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Sample Data-Articles

Latex Writing Tips

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      1. Latex labels: a prefix should be used to avoid duplicates of labels in different chapters/sections in the thesis/paper.
        • Equations: e.g., \label{intro-eq1}, \label{review-eq1}.
        • Figures: e.g., \label{fig:intro-fig1}, \label{fig:review-StabilityRegions}.
        • Sections: e.g., \label{sec:intro-Conclusion}, \label{fig:review-Notations}.
        • Tables: e.g., \label{sec:intro-FeedbackGains}, \label{fig:review-ListsOfSymbols}.
      2. New Commands: Create new commands for Corollary/Lemma/Proposition/Remark/Theorem/
        • Here are the command placed before "\begin{document}":
        • \newtheorem{theorem}{Theorem}
          \newtheorem{lemma}{Lemma}
          \newtheorem{proposition}{Proposition}
          \newtheorem{corollary}{Corollary}
          \newtheorem{remark}{Remark}
        • To start a new remark, for example, \begin{remark} remark content \end{remark}
      3. New Commands: Create short forms for long equations
        • For example, \newcommand{\vectorZZ}{\left[\begin{array}{c} \mathbf{z} \\ \mathbf{z}_s\end{array}\right]}.
        • WHen using "\vectorZZ" in the paper, it will display "\left[\begin{array}{c} \mathbf{z} \\ \mathbf{z}_s\end{array}\right].

Reference labels:

        Suggested format - Last name of the first author + year of publication + the first letters of the first three words of the title.
      • For example, the label for the following paper is "Lam2012son".
      • H.K. Lam, 'Stabilization of nonlinear systems using sampled-Data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach' IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol 42, no. 1, pp. 258 - 267, 2012.

Title of references:

    Put those letters or words in the article title (bibtex) to be displayed in capitals in parentheses
    • For example, the word "LMI" in the article title should be displayed in capitals, use {LMI}.
    • For example, the word "Lyapunov" in the article title should be dislayed in capitals, use {L}yapunov.
  1. Pay attention to the punctuation marks in sentences involving equations.
    • For example, "The characteristic is determined by $$f(x(t)) = a x(t) + b$$." where a full stop is required at the end.
  2. When an expression such as $j = 1, 2, \ldots, c$.  You should use $j$ = 1, 2, $\ldots$, $c$, otherwise the font face of the "comma" in the first expression will be different from the text font face.
  3. When overhead symbols are used, for example, $\hat{\mathbf{x}}$ should be used instead of $\mathbf{\hat{x}}$, otherwise the "hat" will be in bold face as well.

Reference Format:

