生醫材料暨檢測技術領域 Biomedical materials and detection technology  

1. Dielectric properties and capacitor
2. X-ray diffraction and structure
3. TEM selected-area electron diffraction and structure factor
4. Gauss’s law and application in dielectric capacitor
5. The correlation between polarization and dielectric permittivity

  生物技術領域 Biotechnology  

• Gene Engineering

1. Central dogma of molecular biology
2. Cloning and expression: restricted enzyme, ligation, selection, expression
3. Recombination

• Microorganism Engineering

1. Strain identification/ selection
2. Plasmid& Vector
3. Genetic modification

• Cell Engineering

1. Cell fusion
2. Organelle replacement/ transplantation
3. Knock in, knock out, transgenic
4. Cell culture

• Fermentation

1. Host
2. Parameters
3. Optimization

• Cell Therapy

1. Cell isolation
2. Cell activation
3. Customization

• Application

1. Agriculture
2. Medicine (diagnosis, therapy)
3. Forensic
4. Industry
5. Environment
6. Food

  生醫資訊科學領域 Biomedical Information Science  

1. Image Compression Methods:

Huffman coding, Arithmetic coding, Run-length coding, Embedded zerotree, wavelet algorithm (EZW)

Requirement:the steps of these approaches; encode/decode with these methods, working conditions for these methods; examples to show the coding.

2. Edge Detectors:

First-order derivative: Roberts (2x2), Prewitt (3x3), Sobel (3x3)

Second-order derivative: Laplacian (3x3), Double edge, Zero-crossing

Requirement: differences between First-order and Second-order derivative; explain these approaches with figure.

3. Erosion and Dilationt:

the purposes of these approaches; explain these approaches with figure; the steps of these approaches.

4. SVD (Singular Value Decomposition):

the principle and purposes of the approach; example to illustrate the method.

5. FCM (Fuzzy C-Means Clustering):

the principle of the approach; explain the formula; the procedure of the FCM, the difference between FCM and k-means; comparisons of these two approaches.

It would be better to explain FCM by minimizing the objective function.

Pattern recognition:

1. Obtain the equation of the Bayes decision boundary based on the data given in the question.
2. Use the method of function approximation to obtain estimates of probability density function.
3. Given a pattern population and an unknown pattern, calculate Mahalanobis distance.
4. Apply the potential function method to obtain the decision boundary.
5. Explain the following four algorithms. simple cluster-seeking algorithm, maximin-distance algorithm, K-means algorithm, and Isodata algorithm.