WALK-IN INTERVIEW 2017 MASTER AND PHD INTAKE AT CISIR, UTP

10th JUNE 2017, Saturday
CAPE, Level 16, Menara 2, Menara Kembar Bank Rakyat, Jalan Travers, Kuala Lumpur
09:00 - 16:00

BRING YOUR RESUME AND ACADEMIC CERTIFICATES

(For International Student, we will have Skype Interview for later date)
 

Brief Description

The Department of Electrical & Electronic Engineering, and the Centre for Intelligent Signal and Imaging Research (CISIR) strongly welcome those who are interested to pursue PhD and Master in Universiti Teknologi PETRONAS. We are seeking hardworking and committed individuals for the following research topics/areas:

 

[TAKEN] PROJECT 1: Development of clinical diagnostic and decision support tool for early knee osteoarthritis using non-invasive and in-vivo multinuclear magnetic resonance imaging

Project Background:

Osteoarthritis (OA) is the most common type of joint disease that will lead to failure of synovial joint organ and disability. Generally, OA affects the joint of spine, fingers, hips, knees and toe but it is most common in knee.   It is known that knee OA is due to the gradual loss of articular cartilage (AC). The worldwide prevalence estimate for symptomatic OA is 9.6 % among men and 18 % among women.  Disability due to OA has a huge negative impact to the economy (loss of productivity, health costs) of any country.  More importantly, there is an increasing trend of younger people diagnose having OA due to active life style.  Thus, det ecting any changes in AC associated with early onset of OA is important as it allows physicians to dispense advice such as weight control, avoidance of activities involving excessive use of weight bearing joints etc., and to start treatment to delay further degeneration of AC.

1 PhD position (RM 2300-4000 per month)

 

[WITHDRAW] PROJECT 2: Biomedical engineering study of brain neuronal mechanisms using signal analysis of magnetic resonance spectroscopy 

Project Background:

Drug addiction is a mental and health disorder that results in a compulsive behaviour to seek and consume drug. In Malaysia, among the drug addicts around 50% are youth. On average, 0.2-0.3% of a country GDP is spent on rehabilitation of drug addicts. Drug addiction causes structural and functional neuroplasticity in the brain. Drug addiction results in the disruption of control mechanisms and alteration in functionality of neurons in reward pathways. In normal situations, the Nucleus Accumbens (NAcc) inhibits glutamate afferent to Ventral Tegmental Area while impaired neurons in NAcc results in GABA depletion that leads to the drug seeking behaviour. Thus, investigation into neuronal mechanisms by way of measuring.

1 PhD position (RM 2300-4000 per month)

 

 PROJECT 3: Analysis and Classification Of Malignant Lymphoma 

Project Background:

Malignant lymphoma is a cancer affecting lymph nodes. There are three types of malignant lymphoma chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). The ability to distinguish classes of lymphoma from biopsies sectioned and stained with Hematoxylin / Eosin (H+E) would allow for more consistent and less demanding diagnosis of this disease. The use of computer‐aided image analysis techniques will improve the analysis and classification of malignant lymphoma. Â In this work, several feature extraction techniques will be used for extracting features H+E samples. In particular, the use of Gabor filters and local binary pattern (LBP) provide the global and local features of images. This will give better representation of the feature histograms that will be fed into a classifier. The hybrid method is expected to increase the accuracy of the classifier hence give better result and assist medical practitioners in diagnosis and treatment for cancer patients.

Project Research Area: Image Processing, Pattern Recognition, Biomedical Engineering

1 Master position (RM 1300 per month)

 

[TAKEN] PROJECT 4: Analysis of Prostate Cancer in MRI using Multiple Texture Features 

Project Background:

The number of prostate cancer cases is steadily increases especially with increasing number of ageing population. Currently, a common method for diagnosis of prostate cancer is by transrectal ultrasound (TRUS) biopsy, which is performed after a positive prostate specific antigen (PSA) blood test. A combination of TRUS and PSA is proved to reduce prostate cancer mortality by 20% -30%. However, this method has a relatively low specificity.  These problems motivate researchers to look for other image modality that can further improved the diagnosis of prostate cancer. MRI is increasingly used to diagnose prostate cancer as it improved sensitivity and specificity of PSA and TRUS but it is only used as a second-line modality mainly because experienced expertise in reading prostate MRI is not widely available. An automated image analysis and classification of prostate cancer in MRI could help reduce these problems and open the door to prostate cancer screening using MRI. In this work, we will formulate and develop a prostate cancer classification algorithm based on LBP and Gabor filters. The combination of LBP and Gabor filter gives the local and global feature of an image which in turn will provides a better image representation. It is expected that the use of multiple features will better improve the accuracy, sensitivity and specificity of the algorithm.