See IEEE Referencing Guide

General writing tips

  1. The Introduction section should consist of the following:
    1. Problem statement: it defines the problem to be handled and why the problem is important with the support of existing applications if any.
    2. Literature review: it discusses what work and methods are available to deal with the problem.  Do not just simply saying “Method 1 was found in [1]. Method 2 was found in [2]” etc.  Give more details by briefly telling the how they do it, their pros and cons, limitations, etc, which will be some good evidence to support the motivation of your work and proposed approach.  It is good to categorise the existing methods to give an overall picture.  For example, you may say “The existing approaches can be categorised into three main categories.  Category 1 is to deal with the problem using . . . [references] . . . ”.
    3. Your approach and motivations: In the literature review, you have reviewed the limitations of the existing methods. It is the motivations of proposing your own for improvement and deal with the problem.  Here, you briefly tell your method in words and without equations.  Tell what limitations of the existing works and difficulties of the problem your method can handle.
    4. Paper organisation:  it tells the structure of the paper and briefly presents what will appear in each sections. This would be the last paragraph of the introduction section.
  2. Be consistent of using terms, format (citations, equations, figures, tables, references, etc), English (American  or Bristish).  For example, regarding terms, when you use “fuzzy logic system” in the paper, just use “fuzzy logic system” throughout the paper but not sometimes “ fuzzy system”, “fuzzy inference system”, etc, although they refer to the same thing.
  3. Define the short forms/abbreviations for terms at their first time of appearance.  After that point, only use the abbreviation only.  For example, define “fuzzy-model-based” as FMB.  From that point on, only use “FMB”.
  4. Define all variables appeared in the paper (used in equations, figures, tables, etc.).  Variables should stick to standard format, say, scalar variables in italic, vector/matrix in bold. They must be in the same face throughout the paper even in the figures and tables.
  5. Define the dimensions for variables, say in latex format , $\mathbf{X} \in \Re^{n \times n}$, 
  6. Do not use contraction, e.g., “don’t”, “can’t”, use “do not”, “cannot”.
  7. Each caption for figure/table must be unique.
  8. Figures/tables are used in the paper to help explain ideas/results.  Do not just let them sit in the paper and ask the readers to interpret them.  For example, if you just say “The concept is illustrated in Fig. X” and nothing to follow, which is a bad example.  You ask the reader to understand by himself/herself.  After saying “The concept is illustrated in Fig. X”, you should describe the figure (what is in the figure? What do you want to tell through the figure? Define all variables in the figure, etc).  The same applies to Tables.  You should describe what is in the table and how to read the table.  Then, present the results, discussions and observations.
  9. Figures plotting time responses for example must have labels for all axises.
  10. Use recently published journal papers as references for literature review except for those important papers or references for background theory.
  11. All information for a reference should be provided, i.e., auhtors’ names, article title, conference/journal title, volume number, issue number, page numbers, month of publication, year of publication.
    1. Book title should be in title sentence. The first letter of every word should be in capital except adverb. For example, “Stability Analysis of Fuzzy Control Systems using Lyapunov Approach”.
    2. Paper title for Conferences/journals should be in sentence case. Only the first letter of the first word should be in capital, except nouns in the middle of the title. For example, “Stability analysis of fuzzy control systems using Lyapunov approach”.
    3. The first letter of a noun in the title should be in capital. For example, “Lyapunov” in the above example; LMI, etc. In the latex file, use “{L}yapunov” and {LMI} in the bibtex file.
  12. All references in the reference list must be cited in the paper.

UG Projects 2017-18

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Please email me at This email address is being protected from spambots. You need JavaScript enabled to view it. if you are interested in any of these topics and would like to have a discussion.

Keywords: Computer Programming, Computational Intelligence, Fuzzy Logic, Neural Network, Machine Learning, Population-Based Search Algorithms (Genetic Algorithm, Particle Swarm Optimisation, Q-learning, etc.) 

Requirements: Hardworking, self-motivated, creative, good programming skills, willing to learn.

 

[HKL01] Mobile Apps: Prediction Game

This project develops a mobile game application. An idea is that a car is running throught different modules. Each module is characterised by some attributions, for example, wind speed, wind directon, slope, weather conditions (sunny, rainy, snowy, temperature), terrain conditions (sand, bumpy, rocky, with holes, with bridges). The player will use the given module information to determine the car attributions (speed, direction, force) so that the car can run as far and quick as possible.

 

[HKL02] PhP Form Builder + MySQL Database

This project is to develop a PhP form builder with MySQL database feature for storing user input data and generating statistical information. The PhP form builder will create the front end showing the forms (multiple forms) the users will see and the backend managing the database and user accounts. Here is an example: Simfactic Forms

 

[HKL03] Machines Learn to Play

This project uses computational intelligence and machine learning techniques to create a machine which learns to play a computer game (of your design).

References: Playing Atari with Deep Reinforcement Learning; Q-learning tutorials: 1, 2, 3; Fuzzy Q-learning 

Some examples from youtube are shown below:

 

[HKL04] Electronic Arts

This project uses computational intelligence and machine learning techniques to teach a machine which can create arts, say, evolve images, mimic drawings, etc.  

References: Automatic and Interactive Evolution of Vector Graphics Images with Genetic Algorithms, Evolving Line Drawings.

