If you've ever run a data acquisition loop in software and noticed inconsistent sample intervals, you're not alone. This is the single most common problem engineers hit when moving from bench instruments to programmatic DAQ — and the fix is simpler than you think.

The Problem

Most beginner DAQ scripts use a software-timed loop:

using NIDAQMX

task = create_task()
add_analog_input(task, "Dev1/ai0")
start(task)

data = Float64[]
for i in 1:1000
    push!(data, read_single(task))
    sleep(0.001)  # ← This is the problem
end

sleep(0.001) does NOT guarantee 1 ms spacing. Your OS scheduler, garbage collector, and other processes all compete for CPU time. Actual intervals range from 0.5 ms to 15+ ms — that's 30x jitter.

The Fix: Hardware Timing

Let the DAQ card's onboard clock control sample timing:

task = create_task()
add_analog_input(task, "Dev1/ai0")
configure_sample_clock(task, 
    rate = 1000.0,           # 1 kHz sample rate
    samples_per_channel = 1000,
    sample_mode = :finite
)
start(task)
data = read(task, 1000)     # All 1000 samples, hardware-timed

The DAQ card's crystal oscillator is accurate to ±50 ppm. At 1 kHz, that's ±0.05 μs of jitter per sample — versus the milliseconds you get with software timing.

When It Matters Most

• Frequency analysis (FFT) — uneven spacing violates the assumption behind DFT, producing spectral leakage and phantom peaks

• Control loops — inconsistent Δt means your PID gains behave differently at different loop iterations

• Cross-channel correlation — if channels aren't sampled simultaneously, phase measurements are meaningless

Hardware timing isn't optional for serious measurement work. It's the foundation everything else builds on.

This is covered in depth in Chapter 2 of the full DAQ Timing & Triggers course inside SignalFlow Academy.