Project Research Area: Image Processing, Pattern Recognition, Biomedical Engineering

1 PhD position (RM 1600 per month)

 

PROJECT 5: Neuroergonomic Analysis of Human Automatic Interaction Performance -Assessment of Mental Stress State by EEG on Plant Operators.  

Project Background:

Stress is regarded as the most frequent work-related health problem, preceded by musculoskeletal disorder which is due to long exposure to stress. Mental stress at work may affects thinking capabilities, behavior style and overall working capacity of human body. It is therefore important to better assess mental state of workers at work in order to improve their work performance. In this study, EEG data will be used to assess the mental workload of plant operators in their working environment. During the experiment, participant will be assigned to a different workload level (low/high), which will be pseudo-randomly assigned. The different workload level will be run on Multi-Attribute Task Battery-II, a software developed by NASA to study divided attention and multitasking. In the low workload condition, the participants will perform two monitoring tasks using keyboard, i.e., the system monitoring and the resource management tasks. In the high workload condition, they will have additional tracking task to manage in parallel. EEG data will be collected during the experiment to give insight into the operator mental state. In addition, ERPs that correspond to EEG activity that is temporally locked to a given stimulation will also be extracted. It is hope that the result of this study can be used to assess the mental state of plant operators under high workload as well as to quantity the level of high workload. This study can be used as guidelines to plant managers in task allocation to ensure higher job performance and plant safety.

Project Research Area: Signal Processing, Pattern Recognition, Biomedical Engineering

1 PhD position (RM 1600 per month)

 

PROJECT 6: Development of a highly interactive manipulation and human-robot coordination system for Internet of Things (IoT) robot in healthcare industry   

Project Background:

With the advance of robot technology in intelligence, mechanism and strength, robot is not only applied to autonomous industry but is also further employed to home, human society, services business and healthcare industry. However, due to the limitation of autonomous capability, robots are still unable to independently cope with uncertainties and changes in environment. This is a non-trivial task for robots and requires human to manipulate them to execute tasks. Human (e.g. disabled person and elderly) and robot may thereupon stay in the same space, which leads to the safety issue. There are therefore two main issues that fall into the scope of this three-year project: to establish and implement methods to manipulate Internet of Things (IoT) robot and to develop mechanism which ensures human safety while interacting with a robot. For the former one, we will develop a novel robotic manipulation system composed of wireless communication function and graphical interface on portable and mobile devices (e.g., mobile phone and tablet). By combining the real scene and virtual reality scene, the more precise, natural and rapid manipulation could be achieved. For the latter one, degree of freedom, operating range of the robot and 3D image are facilitated to monitor the interaction between human workers, robots and objects in the working environment and to develop an autonomous collision-free strategy avoiding the contact and collision with the unrelated persons and objects during task execution. Eventually, the entire manipulation system along with the safety mechanism will be developed and proper indices will be chosen to evaluate its performance.

 

PROJECT 7: Computer-Aided Spotting System of Pulmonary Tuberculosis Cavities Using X-ray images 

Project Background:

Tuberculosis (TB) remains a top communicable disease killer (as per the WHO report, 9.6 million people fell ill with TB and 1.5 million died in 2014) caused by the bacteria, but fortunately most of TB are preventable if early diagnosed and treated. Many measures are reported in the literature for TB detection, however, sputum smear microscopy is the most widely used one. It is a time-consuming process which requires a medical examination specialist review slides and just limited number of slides could be screened. Developed countries (e.g. Japan) still suffer from the imported TB from immigrants. The proportion of immigrants among all TB cases in Japan has steadily increased from 2.4% in 2000 to 5.6% in 2014. In addition, many countries, including Malaysia, have a lack of well-trained specialists, and they sometimes fail to detect TB cases due to a heavy workload. Automatic methods can be utilized to save the workload of medical examination specialists and therefore decimate the diagnosis costs. Many automatic methods were proposed in the literature either to develop a function of auto focusing of images or to develop a function of automatic segmentation of calculating TB bacilli. But, just only a few attempted to offer a diagnostic system with aforementioned two functions. Among them, only TBDx (Signature Mapping Medical Sciences Inc., USA) is commercially available on the market. But the cost of this system is still too expensive (about US$ 23,000) to developing countries to afford it. In addition, most existing methods fail to identify the T-shape closed bacilli which were usually classified as non-bacilli. There is, therefore, still a plenty room to improve the accuracy of detection results and to develop a low cost automatic TB detection system.

 

PROJECT 8: Development of Hemoglobin Inspection using image processing technique 

Project Background:

The measurement of hemoglobin, a protein found in red blood cells, plays a vital role in the detection, evaluation, and even management of anemia, patients with leukemia and other disorders. Healthcare providers measure hemoglobin either have to draw blood with a needle or intravenous line, or use specialised instrument to non-invasively estimate it. The objectives of this project are shown below:

  1. To develop a method to detect and evaluate hemoglobin concentration using image processing technique
  2. To develop the smartphone camera, if possible, to measure hemoglobin concentrations and screen for anemia, blood conditions without needle sticks.