Some examples from youtube are shown below: 

 

 

 

[HKL05] Artificial Life

This project is to create artificial creatures living in a simulated environment.  Artificial creatures will evolve to adopt the environment for survival using computational intelligence techniques, for example, genetic algorithm/particle swarm optimisation, fuzzy logic and neural networks.  Variable computational techniques will be employed, modified and tested under different scenarios for comparison of performance. 

Some examples from youtube of artifical life are shown below:

  

 


 

UG Projects 2014-15

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Control-Based Projects

[HKL01] Fuzzy control of virtual ball-and-plate system 
      The aim of this project is to control the ball-and-plate system using fuzzy-model-based control method.  A fuzzy model will be developed to facilitate the design of the fuzzy controller.  Stability analysis will then be carried out.  By solving numerically the solution to the stability conditions, a fuzzy controller can be obtained.  To verify the analysis result, the control system will be implemented using a Lego Mindstorm NXT kit/Arduino microcontroller and a smart phone/tablet.  Lego Mindstorm NXT kit/Arduino microcontroller will be used to implement the control platform.  The smart phone/tablet will be used to implement the virtual ball-and-pate system, which is mounted on the Lego Mindstorm/Ardunio platform.  The smart phone/tablet will show a ball on the screen.  The information of the ball, e.g., position, velocity, etc, will be fed back to the Lego Mindstorm/Ardunio platform.  By adjusting the angle of the platform (which controls the angle of the smart phone/tablet), the ball position is driven to the origin.  Student taking this project is required to be hardworking and self-motivated.  Good Matlab and smart phone programming skills are essential to this project. 

[HKL02] Fuzzy control of virtual inverted pendulum system

    Please refer to the description of [HKL01].  Instead of ball-and-plate system, an inverted pendulum system is used. 

[HKL03] Fuzzy control of ball-and-beam system

    This project aims at the implementation of a physical ball-and-beam system using Lego Mindstorm NXT kit or Arduino microcontroller.  An image processing technique will be developed to locate the ball position in the beam or an infrared sensor is used instead.  Various controllers will be employed to stabilize the ball position or drive the ball to keep track of a pre-defined trajectory.  Good programming and hardware building skills are essential to the project. 

Neural network-based projects

[HKL04] Voice command recogniser 

    This project will implement a calculator, which performs calculation according to voice commands.  Speech recognition will be done by the following steps:  1) Student will collect speech data of himself/herself as the command input of calculator.  2) Feature extraction will be done for all collected speech data.  3) Neural networks/support vector machines/fuzzy logic/other classifiers will be employed to implement the voice command recogniser.  The overall systems will be implemented in Matlab or other platforms such as IOS/Android platform.  Recognition performance will be investigated and compared with existing methods.  Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming and smart phone programming skills are essential to this project. 

[HKL05]  Gesture command recogniser 

   Instead of using voice, gestures are used to issue commands.  For example, a web camera/smart phone/Kinect can be used to capture gestures.  Project details is more or less the same as described in [HKL04].

[HKL06] Time-series predictor

This project is to create a predictor which is to forecast the time series using historictal data.  One of an examples for time series is stock market prices.  Taking stock markert price as an example, an application (e.g., mobile application) will collect historial data online (say from Yahoo Finance), implement and train the predictor, and perform prediction.  Predictor will be constructed by neural networks/support vector machines/fuzzy logic/other traditional methods.  Comparison and evaluation will be done by considering different sets of data.  Good Matlab programming and smart phone programming skills are essential to this project. 

