Embedded Algorithms in Data Structures: A Comprehensive Guide

There are embedded algorithms in data structures

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Course Content

1. Course Overview

Course Definition: This is a course that integrates knowledge of data structures with the application of algorithms in embedded systems. Data structures mainly study the organization, storage, and operation methods of data, such as arrays, linked lists, stacks, queues, trees, graphs, etc. Embedded algorithms focus on how to efficiently implement specific functional algorithms in resource-constrained embedded devices (such as microcontrollers, FPGAs, etc.), like sensor data processing algorithms and device driver algorithms.
Course Objectives: To help students understand the importance of data structures in embedded systems, and to design and optimize algorithms suitable for embedded environments. Students will learn how to solve practical problems using appropriate data structures under constraints such as limited memory and low power consumption, such as temperature sensor data collection and analysis in smart home systems, or engine control algorithms in automotive electronic systems.

Practice-Oriented: The course emphasizes practical operations, with a large number of experimental cases. For example, using the step counting algorithm of a smart wristband, students need to use appropriate data structures (possibly a circular buffer to store acceleration sensor sampling data) and design corresponding algorithms to calculate steps. Through actual embedded development boards (such as ARM – Cortex M series development boards), students experience the operation of algorithms in embedded devices.
Interdisciplinary Knowledge Integration: Combines knowledge of data structures from computer science with embedded systems knowledge from electronic engineering. Students can understand the complete process from hardware-level to software algorithm implementation. For instance, when learning about embedded software design for Bluetooth communication modules, it involves data transmission formats (related to data structures) and algorithm implementation of communication protocols.
Rich Case Studies: Covers cases from multiple popular embedded application fields. From PLC (Programmable Logic Controller) programming algorithms in industrial automation to embedded data collection and processing algorithms in medical devices (such as ECG data filtering algorithms), students understand the diversity of data structures and algorithms in different scenarios.

Basics of Data Structures: Detailed explanation of common data structures, including their definitions, operation methods, time complexity, and space complexity. For example, for linked lists, the structural characteristics of singly linked lists, doubly linked lists, and circular linked lists will be introduced, as well as the implementation methods of insertion, deletion, and search operations.
Introduction to Embedded Systems: Introduces the hardware architecture of embedded systems (such as CPU, memory, peripheral interfaces) and software architecture (such as real-time operating systems, device drivers). Also explains the development process and toolchain of embedded systems, such as using the GCC compiler for cross-compilation.
Principles of Embedded Algorithm Design: Teaches how to design appropriate algorithms based on the characteristics of embedded systems (such as resource constraints and real-time requirements). Includes optimization strategies for algorithms, such as time-space trade-offs, and how to leverage hardware features to accelerate algorithm execution.

Basic Experiments: Sets up some simple experiments to consolidate the basic knowledge of data structures and embedded systems. For example, implementing a simple stack structure on a development board to store key event data; or implementing a queue to process data received from UART (Universal Asynchronous Receiver-Transmitter).
Comprehensive Experiments: Through some complex projects, students can comprehensively apply the knowledge learned. For example, designing a small embedded temperature control system, involving temperature sensor data collection (using appropriate data structures to store collected data), data filtering algorithms, control algorithms (such as PID control algorithms), and outputting results through a display or communication interface.

Course Project Part: Requires students to work in groups to complete a complete embedded system project, from requirement analysis, scheme design, code implementation to testing and optimization. Project themes can be an intelligent traffic light control system (involving vehicle and pedestrian sensor data processing, signal light state switching algorithms), or a simple wireless sensor network node for data collection and transmission.

Knowledge Mastery: Students will be able to proficiently master the application of various data structures in embedded environments and deeply understand the design principles and optimization methods of embedded algorithms. For example, students will accurately choose appropriate data structures to store and process data in embedded devices and reasonably analyze and optimize the time complexity and space complexity of algorithms.
Practical Ability: Through experiments and course projects, students will have the ability to independently develop small embedded systems, including hardware circuit connections, software development, debugging, and optimization. For example, students can design and implement an environmental monitoring system based on embedded devices that can collect temperature, humidity, light, etc., and send the data to a server for analysis via the network.
Problem-Solving Ability: When facing complex embedded system problems, students can use the knowledge of data structures and algorithms learned to propose effective solutions. For example, when dealing with memory leak issues in embedded devices, students can use their knowledge of data structures to track memory allocation and release, and use appropriate algorithms to detect and fix memory leak points.

Professional Background Requirements: This course is mainly aimed at students majoring in Computer Science and Technology, Electronic Information Engineering, Automation, and other related fields. These students already have a certain foundation in computer programming and electronic circuit knowledge, which will help them better understand the course content.
Prerequisite Course Requirements: Students should have completed courses in programming languages (such as C/C++), digital circuits, microcomputer principles, etc. For example, with a foundation in C language programming, students will find it easier to implement embedded algorithm codes; knowledge of digital circuits and microcomputer principles will help students better understand the hardware-level principles of embedded systems.

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Embedded Algorithms in Data Structures: A Comprehensive Guide