Difficulty: Foundational to Intermediate | Prerequisites: Undergraduate-level maths (calculus), basic programming
Source: CEN MS Graduate Handbook 2024-2025, Appendix I, Arizona State University
Tags: EEE 350, MAT 243, EEE 203, CSE 230, CSE 310, random signal analysis, discrete maths, digital signal processing, computer architecture, algorithms, data structures, prerequisite courses
The CEN MS programme expects incoming students to have covered five foundational subjects before (or shortly after) starting graduate coursework. These are not electives; they feed directly into the two core courses (CSE 551 requires CSE 310; EEE 554 requires EEE 350) and into much of the CEN Area coursework. If you are coming from a non-traditional background or a different engineering discipline, this is your checklist of gaps to fill. The handbook provides a recommended textbook and topic list for each.
Five prerequisite subjects: Random Signal Analysis (EEE 350), Discrete Mathematics (MAT 243), Digital Signal Processing (EEE 203), Computer Architecture and Organisation (CSE 230), and Algorithms and Data Structures (CSE 310). Each has a recommended textbook and a specific list of topics you should be comfortable with before starting the MS.
Textbook: Yates and Goodman, Probability and Stochastic Processes, 2nd edition, Wiley, 2005.
Why it matters: This is the prerequisite for EEE 554 (Probability and Random Processes), one of the two core courses. Without this foundation, you will struggle with the graduate-level treatment of stochastic models.
Topics to know:
Axiomatic probability
Random variables, distribution functions, and density functions
Special distributions: Gaussian, exponential, and others
Expectation and variance
Multiple random variables
Central limit theorem and law of large numbers
Maximum-likelihood estimation and confidence intervals
Random processes
Statistical analysis using sample statistics, histograms, and linear regression
Real-world application: These concepts underpin communications system design, machine learning feature modelling, and any engineering work where noise and uncertainty must be quantified.
Textbook: Kenneth H. Rosen, Discrete Mathematics and Its Applications, McGraw-Hill, 7th edition.
Why it matters: Discrete maths provides the logical and combinatorial foundations for algorithms, formal verification, and circuit design. Many CEN Area courses assume fluency in proof techniques, graph theory, and combinatorics.
Topics to know:
Foundations: Logic and Proofs (Ch. 1 and 5) - mathematical reasoning, constructing proofs, mathematical induction
Combinatorial Analysis (Ch. 6 and 8) - solving counting problems
Elementary Number Theory (Ch. 4)
Discrete Probability (Ch. 7) - probability theory fundamentals, conditional probability, random variables
Graph Theory (Ch. 10-11) - basics of graph theory, properties of trees
Boolean Algebra (Ch. 12) - Boolean functions, representation, minimisation of Boolean circuits
Real-world application: Boolean algebra is the language of digital circuit design. Graph theory appears in network routing, scheduling, and dependency analysis.
Textbook: Oppenheim, Willsky and Nawab, Signals and Systems, Prentice Hall, 2nd edition.
Why it matters: DSP concepts feed into the Multimedia and Signal Processing (MSP) CEN Area and many communications-related graduate courses. Understanding transforms and system properties is essential for anyone working with real-world data acquisition or communications.
Topics to know:
Signals (Ch. 1) - continuous-time and discrete-time signals, unit step, unit impulse, sinusoids, time-variable transformations
Systems (Ch. 1-2) - LTI systems (linearity, time-invariance, causality, stability), impulse response, convolution (graphical and analytical), block diagrams, input-output equations
Fourier Transform (Ch. 4) - forward and inverse transform, FT properties, frequency response
Discrete-Time Fourier Transform (DTFT) (Ch. 5) - forward and inverse, DTFT properties, frequency response
Sampling (Ch. 7) - converting continuous-time to discrete-time, sampling theorem
z-Transform (Ch. 10) - forward and inverse transform, region of convergence, properties
Real-world application: Sampling theory is why your phone can capture and reconstruct audio. The z-Transform is the workhorse of digital filter design.
Textbook: David A. Patterson and John L. Hennessy, Computer Organization and Design: The Hardware-Software Interface, 4th edition.
Why it matters: This is foundational for the VLSI, Architecture, and Embedded Systems (VAES) CEN Area and for understanding how software maps to hardware. Graduate courses in this area assume you can reason about pipelines, caches, and instruction sets.
