Embedded Algorithms for Data Structures

There are embedded algorithms for data structures.

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Course Content

1. Course Overview

“Embedded Algorithms for Data Structures” is a comprehensive course that integrates knowledge of data structures with the design of algorithms for embedded systems. It primarily focuses on how to efficiently organize and process data in resource-constrained environments of embedded devices, as well as designing optimized algorithms to meet specific application requirements.

This course will start with the basic concepts of data structures such as arrays, linked lists, stacks, queues, trees, and graphs, explaining their storage methods, operational characteristics, and applicable scenarios in embedded systems. At the same time, it will delve into the design principles of embedded algorithms, including optimizations for time complexity and space complexity, to ensure that algorithms can run efficiently on embedded devices.

The course content will be combined with practical cases of embedded devices, such as microcontrollers and sensor nodes, allowing students to understand how data structures and algorithms function in these devices for data collection, processing, storage, and transmission. For example, in a simple temperature sensor embedded system, an appropriate data structure will be used to store the collected temperature data sequence, and an effective sorting algorithm will be employed to sort and analyze this data for subsequent temperature control or data reporting operations.

2. Course Highlights

(1) Practice-Oriented

Emphasizes hands-on practical skills, with a large number of experimental cases and project practices included in the course. Students will have the opportunity to implement various data structures and algorithms on actual embedded development boards, such as using ARM Cortex-M series development boards. By writing C or C++ code, they will apply linked list data structures in task scheduling systems, allowing them to intuitively feel the practical effects of algorithms and data structures in an embedded environment.
Tightly integrated with actual industrial applications, such as typical application cases in embedded fields like smart home control systems and automotive electronic systems, helping students understand how the knowledge learned translates into functional modules in actual products.

(2) Systematic Knowledge Framework

Builds a complete knowledge framework for data structures and embedded algorithms. From basic principles of data structures to advanced algorithm design techniques, and special considerations in embedded systems, such as resource constraints and real-time requirements, comprehensive explanations are provided. The course will clearly display the flow of data in different data structures and the execution process of algorithms through flowcharts and state diagrams, helping students better understand and memorize the content.
Focuses on the coherence and progression of knowledge. Starting with simple linear data structures, gradually introducing complex nonlinear data structures and corresponding embedded algorithms, allowing students to master the knowledge system step by step, avoiding confusion caused by leapfrog learning.

(3) Integration of Cutting-Edge Technologies

Introduces the latest research results in embedded technologies and algorithms. For example, with the rise of artificial intelligence in edge computing (computing at the embedded device end), the course will cover some simple machine learning algorithm applications in embedded systems, as well as how to use appropriate data structures to store and process machine learning model parameters.
Focuses on updates to industry standards and specifications. For instance, as the requirements for safety and reliability of embedded systems increase, the course will explain how to adhere to relevant safety standards in the design of data structures and the implementation of algorithms, such as the application of encryption algorithms in embedded secure communication and fault-tolerant design of data structures.

3. Course Composition

(1) Theoretical Teaching Part

Fundamentals of Data Structures Module

Detailed explanations of the definitions, representation methods, and basic operations of common data structures. For example, for arrays, the storage methods of one-dimensional and two-dimensional arrays will be explained, along with how to access, insert, and delete elements; for linked lists, the structural characteristics, node definitions, and operations such as traversal, insertion, and deletion of singly linked lists, doubly linked lists, and circular linked lists will be analyzed in depth.
Introduction to the concept of Abstract Data Types (ADT) of data structures, helping students understand the separation of logical interfaces from actual implementations, thereby enhancing their abstract thinking abilities. With the ADT approach, students can focus more on the functional use of data structures without being troubled by specific implementation details.

Fundamentals of Embedded Systems Module

Covers the hardware fundamentals of embedded systems, including the basic architecture of microprocessors and microcontrollers, the functions and roles of memory management units (MMUs), and the working principles of various peripherals (such as UART, SPI, I2C interfaces). This knowledge provides hardware background support for understanding the storage and access of data structures in embedded devices.
Explains the basic concepts and task scheduling mechanisms of embedded operating systems (such as FreeRTOS, uC/OS, etc.). Students need to understand how data structures are shared and manipulated by different tasks in a multitasking environment, and how to ensure data consistency and integrity through synchronization and mutual exclusion mechanisms provided by the operating system.

Embedded Algorithm Design Module

In-depth study of design methods and strategies for embedded algorithms. This includes applications and design ideas of greedy algorithms, dynamic programming algorithms, divide-and-conquer algorithms, etc., in embedded systems. For instance, using dynamic programming algorithms, an actual embedded path planning problem (such as the optimal path search for robots in a limited map) will be explained, demonstrating how to use the idea of dynamic programming and appropriate data structures (such as two-dimensional arrays) to store intermediate results to effectively solve problems.
Analysis of performance evaluation indicators of algorithms, primarily focusing on time complexity and space complexity. Students will learn how to analyze and calculate the time consumption and space occupation of an embedded algorithm under different input scales, and verify their analysis results through actual code testing and performance analysis tools (such as performance analyzers).

