Vikneswaran Vijean
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Preferred name
Vikneswaran Vijean
Official Name
Vijean, Vikneswaran
Alternative Name
Vikneswaran
Vikneswaran, V.
Vijean, V.
Main Affiliation
Scopus Author ID
54785424700
Researcher ID
D-2539-2015

Research Output
Now showing 1 - 8 of 8
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Publication

Simulation of PLC Ladder Logic Programming for an Automated Glass Bottle Molding and Refilling Plant

2021-01-01 , Khan M.M. , Shawareb O.M.A. , Palaniappan R. , Vikneswaran Vijean , Nabi F.G. , Shawareb A.H.A. , Khan N.K.

Automation has been gaining interest in every branch today. The reason for the popularity of automation in industries today is due to its capability to reduce labour cost, reduce material wastage, increase the production quantity, to improve the quality of the product and to reduce idle time in manufacturing industry. In this work, an industrial process of glass molding and filling operation is simulated using the Fiddle PLC simulator. GRAFCET based modelling of discrete event is used in developing the PLC ladder logic program for the industrial process. The proposed method reduces labour cost by 40%, Increases production value by 55%, and reduces material wastage by 20% compared to manual operation. The proposed GRAFCET based model was found to be reliable, efficient, and accurate in performing the control sequence of the glass molding and filling operation. In future, it is proposed to develop the HMI (human machine interface) and the corresponding hardware will be developed for the same application.

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Differential diagnosis tool in healthcare application using respiratory sounds and convolutional neural network

2023-08-03 , Palaniappan R. , Sundaraj K. , Nabi F.G. , Vikneswaran Vijean

This chapter focuses on the classification of respiratory pathology using breathsound signals. The development of a computerised breath-sound analysis system could improve the standard of living of people affected by respiratory-related disease and further also be used as a differential diagnosis tool in affective computing. Accordingly, in this chapter, respiratory sounds recorded according to the Computerised Respiratory Sound Acquisition standard were obtained from subjects with respiratory sounds belonging to five different respiratory pathologies, namely, normal, wheezes, rhonchi, fine crackles, and coarse crackles.

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Effect of Mindfulness Meditation toward Improvement of Concentration based on Heart Rate Variability

2020-12-20 , Lim Chee Chin , Rosli F.F.B. , Fook C.Y. , Vikneswaran Vijean , Saidatul Ardeenawatie Awang , Palaniappan R.

Mindfulness meditation is a type of therapy for a psychological cure like depression and anxiety that can significantly increase peoples' ability to concentrate and focus. Thus, this paper describes the analysis of mindfulness meditation effect toward concentration study in term of heart rate variability (HRV) signal. A memory test is used as a medium to test the concentration level of 20 participants, and their performance of the electrocardiogram signal was recorded. Peaks detection method and Pan-Tompkin method are used to extract the features like PQRST peaks and R-R interval from the ECG signal. Then, the extracted ECG signal features are classified using KNN method for before and after meditation during the memory test. The result shows that the effect of mindfulness meditation can improve the performance of participants' concentration level. The highest accuracy, sensitivity and specificity performance is obtained from the combination of all six features (P, Q, R, S, T peaks, and R-R interval value), which is 84.58 %, 88.77% and 80.39%. The analysis of memory test produces higher memory test score (69.2%), lesser miss selection (60.8%) and shorter taken time to complete the memory test (2.268 minutes) after mindfulness meditation compared to before mindfulness meditation. The R-R interval value represents heart rate variability (HRV) is important to prove that most of the participants are more relax and can handle their stress better after doing mindfulness meditation.

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Assessments of cognitive state of Mitragyna speciosa (ketum) users during relaxation state

2023-02-21 , Fadhilah A.W. , Vikneswaran Vijean , Ahmad Faizal Salleh , Rashid R.A. , Palaniappan R. , Mutusamy H. , Helmy K.

The abuse of Mitragyna speciosa or commonly known as ketum leaves is widespread across Asian countries. Ketum leaves that were originally used as medicine were abused for the purpose of deluding their minds. As it has intoxicated properties that similar to drugs, EEG signals of ketum users may differ from normal people as the ketum may alter the brain signal and the cognitive state of ketum users may decrease. Therefore, this study was conducted to assess the cognitive state between ketum users and non-ketum users in terms of their relaxation state by using brain signal characteristics. A total of 8 subjects were involved in the experimental session. The 8 subjects were divided into two groups which are 4 subjects were ketum users for at least one year while the other 4 subjects were non-ketum users, had enough sleep for at least 6 hours and had no mental disorders. The EEG data was recorded during awaken relaxed state and was filtered using a notch filter and Independent Component Analysis (ICA) to remove the powerline artefacts, eye blinking and eye movement. Stockwell Transform was used to reduce the amount of the large data and extract useful features from the signal. Student's t-test is performed in order to compute the percentages of the differences between the ketum users and non-ketum users in each brain lobe. Mean of Shannon Entropy, mean of Tsallis Entropy, and mean of Hurst Exponent features used were able to elucidate the differences in brain activities between the two groups investigated.

