Introduction / Context:
DSP applies mathematical operations to sampled data to enhance, analyze, or transform signals originating in the analog world. Typical aims include noise reduction, equalization, and band selection for measurement, communication, and audio applications.
Given Data / Assumptions:
- Signals are already digitized by an ADC.
- We want primary use-cases, not peripheral tasks.
- “Conditioning” includes scaling, windowing, equalization, and normalization in the digital domain.
Concept / Approach:
Filtering (low-pass, high-pass, band-pass, adaptive) is central to DSP. Conditioning encompasses operations that prepare signals for subsequent analysis or control, such as gain normalization, de-trending, or resampling. Together they represent a core DSP workload.
Step-by-Step Solution:
Sample the analog signal with sufficient rate and resolution.Apply digital filters to remove noise or isolate bands.Perform conditioning (scaling, offset correction, windowing) prior to feature extraction or control.
Verification / Alternative check:
Standard DSP curricula emphasize filtering and conditioning as first-line treatments of acquired data.
Why Other Options Are Wrong:
sending, receiving: Communication tasks are broader; core DSP still centers on filtering/conditioning.digitizing, weighting: Digitizing is pre-DSP (ADC); “weighting” is too narrow.leveling, translating: Vague and not representative of the main workload.
Common Pitfalls:
Equating DSP solely with Fourier transforms; many tasks are time-domain conditioning.
Final Answer:
filtering, conditioning
Discussion & Comments