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

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  • Publication
    Potential of pretreated palm kernel shell on pyrolysis
    ( 2023-01-01)
    Razi Ahmad
    ;
    Ragunathan Al Santiagoo
    ;
    Abdul Ghapar Ahmad
    ;
    Syakirah Afiza Mohammed
    ;
    Wan Ahmad W.A.M.
    ;
    Vikneswaran Vijean
    ;
    Ibrahim N.R.
    The impact of pretreatment on palm kernel shell (PKS) with torrefaction for the possibility of pyrolysis is discussed in this study. PKS samples were torrefied at different holding times of 30 and 60 minutes at temperatures of 200, 225, 250, 275, and 300 °C. In a fixed-bed reactor with a constant nitrogen flow rate of 500 ml/min, torrefaction pretreatment was carried out. The elemental composition, mass, and energy yield, as well as proximate analysis, were all performed on the pretreated PKS. The optimised pretreated PKS was pyrolyzed next at a temperature of 400 to 550 °C in a fixed-bed reactor. The outcomes demonstrated that the pretreated PKS had a significant mass and energy yield at a temperature of 250 °C and a holding time of 30 min. PKS's calorific value and carbon content both rose after pretreatment. However, the oxygen and moisture content decreased for pretreated PKS. The maximum bio-oil production of 58% was achieved during the pyrolysis of pretreated PKS at a temperature of 500 °C. At higher temperature of 550 ℃, the bio-oil decreased due to secondary cracking reaction. Consequently, the pretreated PKS has greater potential as effective feedstock for successive proses particularly pyrolysis for bio-oil production.
  • Publication
    Review Article A Review of Optical Ultrasound Imaging Modalities for Intravascular Imaging
    ( 2023-01-01)
    Rushambwa M.C.
    ;
    Suvendi R.
    ;
    Pandelani T.
    ;
    Palaniappan R.
    ;
    Vikneswaran Vijean
    ;
    Nabi F.G.
    Recent advances in medical imaging include integrating photoacoustic and optoacoustic techniques with conventional imaging modalities. The developments in the latter have led to the use of optics combined with the conventional ultrasound technique for imaging intravascular tissues and applied to different areas of the human body. Conventional ultrasound is a skin contact-based method used for imaging. It does not expose patients to harmful radiation compared to other techniques such as Computerised Tomography (CT) and Magnetic Resonance Imaging (MRI) scans. On the other hand, optical Ultrasound (OpUS) provides a new way of viewing internal organs of the human body by using skin and an eye-safe laser range. OpUS is mostly used for binary measurements since they do not require to be resolved at a much higher resolution but can be used to check for intravascular imaging. Various signal processing techniques and reconstruction methodologies exist for Photo-Acoustic Imaging, and their applicability in bioimaging is explored in this paper.
  • Publication
    Comparison between predicted results and built-in classification results for brain-computer interface (BCI) system
    ( 2021-05-03)
    Ong Z.Y.
    ;
    Saidatul Ardeenawatie Awang
    ;
    Vikneswaran Vijean
    Brain-computer interface (BCI) system is a system of receiving information and transferring responses by communication between a computer and human brain. BCI system acts as assistive device to help the severe motor disabilities patients to live like a normal human being. Classification results used to validate the performances of BCI system. Several classification methods have been used in BCI system. However, previous researchers did not compare the classification results with predicted results. In this study, the predicted results were calculated from the questionnaire which collected from participants after completed the experiments. These predicted results were used to compare with the results from classification learner tool. The built-in classification methods included decision tree, support vector machine (SVM), k-nearest neighbor (KNN) and ensemble classifiers. Based on the results, the average difference of predicted results and built-in classification results for cubic SVM is the smallest which is 2.41% and 1.81% for motor imagery 1 and motor imagery 2 respectively. This finding shows that the cubic SVM classifier can detect the mistake that did by the subjects during the experiment.
  • Publication
    Intelligent fall detection system using traditional and non-traditional machine learning algorithm based on MQTT
    ( 2021-07-21)
    Cheong C.Y.
    ;
    Lim C.C.
    ;
    Chong Y.F.
    ;
    Vikneswaran Vijean
    ;
    Ahmad Faizal Salleh
    ;
    Affandi M.
    The population of elderly people exposed to the risk of fall increases each year as reported by World Health Organization (WHO). Fall detection system presented normally is high cost, large size and not efficient. Wearable-based sensor fall detection system developed in this project which were small size, portable and low-cost. The concept of Message Queuing Telemetry Transport (MQTT) applied in this fall detection system to ease the process of data transmission from motion sensor to Raspberry Pi for classification via Wi-Fi. A small size and lightweight microcontroller (Wemos D1 mini ESP 8266) integrated with MPU6050 motion sensor to sense and publish the motion data. Raspberry Pi 3 Model B applied to carry out classification of the motion data. Machine learning algorithms used for classification in comparison were k-Nearest Neighbors (k-NN) and Long Short-Term Memory (LSTM) of Recurrent Neural Network (RNN). LSTM achieved better result at 97% than k-NN at 94%. Smartphone used to publish the notification via an application known as Blynk.
