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Digital acquisition converts a continuous voltage signal into timed numerical samples. A practical chain is a suitably buffered and amplified sensor signal, an analogue anti-aliasing filter, a sampling or sample-and-hold stage, an analog-to-digital converter, and a clocked data-storage system. Choose the gain so that the interesting variation uses the available input range without clipping. Finite input impedance can load a sensor, so an appropriate buffer or operational amplifier may be needed.

Sampling makes time discrete. If the conditioned signal is confined below frequency $f_{\max }$, choose a sampling rate $f_s>2f_{\max }$, allowing a transition band for the physical filter. The Nyquist–Shannon sampling theorem permits ideal reconstruction of a suitably bandlimited function from exact samples. Without filtering, components separated by integer multiples of $f_s$ have identical samples; in particular real sinusoids at $f$ and $f_s-f$ can be indistinguishable. This aliasing cannot generally be undone by later digital filtering. Oversampling can ease filter design, but does not replace the requirement to suppress unwanted high-frequency content before sampling.

Amplitude quantization is separate from sampling. A uniform $b$-bit converter covering voltage span $V_{\mathrm{span}}$ has step $q\simeq V_{\mathrm{span}}/2^b$. Rounding an in-range input gives the uniform-quantization error bound $|ϵ|\le q/2$. If the rounding errors can additionally be modeled as uniform and uncorrelated, their standard deviation is $q/\sqrt{12}$. This statistical estimate is not a guarantee for a constant input or a coherent periodic signal. More bits increase resolution, but accuracy also depends on voltage-reference calibration, offset, gain error, nonlinearity and electronic noise. Out-of-range signals clip and invalidate the rounding bound.

Clock quality matters too. Timing jitter $\delta t$ gives a voltage error approximately $(dV/dt)\delta t$; for a sinusoid its scale grows with $2\pi f{V}_p{\sigma }_t$. A clock calibration error biases measured frequencies and time intervals. Simultaneous multichannel signals need known relative timing, and the sampling and analogue settling times must be appropriate to the signal bandwidth.

For $n_c$ channels the raw recording rate is approximately $n_c{f}_{s}b$ bits per second, before overhead, so storage and sustained transfer speed must prevent missing data. Store units, channel identities, timestamps, sampling rate, input ranges and calibration information along with the readings. Preserve the original samples when applying digital filters or other processing. Averaging can improve precision for independent fluctuating errors, but cannot restore aliased frequencies, clipped peaks or a fixed rounding bias. Digital recording makes reproducible analysis and transmission possible when its sampling, amplitude and timing limitations are all controlled.

For a real signal with highest frequency $f_{\max }=40\mathrm{MHz}$, the Nyquist–Shannon sampling theorem gives the nominal Nyquist sampling rateTo cover an arbitrary component exactly at the band edge, choose $f_s>80\mathrm{MHz}$: a $40\mathrm{MHz}$ sine sampled at exactly $80\mathrm{MHz}$ can have every sample equal to zero for an unfortunate phase. Practical aliasing suppression also calls for a margin above the nominal limiting rate.

With rounding to the nearest digitizer level, quantization with step $q=0.1\mathrm{mV}$ has the uniform-quantization error boundThus the stated digitizer meets the requested nominal amplitude precision per sample, assuming no clipping or other errors. The bandwidth sets the nominal minimum sampling rate; the level spacing sets the rounding precision. Strictly, the sampling theorem reconstructs exactly from exact samples. A uniform $0.05\mathrm{mV}$ error bound on the entire reconstructed continuous waveform is not guaranteed by the level spacing and sampling rate alone; that stronger requirement needs a specified reconstruction and noise model.

For the $110\mathrm{Hz}$ fundamental alone, the Nyquist–Shannon sampling theorem places the limiting sampling rate at twice that frequency:Use a rate strictly above this boundary for a sinusoid of unknown phase: exactly two samples per cycle can, for a particular phase, all fall at zero. If higher Fourier harmonics are retained, the sampling rate must instead exceed twice the highest retained frequency, or those components must be removed to prevent aliasing.