 

PROJECT 9: Development of next generation blood pressure monitor

Project Background:

Various physiological vital signs have been widely used to primary disease detection and prevention. Blood pressure is one of them to observe the probability of occurrence of cardiovascular diseases. Currently, healthcare providers mostly used pump to generate pressure on the wrist to measure the blood pressure. People normally feel uncomfortable when the machine put pressure on the arm, which probably results in the error on the measurement and is unlikely to do real-time monitoring. There is a need to develop a new non-invasive blood pressure measurement without a cuff.

 

PROJECT 10: Quantitative EEG analysis for Long-term Memory (LTM)

Project Background:

Long-term memory is the ability of storing and retrieval of information over a long period of time. Theoretically, the capacity and retention duration of long term memory could be unlimited. However, the ability of LTM decays with the passage of time. Thus, individual differences in LTM exist among people, including adults and old age individuals. This study, will explore the pattern of EEG among a group of young adults during two LTM processes, encoding, which is the stage of creating new memory and retrieval, which is the stage of recollection of stored information. The EEG patterns which could explain the individual differences in the LTM processes will be identified. It is expected that EEG analysis will be beneficial for understanding the individual differences in LTM.  
Project Research Area: Signal Processing, Pattern Recognition, Neuroscience

Requirement: Basic Knowledge of Signal Processing

 1 MSc position

 

PROJECT 11: Neuronal Mechanism of Long-term Memory (LTM) using simultaneous EEG-fNIR 

Project Background:

Long-term memory is the ability of storing and retrieval of information over a long period of time. Theoretically, the capacity and retention duration of long term memory could be unlimited. However, the ability of LTM decays with the passage of time. Thus, individual differences in LTM exist among people, including adults and old age individuals. This study, will explore the pattern of EEG among a group of young adults during two LTM processes, encoding, which is the stage of creating new memory and retrieval, which is the stage of recollection of stored information. The EEG patterns which could explain the individual differences in the LTM processes will be identified. It is expected that EEG analysis will be beneficial for understanding the individual differences in LTM.  

Project Research Area: Signal Processing, Pattern Recognition, Neuroscience

Requirement: Basic Knowledge of EEG and fNIR

1 PhD position

 

[TAKEN] PROJECT 12: Improving learning and memory disabilities using Neurofeedback therapy with EEG technique

Project Background:

Learning and memory are two interrelated cognitive processes. Learning enables us to get new knowledge or experience; while memory allows to retain the learned experience or knowledge. The issues about these cognitive processes are reported by clinical research studies, including children and adults. A non-invasive technique of improving cognitive skills based on operand conditioning is known as Neurofeedback (NFB). NFB therapy allows to regulate certain brain pattern associated with a certain cognitive process, e.g., Alpha frequency with peak performance. In this study, a group of individuals will be studied with learning and memory disability in comparison with control group by employing NFB therapy with scalp EEG signals. NFB is a passive technique, which show effects after giving a regular therapy for modulating the target EEG pattern for a certain period of time. Thus, a series of experimentations will be conducted on selected participants to modulate EEG patterns. The exiting NFB protocols will be utilized for improvements, while new protocol will be proposed to be used for both children and adults individuals.

Project Research Area: Signal Processing, Pattern Recognition, Biomedical Engineering

Requirement: Basic knowledge of experiment design in E-prime

1 PhD position

 

PROJECT 13: Wearable EEG-based Stress Monitoring and Control System

Project Background:

In this research work we propose a complete system that is smart in a sense that it utilises minimum resources and provide self-care remedies for stress patients. The system includes a wearable Ear-EEG device with a micro amplifier wirelessly connected to intelligent stress monitoring and treatment system. The stress treatment and monitoring system will be embedded in a mobile app. The proposed system will continuously monitor the mental states of the stressed patient and classify the patient condition according to the severity of the mental state. The stress treatment and monitoring system will be connected online with the centralised system in the clinic or hospital, where instant remedial actions can be initiated. Developing a mobile app in such an arrangement will help utilise the mobile gadgets used commercially. The system has a benefit to be used on the go at workplace or home and the patient can also be advised remotely on his condition by the psychiatrist. It is expected that a very important aspect of treating the stressed patient, i.e. treatment withdrawal can be eliminated and the patient will continue to be a useful part of the society.

The stress monitoring and control system is based on wearable Bluetooth device which will measure EEG data and transfers it in real-time to a centralised system (placed in a hospital or clinic) through the internet using the patient's mobile.  

