scanning electron microscope (SEM).Keywords: Nano electronics, bioelectronics, scanning electron microscope (SEM), packaging, atomic force microscope (ATM)


2021 ◽  
Vol 3 (2) ◽  
pp. 99-113
Author(s):  
Karuppusamy P.

In several industrial applications, rotating machinery is widely utilized in various forms. A growing amount of study, in the academic and industrial fields, as a potential sector for the confidentiality of modern industrial labor systems, has been drawing early fault diagnosis (EFD) techniques. However, EFD plays an essential role in providing sufficient information for performing maintenance activities, preventing and reducing financial loss and disastrous defaults. Many of the existing techniques for identifying rotations were ineffective. For the identification of spinning machine faults, many in-depth learning methods have recently been developed. This research report has included and analysed a number of research publications that have higher precision than standard algorithms for detecting early failures in rotating machinery. In addition to the artificial intelligence monitoring (AIM) model, detecting the defects in rotating machine was also realized through the simulation output. AIM framework model is also testing the rotating machinery in three different stages, which is based on the vibration signal obtained from the bearing system and further it has been trained with the neural network preceding. Compared to other traditional algorithms, the AIM model has achieved greater precision and also the other performance measures are tabulated in the result and discussion section.


Author(s):  
Karuppusamy P.

In several industrial applications, rotating machinery is widely utilized in various forms. A growing amount of study, in the academic and industrial fields, as a potential sector for the confidentiality of modern industrial labor systems, has been drawing early fault diagnosis (EFD) techniques. However, EFD plays an essential role in providing sufficient information for performing maintenance activities, preventing and reducing financial loss and disastrous defaults. Many of the existing techniques for identifying rotations were ineffective. For the identification of spinning machine faults, many in-depth learning methods have recently been developed. This research report has included and analysed a number of research publications that have higher precision than standard algorithms for detecting early failures in rotating machinery. In addition to the artificial intelligence monitoring (AIM) model, detecting the defects in rotating machine was also realized through the simulation output. AIM framework model is also testing the rotating machinery in three different stages, which is based on the vibration signal obtained from the bearing system and further it has been trained with the neural network preceding. Compared to other traditional algorithms, the AIM model has achieved greater precision and also the other performance measures are tabulated in the result and discussion section.


2021 ◽  
Vol 3 (2) ◽  
pp. 126-137
Author(s):  
Karthigaikumar P.

Based on an assessment of production capabilities, manufacturing sectors' core competency is increased. The importance of product quality in this aspect cannot be overstated. Several academics have introduced Deming's 14 principles, Shewhart cycle, total quality management, and other approaches to decrease the external failure costs and enhance product yield rates. Analysis of industrial data and process monitoring is becoming increasingly important as a part of the Industry 4.0 paradigm. In order to reduce the internal failure cost and inspection overhead, quality control (QC) schemes are utilized by industries. The final product quality has an interactive and cumulative effect of various parameters like operators and equipment in multistage manufacturing processes (MMP). In other cases, the final product is inspected in a single workstation with QC. It's challenging to do a cause analysis in MMP whenever a failure occurs. Several industries are looking for the optimal quality prediction model in order to achieve flawless production. The majority of current approaches solely handles single-stage manufacturing and is inadequate in dealing with MMP quality concerns. To overcome this issue, this paper proposes an industrial quality prediction system with a combination of multiple Program Component Analysis (PCA) and Decision Stump (DS) algorithm for MMP quality prediction. A SECOM (SEmiCOnductor Manufacturing) dataset is used for verification and validation of the proposed model. Based on the findings, it is clear that this model is capable of performing accurate classification and prediction in the field of industrial quality.


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