arXiv subjects

Arttu Nieminen

Publications and source records attributed to Arttu Nieminen.

5 recordsLinked to original sources

arXiv2026

Meta-optical processors for broadband complex-field image operations

All-optical image processing provides a fast and energy-efficient alternative to conventional electronic systems by directly manipulating optical wavefronts. However, metasurface-based optical processors reported to date are often limited in functionality, operating bandwidth, or input modality, which restricts their adaptability across different image processing tasks. Here, we demonstrate a broadband metasurface platform capable of performing diverse analog image processing operations on both amplitude- and phase-encoded inputs. This platform is realized using a single-layer dielectric metasurface designed through an end-to-end, task-driven inverse design framework. By tailoring the spatial-frequency components of incident image wavefronts, the metasurface implements analog operations such as edge detection and pattern recognition across a 200nm wavelength bandwidth in the visible spectrum. Furthermore, we develop a compact processor architecture that integrates imaging and computation within a reduced optical footprint. These results establish a flexible and compact metasurface-based optical processor with strong potential for integration into practical imaging and optical computing systems.

physics.optics↗

arXiv2025

Analytical Framework of Orbital Angular Momentum Beam under Misaligned Detection

This work presents an analytical framework for modeling a detected orbital angular momentum (OAM) spectrum of an optical beam subject to tilt and lateral displacement. Firstly, we demonstrate that both types of misalignment generate OAM sidebands governed by the same functional form, each characterized by a distinct dimensionless parameter that depends on the beam's size and wavelength. Secondly, our analysis reveals that increasing the beam's topological charge broadens the detected OAM spectrum. Lastly, we show that when both tilt and lateral displacement are present, the contribution of the original OAM mode can be tuned: specifically, by orienting the tilt and displacement in perpendicular directions, the resulting misalignment effects interfere destructively, thereby reducing crosstalk.

physics.optics↗

arXiv2022

Harmonic generation in bent graphene with artificially-enhanced spin-orbit coupling

We theoretically investigate the nonlinear response of bent graphene, in the presence of artificially-enhanced spin-orbit coupling, which can occur either via adatom deposition, or by placing the sheet of bent graphene in contact with a spin-orbit active substrate. We discuss the interplay between the spin-orbit coupling and the artificial magnetic field generated by the bending, for both the cases of Rashba and intrinsic spin-orbit coupling. For the latter, we introduce a spin-field interaction Hamiltonian addressing directly the electron spin as a degree of freedom. Our findings reveal that in this case, by controlling the amount of spin-orbit coupling, it is possible to significantly tune the spectrum of the nonlinear signal, achieving, in principle, efficient conversion of light from THz to UV region.

cond-mat.mes-hall↗

arXiv2019

Goos-Hänchen and Imbert-Fedorov Shifts for Epsilon-Near-Zero Materials

We investigate the reflection of a Gaussian beam impinging upon the surface of an epsilon-near-zero (ENZ) medium. In particular, we discuss the occurrence of Goos-Hänchen and Imbert-Fedorov shifts. Our calculations reveal that spatial shifts are significantly enhanced owing to the ENZ nature of the medium, and that their value and angular position can be tuned by tuning the plasma frequency of the medium.

physics.optics↗

arXiv2017

Efficient differentially private learning improves drug sensitivity prediction

Users of a personalised recommendation system face a dilemma: recommendations can be improved by learning from data, but only if the other users are willing to share their private information. Good personalised predictions are vitally important in precision medicine, but genomic information on which the predictions are based is also particularly sensitive, as it directly identifies the patients and hence cannot easily be anonymised. Differential privacy has emerged as a potentially promising solution: privacy is considered sufficient if presence of individual patients cannot be distinguished. However, differentially private learning with current methods does not improve predictions with feasible data sizes and dimensionalities. Here we show that useful predictors can be learned under powerful differential privacy guarantees, and even from moderately-sized data sets, by demonstrating significant improvements with a new robust private regression method in the accuracy of private drug sensitivity prediction. The method combines two key properties not present even in recent proposals, which can be generalised to other predictors: we prove it is asymptotically consistently and efficiently private, and demonstrate that it performs well on finite data. Good finite data performance is achieved by limiting the sharing of private information by decreasing the dimensionality and by projecting outliers to fit tighter bounds, therefore needing to add less noise for equal privacy. As already the simple-to-implement method shows promise on the challenging genomic data, we anticipate rapid progress towards practical applications in many fields, such as mobile sensing and social media, in addition to the badly needed precision medicine solutions.

stat.ML↗