Project Research Area: Stress and Depression, Neuro-Signal Processing

Requirement: Knowledge of EEG Analysis   

1 PhD position
1 MSc position

 

PROJECT 14: Neuroergonomic Analysis of Human Automatic Interaction Performance -Assessment of Mental Stress State by EEG on Plant Operators. 

Project Background:

Stress is regarded as the most frequent work-related health problem, preceded by musculoskeletal disorder which is due to long exposure to stress. Mental stress at work may affects thinking capabilities, behavior style and overall working capacity of human body. It is therefore important to better assess mental state of workers at work in order to improve their work performance. In this study, EEG data will be used to assess the mental workload of plant operators in their working environment. During the experiment, participant will be assigned to a different workload level (low/high), which will be pseudo-randomly assigned. The different workload level will be run on Multi-Attribute Task Battery-II, a software developed by NASA to study divided attention and multitasking. In the low workload condition, the participants will perform two monitoring tasks using keyboard, i.e., the system monitoring and the resource management tasks. In the high workload condition, they will have additional tracking task to manage in parallel. EEG data will be collected during the experiment to give insight into the operator mental state. In addition, ERPs that correspond to EEG activity that is temporally locked to a given stimulation will also be extracted. It is hope that the result of this study can be used to assess the mental state of plant operators under high workload as well as to quantity the level of high workload. This study can be used as guidelines to plant managers in task allocation to ensure higher job performance and plant safety.

Project Research Area: Image Processing, Pattern Recognition, Biomedical Engineering

1 PhD position

 

PROJECT 15: Mindfulness in Emotion Self-Regulation based on Multi-Biosignals analysis for Prehypertension

Project Background:

Emotional self-regulation is very critical in maintaining a good mental and physical well-being in a person's life. Poor emotion regulation can lead to prolong stress and causes emotional problems adjustment that can further lead to health conditions ranging from obesity to heart disease, Alzheimer's disease, diabetes, depression, gastrointestinal problems, cancer and asthma to name a few. To date, mindfulness training (MT) has benefited numerous patients with a variety of health conditions and concerns by enhancing cognitive functioning and emotional stability through increase of self-awareness and satisfaction that promotes relaxation of body and mind. The MT effects can be observed in brain regions that corresponds to the cognitive and emotional changes. In this study, we will investigate the effect of MT in improving emotion self-regulation based on biosignals obtained from the brain and body (muscle, heart, skin) among prehypertensive patients. This study will involve recognition of biomarkers for emotion self-regulation from the biosignals obtained using signal processing techniques. In addition to that, the recognized biomarkers will be used and analyzed to characterize and validate MT. 

Project Research Area: Image Processing, Pattern Recognition, Biomedical Engineering

Requirement: Knowledge of Bio-signals analysis  

1 PhD position
1 MSc position

 

[TAKEN] PROJECT 16: Automated Analysis of 3D Breast Images for Cancer Detection  

Project Background:

We propose to investigate different feature extraction methods using the 3D information of Breast images. A few feature extraction and classification methods have shown good performances based on 2D images. In particular, feature extraction techniques based on Multiresolution decompositions (e.g. Wavelet, Curvelet) have shown discriminative properties in classification of mammograms into normal versus abnormal images. The extension of these techniques to 3D mammogram images would be investigated in this work. The sought technique would exploit the 3D information to extract robust features while including strategies to minimize the computational time.

Project Research Area: Image Processing, Pattern Recognition, Biomedical Engineering

Requirement: Knowledge of Image Processing  

The work is ideally suited for highly motivated and enthusiastic applicants with a good programming skill in MATLAB and experience in Image processing.

1 PhD position

 

PROJECT 17: Modulating Brain Waves using Neurofeedback therapy for workforce stress and anxiety  

Project Background:

Stress and Anxiety are usually a person's response to stress, which can come from psychological, physical, or environmental sources, like high work load with tight deadlines. Once a person under stress longer time, its start affecting the work performance and ultimately health as well. For stress and anxiety sufferers, learning how to modulate or turn off chronic stress responses is life changing. Neurofeedback provides physiological assistance and helps people learn to change their responses to stress. With brain training, they can develop the skills they need to reduce or eliminate anxiety in their lives. In this study, the stress and anxiety in workforce will be treatment with neurofeedback therapy to reduce it and improve the work performance of individuals suffered with stress. This study will provide a solution to reduce the stress and anxiety that will benefit to everyone in the society suffering with workforce stress.

Project Research Area: Signal Processing, Pattern Recognition, Biomedical Engineering

Requirement: Knowledge of Signal Processing  

The work is ideally suited for highly motivated and enthusiastic applicants with a good programming skill in MATLAB and experience in Signal processing.

