The goals of this course are to provide the student with the following: - The ability to use the mathematical techniques such as linear algebra, Fourier theory and sampling in more advanced signal processing settings. - Fundamentals of multiresolution/wavelet techniques. - In-depth coverage of bioimaging applications, such as compression, denoising and others. Upon successful completion of this course, the student will be able to: - Explain the importance and use of signal representations in building more sophisticated signal processing tools, such as wavelets. - Think in basic time-frequency terms. - Describe how Fourier theory fits in a bigger picture of signal representations. - Use basic multirate building blocks, such as a two-channel filter bank. - Characterize the discrete wavelet transform and its variations. - Construct a time-frequency decomposition to fit the signal you are given. - Explain how these tools are used in various applications. - Apply these concepts to solve a practical problem through an independent project.