ENG311 Digital Signal Processing SUSS Assignment Sample Singapore

ENG311 Digital Signal Processing is an exciting course for those interested in exploring the technology that allows us to communicate through the electronic exchange of data. It introduces students to the fundamentals of analog and digital signal processing and explores topics such as signal analysis, filtering, synthesis, and sampling.

The course provides a great opportunity for students to deepen their understanding of signals, systems and operations related to DSP algorithms and their implementation on microprocessors. With a strong emphasis on effective problem-solving techniques, ENG311 prepares students ready to face real-life challenges in this rapidly evolving field.

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Let’s review the objectives for the assignment:

Assignment Objective 1: Discuss the properties of Linear Time-Invariant (LTI) systems.

Linear Time-Invariant (LTI) systems use mathematical operations to represent physically observable behavior. These systems are characterized by three primary properties: they are linear, shift-invariant, and time-invariant. Linearity means that the output of a system is a simple multiple of the input – changing the amplitude or frequency of input signals won’t have any additional effect on their output characteristics.

Shift invariance refers to their ability to maintain linearity at various locations due to their inherent lack of memory; changes in an input signal’s temporal location will not affect its output characteristics. Lastly, these systems are time-invariant due to their independence from external stimuli; adjustments made to the environment or parameters outside of the system will not alter its response. Therefore, LTI systems process input signals without any distortion over long periods of time allowing it to be utilized in many industrial applications.

Assignment Objective 2: Calculate sampling frequency, circular convolution, quantization parameters, and other signal parameters.

Analyzing signal parameters is an important part of processing data to obtain optimal results. The frequency of sampling, circular convolution, and quantization parameters all play a role in the precision of the final product. Calculating these signal parameters requires knowledge and careful consideration; inaccurate calculations can lead to degradation or even direct errors in the processed data.

Thus, it is essential to apply accurate methods and the correct techniques when calculating sampling frequency, circular convolution, and quantization parameters. Utilizing these techniques will help ensure that your signal parameters are properly calculated and your processed data is accurate.

Assignment Objective 3: Analyze LTI systems and signals in the time and frequency domains.

Linear Time-Invariant (LTI) systems are ubiquitous within control theory and signal processing. Being able to analyze these systems within both the time and frequency domains is essential for gaining a thorough understanding of their behavior. Through the use of specialized techniques, LTI systems can be efficiently examined in the temporal realm, allowing for proper characterization in terms of response to disturbances or external inputs.

Using Fourier analysis, parameters such as magnitude and phase shifts can be determined from the frequency domain perspective. Ultimately, this allows for an effective assessment of the stability and performance of such LTI systems.

Assignment Objective 4: Apply the properties of Fourier methods (Fourier Series, Fourier Transform, Discrete Fourier Transform) to examine signals and systems.

Fourier methods are a powerful tool for examining signals and systems. Using these methods, we can resolve signals and systems into a combination of sines and cosines (Fourier Series), use complex exponentials to determine the frequency content of a signal (Fourier Transform) or turn to fast algorithms to calculate the FFT (Discrete Fourier Transform). Furthermore, this technique can be used in many different fields, such as speech recognition, image processing and astronomy. This demonstrates just how versatile and effective Fourier methods are when it comes to analyzing signals and systems.

Assignment Objective 5: Implement Finite Impulse Response Filters (FIR) using windowing, frequency-sampling and optimal equity-ripple methods.

The implementation of finite impulse response (FIR) filters is a common task used across many industries, from audio engineering to data analysis. The three most popular methods for implementing FIR filters are windowing frequency-sampling and optimal equity-ripple methods. Windowing allows the use of a non-recursive filter design based on Kaiser-Bessel weighting functions, while the frequency-sampling approach sets evenly spaced points in the frequency domain of a sampled design.

Finally, the optimal equity-ripple method allows for a linear-phase filter with equiripple decrease in amplitude towards the stopband frequencies. Each method comes with its own advantages depending upon requirements and specifications, making them invaluable tools to any engineer designing FIR filter systems.

Assignment Objective 6: Construct Infinite Impulse Response Filters (IIR) using either Impulse Invariance or the Bilinear Transformation.

Constructing Infinite Impulse Response (IIR) Filters is an essential step in a range of digital signal processing applications. There are two popular methods recommended for this task: Impulse Invariance and the Bilinear Transformation.

Impulse Invariance uses discrete impulse responses to mimic the behavior of their continuous counterparts, while the Bilinear Transformation is designed such that frequencies at the Nyquist frequency are mapped correctly into the digital domain. Each technique has its pros and cons; however, both can be very successful in achieving good filter design for various IIR applications.

Assignment Objective 7: Formulate algebraic expressions to represent signals and systems.

Algebraic expressions allow us to quantify the various components of a signal and system. Through this formal mathematical language, we can determine how a given system will behave in any number of scenarios by analyzing the mathematical equations. This heightened specificity helps to uncover potential problems before they arise. Moreover, algebraic expressions are extremely versatile and can easily be tailored to fit new signals and systems as needed for greater accuracy and prediction. Ultimately, algebraic expressions offer an invaluable tool for engineers wanting an intuitive understanding of their signals and systems.

Assignment Objective 8: Draw the block diagrams, impulse response, magnitude response, phase response, and other characteristics of signals/systems.

Block diagrams are an effective method of visualizing the relationship between various signals and systems. Using these diagrams, we can see the inputs, outputs, and any components in between which make up a system. Additionally, they allow us to gain insight into how external factors such as noise and disturbances interact with the system.

Impulse response and magnitude response are two other important characteristics that come into play when analyzing signals and systems – impulse response shows how a signal is affected by a system over time, while magnitude response reveals the strength of a signal relative to internal or external factors. Combining all of this data allows us to give a comprehensive overview of the state of a given signal or system at any select point in time.

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