Longitude convention and octal values

Recently, I encountered a bug in code that handled longitude values, where 33 degrees longitude is typically written as 033. Upon investigation, I discovered that Java was calculating 033 + 1 as 28 instead of 34. This happened because, when we write longitude values like "033" in geographic notation, we mean decimal 33 degrees. However, if we write it directly in Java, C++, or similar languages, it is interpreted as octal 33 (which equals decimal 27) due to the leading zero!

This is a classic example of why it's important to be careful when working with domain-specific number formats - what makes sense in geographic notation can have unexpected behavior when directly translated to programming language syntax!

Verifying Simulink generated C code

I generated embedded C code for the plant in the following MATLAB Simulink model:

To verify that the generated code behaves the same as the Simulink model, I could save the controller outputs to a file, create a C project with a main() function to read those outputs, and feed them to the plant C code at each time step—essentially converting a closed-loop simulation into an open-loop one. This approach would work if I use a single-step numerical integrator/solver like ode1 (Euler method). However, I usually prefer ode4 (Runge-Kutta 4) due to its better accuracy with less computation. Despite RK4 having 4 intermediate computations, it has a global error of O(h^4) and Euler O(h). If RK4 uses a step size of h, Euler would need h^4 to achieve similar accuracy. So, in total Euler would require more function evaluations than RK4—specifically, (1/h^3)/4 times more. For example, if hRK4 = 0.1, Euler would need 250x more computations.

Unfortunately, RK4 is a multi-step method with minor step sizes. Since the Simulink model saves controller outputs only at major steps, reading them from a file to drive the plant might result in divergent behavior—especially if the system has high-frequency dynamics—because controller outputs could differ at minor steps, while reading from the file only accounts for major step outputs.

You can only fully verify the system by integrating the C code into your embedded setup, which generates controller outputs based on real time plant-sensor data. Since testing on an embedded system takes time, to catch issues such as uninitialized variables in the plant code, you can create a C project, build and run your plant model with constant controller inputs. If it compiles and runs without NaN values, you can proceed to the embedded testing phase.

If you want to be more rigorous, you could save the plant outputs together with the controller outputs. In your C project, you read the controller output, feed it to the plant, run it for only one time step, read the Simulink plant output for that time instance from a file, and compare it with the C code plant output. Even this comparison is not perfect and only works for the first time step because Simulink integrator block outputs depend on the system state in the previous time step. To check the whole time span, you also have to save the system state and initialize the system with the correct state at each time step.

Linux: Dynamic vs Static Linking

Today, we had a chat with a colleague about whether a C/C++ binary (ELF) built on one Linux distribution would work on another. After some research, I found that default GCC builds are dynamically linked, and the ELF file contains:

1. Your program's code
2. A list of dynamic dependencies (shared libraries) it needs
3. Symbols that need to be resolved at runtime

However, it does not contain the actual shared libraries - those need to be present on the system where you run the program. You can see these dependencies using ldd. For example, a simple C "hello world" program with only a printf() call can be built with gcc hello.c -o hello. ldd hello output:

linux-vdso.so.1 (0x00007fff6a0e1000)

libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007fdaf6400000)

/lib64/ld-linux-x86-64.so.2 (0x00007fdaf66b3000)

The files size is 15960 bytes. To make is truly independent of any shared library we would build it with gcc -static hello.c -o hello_static. ldd hello_static shows:

not a dynamic executable

The size of hello_static is a whopping 900344 bytes, 56X more than the dynamically linked build.

The good news is that these libraries are present by default on virtually every Linux distribution, so including them in every executable would waste a lot of space. However, you must ensure that C++ version (C++17, C++20, etc.) specific features used in your code are supported by the gcc/g++ version on the target Linux distribution. Of course, the CPU architecture has to be the same too — that goes without saying.

On Windows, C++ libraries come with Visual C++ Redistributable:

Note that  if a Windows PC can successfully load and run a DLL compiled with a specific C++ version, it can also run an EXE compiled with that C++ version because the C++ runtime requirements are the same whether the code is in a DLL or EXE. The only difference is how the code is packaged and loaded, not its runtime requirements.

Music: André Rieu & Mirusia - Ave Maria

C++ std::endl vs "\n"

I have a thread safe print function that uses lock_guard and std::cout and is called from different threads. When I replaced std::endl expressions with "\n", my program stopped printing to console. The main difference is that std::endl does two things: it inserts a newline character AND flushes the output buffer, while "\n" only inserts a newline character. In a multithreaded environment, without flushing, the output might stay in the buffer and not be visible immediately.

Memory Insights from a Segfault

Recently, a program crashed with segmentation fault (sigsegv). Since segmentation faults can happen for a variety of reasons, it took some time to find out that the root cause was insufficient stack (stack overflow) due to a function allocating extra 16kB for a local variable, float var[4096]. The quick fix was to increase the stack size of the thread calling that function from 32kB to 1MB. 