1 PhD position (RM 1600)

 

PROJECT 18: Machine Learning techniques for Computer Aided Diagnosis of Diabetic Retinopathy  

Project Background:

Eye diseases such as Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are the most common causes of irreversible vision loss in individuals with diabetes. DME is a result of DR, it is a complication of diabetes caused by fluid accumulation in the macula, or central portion of the eye. DME is characterized as an increase in retinal thickness within 1 disk diameter of the fovea center with or without hard exudates and sometimes associated with cysts . Diabetes is affecting one adult over five in Malaysia and therefore with a progression of the related complications such as DR and DME.

Spectral Domain Optical Coherence Tomography (SD-OCT), since its introduction a little more than a decade ago, has now proven to be one of the imaging tool of choice for diagnosis and management of retinal disease . Meanwhile, recently, there has been a revolutionary step forward in machine learning techniques with the advent of deep learning where a many-layered neural network is trained to extract the former "hand crafted features" purely from training data. Specifically, the development of convolutional neural network (CNN) allowed for significant gains in the ability to classify images and detect objects in a picture.

The lack of work on DME detection based on SD-OCT data is mainly explained by the lack of annotated data, as SD-OCT is a rather new imaging tools. It was recently shown that, option such as transfer learning, fine tuning of pre-trained networks such as AlexNet, VGG , GoogleNET or a wise selection of training images can perform as well as fully trained network on a several sets of medical imaging of restricted size. In this project will investigate these aspects.

Project Research Area: Signal Processing, Pattern Recognition, Biomedical Engineering

Requirement: Knowledge of Signal Processing  

The work is ideally suited for highly motivated and enthusiastic applicants with a good programming skill in MATLAB and experience in Signal processing.

1 MSc position (RM 1300)

 

PROJECT 19: Machine Learning techniques for Computer Aided Diagnosis of Prostate Cancer Detection  

Project Background:

Computer Aided Diagnosis for prostate cancer using Multi-parametric data has proven to achieve the highest performances in term of detection [1]. However, distinguishing cancer in Central Gland (CG) versus Peripheral Gland (PG) is still an open issue. This work will concentrate on designing a classifier for CG and PG separately from manually annotated Multi-parametric data and compared its performance with existing state of the art systems which do not distinguish the two areas during the training. New architectures based on CCNs could be investigated to extract useful features in the different areas.

Usefull Readings:
Computer-Aided Diagnosis for Prostate Cancer using Multi-Parametric Magnetic Resonance Imaging
PhD thesis, G Lemaitre, Universite de Bourgogne; Universitat de Girona, Nov 2016.
"Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review", G Lemaître, R Martí, J Freixenet, JC Vilanova, PM Walker, F Meriaudeau, Computers in biology and medicine, 60, 8-3, 2015.
 "A Supervised Learning Framework of Statistical Shape and Probability Priors for Automatic Prostate Segmentation in Ultrasound Images", Soumya Ghose, Arnau Oliver, Jhimli Mitra, Robert Marti, Xavier Llado, Jordi Freixenet, Désiré Sidibé, Joan C. Vilanova, Josep Comete, Fabrice Meriaudeau, Medical Image Analysis, vol. 17(6), pp. 587-600, 2013.
 "A Spline-Based Non-linear Diffeomorphism for Multimodal Prostate Registration",J. Mitra, Z. Kato, R. Marti, A. Oliver, X. Llado, D. Sidibé, S. Ghose, J. C. Vilanova, J. Comet, F. Meriaudeau, , in Medical Image Analysis, vol. 16(6), pp. 1259-1279, 2012.
"A Survey of Prostate Segmentation Methodologies in Ultrasound, Magnetic Resonance and Computed Tomography Images", S. Ghose, A. Oliver, R. Marti, X. Llado, J. C. Vilanova, J. Freixenet, J. Mitra, D. Sidibé, F. Meriaudeau, in Computer Methods and Programs in Biomedecine, vol. 108(1), pp. 262-287, 2012.

Project Research Area: Signal Processing, Pattern Recognition, Biomedical Engineering

1 MSc position (RM 1300)

 

[TAKEN] PROJECT 20: Analysis of Lower Limb Prosthetic Socket Design 

Project Background:

Prosthetic is a must to those whom lost their body parts to continue life as a normal human being. There are few types of prosthetic, lower limb and upper limb prosthetic. The ability to reconstruct perfect fit of lower limb prosthetic socket would give a high comfortability to the user. The use of computer-aided design and computer-aided manufacturing (CAD/CAM) will improve the analysis and classification of prosthetic socket. In this work, several image processing techniques will be used for reconstructing 3D image of a lower limb prosthetic. Data were taken by using camera and the pre-processing technique is required to reconstruct the image. This will give a better representation of the reconstructed image with high accuracy. The reconstructed image is expected to increase the accuracy of the prosthetic socket and assist medical practitioners to come out with a perfect socket for the patients.

Project Research Area: Image Processing, Signal Processing, Biomedical Engineering

Requirement: Proficiency in Matlab/C++ languages.