For a straightforward discrete Fourier transform estimate, Fourier frequency resolution obeys $\Delta f\sim 1/T_{\mathrm{obs}}$. Resolving $0.07\mathrm{Hz}$ therefore suggestsAt the limiting sampling rate this is about $f_s{T}_{\mathrm{obs}}\simeq 3143$ samples. These are the usual sampling and spectral-resolution estimates. The $14.3\mathrm{s}$ value is not a universal lower bound on single-tone frequency estimation: fitting a sinusoidal model with sufficiently low noise can estimate its frequency more precisely than the spacing of discrete Fourier transform bins. The question gives no noise level or estimator, so a sharper universal minimum time cannot be deduced.

The fastest clearly resolved late oscillations occupy roughly $0.004$–$0.005\mathrm{s}$ per cycle when compared with the $0.05\mathrm{s}$ major-tick spacing. Thus $f_{\max }$ is of order $200$–$250\mathrm{Hz}$ and the Nyquist–Shannon sampling theorem requiresA sampling rate of order $500\mathrm{samples}{\mathrm{s}}^{-1}$ is the estimated lower boundary for the plotted signal bandwidth; practical acquisition needs anti-alias filtering and margin. This is not a determination of LIGO's actual acquisition rate.^[ref]

The bit-depth requirements for waveform digitization need a specified precision. The numerical bit depth is underdetermined by the figure. If the digitizer covers a full-scale range $V_{\mathrm{FS}}$ and the required quantization step is at most $\Delta$, thenFor a uniform quantizer its ideal quantization-noise rms is approximately $\Delta /\sqrt{12}$. No permitted quantization error, noise criterion or calibrated digitizer range is specified. The plotted reconstruction band does not itself define one. Therefore no unique finite bit count, or exact lossless digitization of an arbitrary real-valued waveform, follows from the supplied data.

The Nyquist–Shannon sampling theorem gives a Nyquist rate of $200$ samples per second for a $100$ Hz upper frequency. In practice choose $f_s>200$ Hz, for example $500$ Hz with $\Delta t=2$ ms, and use an anti-aliasing filter whose passband retains the required signal below the Nyquist frequency. Exactly $200$ Hz leaves no filter transition band and can entirely miss a $100$ Hz sinusoid at an unfortunate phase; it is a limiting rate, not a robust acquisition choice.

The conventional Nyquist frequency isFrequencies above this cutoff are indistinguishable from lower-frequency sampled sinusoid aliases. The Nyquist–Shannon sampling theorem requires a band limit strictly below the cutoff for general phase information: at exactly the cutoff, some phases can give zero samples. Thus $32$ hours is the conventional limiting period, with unambiguous reconstruction requiring a longer period under those sampling assumptions.

For a Schwartz function on the real numbers, its periodization $P_f$ is a smooth periodic function of period of a function one. Rapid decay gives uniform convergence on $[0,1]$ of the defining series and each differentiated series. Its $k$th Fourier coefficient isThe change of variable $u=x+n$ uses $e^{2\pi ikn}=1$. Rapidly decreasing Fourier coefficients give the absolutely and uniformly convergent Fourier series $P_f(x)={\sum }_k\hat{f}(k)e^{2\pi ikx}$. Evaluation at $x=0$ proves the Poisson summation formula.^[ref]

The same identity explains aliasing in the Nyquist–Shannon sampling theorem: applying the Poisson summation formula to the Schwartz function $x\mapsto f(x)e^{-2\pi i\xi x}$ givesThe sampled spectrum is periodic in frequency, and shifted copies of the continuous Fourier transform add together. When those copies overlap, the samples cannot separate their contributions. The Nyquist–Shannon sampling theorem adds a bandlimited function hypothesis to make reconstruction possible.

A point-sampling model assigns $p_{rc}=f(x_c,y_r)$ to a pixel on a grid, where $f$ is a continuous function representing an image signal. An area-sampling model instead assigns the average value of a function over a specified footprint. These are different models: a narrow feature between point samples can be missed, whereas its contribution to an area sample depends on its width and shade. Neither model says that the recorded value uniquely determines everything inside a grid cell.

The upper panels start with exactly the same $6\times 4$ array. Nearest-neighbor interpolation repeats its values into a $24\times 16$ display; successive linear interpolations in the two coordinate directions produce a smoother $24\times 16$ display. Original sample positions are marked. Both displays use 384 output pixels, but only the same 24 recorded values. The extra values follow the chosen interpolation rule. Between-grid appearance is inferred, not independently recorded.