Topics to know:
Assembly Language Programming - writing assembly programs for simple problems
Procedure Calling Convention - caller-saved, callee-saved, argument and return value registers; writing procedures and recursive functions in assembly
Data Representation - unsigned, two's complement, and floating point; arithmetic operations; hardware structures for these operations
Pipelined Processor Design - single-cycle and pipelined processors, pipeline hazards and avoidance techniques
Memory Hierarchy - rationale for memory organisation, cache operation
I/O - basic understanding of storage and input/output
Advanced Computer Architecture - superscalar, multi-threading, multi-core architectures
Real-world application: Understanding caches and pipelines is how engineers optimise software performance. Multi-core architecture knowledge is essential for anyone writing concurrent or parallel code.
Textbook: Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, Introduction to Algorithms, 3rd edition (commonly known as CLRS).
Why it matters: This is the prerequisite for CSE 551 (Foundations of Algorithms), one of the two core courses. Graduate-level algorithm work builds directly on the sorting, searching, graph, and complexity concepts covered here.
Topics to know:
Basics of algorithm design and analysis (Ch. 1-3) - asymptotic notation, recurrences
Divide and Conquer (Ch. 4)
Elementary Data Structures and hash tables (Ch. 10-11)
Sorting: Heapsort (Ch. 6), Quicksort (Ch. 7), Radix Sort and Bucket Sort (Ch. 8)
Searching: Binary Search Trees (Ch. 12), Red-Black Trees (Ch. 13.1-4)
Dynamic Programming (Ch. 15)
Greedy Algorithms (Ch. 16)
Minimum Spanning Tree (Ch. 23)
Shortest-Path Problems (Ch. 24-25)
Elementary Graph Theory (Ch. 22)
String Matching (Ch. 32)
NP-completeness (Ch. 34)
Real-world application: Shortest-path algorithms power every navigation app. NP-completeness tells you which problems are worth trying to solve optimally and which need heuristics.
"I only need to review the prerequisites for my concentration." Both core courses (CSE 551 and EEE 554) are required for all students, so CSE 310 and EEE 350 matter regardless of whether you are CEN-CS or CEN-EE.
"I can skip DSP and Computer Architecture if I am in the CEN-CS concentration." While these are not formal prerequisites for the core courses, many CEN Area courses assume this knowledge. You may find yourself underprepared for electives.
"The textbook editions do not matter." The topic lists in Appendix I reference specific chapters. If you use a different edition, the chapter numbering may differ, so cross-reference topics rather than chapter numbers.
⚠️ CSE 310 is the prerequisite for CSE 551. If you are deficient, you can take it concurrently or petition to take it first.
⚠️ EEE 350 is the prerequisite for EEE 554. Same options apply.
⚠️ These deficiency courses do not count toward the 30-credit degree requirement.
⚠️ If your undergraduate programme did not cover all five subjects, plan to fill the gaps in your first semester or before admission.
Fill in the blank: CSE 310 is the prerequisite for the core course _______. (CSE 551)
True or False: EEE 350 covers Fourier Transforms and z-Transforms. (False, that is EEE 203. EEE 350 covers probability and random processes.)
Fill in the blank: The recommended textbook for CSE 310 is commonly abbreviated as _______. (CLRS)
Q: Which prerequisite subject is the foundation for EEE 554?
A: Random Signal Analysis (EEE 350).
Q: Name four sorting algorithms covered in CSE 310.
A: Heapsort, Quicksort, Radix Sort, and Bucket Sort.
Q: What is the sampling theorem, and which prerequisite course covers it?
A: The sampling theorem describes the conditions under which a continuous-time signal can be perfectly reconstructed from its discrete-time samples. It is covered in EEE 203 (Digital Signal Processing).
Q: In Computer Architecture (CSE 230), what are the three data representation formats students should understand?
A: Unsigned, two's complement, and floating point.
Q: What topics from Discrete Mathematics (MAT 243) overlap with algorithms work?
A: Graph theory (which underpins graph algorithms in CSE 310), combinatorial analysis (which underpins algorithm analysis), and logic/proofs (which underpin correctness proofs).
CSE 310 and EEE 350 are the direct gateways to the two core courses covered in the Degree Requirements study notes. Filling gaps in these prerequisites is the first practical step after understanding the programme structure. The Admissions notes cover how deficiency courses interact with your credit total and iPOS.
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