 1 MSc position (RM 1300)

 

PROJECT 21: Brain network analysis of multimodal MRI images of brain reward pathways in Ketum addiction 

Project Background:

Dysfunction in the brain reward system is associated with compulsive, impulsive and addictive behavior. Recent advances in the analysis of multimodal MRI imaging such as resting state functional MRI and diffusion tractography now permit comparison and assessment of brain structural and functional changes. Using these tools, we seek to characterize the anatomical and functional changes associated with Ketum use and abuse and its impact on various cognitive and executive mental functions. The outcome will be an analysis method to grade the severity of structural and functional deficits in the brain reward pathway. The results may be generalized to other psychiatric diseases of compulsion, impulsivity and addiction.

Project Research Area: Biomedical signal processing, 4D MRI image and graph analysis, neuroinformatics

Requirement: Proficiency in Python/Matlab languages.

1 MSc position (RM 1300) / 1 PhD position (RM 1600)

 

PROJECT 22: Deep machine learning analysis of brain function for epilepsy  

Project Background:

Deep machine learning is a novel and powerful approach to pattern recognition and classification. It has been successfully applied to the image recognition task. In this project, we will develop novel deep learning methods to assess different pathologies of epilepsy from time-based measurements of the brain. With the advent of IoT wearable devices, this research will also approach the classification problem from a big data analytics perspective. The researcher will evaluate and innovate on a variety of deep learning architectures and training algorithms to find the most suitable technique for epilepsy monitoring and classifaction.

Project Research Area: Deep learning, machine learning, 4D MRI image and graph analysis, EEG signal processing and source localization, neuroinformatics

Requirement: Proficiency in Python/Matlab languages.

1 MSc position (RM 1300) / 1 PhD position (RM 1600)

 

PROJECT 23: Cancer immunotherapy point-of-care device using microfluidic manipulation 

Project Background:

Immunotherapy is a promising new approach to treating cancer. The researcher will join ongoing work to develop a portable point of care device to apply immunotherapy to treatment of cancer. The research involves prototyping of electronic, microfluidic and optical systems to manipulate living cells and their interaction with proteins and biologics in high throughput fashion using inertial microfluidics. There is further possibility to develop integration of this system with electronic nano-sensors for cellular monitoring.

Project Research Area: Biomedical signal processing, microfluidic device design and prototyping

Requirement: Ability to prototype miniature optical imaging systems

1 MSc position (RM 1300) / 1 PhD position (RM 1600)

 

PROJECT 24: Automated optical and neurological assessment system

Project Background:

Observation of ocular activity is a classical tool for neurological assessment. Advancements in integrated and miniaturized cameras and optics make it possible to develop an automated system to track and grade ocular activity as an indicator of cognitive and executive function or neurological damage. This is a multidisciplinary project to develop such a tool as an IoT device for easy and long-term monitoring of brain function through eye tracking. The researcher will be expected to develop a prototype of the optical imaging system, utilize machine learning algorithms to track and classify eye trajectory and validate the results against analysis of MRI structural & functional images.

Project Research Area: 4D MRI image and graph analysis, neuroinformatics, optical systems prototyping and development, machine learning for video processing

Requirement: Ability to prototype miniature optical imaging systems

1 MSc position (RM 1300) / 1 PhD position (RM 1600)

 

[TAKEN] PROJECT 25: Blockchain techniques for secure data exchange in telemedicine system

Project Background:

With the advent of Internet of Things wearables and telemedicine portals, virtual health consultation with physicians will become increasingly common. A variety of big data analytics algorithms will also be deployed to improve health monitoring and encourage healthy living. Tremendous quantities of sensitive medical data will be exchanged across online platforms in this new mode of healthcare. There is a need to reinvent an inherently secure method of data exchange to protect ownership and privacy rights of individual patients. Blockchain technology has recently been invented to permit the exchange of digital currencies through fundamentally unsecure network of computers. This research will be to develop an innovative protocol using blockchain technology to enable secure exchange of medical data over unsecure distributed systems.

Project Research Area: Virtual health systems, network and security in public access systems

Requirement: Proficiency in Python/Matlab languages.

1 MSc position (RM 1300) / 1 PhD position (RM 1600)

 

PROJECT 26: Modelling and Simulation of Human Glioma Growth Model

Project Background:

Gliomas are the most common types of brain cancer (accounting for about 50% of all primary brain tumors), well known for their aggressive proliferation and the invasive behavior leading to a high mortality rate. The understanding of malignant glioma growth is still far from being complete, mostly because gliomas proliferate as solid tumors and invade the surrounding brain parenchyma actively. Proliferation and specially migration of gliomas represent a very challenging problem from a mathematical viewpoint. Mathematical modelling of glioma is an extensively explored area with a large variety of mathematical models exploring multiple complexities. Several mathematical models have been developed for identifying the interactions between glioma cells and tissue microenvironment, which play an important role in the mechanism of the tumor formation and progression. Generally, reaction-diffusion modelling is used commonly to represent glioma growth problem. Consequently, in this research, modelling of the three-dimensional anisotropic reaction-diffusion equation and implementation of the numerical techniques for solving governing model will be conducted. Performance analysis of the proposed method in solving the three-dimensional anisotropic reaction-diffusion model for glioma growth will be investigated.