MSC Projects 2013-14

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Control-Based Projects

[HKL01] Fuzzy-model-based control of virtual ball-and-plate system

    The aim of this project is to control the ball-and-plate system using fuzzy-model-based control method.  A fuzzy model will be developed to facilitate the design of the fuzzy controller.  Stability analysis will then be carried out.  By solving numerically the solution to the stability conditions, a fuzzy controller can be obtained.  To verify the analysis result, the control system will be implemented using a Lego Mindstorm NXT kit and a smart phone/tablet.  The Lego Mindstorm NXT kit will be used to implement the control platform.  The smart phone/tablet will be used to implement the virtual ball-and-pate system, which is mounted on the Lego Mindstorm platform.  The smart phone/tablet will show a ball on the screen.  The information of the ball, e.g., position, velocity, etc, will be fed back to the Lego Mindstorm platform.  By adjusting the angle of the platform (which controls the angle of the smart phone/tablet), the ball position is driven to the origin.  Student taking this project is required to be hardworking and self-motivated.  Good Matlab and smart phone programming skills are essential to this project.

Another option: Instead of virtual ball-and-pate system, a physical system can be implemented using Lego Mindstorm NXT kit or Arduino microcontroller.  An image processing technique will be developed to locate the ball position in the beam or an inferred sensor is used instead.  Various controllers will be employed to stabilize the ball position or drive the ball to keep track of a pre-defined trajectory.  Good programming and hardware-building skills are essential to the project. 

[HKL02] Fuzzy control of virtual inverted pendulum system

    Please refer to the description of [HKL01].  Instead of ball-and-plate system, an inverted pendulum system or a system of your choice is considered. 

[HKL03] Fuzzy control of vehicle active suspension systems

    The aim of the project is to design a fuzzy controller to control the vehicle  active suspension system, subject to the performance constraints and user preferences. This project can be broken down into the following tasks.  1) Fuzzy model will be constructed describing the system dynamics for analysis and design. 2) Stability analysis and control synthesis will be investigated based on the Lyapunov stability theory.  A set of linear matrix inequalities (LMIs) will be derived to determine the system stability and feedback gains of the fuzzy controller.  3) Computer simulations will be done to verify the analysis result and control performance will be compared with other control schemes.  4) A Graphic User Interface (GUI) will be implemented based on Matlab which involves an animated inverted pendulum. Student taking this project is required to be hardworking and self-motivated. Good Matlab programming skill and mathematical background are essential to this project.

[HKL04] Position and tracking control of wheeled mobile robots

    The aim of this project is to design a fuzzy controller for wheeled mobile robot for position and tracking control.  For position control, the wheeled mobile robot is driven to reach a target position.  For tracking control, the wheeled mobile robot is driven to move along a pre-defined trajectory.  This project can be broken down into the following tasks.  1) The dynamic model of the wheeled mobile robot is developed.  Based on the model, various types of controllers, such as fuzzy controller, PID controller, linear state-feedback controller, will be employed to perform the control.  2) The system performance will be investigated and optimised by genetic algorithm (GA) or particle swarm optimization (PSO).  3) A path planning algorithm will be developed to find the shortest path to reach the target with the ability of obstacle avoidance.  4) A Matlab software with Graphic User Interface (GUI) will be implemented to facilitate computer simulations and control synthesis with animated output.  5)  The system will be implemented physically using a Lego Mindstorm NXT kit.  Hardware results will be recorded and compared.  Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill and mathematical background are essential to this project.

[HKL04] Regulation of DC-DC switching converters using computational intelligence techniques

    This project aims at regulating the output voltage of the DC-DC switching converter subject to changing input voltage and/or output load using fuzzy control approach.  A fuzzy controller incorporating expert knowledge is employed to realise the output regulation.  Expert knowledge extracted from the characteristic of DC-DC switching converter is expressed by linguistic rules in IF-THEN format.  By properly designing the membership functions of the fuzzy controller using computational intelligence technique (Genetic algorithm (GA)/Particle Swarm Optimisation (PSO)), a well-performed fuzzy controller can be realised.  Various types of DC-DC switching controller and fuzzy controllers will be studied and investigated.  MATLAB and fuzzy logic toolbox will be employed for implementation of the algorithms and carrying out simulations.  A Graphic User Interface (GUI) will be implemented.  Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill is essential to this project.