      17  1
  • Publication
    Investigation on Medicated Drugs in ECG of Healthy Subjects
    ( 2022-01-01)
    Vikneswaran Vijean
    ;
    Abdul Ghapar Ahmad
    ;
    Syakirah Afiza Mohammed
    ;
    Razi Ahmad
    ;
    Wan Amiza Amneera Wan Ahmad
    ;
    Ragunathan Al Santiagoo
    ;
    Lim Chee Chin
    Heart diseases are now the leading cause of death worldwide, it is estimated that around 7 million patients who are living in developed countries, lost their lives due to diseases related to their cardiovascular system. In Malaysia, cardiovascular diseases represents one fifth of total deaths in the country in the past three decades. Currently patients need some sort of drugs that help them to stabilize and restore the regular patterns of their heart beat because if the patients cannot manage to restore the normal heart beat pattern, the undesired heart condition could lead life threatening situations. Advancement of biotechnology has enabled the creation of new medicated drugs to provide better treatment options. However, when this treatment option fails and there is a need to provide emergency intervention to the patients in hospitals, the medical experts often need to know about the patients' intake of any medications prior to hospital admittance for providing suitable treatments. Sometimes, this would be a difficult task as the patient might be admitted in semi-conscious or unconscious state. Therefore, this study focusses on identification of different medicated drugs usage through analysis of ECG data of the users. The data for the experiment was obtained from physionet library, which provides ECG data of subjects administered with a combination of Dofetilide, Mexiletine, lidocaine, Moxifloxacin and Diltiazem medicated drugs. The use of morphological and non-linear features derived from the ECG signals were able to provide prediction accuracy of 77.26% using SVM classifier.
      42  2
  • Publication
    Influence of Torrefaction on Sewage Sludge
    ( 2023-01-01)
    Razi Ahmad
    ;
    Abdul Ghapar Ahmad
    ;
    Mohammed S.A.
    ;
    Wan Ahmad W.A.M.
    ;
    Vikneswaran Vijean
    ;
    Ragunathan Al Santiagoo
    ;
    Ibrahim N.R.
    ;
    Udin N.
    Sewage sludge (SS) from sewage treatment plants has been seen as a waste for decades and little attention to investigate the potential and its beneficial product. SS contain a large amount of recoverable energy; however, it has high moisture, ash, heavy metals, and organic contents. Thus, the goal of this research is to use torrefaction to produce upgraded or torrefied SS. The proximate and ultimate analysis was used to determine the physical and chemical characteristic of raw SS. Torrefaction was performed through inert atmosphere in fixed bed reactor. According to the findings, at torrefaction temperature of 300 °C, torrefaction had improved the quality of raw SS, increasing its fixed carbon content by 47.8%, and its moisture content by 61.3%. The optimum temperature for torrefaction was at 300 ℃ with holding time of 60 minutes. This study improved the quality of sewage sludge to be utilised in subsequent thermochemical conversion.
      5  34
  • Publication
    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.
      5  19
  • Publication
    Clinical validation of 3D mesh reconstruction system for spine curvature angle measurement
    ( 2023-02-21)
    Shanyu C.
    ;
    Lim Chee Chin
    ;
    Fook C.Y.
    ;
    Azizan A.F.
    ;
    Vikneswaran Vijean
    ;
    Ahmad Faizal Salleh
    ;
    Shafriza Nisha Basah
    Spine curvature disorders are scoliosis, lordosis, and kyphosis. These disorders are mainly caused by the bad habits of the person during sitting, standing, and lying. There are about 3 to 5 out of 1,000 people who are affected by spine curvature disorder. The current conventional method used for diagnose this disorder, such as radiography, goniometry and palpation. However, these conventional methods require human skills and can be time-consuming, resulting to exhaustion of logistic. Therefore, there is a need to solve this problem by creating a Graphical User Interface (GUI) to analyse the human body posture through the 3D reconstructed model of the person. Hence, 3D map meshing reconstruction of the human body method is proposed. This project divided into three parts, which are the development of the GUI for human posture analysis, clinical validation and posture analysis of the 3D model. The 3D model reconstructed from 3D mapping parameters shows 100% accuracy of the assessed point. The lowest difference of angle for the comparison between clinical method (goniometer) and the GUI for male is (A.Pe) 0.930±0.870 and 1.240±0.860 for female (P.Pe). This finding of 3D model assessment system can be helpful for medical doctor to diagnose patient who have spine problem.
      1  32
  • Publication
    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.
      1  27