(2) Practical Teaching Part

Experimental Courses Module

Design a series of experimental projects, each closely revolving around one or more themes of data structures and embedded algorithms. For example, one experimental project is to implement a simple embedded file system, where students need to use tree data structures (such as B-trees) to organize files and directories while using embedded algorithms to implement file read and write operations and manage storage space.
During the experiment, students are required to use actual embedded development toolchains (such as ARM-GCC, Keil MDK, etc.) for code writing, compilation, and debugging. Students need to learn to use debugging tools (such as J-Link debuggers) to track the execution process of programs, observe changes in data structures, and the execution steps of algorithms, thereby deepening their understanding of theoretical knowledge.

Course Project Module

Arrange a comprehensive course project, such as designing a small embedded IoT system. Students need to comprehensively apply the knowledge of data structures and embedded algorithms learned, from data collection (through sensor interfaces and corresponding data structures to store collected data), data transmission (designing appropriate communication protocols and data structures to package and transmit data) to data processing (analyzing and processing data using algorithms on embedded devices or cloud servers).
In the course project, students are encouraged to work in teams, fostering their teamwork and project management skills. Students need to develop project plans, allocate tasks, integrate and test code, and ultimately present and report project results.

4. Course Outcomes

(1) Knowledge and Skill Acquisition

Students will be proficient in the application of various data structures in embedded systems, including but not limited to selecting appropriate data structures based on actual needs, such as using circular queues to store the latest data samples in real-time data collection systems, and utilizing hash tables for quick lookups and matches in network protocol stack implementations.
Learn to design and optimize embedded algorithms, capable of developing efficient algorithm solutions for specific embedded application scenarios, such as motor control algorithms in industrial automation, signal processing algorithms in medical devices, etc., using learned algorithm design strategies (e.g., greedy algorithms for resource allocation problems). They will also be able to accurately evaluate and optimize the performance of algorithms to meet the resource and performance requirements of embedded systems.
Proficient in embedded development tools and processes, including hardware platform setup, software development environment configuration, code writing, compilation, debugging, and downloading operations. They will be able to independently complete the development of the software portion of a simple embedded system, from low-level driver development (involving initialization and operation of data structures) to high-level application development (using embedded algorithms to implement specific functions).

(2) Skill Enhancement

Cultivate students’ problem-solving abilities. When faced with complex embedded system design issues, such as resource conflicts and unmet real-time requirements, students will be able to apply their knowledge of data structures and algorithms to analyze problems, build models, and select appropriate algorithms and data structures to resolve issues. For example, in a multitasking embedded system, when a task deadlock occurs, students will be able to use graph data structures to analyze dependencies between tasks and solve the deadlock problem using appropriate algorithms (such as resource allocation graph reduction algorithms).
Enhance students’ innovative thinking abilities. Some open-ended experimental projects and course projects in the course encourage students to try new combinations of algorithms and data structures or improve and innovate existing algorithms. For instance, in embedded image recognition systems, students may explore how to use new types of data structures (such as sparse matrices) to store and process image data, thereby improving recognition efficiency and reducing storage costs.
Strengthen students’ teamwork and communication skills. Through teamwork in course projects, students learn to collaborate with team members to complete a complex embedded system project. In this process, students need to communicate effectively, including discussions on technical solutions, defining and integrating code interfaces, and tracking and coordinating project progress.

5. Target Audience

(1) Undergraduate students majoring in Computer Science and Technology, Electronic Information Engineering, and related fields

For students in computer-related majors, this course can deepen their understanding of data structure knowledge and apply it to the specific field of embedded systems. By taking this course, they can broaden their employment direction, venturing beyond traditional software development fields into roles such as embedded software engineers.
Students majoring in Electronic Information Engineering can combine their hardware knowledge with software algorithms through this course. After learning about embedded hardware, they will further master how to efficiently implement data structures and algorithms on hardware platforms, thereby enhancing their overall capabilities in embedded system design and laying a solid foundation for engaging in embedded system development and IoT device development.

(2) Professionals engaged in embedded system development

For engineers already working in the embedded field, this course presents a good opportunity to enhance their technical skills. As embedded systems become increasingly complex, the requirements for data processing and algorithms are also rising. By learning this course, they can systematically study the latest knowledge of data structures and algorithms and apply it to actual work projects to improve product performance and competitiveness.
For example, in the automotive electronics field, with the development of autonomous driving technology, there is a need to process large amounts of sensor data and run complex control algorithms. Professionals taking this course can better understand how to use efficient data structures to store and process sensor data, such as lidar data and camera image data, as well as how to design and optimize embedded algorithms related to autonomous driving, such as path planning algorithms and target recognition algorithms.