[HKL06] Control of backward movement of articulated vehicle based on fuzzy-model-based control approach 

    A fuzzy controller is designed to control the backward movement of an articulated vehicle using fuzzy-model-based control approach.  The system dynamics of the articulated vehicle is described by a T-S fuzzy model.  Based on the T-S fuzzy model, a stable fuzzy controller is designed using Lyapunov approach.  Stability conditions in terms of linear matrix inequalities (LMIs) are obtained to guarantee the system stability and synthesise the controller.  The solution to the LMI conditions will be solved numerically using convex programming techniques such as MATLAB LMI tool box.  This project can be broken down into the following tasks.  1) A fuzzy model will be developed to describe the system dynamics of the articulated vehicle.  A fuzzy controller will then be proposed to control the system.  2) System stability will be investigated based on the Lyapunov stability theory.  A set of linear matrix inequalities (LMIs) will be obtained to guarantee the system stability and feedback gains of controller.  3) Computer simulations will be done to verify the analysis result.  4) The system will be implemented physically using the Lego Mindstorm NXT kit.  Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill and mathematical background are essential to this project.

Computational Intelligence-Based Projects

[HKL07] Mind recogniser and its Applications.

        The aim of this project is develop a recogniser which is able to recognise someone’s mind.  A headset (Emotiv EEG Headset) will be employed to capture your mind.  Brain signals will be recorded and features will be extracted for recognition purposes.  Neural networks or other classifier will be  developed to classify the intentions of the user where the recognised command will used for an application for demonstration purposes.  A suggested application is the control of virtual mobile robot on the screen.  The robot will react according to the user’s intentions in his/her mind.  Student taking this project is expected to explore how the EEG headset works and data collection.  Also, good Matlab programming skill is essential to this project. 

[HKL08] Classification of Epilepsy using Computational Intelligence Techniques

        This project aims at classifying the epilepsy status of patients.  Real clinical data will be used for this project.  Given EEG epochs obtained from patients, classifiers are designed to recognise three status, namely seizure-free, pre-seizure and seizure.  Features will be extracted from the raw data which will be used as the input of classifiers.  Different computational intelligence techniques including neural networks, support vector machines, fuzzy logic systems, self-organisation map and traditional classifiers including k-NN and naive Bayes will be employed for classifying the epilepsy status and compare their performance.  Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill is essential to this project.

[HKL09] Ship Traffic Prediction using Computational Intelligence Techniques

      This project aims at designing a ship traffic predictor using computational intelligence techniques, for example, neural networks, fuzzy logic systems, support vector regressors.  Real ship traffic data will be used for this project. For comparison purposes, traditional predictors such as linear/nonlinear regressors will be employed. The proposed traffic ship predictor demonstrates a potential solving the sea traffic problem.  Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill is essential to this project.

[HKL10] Classification of EMG signals using Neural Networks

    This project aims at classification of EMG signals using neural networks.  EMG data of different movement actions will be collected by the student taking this project. Various feature points will be extracted for the purposes of classification. Various neural-network-based classifiers will be proposed and implemented using Matlab.  Recognition performance will be investigated and compared with existing methods. Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill is essential to this project.  

[HKL11] Voice commanded calculator

    This project will implement a calculator, which performs calculation according to voice commands.  Speech recognition will be done by the following steps:  1) Student taking this project will collect speech data of himself/herself as the command input of calculator.  2) Feature extraction will be done for all collected speech data.  3) Neural networks or support vector machines will be employed to implement the speech classifier.  The voice commanded calculator will be implemented using Matlab.  Recognition performance will be investigated and compared with existing methods.  Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill is essential to this project.

[HKL12] Recognition of handwritten characters using neural networks

    The aim of this project is to recognise handwritten characters using neural-network-based techniques.  A number of neural networks will be investigated and employed to implement the recognisers.  An algorithm for pattern recognition will be developed to capture the feature points from the images of the characters.  Various recognisers constructed by neural networks will be proposed to perform recognition using the captured feature points.  Learning and optimisation algorithms will be employed to train the neural networks.  The proposed approaches will be compared with the traditional ones in terms of recognition accuracy.  Matlab will be used to implement the proposed algorithms and a graphic user interface (GUI). Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill is essential to this project.