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Early Detection of Diabetic Foot Ulcers through Wearable Shoe Design

2022-01-01 , Vikneswaran Vijean , Syakirah Afiza Mohammed , Razi Ahmad , Wan Amiza Amneera Wan Ahmad , Ragunathan Al Santiagoo , Abdul Ghapar Ahmad , Palaniappan R.

Diabetes Mellitus is categorized as a chronic metabolic disease where blood glucose levels are abnormal. Diabetic foot ulcer is a complication often associated with this disease. Diabetes foot ulcer is also commonly known as diabetes foot pain. It is a type of foot damage medical condition that progresses from diabetes mellitus. According to scientific data, almost 15% of diabetes patients may develop diabetes foot ulcer in their lifetime [1]. A foot ulcer is an open wound that commonly found under the feet, it can be a shallow open wound on the surface of the skin (less severe) or it can be a deep wound which exposes bones, tendons and joints [2]. However, if early prevention is carried out, diabetes patients might be able to avoid problems from diabetes foot ulcer. Thus, in this study, a wearable shoe prototype for early detection of foot ulcers is proposed to be used in home. The developed device will be associated with temperature sensor, vibration motor and pressure sensor. This device enables diabetes patients to carry out evaluation on their foot in daily life. With this device, early symptoms of foot ulcer can be detected and the seriousness of foot ulcer can be monitored.

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DT-CWPT based Tsallis Entropy for Vocal Fold Pathology Detection

2020-10-26 , Farah Nazlia Che Kassim , Vikneswaran Vijean , Muthusamy H. , Zulkapli Abdullah , Rokiah Abdullah , Palaniappan R.

The study of voice pathology has become one of the valuable methods of vocal fold pathology detection, as the procedure is non-invasive, affordable and can minimise the time needed for the diagnosis. This paper investigates the Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) based Tsallis entropy for vocal fold pathology detection. The proposed method is tested with healthy and pathological voice samples from Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and Saarbruecken Voice Database (SVD). A pairwise classification using k-Nearest Neighbors (k-NN) classifier gave 91.59% and 85.09% accuracy for MEEI and SVD database respectively. Higher classification accuracy of 93.32% for MEEI and 85.16% for SVD database achieved using Support Vector Machine (SVM) classifier.

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Data Acquisition System for Web-based Multi-modal Data Repository

2021-03-25 , Rushambwa M.C. , Mukherjee A. , Maity M. , Palaniappan R. , Vikneswaran Vijean , Ghulam Nabi F.

The multimodal medical data with annotation helps to build different automated algorithms. Each reported work has used a specific disease database and developed a CAD based on the considered database. Therefore, the availability and quality of medical database play the most crucial role in developing any CAD. However, it has been observed that most of the reported studies used public database (created by foreign universities/centers) or private database. Unfortunately, the availability of a national medical database in India is negligible. However, development of such medical database is possible. Such medical database can encourage new research activity and help a large research community. The proposed study focuses on developing an online public medical multimodal database platform. Here, the data acquisition software is build for collecting various information.

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Cloud based analysis and classification of EEG signals to detect epileptic seizures

2021-03-25 , Rushambwa M.C. , Gezimati M. , Govindaraj P. , Palaniappan R. , Vikneswaran Vijean , Ghulam Nabi F.

Epileptic seizures are explained as the abnormal electrical activity occurring in the brain due to an internal or external triggering factors. EEG (Electroencephalograph) is used to record brain activity and can be used to detect the seizures before, during or after they occur. These signal characteristics, however differ from patient to patient due to the different emotional and physical wellbeing of the various individuals. In normal circumstances, anti-epileptic medication is used to treat patients but very few systems have been developed to manage and track the seizures. In most extreme and rare cases, some patients undergo invasive surgery to treat the seizures and this is common in seizures that are caused by tumors or physical brain damage. Non-invasive surface electrode EEG measurement gives an estimate of the seizure onset but more invasive intracranial electrocorticogram (ECoG) are required at times for precise localization of the epileptogenic zone. This project aims at designing and implementing a device that can be used to detect and monitor the attention and meditation values of a person in real time. The system measures the EEG waves of the brain, performs feature extraction, classification and sends the control command over wireless to a remote controller. The remote controller in turn issues commands with corresponding brain wave frequency and sends it to the cloud for remote analysis and classification.

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