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

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Now showing 1 - 10 of 33
  • 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
    Compatibilizers Effect on Recycled Acrylonitrile Butadiene Rubber with Polypropylene and Sugarcane Bagasse Composite for Mechanical Properties
    ( 2023-10-16)
    Zainal M.
    ;
    Ragunathan Al Santiagoo
    ;
    Razi Ahmad
    ;
    Mohammed S.A.
    ;
    Abdul Ghapar Ahmad
    ;
    Vikneswaran Vijean
    ;
    Wan Amiza Amneera Wan Ahmad
    ;
    Allan Melvin Andrew
    Compatibilizers effect on recycled acrylonitrile butadiene rubber (NBRr) with polypropylene (PP) and sugarcane bagasse (SCB) composite for mechanical properties is evaluated. Trans-Polyoctylene Rubber (TOR) and Bisphenol a Diglycidyl Ether (DGEBA) are used as compatibilizers in this study. Three (3) different composites (80/20/15, 60/40/15, and 40/60/15), with fixed filler (15 phr) and compatibilizers (10 phr) content, were carried out. These composites were arranged via melt mixing technique utilizing a heated two-roll mill at a temperature of 180 C for 9 minutes employing a 15-rpm rotor speed. Tensile and morphological properties were evaluated. The result shown average tensile strength dropped by 48.50% as the recycle NBR content rises 20 phr. Nevertheless, subsequent compatibilization reveals that the compositesâ tensile properties were all greater than control composites. The morphology discovered validates the tensile properties, indicating a stronger interaction between the PP/SCB and recycle NBR composites with the addition of compatibilizer DGEBA.
      3  44
  • Publication
    Dual-tree complex wavelet packet transform for voice pathology analysis
    ( 2020-07-01)
    Farah Nazlia Che Kassim
    ;
    Muthusamy H.
    ;
    Vikneswaran Vijean
    ;
    Zulkapli Abdullah
    ;
    Rokiah Abdullah
    Voice pathology analysis has been one of the useful tools in the diagnosis of the pathological voice, as the method is non-invasive, inexpensive, and can reduce the time required for the analysis. This paper investigates feature extraction based on the Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) using energy and entropy measures tested with two classifiers, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and Saarbruecken Voice Database (SVD) were used. Five datasets of voice samples were used from these databases, including normal and abnormal samples, Cysts, Vocal Nodules, Polyp, and Paralysis vocal fold. To the best of the authors’ knowledge, very few studies were done on multiclass classifications using specific pathology database. File-based and frame-based investigation for two-class and multiclass were considered. In the two-class analysis using the DT-CWPT with entropies, the classification accuracy of 100% and 99.94% was achieved for MEEI and SVD database respectively. Meanwhile, the classification accuracy for multiclass analysis comprised of 99.48% for the MEEI database and 99.65% for SVD database. The experimental results using the proposed features provided promising accuracy to detect the presence of diseases in vocal fold.
      23  4
  • Publication
    Investigation on effect of gas concentration in distinguishing conventional plastic and bioplastic for plastic recycling
    ( 2022-12)
    Ragunathan Al Santiagoo
    ;
    Allan Melvin Andrew
    ;
    Wan Amiza Amneera Wan Ahmad
    ;
    Razi Ahmad
    ;
    Syakirah Afiza Mohammed
    ;
    Abdul Ghapar Ahmad
    ;
    Vikneswaran Vijean
    ;
    Mustaffa Zainal
    Distinguishing type of plastic was important for the recycling process. In this project, the effect on gas concentration released from composite was studied to distinguish between conventional plastic and bioplastic. This project involved the fabrication of a composite from polypropylene (PP), empty fruit bunches (EFB), and recycle acrylonitrile butadiene rubber (NBRr), with PP used as a conventional plastic and PP/NBRr/EFB used as a bioplastic. Trans-polyctylene (TOR) was used as a compatibilizer to evaluate the effect on the PP/NBRr/EFB. Tensile testing and SEM were conducted to study the mechanical properties and morphological properties on the PP/NBRr/EFB and the PP/NBRr/EFB/TOR composite. The gas sensor (MQ135) was used in this study to detect the presence of NH3 and CO2 released from heating conventional plastic and bioplastic. From the overall result, composite with TOR as compatibilizer has shown better performance than composite without TOR in mechanical, morphological and gas sensor testing. By using MATLAB software, it shows that from gas sensor testing, it can be verified to distinguish between conventional plastic and bioplastics for plastic recycling. The average classification obtained from the Probabilistic Neural Network (PNN) was 99.29 % accurate.
      30  1
  • 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.
      22  1
  • Publication
    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.
      3  32
  • Publication
    Identification of habitual smokers through speech signal
    ( 2024-02-08)
    Vikneswaran Vijean
    ;
    Ragunathan Al Santiagoo
    ;
    Abdul Ghapar Ahmad
    ;
    Syakirah Afiza Mohammed
    ;
    Razi Ahmad
    ;
    Wan Amiza Amneera Wan Ahmad
    Smoking is an addictive behavior and can result major health complications. Nowadays, many young adults tend to pick up this unhealthy habits which could potentially harm their health and affects the future workforce of the nation. Most of the habitual smokers have difficulties in ceasing this habit and require external assistance in the form of group therapy, medical interventions to quit smoking. Therefore, the main aim of this study is to investigate the speech signals of the subjects in an effort to identify the habitual smokers non-invasively. Through this detection, young smokers could be identified. Voice samples from VOice ICar fEDerico II from PhysioNet database were used for this study. Wavelet Packet Transform was used to extract non-linear features from the signals. Due to uneven data, ADASYN algorithm was used to produce a balanced dataset through synthetic data sampling. Subspace KNN and SVM classifiers were used for the investigations and classification accuracies up to 83.7% and 94% of AUC curve were observed from the analysis. The results suggests that the proposed method is effective in detecting habitual smokers, and can be considered as an early screening for smoking habits in young adults for targeted rehabilitation strategies.
      29  1
  • Publication
    Real and complex wavelet transform using singular value decomposition for malaysian speaker and accent recognition
    ( 2021-01-01)
    Rokiah Abdullah
    ;
    Vikneswaran Vijean
    ;
    Muthusamy H.
    ;
    Farah Nazlia Che Kassim
    ;
    Abdullah Z.
    This paper presents a new approach for Malaysian speaker and accent recognition using wavelet feature extraction method, namely Wavelet Packet Transform (WPT), Discrete Wavelet Packet Transform (DWPT) and Dual Tree Complex Wavelet Packet Transform (DT-CWPT). Since Singular Value Decomposition (SVD) was based on factorization and summarization technique which reduces a rectangular matric, it is applied on those features to evaluate the performance for speaker and accent recognition. The features are derived from wavelets and SVD classified with three different classifiers namely k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). In this work, English digits (0–9) and Malay words database uttered from 75 undergraduate students of Universiti Malaysia Perlis (UniMAP) which are Malays, Chinese and Indian. The Malay words had a combination of consonants and vowels in monosyllable and bi-syllable structure. The accuracy of file-based analysis achieved were above 81% while for frame-based analysis, 93.87% and above were obtained using three different classifiers (k-NN, SVM and ELM) for speaker and accent recognition. Through the experiments, it is observed that accent recognition achieved high recognition rate of 100% for both framed-based analysis and file-based analysis using SVM. The experimental results show the proposed features using SVD achieved high accuracy of 100% using SVM through English digits and Malay words in accent recognition. This indicated that feature extraction using wavelets (WPT, DWPT and DT-CWPT) with SVD can achieve a good performance for both English digits and Malay words.
      2  47