Linearizing nonlinear optics

In the framework of linear optics, light fields do not interact with each other in a medium. Yet, when their field amplitude becomes comparable to the electron binding energies of matter, the nonlinear motion of these electrons emits new dipole radiation whose amplitude, frequency and phase differ from the incoming fields. Such high fields are typically achieved with ultra-short, femtosecond (1fs = 10-15 sec.) laser pulses containing very broad frequency spectra. Here, the matter not only couples incoming and outgoing fields but also causes different spectral components to interact and mix through a convolution process. In this contribution, we describe how frequency domain nonlinear optics overcomes the shortcomings arising from this convolution in conventional time domain nonlinear optics1. We generate light fields with previously inaccessible properties because the uncontrolled coupling of amplitudes and phases is turned off. For example, arbitrary phase functions are transferred linearly to the second harmonic frequency while maintaining the exact shape of the input power spectrum squared. This nonlinear control over output amplitudes and phases opens up new avenues for applications based on manipulation of coherent light fields. One could investigate c.f. the effect of tailored nonlinear perturbations on the evolution of discrete eigenmodes in Anderson localization2. Our approach might also open a new chapter for controlling electronic and vibrational couplings in 2D-spectroscopy3 by the geometrical optical arrangement

Decoupling frequencies, amplitudes and phases in nonlinear optics

In linear optics, light fields do not mutually interact in a medium. However, they do mix when their field strength becomes comparable to electron binding energies in the so-called nonlinear optical regime. Such high fields are typically achieved with ultra-short laser pulses containing very broad frequency spectra where their amplitudes and phases are mutually coupled in a convolution process. Here, we describe a regime of nonlinear interactions without mixing of different frequencies. We demonstrate both in theory and experiment how frequency domain nonlinear optics overcomes the shortcomings arising from the convolution in conventional time domain interactions. We generate light fields with previously inaccessible properties by avoiding these uncontrolled couplings. Consequently, arbitrary phase functions are transferred linearly to other frequencies while preserving the general shape of the input spectrum. As a powerful application, we introduce deep UV phase control at 207 nm by using a conventional NIR pulse shaper

MAG-PUFs:Authenticating IoT devices via electromagnetic physical unclonable functions and deep learning

The challenge of authenticating Internet of Things (IoT) devices, particularly in low-cost deployments with constrained nodes that struggle with dynamic re-keying solutions, renders these devices susceptible to various attacks. This paper introduces a robust alternative mitigation strategy based on Physical-Layer Authentication (PLA), which leverages the intrinsic physical layer characteristics of IoT devices. These unique imperfections, stemming from the manufacturing process of IoT electronic integrated circuits (ICs), are difficult to replicate or falsify and vary with each function executed by the IoT device. We propose a novel lightweight authentication scheme, MAG-PUFs, that uses the unintentional Electromagnetic (EM) emissions from IoT devices as Physical Unclonable Functions (PUFs). MAG-PUFs operate by collecting these unintentional EM emissions during the execution of pre-defined reference functions by the IoT devices. The authentication is achieved by matching these emissions with profiles recorded at the time of enrollment, using state-of-the-art Deep Learning (DL) approaches such as Neural Networks (NN) and Autoencoders. Notably, MAG-PUFs offer compelling advantages: (i) it preserves privacy, as it does not require direct access to the IoT devices; and, (ii) it provides unique flexibility, permitting the selection of numerous and varied reference functions. We rigorously evaluated MAG-PUFs using 25 Arduino devices and a diverse set of 325 reference function classes. Employing a DL framework, we achieved a minimum authentication F1-Score of 0.99. Furthermore, the scheme's efficacy in detecting impostor EM emissions was also affirmed, achieving a minimum F1-Score of 0.99. We also compared our solution to other solutions in the literature, showing its remarkable performance. Finally, we discussed code obfuscation techniques and the impact of Radio Frequency (RF) interference on the IoT authentication process.</p

Deterministic Identification Over Multiple-Access Channels

Deterministic identification over K-input multiple-access channels with average input cost constraints is considered. The capacity region for deterministic identification is determined for an average-error criterion, where arbitrarily large codes are achievable. For a maximal-error criterion, upper and lower bounds on the capacity region are derived. The bounds coincide if all average partial point-to-point channels are injective under the input constraint, i.e. all inputs at one terminal are mapped to distinct output distributions, if averaged over the inputs at all other terminals. The achievability is proved by treating the MAC as an arbitrarily varying channel with average state constraints. For injective average channels, the capacity region is a hyperrectangle. The modulo-2 and modulo-3 binary adder MAC are presented as examples of channels which are injective under suitable input constraints. The binary multiplier MAC is presented as an example of a non-injective channel, where the achievable identification rate region still includes the Shannon capacity region.Comment: ISIT 2023 versio