【Qualified Exams】Outline of Examination Questions in Various Fields in the First Semester of 2024
生醫材料暨檢測技術領域 Biomedical materials and detection technology
- Dielectric properties and capacitor
- X-ray diffraction and structure
- TEM selected-area electron diffraction and structure factor
- Gauss’s law and application in dielectric capacitor
- The correlation between polarization and dielectric permittivity
生物技術領域 Biotechnology
- Gene Engineering
- Central dogma of molecular biology
- Cloning and expression: restricted enzyme, ligation, selection, expression
- Recombination
- Microorganism Engineering
- Strain identification/ selection
- Plasmid& Vector
- Genetic modification
- Cell Engineering
- Cell fusion
- Organelle replacement/ transplantation
- Knock in, knock out, transgenic
- Cell culture
- Fermentation
- Host
- Parameters
- Optimization
- Cell Therapy
- Cell isolation
- Cell activation
- Customization
- Application
- Agriculture
- Medicine (diagnosis, therapy)
- Forensic
- Industry
- Environment
- 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:
- Obtain the equation of the Bayes decision boundary based on the data given in the question.
- Use the method of function approximation to obtain estimates of probability density function.
- Given a pattern population and an unknown pattern, calculate Mahalanobis distance.
- Apply the potential function method to obtain the decision boundary.
- Explain the following four algorithms. simple cluster-seeking algorithm, maximin-distance algorithm, K-means algorithm, and Isodata algorithm.