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Data Acquisition Channel Budget and Sampling Plan Design

30 Chapters
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01
Introduction to data acquisition channel budgets
defining channel count, per-channel cost drivers, and the impact of budget constraints on sampling plan design.
acquisitiondefining
02
Signal characteristics and channel requirements
analyzing signal bandwidth, amplitude range, source impedance, and sensor types to determine per-channel specifications.
characteristicsrequirements
03
Sampling theory fundamentals for channel budgeting
Nyquist criterion, anti-aliasing filter requirements, and oversampling trade-offs in multi-channel systems.
Samplingbudgeting
04
ADC architecture selection and channel budget impact
comparing multiplexed, simultaneous-sampling, and per-channel ADC topologies in terms of cost, speed, and channel density.
ADCarchitecture
05
Channel count and multiplexing strategies
calculating effective per-channel sampling rate, settling time requirements, and crosstalk considerations in multiplexed architectures.
multiplexingstrategies
06
Budgeting for signal conditioning per channel
amplifier gain stages, filter complexity, isolation, and their cost and board-space contributions.
Budgetingconditioning
07
Power and thermal budget allocation across channels
estimating per-channel power consumption and thermal density in high-channel-count data acquisition systems.
allocationchannels
08
Clocking and synchronization budget
distributing sample clocks, managing jitter, and synchronizing multiple ADCs within a fixed channel budget.
Clockingsynchronization
09
Data throughput and interface bandwidth budgeting
calculating aggregate data rates, selecting serial/parallel interfaces, and ensuring interface capacity matches channel count and sampling rate.
throughputinterface
10
Memory and buffering budget for multi-channel acquisition
sizing FIFOs, DMA buffers, and local storage based on channel count, sample rate, and latency requirements.
bufferingmulti-channel
11
Error budget analysis per channel
allocating gain, offset, noise, and nonlinearity errors across the signal chain to meet system accuracy targets.
analysisallocating
12
Noise budgeting in multi-channel systems
calculating thermal noise, flicker noise, and ADC noise contributions, and optimizing channel budget for SNR requirements.
budgetingmulti-channel
13
Calibration and compensation budget
planning per-channel calibration routines, reference distribution, and cost of calibration hardware and time.
Calibrationcompensation
14
Channel budget vs. sampling rate trade-off analysis
quantifying the relationship between number of channels, per-channel sampling rate, and total system cost.
samplingtrade-off
15
Sampling plan design for periodic signals
selecting sampling frequency and record length to avoid spectral leakage and ensure coherent sampling across channels.
Samplingperiodic
16
Sampling plan design for transient and event capture
trigger strategies, pre-trigger buffering, and sample rate selection for capturing fast events within channel constraints.
Samplingtransient
17
Multi-rate sampling plan design
assigning different sampling rates to channels based on signal dynamics while respecting total throughput and memory budgets.
Multi-ratesampling
18
Anti-aliasing filter design under channel budget constraints
choosing filter order, cutoff frequency, and implementation technology for multiple channels cost-effectively.
Anti-aliasingconstraints
19
Aperture and timing uncertainty budgeting
analyzing sample-and-hold aperture jitter, clock jitter, and their effect on effective number of bits (ENOB) per channel.
Apertureuncertainty
20
Channel-to-channel matching and skew budgeting
specifying and achieving gain, offset, and timing matching across channels in simultaneous and multiplexed systems.
Channel-to-channelmatching
21
PCB layout and grounding budget for multi-channel acquisition
partitioning analog and digital domains, minimizing crosstalk, and managing return currents within channel density constraints.
PCBgrounding
22
Reference and excitation distribution budgeting
designing low-noise reference and sensor excitation networks for multiple channels with minimal drift and loading errors.
Referenceexcitation
23
Digital filtering and decimation budget
allocating processing resources for per-channel digital filters and decimators, and their impact on channel count and power.
filteringdecimation
24
System-level channel budget verification
building a requirements traceability matrix and performing worst-case analysis to validate channel count and sampling plan.
System-levelverification
25
Cost modeling and optimization of channel budgets
developing parametric cost models for channels and sampling plans, and applying optimization techniques.
modelingoptimization
26
Case study
designing a 64-channel vibration monitoring system—channel budget, sampling plan, and component selection under cost constraints.
designingvibration
27
Case study
high-speed simultaneous sampling for power quality analysis—balancing channel count, sampling rate, and isolation requirements.
high-speedsimultaneous
28
Case study
low-power wireless sensor node with 8 channels—budgeting power, sampling rate, and duty cycling for battery life.
low-powerwireless
29
Simulation and modeling of channel budgets
using behavioral models to predict system performance and validate sampling plan before hardware implementation.
Simulationmodeling
30
Review and best practices
summarizing key principles, common pitfalls, and a checklist for designing robust channel budgets and sampling plans.
practicessummarizing