[HKL13] Graffiti interpretation using support vector machines

    The aim of this project is recognise graffiti using support vector machines.  Different types of support vector machines, i.e., support vector classifiers and support vector regressors will be employed to construct the graffiti interpreters.  Based on optimisation theory, with the sample points of the graffiti, the optimal parameters of the support vector machines will be determined.  The graffiti interpreters will be implemented by Matlab and the performances will be investigated through computer simulation.  Good Matlab programming skill is essential to this project.  Student taking this project is required to be hardworking and self-motivated.

MSC Projects 2014-15

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If you are interested in any of the following projects, please send me an email, This email address is being protected from spambots. You need JavaScript enabled to view it.

Control-Based Projects

[HKL01] Fuzzy-Model-Based Control of Virtual Ball-and-Plate System

    The aim of this project is to control the ball-and-plate system using fuzzy-model-based control method.  A fuzzy model will be developed to facilitate the design of the fuzzy controller.  Stability analysis will then be carried out.  By solving numerically the solution to the stability conditions, a fuzzy controller can be obtained.  To verify the analysis result, the control system will be implemented using a Lego Mindstorm NXT kit and a smart phone/tablet.  The Lego Mindstorm NXT kit will be used to implement the control platform.  The smart phone/tablet will be used to implement the virtual ball-and-pate system, which is mounted on the Lego Mindstorm platform.  The smart phone/tablet will show a ball on the screen.  The information of the ball, e.g., position, velocity, etc, will be fed back to the Lego Mindstorm platform.  By adjusting the angle of the platform (which controls the angle of the smart phone/tablet), the ball position is driven to the origin.  Student taking this project is required to be hardworking and self-motivated.  Good Matlab and smart phone programming skills are essential to this project.

Another option: Instead of virtual ball-and-pate system, a physical system can be implemented using Lego Mindstorm NXT kit or Arduino microcontroller.  An image processing technique will be developed to locate the ball position in the beam or an inferred sensor is used instead.  Various controllers will be employed to stabilize the ball position or drive the ball to keep track of a pre-defined trajectory.  Good programming and hardware-building skills are essential to the project. 

[HKL02] Fuzzy Control of Virtual Inverted Pendulum System

    Please refer to the description of [HKL01].  Instead of ball-and-plate system, an inverted pendulum system or a system of your choice is considered. 

[HKL03] Fuzzy Control of Vehicle Active Suspension Systems

    The aim of the project is to design a fuzzy controller to control the vehicle  active suspension system, subject to the performance constraints and user preferences. This project can be broken down into the following tasks.  1) Fuzzy model will be constructed describing the system dynamics for analysis and design. 2) Stability analysis and control synthesis will be investigated based on the Lyapunov stability theory.  A set of linear matrix inequalities (LMIs) will be derived to determine the system stability and feedback gains of the fuzzy controller.  3) Computer simulations will be done to verify the analysis result and control performance will be compared with other control schemes.  4) A Graphic User Interface (GUI) will be implemented based on Matlab which involves an animated inverted pendulum. Student taking this project is required to be hardworking and self-motivated. Good Matlab programming skill and mathematical background are essential to this project.

[HKL04] Control of Electro-hydraulic Systems using Intelligent Control

    Electro-hydraulic systems have demonstrated a wide range of applications including robot manipulators and anti-lock bracking systems.  As the presences of uncertainties and nonlinearities, it makes the analysis and design difficult.  The aim of this project is to design an intelligent fuzzy controller for the control of electro-hydraulic systems.by perfoming the following tasks:  1) The dynamic model of the electro-hydraulic systems is developed.  Based on the model, various types of controllers, such as fuzzy controller, PID controller, linear state-feedback controller, will be employed to perform the control.  2) Stability analysis will be conducted to come up with stability conditions for control synthesis.  3) A Matlab software with Graphic User Interface (GUI) will be implemented to facilitate computer simulations and control synthesis with animated output. Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill and mathematical background are essential to this project.