Spatial resolution needs a stated meaning. A grid over a rectangle of width $L_x$ and height $L_y$ has nominal cell pitches $\Delta x=L_x/W$ and $\Delta y=L_y/H$. Over the same rectangle, doubling both dimensions halves these pitches and quadruples the pixel count. That is a finer grid, not a guarantee of more recoverable detail. Blurring before sampling, quantization and aliasing can make different originals share the same recorded array. Enlarging the rectangle without adding samples instead increases the displayed pitch.

A reproducible computational demonstration makes the ambiguity exact. Take the one-dimensional profiles $f(x)=0.5+0.4\sin (2\pi x)$ and $g(x)=0.5+0.4\sin (10\pi x)$, repeated identically along every row. Sample at $x_j=j/4$ for $j=0,\dots ,8$. Since $10\pi x_j=2\pi x_j+2\pi j$, the fact that the sine is a periodic function gives $f(x_j)=g(x_j)$. Both yield $0.5,0.9,0.5,0.1,0.5,0.9,0.5,0.1,0.5$, although at $x=1/8$ they give approximately $0.78284$ and $0.21716$. The lower panel draws both originals and their common samples: no operation on those samples alone can identify the original profile. This is aliasing in an ideal point-sampling model, without image noise or rounding.

The Nyquist–Shannon sampling theorem describes reconstruction under a bandwidth restriction and ideal sampling assumptions; it does not grant uniqueness for arbitrary image signals from a finite raster image.^[ref] The beautiful lesson is constructive: choose what detail the grid should preserve before sampling, and choose a reconstruction rule suited to the information actually retained.

For a roughly square array of retinal receptors with surface density $n$ and image distance $f$, the pitch is $s\simeq n^{-1/2}$ and its angular scale is $s/f$. Reconstructing a periodic spatial pattern requires at least two samples per period under the Nyquist–Shannon sampling theorem. Receptor density alone does not specify lattice geometry, contrast sensitivity or a unique point-source resolution criterion.

Signal sampling records a continuous signal at discrete times. Uniform sampling creates shifted frequency spectra separated by the sample rate; avoiding overlap requires suitable bandwidth limits and an anti-aliasing filter. The Nyquist–Shannon sampling theorem describes exact reconstruction for an ideal band-limited signal.

In spatial sampling of a wave, a detector records a wave at positions $x_n=np$ separated by pitch $p>0$. Angular wavenumbers differing by $2\pi m/p$, with integers $m$, give identical samples of a complex Fourier mode:This is spatial aliasing. To distinguish arbitrary sinusoids within a prescribed band, require $|k|<\pi /p$, equivalently $|{\nu }_x|<1/(2p)$, the Nyquist spatial frequency. Thus $p<{\lambda }_{\min }/2$ for the shortest allowed wavelength. At equality a sine can vanish at every sample, so phase information can be lost. The Nyquist–Shannon sampling theorem states the precise reconstruction conditions for a bandlimited function; a finite scan introduces further limitations.

The synthetic two-tone snapshot iswhere both ${\nu }_j$ count cycles per metre. The component wavelengths are $0.50\mathrm{m}$ and $0.125\mathrm{m}$. With $p=0.05\mathrm{m}$, both lie below the Nyquist spatial frequency of $10$ cycles per metre. With $p=0.10\mathrm{m}$, the $8$-cycle component aliases to the $2$-cycle component: at every $x_n=n/10$, $\cos (2\pi \cdot 8x_n)=\cos (2\pi \cdot 2x_n)$. The sampled sum is therefore indistinguishable from $1.5\cos (4\pi x)$, although the continuous curves differ between samples. Drawing lines between sample points cannot resolve this ambiguity.

Rounding a voltage to the nearest level in a uniform grid of spacing $q$ gives a quantization error of magnitude at most $q/2$, provided the input is within the converter's range. This follows because each grid point represents the interval bounded by the midpoints to its two neighbors. Clipping invalidates the bound.

The bound concerns recorded samples, whereas the Nyquist–Shannon sampling theorem concerns reconstruction from exact samples of a bandlimited function. If a reconstruction is $\tilde{v}(t)={\sum }_n{Q}_q(v_n)w_n(t)$, its error is ${\sum }_n{ϵ}_n{w}_n(t)$, so a general bound involves $(q/2){\sum }_n|w_n(t)|$, not simply $q/2$. Thus sampling bandwidth and per-sample rounding precision are distinct specifications. Under an additional uniform independent-error model, the quantization-noise standard deviation is $q/\sqrt{12}$, an rms statement rather than a worst-case reconstruction guarantee.