Project Research Area: Mathematical Biology, Numerical Analysis

Requirement: Proficiency in Matlab/C/C++ languages.

1 PhD position (RM 1600)

 

PROJECT 27: Numerical Solution of Cable Equation

Project Background:

Dendrites are extensions to the neuronal cell body in the brain which are posited in several functions ranging from electrical and chemical compartmentalization to coincident detection. Dendrites differ across cell types but one common feature is a branched structure. The evolution of voltage in the dendrite can be describes by using cable equation, which is a partial differential equation. In this research, modelling of the cable equation and implementation of the numerical techniques for solving governing equation will be conducted. Performance analysis of the proposed methods in solving the cable equation will be investigated.

Project Research Area: Applied Mathematics, Numerical Analysis

Requirement: Proficiency in Matlab/C/C++ languages.

1 MSc position (RM 1300)

 

PROJECT 28: Comparison of different EEG-references for diagnosing depression

Project Background:

The effects of different EEG references on EEG-features has been less studied and not clear. Therefore, in this research work, the EEG data rerecorded in different EEG references will be compared for the diagnosing depression. The EEG features such as the signal power, interhemispheric asymmetry and coherence will be computed and compared based on the classification accuracy in different well known EEG references. For this purpose, the EEG data recorded during link-ear reference, average reference, and infinity reference will be utilized. The project will highlight the important data regarding appropriate reference that could be more efficient when a choice is given on the selection of different references.

Project Research Area: Neuro-signal processing, Pattern Recognition, Biomedical Engineering

Requirement: Proficiency in Matlab/C/C++ languages.

1 MSc position (RM 1300)

 

PROJECT 29: Diagnosis of Unipolar and Bipolar Depression using EEG Analysis

Project Background:

An early treatment for depression could become a challenge due to misdiagnosis of unipolar and bipolar depression due to their overlapping symptoms. The difficulty could be alleviated by identification of robust neurophysiological biomarker for both unipolar and bipolar depression. In this study, an EEG-based method will be developed that can specifically identify the depression from bipolar disorder.  For this purpose, 3 different groups of participants will be recruited involving the depressed, bipolar and healthy participants. The objective is to identify the EEG features that can distinctly identify different groups with high accuracy. The project help in the identification of an automated method for differential diagnosis of depression. Hence, this proposed method could form a basis to standardizing differential diagnosis of unipolar and bipolar depression.  

Project Research Area: Neuro-signal processing, Pattern Recognition, Biomedical Engineering, Image Processing

Requirement: Proficiency in Matlab/C/C++ languages.

1 PhD position (RM 1600)

 

PROJECT 30: A Neuromarker Development for Schizophrenia Disorder

Project Background:

Schizophrenia is a chronic and severe mental disorder, which causes a range of different psychological symptoms involving a breakdown in the relation between thought, emotion, and behavior, leading to faulty perception, inappropriate actions and feelings. In Malaysia, one out of every hundred citizen is suffering from schizophrenia disorder. In addition, the ratio of psychiatrists to the people in Malaysia is below the international standards. This is a serious issue and need to develop an automatic EEG based diagnostic tool to be used as a neuromarker for early diagnosis of Schizophrenic disorder and support the clinicians in the diagnostic process. This research study will be helpful for schizophrenic patients as well as for psychiatrics and clinicians to advance the diagnosis process of the patients.

Project Research Area: EEG Signal Processing, Schizophrenia disorder and Biomedical Engineering

Requirement: Proficiency in Matlab languages.

1 PhD position (RM 1600)

 

[TAKEN] PROJECT 31: Development of Epileptic Seizure forecasting System using EEG signals.

Project Background:

Epilepsy is one of the most common neurological disorders, which affects more than 1% of the world population. Regardless of the fact that seizures occur infrequently, patients with epilepsy experience persistent nervousness due to the probability of a seizure occurring. Much work has been done on diagnosis of seizure activity using scalp EEG signals. However, prediction of seizure activity (Seizure forecasting system) has the potential to help patients with epilepsy lead more normal lives. In order for EEG-based seizure forecasting systems to work effectively, computational algorithms must reliably identify periods of increased probability of seizure occurrence. The early prediction/forecasting of seizure activity will be helpful to warn patients of approaching seizures activities. In addition, patients would be alerted to avoid potentially dangerous activities like driving, swimming. Finally, medications could be controlled only when required to coming seizures and reducing the side-effects of the medicines.

