Data Processing and Integration Challenge

What is the primary goal of image processing?

• To create artistic images.

• To compress image file sizes.

• To change the color of images.

• To enhance or extract useful information from images.

Which technique is commonly used for noise reduction in signals?

• Amplification

• Filtering

• Modulation

• Encoding

What is the purpose of multi-source data integration?

• The purpose of multi-source data integration is to create a comprehensive and cohesive dataset from diverse data sources.

• To simplify data storage by using only one format.

• To enhance the performance of individual data sources.

• To limit data access to a single source.

Name a popular library used for image processing in Python.

• PIL

• NumPy

• OpenCV

• Matplotlib

What is the difference between supervised and unsupervised signal processing?

• Supervised signal processing is faster than unsupervised.

• Unsupervised signal processing requires more data than supervised.

• Supervised signal processing uses labeled data for training, while unsupervised signal processing uses unlabeled data to find patterns.

• Supervised signal processing is only used in image processing.

How can you handle missing data when integrating multiple data sources?

• Assume missing data is zero

• Replace missing data with random values

• Use imputation, data transformation, exclusion, or flags to handle missing data.

• Ignore missing data completely

What is the role of filters in signal processing?

• Filters convert analog signals to digital signals.

• Filters amplify all frequencies equally.

• Filters are used to store signals for later use.

• Filters are used to manipulate signals by allowing certain frequencies to pass and attenuating others.

Describe a common method for feature extraction in image processing.

• Texture analysis using Gabor filters

• Color histogram analysis

• Edge detection using the Canny edge detector

• Principal component analysis (PCA)

What is the significance of data normalization in multi-source integration?

• Data normalization complicates the integration process across multiple sources.

• Data normalization is significant as it improves data consistency, quality, and integration across multiple sources.

• Data normalization is only relevant for single-source data management.

• Data normalization reduces data redundancy but does not affect quality.

Which format is commonly used for storing images in a compressed manner?

• JPEG

• BMP

• GIF

• PNG