[HKL05] Controlling Chaos in a Memristor Based Circuit using Intelligent Control 

    Chaos is a random-like but deterministic signal, which demonstrates an application on signal encription and decryption for communication systems.  Memristor is the missing fourth element relating electric charge and magnetic flux linkage. This project aims to study the chaotic beheaviour of a memristor based circuit.  Intelligent control techniques are emplyed to control the chaotic of the circuit.  A fuzzy model will first be constructed to describe the dynsmics of the circuit.  Stability analysis will be conducted to obtain stability conditions for control synthesis. A Graphic User Interface (GUI) will be implemented for demonstration purposes.  Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill is essential to this project.

Computational Intelligence-Based Projects

[HKL06] Solving Shop Scheduleing Problems using Evolutionary Algorithms

        The aim of this project is to solve shop scheulding problem which is a class of multi-stage schedulding problems.  More details can be found in wikipedia.  Shop scheulding demonstrates a wide range of applications such as manufacturing and production process, for example, which requires to determine the best flow of different components (raw materials, unfinished products, tools) to complete a task.  Evolutionary algorithms are investigated, improved and applied to solve various shop scheduling problems.  Comparisons will be made between various evolutionary algorithms.  Student taking this project is required to be hardworking and self-motivated. Good Matlab programming skill and mathematical background are preferred.

[HKL07] Classification of Epilepsy using Computational Intelligence Techniques

        This project aims at classifying the epilepsy status of patients.  Real clinical data will be used for this project.  Given EEG epochs obtained from patients, classifiers are designed to recognise three status, namely seizure-free, pre-seizure and seizure.  Features will be extracted from the raw data which will be used as the input of classifiers.  Different computational intelligence techniques including neural networks, support vector machines, fuzzy logic systems, self-organisation map and traditional classifiers including k-NN and naive Bayes will be employed for classifying the epilepsy status and compare their performance.  Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill is essential to this project.

[HKL08] Classification of EMG Signals using Neural Networks

    This project aims at classification of EMG signals using neural networks.  EMG data of different movement actions will be collected by the student taking this project. Various feature points will be extracted for the purposes of classification. Various neural-network-based classifiers will be proposed and implemented using Matlab.  Recognition performance will be investigated and compared with existing methods. Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill is essential to this project.  

[HKL09] Voice Command Recognition

    This project will implement a voice command recogniser for a robot.  Speech recognition will be done by the following steps:  1) Voice data will be collected as input commands.  2) Feature extraction will be done for all collected speech data.  3) Neural networks or support vector machines will be employed to implement the recogniser.  The voice commanded recognizer will be implemented using Matlab.  Recognition performance will be investigated and compared with existing methods.  It will then implement the voice commanded recogniser into a robot for demonstration purposes.  Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill is essential to this project.

[HKL10] Recognition of Handwritten Characters using Neural Networks

    The aim of this project is to recognise handwritten characters using neural-network-based techniques.  A number of neural networks will be investigated and employed to implement the recognisers.  An algorithm for pattern recognition will be developed to capture the feature points from the images of the characters.  Various recognisers constructed by neural networks will be proposed to perform recognition using the captured feature points.  Learning and optimisation algorithms will be employed to train the neural networks (or support vector machines).  The proposed approaches will be compared with the traditional ones in terms of recognition accuracy.  Matlab will be used to implement the proposed algorithms and a graphic user interface (GUI). Student taking this project is required to be hardworking and self-motivated.  Good Matlab programming skill is essential to this project.

More Articles...

  1. UG Projects 2015-16
  2. MSC Projects 2015-16
  3. MSC Projects 2017-18
  4. MSC Projects 2017-18 (2)

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