Project Research Area: EEG Signal Processing, Epilepsy disorder and Biomedical Engineering

Requirement: Proficiency in Matlab languages.

1 PhD position (RM 1600)

 

PROJECT 32: The effect of Repair Welding of API 5L X52 Steel on the Residual Stress Fields for Offshore Pipelines Application

Project Background:

Repair welding are widely used in steel and offshore structures either to remedy initial fabrication defects, or to rectify in-service degradations of the components. Previous investigations found that repair welding is likely to pose adverse effects on the long-term integrity of the structure exposed to high pressure and temperature actions. Furthermore, it is believed that high residual stresses, associated with the repair process, most probably play an important role in many of subsequent failures. In addition, repair welds might aggravate the size, magnitude and distribution of the tensile residual stresses in the weldments. These adversely affect the component structural integrity and remaining life. So far, no generally accepted guideline is available to provide reliable evaluations on the possible side effects from the repair welding in offshore oil/gas pipelines. This project will focus on the investigation of the residual stress measurement on single/double and partial/full repair welds of  API 5L X52 steel for offshore pipelines application. Furthermore, the semi destructive blind hole drilling and destructive sectioning methods will be employed to measure the residual stress fields in each case. The results of the two measurement methods will be widely compared. Residual stresses which are caused by full and partial repairs in this study will be compared to the as-welded condition.  

Project Research Area: Repair Welding, Residual stress, Finite element

Requirement: Bachelor degree in Mechanical/Material/Metallurgy/Industrial Engineering. Proficiency in Material characterization, metallurgy, and mechanical testing

1 MSc position (RM 1300)

 

PROJECT 33: Fatigue Crack Growth Investigation on a Maintenance Welding Repair

Project Background:

 Fatigue crack usually initiates at welded joints of structural elements. For a high-responsibility aeronautic component, called "Motor-Cradle" - critical to the flight safety -, successive weld repairs are carried out toward restoring the original condition of the aircraft and extending the in-service life of them. Motivated by the high fracture incidence on this particular component an extensive research program to evaluate the effects of maintenance weld repairs on the structural integrity, mechanical properties and microstructural changes will be developed. In this research-work, a maintenance weld repair procedures will be employed along the useful life of aircrafts, characterized by overlapping the weld bead by gas tungsten arc welding with filler metal will be investigated. The fatigue crack growth will be widely investigated. 

Project Research Area: Repair Welding, Residual stress, Fatigue crack

Requirement: Bachelor degree in Mechanical/Material/Metallurgy/Industrial Engineering. Proficiency in Material characterization, metallurgy, and mechanical testing

1 MSc position (RM 1300)

 

PROJECT 34: Effect of Repair Welding on the Creep Behavior of a 12Cr1MoV/GTR-2CM/P91 Dissimilar Joint

Project Background:

Repair welding is widely used for improving the life of different structure. In this paper, weldment of the original 12Cr1MoV/GTR-2CM/P91 dissimilar joint will be totally removed and then repaired with the same weld metal, and subsequent uniaxial creep tests will be performed both on the pre repaired and post repaired dissimilar joint at 550 ̊C for stresses ranging from 140 to 200 MPa. The aim of this research is to discuss the effect of repair welding on the creep behavior of the original dissimilar joint from comparing the results of these specimens. Several features of the work will be presented, such as the the fracture mode, creep life compared with the original weldment, and the creep behaviours for both joints.

Project Research Area: Repair Welding, Residual stress, Finite element

Requirement: Bachelor degree in Mechanical/Material/Metallurgy/Industrial Engineering.
Proficiency in Material characterization, metallurgy, and mechanical testing

1 MSc position (RM 1300)

 

[TAKEN] PROJECT 35: Adapt OCDMA to Imaging

1 PhD position (RM 1600)

 

Requirements

Malaysian/International

Qualification in  Electrical & Electronic Engineering /Computer Engineering /  Biomedical Engineering and equivalent from a recognized institution.

Good MATLAB programming skills

Fullfill UTP admission criteria

Max Age PhD – 35 year

 

CONTACT US

Application instructions

If you interested please send your resume to This email address is being protected from spambots. You need JavaScript enabled to view it.  and state which project that you interested. We will email you about the interview once we review your resume.
If you have any inquiry kindly contact:
 

Nadira Nordin
Centre for Intelligent Signal & Imaging Research (CISIR)
Block 22, Universiti Teknologi PETRONAS, Bandar Seri Iskandar,
31750 Tronoh, Perak, Malaysia.

Email : This email address is being protected from spambots. You need JavaScript enabled to view it.

Tel      : +605 368 7888  |   Website: http://cisir.utp.edu.my/

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