## 4 Video Rate Identification  
In this section we proceed to evaluate the ability of a current commercial THz system to precisely and rapidly get the signature of the THID tag described previously. To ensure a usable tag reading, the SNR of the chosen THz system must be large enough in the considered bandwidth, i.e., from 0.25 THz to 0.38 THz in the present case. Otherwise, the commercial TDS-THz systems that are currently available permit very fast acquisition of the frequency-dependent transmission coefficient of any sample under test, over a bandwidth spreading from 0.1 to 4 THz with a spectral resolution of several GHz. Such systems, as well as CW ones, can be obviously considered as good candidates for video-rate identification application, especially since their SNR remain sufficient. However, as the noise level is getting higher when measurement averaging is getting lower, the identification process becomes more and more hazardous as the acquisition time decreases. Therefore, we address below the problem to determine the highest reading speed compatible to a reliable identification process of the tag signature. To estimate the shortest acquisition duration required to accurately identify the tag when read by the TAS/500 SP system, we compared one hundred spectral signatures measured on the 19-layer tag with the reference signature. For each set of measurements, we vary the number of averaging and thereby the whole measurement acquisition time, whereas the reference signature has been obtained using the largest averaging number (32,768), leading to an acquisition time of a few minutes (red curve in Figure 6). Then, all the binary codes obtained by using the coding technique previously described on all of these signatures, are calculated and compared to the reference one: \(<\)010101\(>\). Finally, the identification reliability is quantified by calculating the BER, which is simply obtained by counting the erroneous binary codes among the hundred measurements of each set. For all these steps, we used a homemade software based on a find-peak algorithm to objectively retrieve the binary code hidden in the signature. More precisely, the algorithm consists in two main steps:  
* First, the spectral position and width of the PBG is determined by scanning the whole spectral signature. The PBG bandwidth is therefore simply defined as the spectral region where the modulus of the transfer function is lower than a given threshold, empirically chosen at -6 dB in the present study (see horizontal black dashed line in Figure 6);
* Secondly, peaks are detected within the PBG using a peak-find algorithm considering only transmission peaks (1) whose magnitudes are greater than a given \"decision threshold\" (see horizontal black continuous line in Figure 6), and (2) which have spectrally separated each other by at least one channel width. As previously mentioned, this latter condition ensures a reliable detection of close peaks by taking into account the spectral resolution of the THz reader and FWHM of the peaks. Figure 5: Variation of the channel width \(\hat{s}\)\(f\) (in blue) and of the corresponding coding capacity \(\rho\) (in red) versus the absorption coefficient of the materials used to fabricate the 19 layers’ tag. As an example, Figure 6 illustrates these two steps in the case of an 8 ms acquisition time measurement (blue doted curve), compared to the reference one (red curve). It is easy to see that the longer the acquisition time, the smaller the noise, the clearer the peaks appear in the PBG, and the better the binary code can be extracted. For 8 ms measurement duration, the noise level (represented by the grey error bars) is large enough to induce erroneous binary code extraction.