Another solution is to move var to static storage instead of using the stack by changing the definition to static float var[4096]. Upon closer inspection of the code, I saw that var was just used to copy a static array before sending it to another function. Since that function was not modifying the array, there was no reason for the copy. Removing var removed large stack allocation. 

Problems like this are stressful in the short term but provide an opportunity to review the concept of memory regions. Here's a concise breakdown:

Static Storage

- Memory allocated at program start, lives for entire program duration

- Size must be known at compile time 

- Good for: fixed-size buffers that exist for whole program 

- Example: static uint8_t buffer[1024];

- Zero runtime allocation overhead 

- Can't be resized 

Stack

- Memory allocated/deallocated automatically when entering/leaving scope 

- Very fast allocation/deallocation - Limited size (often few MB) 

- Good for: small-to-medium temporary buffers 

- Example: void foo() { uint8_t temp[1024]; }

- Risk of stack overflow with large allocations

Heap 

- Dynamic runtime allocation 

- More flexible but slower than stack 

- Larger size available 

- Good for: large buffers or unknown sizes 

- Example: auto* buffer = new uint8_t[1024];

- Risk of fragmentation 

- Must manually manage memory 

For embedded systems with fixed-size allocation needs: 

1. Use static storage for long-lived, known-size buffers 

2. Use stack for small temporary buffers 

3. Consider static memory pools instead of raw heap allocation if you need dynamic allocation

In Visual Studio 2022, you can use Build > Run Code Analysis to detect functions with large stack allocations, they will be marked with C6262 excessive stack usage warnings.

Embedded Software: Simple vs. Complex

Embedded software is integral to modern technology, ranging from simple home appliances to advanced autonomous systems. It can be broadly classified into two categories: simple (Non-OS) and complex (OS-driven) embedded software.

Simple Embedded Software: When simplicity and low cost are priorities and an OS would be overkill

Examples:

1. Power or temperature monitoring systems.
2. Simple applications in household appliances like ovens and washing machines.

Characteristics:

1. Typically designed for applications with few tasks.
2. No operating system necessary.
3. Software interacts directly with the microcontroller’s hardware (registers etc.), forcing rewrites if the hardware changes.

Advantages:

1. Low power consumption, low cost.
2. Can be developed by electronics engineers, no need for computer engineers because basic embedded programming knowledge is sufficient.
3. Deterministic: By sidestepping the complexity of OS schedulers, simple systems achieve predictable performance.
4. Fewer abstraction layers make verification and validation straightforward, which is a huge advantage for safety-critical certification.

Complex Embedded Software: When multi-tasking, file operations and networking necessitate an OS

Examples:

1. IoT devices requiring seamless connectivity.
2. Systems involving advanced sensor integration or navigation.

Characteristics:

1. Runs on an operating system that manages tasks and system resources.
2. Capable of handling multiple tasks and applications simultaneously.
3. Safety-critical certification is difficult. To make it easier, safety-critical parts should be developed as separate, simpler modules.

Advantages:

1. Less competition and higher profit margins, provided that you have a strong technical team.
2. Requires computer engineers to lead the development because of increased software complexity. Besides embedded software courses, related concepts of algorithms, data structures, and operating systems are also a core parts of computer engineering but not electronics engineering.
3. The OS abstracts low-level hardware management, enabling developers to focus on application logic. A POSIX-compliant application, for instance, can run on any POSIX-supporting OS with minimal changes.
4. Easier for new developers to adapt and contribute due to less hardware dependency.
5. A broad range of pre-existing libraries simplifies development.
6. Operating systems provide abstraction layers (e.g., Linux Device Model), allowing drivers to expose standard interfaces while interacting with specific hardware.
7. Simplifies adding new functionality (e.g. telemetry) or adapting to new hardware (e.g. new/different sensors).
8. With minor modifications, software can be tested on a PC, speeding up testing with less effort (no need for electronic cards, power supplies, etc.) and reducing bugs.

Operating systems can also be categorized as either simple (e.g., FreeRTOS) or complex (e.g., real time Linux with ROS) - but let's leave that topic for another blog post.

Music: Peyk - Don Kafa

Serialization

In C++, to serialize simple data, aka plain old data (POD), where the layout in memory is predictable, you can use a char* (byte) buffer:

This method cannot be used for non-POD types (e.g., those with pointers or virtual methods) because their memory layout is not portable. Examples are std::string, std::vector. For such types, you can use std::ostringstream:

Both approaches assume that the serialized data format and endianness match between serialization and deserialization. For more complex cases, use libraries like nlohmann/json for JSON-based serialization and Boost.Serialization for binary/text serialization with more features.