Multimedia Computing: Algorithms, Systems, and Applications, Fall 2013

Latex or MS Word Template for Project Report

Please use this link to download the Latex template or Microsoft Word template for your project report.

Requirement for Project Report

When writing your project report and presentation, please include the following items explicitly:

1. Define and explain your problem clearly. Provide sufficient motivation for the work and present how your work is connected with the existing/previous work. This part includes three parts (you can refer your proposal for this section)

1.1 Introduction: What is your research/development question? What is the ultimate goal of your proposed system?

1.2. Motivations: Why this is an interesting question, or why it is important to develop a system to solve this question?

1.3. Related work:  What kind of existing research/development work has tried to answer the same or a similar question, what is still unknown?

2. Proposed Approach: Introduce your methods and implementations with sufficient details

3. Experimental Results and Discussions: Discuss the research/development results and quantify the performance study

4. Conclusions and Future Work: Summarize your work, give your conclusions if possible, and point out the possible future work (how the work can be further improved/extended)


The length of the paper should between 5 pages to 10 pages (font 10, double column) without counting any necessary appendices.  A good report is not just a simple description of what you did. Remember, you want your readers to learn something from your report and convince them you have done a very good job (why do you want to work in this project? how does your approach work for the problem? what is your conclusion?) Be sure to include sufficient arguments and evidence to support your main points. 


Requirement for Project Proposal

When writing your proposal and presentation, please include the following items explicitly:

1. Introduction: What is your research/development question? What is the ultimate goal of your proposed system?

2. Motivations: Why this is an interesting question, or why it is important to develop a system to solve this question?

3. Related work: What kind of existing research/development work has tried to answer the same or a similar question, what is still unknown?

4. Proposed approach: What is your plan for working out the solutions to the question? what are the main features in your proposed system? How do you implement your proposed system (e.g., what kind of database do you choose, what are your main programming languages, are there any particular techniques you are going to use)? 

5. Evaluation: How are you going to evaluate your solution. In another word, what is your plan to demonstrate that your solution/answer is good or is reasonable?

6. Timeline: A rough timeline to show when you expect to finish what.



One interesting feature of this class is called "project: early intervention". Specifically, in the beginning of the semester, the instructor will introduce potential projects (include background, objectives,challenges, on-going research efforts, possible research and activities, expected outcomes, etc.) to the students. Based on students' own interests, their own paper reading, and consulting with instructor, the students will pick an appealing project. Then, the instructor will assign more research papers to each student and the students will also select papers by themselves with the approval from the instructor.Based on the project picked by the students, the instructor may group them into different teams, each team for one research and/or development area. We hope after the first few weeks, each student will have a clear idea and vision on his/her course project.


List of Potential Projects

We are listing some of the potential project topics as below. Please keep in mind, the projects listed here are for your reference. We (studetns and faculty) will work together to determine the projects in the class.

Xbox 360 and Kinect-based motion sensing for in-home rehabilitation

The goal of this project is to research new motion sensing algorithms and system for in-home rehabilitation using new gaming motion tracking technologies, such as Microsoft Xbox 360 and Kinect. The home environment is the easiest place to help patients to recovery by conducting intensive, repetitive practice of functional movement. Recent developments in the gaming industry and gaming motion tracking devices, such as Microsoft Xbox 360 and Kinect, have great potential to transform in-home rehabilitation. In this research, we will explore the possibility of utilizing Kinect for in-home rehabilitation. Specific research tasks in this project include the research and development of Kinect-based software to capture, track, analyze, and interpret the 3D motion images to facilitate in-home rehabilitation, GPU-enhanced image and video analysis.

Smartphone-based activity monitoring

The objective of this project is to research into new algorithms and software to detect and classify the physical activity for long-term lifestyle and healthcare monitoring using smartphone platform, such as iPhone and/or android. Physical activity is one of the leading health indicators to measure the mobility level, latent chronic diseases and aging process. Smartphone is an ideal platform to monitor the physical activity since it is usually carried by user most of the time and it could measure the activity passively and automatically in a non-intrusive fashion. Specific research tasks in this project include feature extraction from inertial sensors (e.g., accelerometers and gyroscopes), new machine learning algorithms for activity classification.

Ubiquitous sensing based on smart vision techniques for managed homecare

This project aims to investigate new software system for pervasive home monitoring using smart vision techniques. Pervasive home monitoring plays a vital role in maintaining the independence and improving the life quality for aging population. Low cost vision-based system can be used to monitor and valuate daily activities of the occupants. Specific research tasks in this project include: image sensing device level visual appearance filtering techniques to ensure privacy of occupancy, shape and motion vector analysis for event detection (e.g., vital sign analysis, adverse events detection).

Granger Causality Based Brain-Computer Interface

A brain-computer interface (BCI) (A.K.A., mind-machine interface (MMI), or direct neural interface, brain-machine interface (BMI)) is a direct communication pathway between the brain and an external device. Granger causality (GC), one of the key enabling, is one of the most popular measures to reveal causality influence of time series and has been widely applied in economics and neuroscience. This project focus on developing new causality measures (in time and frequency domains) for the linear regression model. We envison the new causality measure will more reasonable and understandable than GC or Granger-like measures.

Data Visualization for Clinical Decision Support

The objective of this project is to discover the most effective and efficient way to visualize our specific, multimodal, biomedical data. The ultimate goal is to integrate this visualization methodology into clinical decision support software. This software would be used by doctors and therapists to more easily make decisions regarding their patients, based on these visualizations. The data I will be working with comes from several different sources and in a variety of different forms. These include Kinect motion data, video and/or image data, accelerometer and gyroscope data, and ECG/EEG data. The Kinect provides data about the position and movement of various points on the patient's body. The video data will come from cameras monitoring the patient. Mobile phones will provide the gyroscope and accelerometer data, which track which direction the phone is pointed in and its movement in three dimensions. The EEG and ECG data measure the electrical activity of the patient's brain and heart, respectively. All the data will also have a timestamp component.

Medical imaging indexing and search

Images are ubiquitous in biomedicine as they play a vital role in medical diagnosis, clinical treatment, and biomedical research/education. The ultimate goal of this project is to research, develop, evaluate, and demonstrate a data-intensive and scalable intelligent medical image modeling and retrieval system with the capacity of finding the most clinically relevant images to support clinical decision making during diagnosis and treatment. The main challenges of this project take root in the unique characteristics of medical image data, which is large volume, heterogynous, and semantic-rich Specific research tasks in this project include Hadoop and MapReduce-based massive image indexing and retreival, new multi-modal (e.g., text and image) image feature extraction techniques by expanding Bags of Words (BOW) visual features extraction methods and Latent Dirichlet Allocation-based text feature extraction methods, new image and textual modeling and parsing techniques (e.g., cross-modal correlations using canonical correlation analysis (CCA), GPU-based feature extraction.

Risk Analysis for Acute Coronary Syndromes in Chest Pain Patients

The use of nuclear cardiac stress testing has been incorporated into chest pain unit (CPU) evaluation protocols in the evaluation of patients deemed at low to intermediate risk of acute coronary syndromes (ACS) defined as unstable angina or acute myocardial infarction (AMI). The objective of this project is to develop a computer-aided predicative model to investigate the risk factors (e.g., age, sex, cardiac risk factors) on the incidence of ACS for the purpose of developing a tool that may assist physicians to predicate the ACS in chest pain patients.



91.460.204 and 91.530.204: Multimedia Computing: Algorithms, Systems, and Applications, Fall 2013

by Dr. Yu Cao, Department of Computer Science, UMass Lowell

For questions, please contact Dr. Yu Cao (