Dual-band, double-negative, polarization-independent metamaterial for the visible spectrum

We present the first dual-band negative index metamaterial that operates in the visible spectrum. The optimized four-functional-layer metamaterial structure exhibits the first double-negative (i.e., simultaneously negative permittivity and permeability) band in the red region of the visible spectrum with a figure of merit of 1.7 and the second double-negative band in the green region of the visible spectrum with a figure of merit of 3.2. The optical behavior of the proposed structure is independent of the polarization of the incident field. This low-loss metamaterial structure can be treated as a modified version of a fishnet metamaterial structure with an additional metal layer of different thickness in a single functional layer. The additional metal layer extends the diluted plasma frequency deep into the visible spectrum above the second order magnetic resonance of the structure, hence provides a dual band operation with simultaneously negative effective permittivity and permeability. Broadband metamaterials with multiple negative index bands may be possible with the same technique by employing higher order magnetic resonances. The structure can be fabricated with standard microfabrication techniques that have been used to fabricate fishnet metamaterial structures.Comment: 26 Pages, 6 Figures, 2 Tables, 2 Medi

Plasmonic Superlens Imaging Enhanced by Incoherent Active Convolved Illumination

We introduce a loss compensation method to increase the resolution of near-field imaging with a plasmonic superlens that relies on the convolution of a high spatial frequency passband function with the object. Implementation with incoherent light removes the need for phase information. The method is described theoretically and numerical imaging results with artificial noise are presented, which display enhanced resolution of a few tens of nanometers, or around one-fifteenth of the free space wavelength. A physical implementation of the method is designed and simulated to provide a proof-of-principle, and steps toward experimental implementation are discussed

Negative Refraction Gives Rise to the Klein Paradox

Electromagnetic negative refraction in metamaterials has attracted increasingly great interest, since its first experimental verification in 2001. It potentially leads to the applications superior to conventional devices including compact antennas for mobile stations, imaging beyond the diffraction limit, and high-resolution radars, not to mention the anamolous wave propagation in fundamental optics. Here, we report how metamaterials could be used to simulate the "negative refraction of spin-zero particles interacting with a strong potential barrier", which gives rise to the Klein paradox--a counterintuitive relativistic process. We address the underlying physics of analogous wave propagation behaviours in those two entirely different domains of quantum and classical.Comment: 4 journal pages, 2 figure

Enhancing the resolution of hyperlens by the compensation of losses without gain media

We present a method to improve the resolution of available hyperlenses in the literature. In this method, we combine the operation of hyperlens with the recently proposed plasmon injection scheme for loss compensation in metamaterials. Image of an object, which is otherwise not resolvable by the hyperlens alone, was reconstructed up to the minimum feature size of one seventh of the free-space wavelength.Comment: 4 pages, 5 figure

Demet Ş. Dinler, Workers Exictence Problem [İşçinin Varlık Problemi]

Book Revie

Workforce Efficiency Increase For The Online Sales In-Store Picking Operation

The online food retailing got more attention to itself with the global coronavirus outbreak. Online marketing growth rate reached above 40% in the lockdown months. It became more important to deliver the orders faster than opponents. This study examines one of the biggest food retails company’s in-store picking operation for workforce efficiency increase. The picker job description and daily work routines observed through observation forms. The results are validated by data extracted from the Collectify system, which is used for picking operation and has all the time stamp of picker’s action. Analyzing data and combining with the output of observation forms, the new picking model proposed for the fresh departments (meat, deli, fruit & vegetable) products. Having two weeks of trial and after realizing the results with the Collectify system data, the new model helped operating costs decrease by 7,7% in the picking operation

Turkish-Russian adversarial collaboration in Syria, Libya, and Nagorno-Karabakh

Russia and Turkey are backing opposing warring parties in three active conflicts. How­ever, this adversarial positioning has not hindered cooperation between Moscow and Ankara. They reign in opposing sides and, in effect, stage-manage their respective theatres of wars. Through multilateral arrangements, Europe is an enabler of Tur­key's position and could leverage its support to push Ankara to cooperate more effec­tively with its Western partners. (author's abstract

Plasmon Injection to Compensate and Control Losses in Negative Index Metamaterials

Metamaterials have introduced a whole new world of unusual materials with functionalities that cannot be attained in naturally occurring material systems by mimicking and controlling the natural phenomena at subwavelength scales. However, the inherent absorption losses pose fundamental challenge to the most fascinating applications of metamaterials. Based on a novel plasmon injection (PI or \Pi) scheme, we propose a coherent optical amplification technique to compensate losses in metamaterials. Although the proof of concept device here operates under normal incidence only, our proposed scheme can be generalized to arbitrary form of incident waves. The \Pi-scheme is fundamentally different than major optical amplification schemes. It does not require gain medium, interaction with phonons, or any nonlinear medium. The \Pi-scheme allows for loss-free metamaterials. It is ideally suited for mitigating losses in metamaterials operating in the visible spectrum and is scalable to other optical frequencies. These findings open the possibility of reviving the early dreams of making 'magical' metamaterials from scratch.Comment: Main text, 8 pages with 4 figures; supplemental material, 21 pages with 21 figure

Combining Features and Semantics for Low-level Computer Vision

Visual perception of depth and motion plays a significant role in understanding and navigating the environment. Reconstructing outdoor scenes in 3D and estimating the motion from video cameras are of utmost importance for applications like autonomous driving. The corresponding problems in computer vision have witnessed tremendous progress over the last decades, yet some aspects still remain challenging today. Striking examples are reflecting and textureless surfaces or large motions which cannot be easily recovered using traditional local methods. Further challenges include occlusions, large distortions and difficult lighting conditions. In this thesis, we propose to overcome these challenges by modeling non-local interactions leveraging semantics and contextual information. Firstly, for binocular stereo estimation, we propose to regularize over larger areas on the image using object-category specific disparity proposals which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The disparity proposals encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class 'car' into a superpixel-based graphical model and demonstrate its benefits especially in reflective regions. Secondly, for 3D reconstruction, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by localizing objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows to reduce noise while completing missing surfaces as objects of similar shape benefit from all observations for the respective category. Evaluations with respect to LIDAR ground-truth on a novel challenging suburban dataset show the advantages of modeling structural dependencies between objects. Finally, motivated by the success of deep learning techniques in matching problems, we present a method for learning context-aware features for solving optical flow using discrete optimization. Towards this goal, we present an efficient way of training a context network with a large receptive field size on top of a local network using dilated convolutions on patches. We perform feature matching by comparing each pixel in the reference image to every pixel in the target image, utilizing fast GPU matrix multiplication. The matching cost volume from the network's output forms the data term for discrete MAP inference in a pairwise Markov random field. Extensive evaluations reveal the importance of context for feature matching.Die visuelle Wahrnehmung von Tiefe und Bewegung spielt eine wichtige Rolle bei dem Verständnis und der Navigation in unserer Umwelt. Die 3D Rekonstruktion von Szenen im Freien und die Schätzung der Bewegung von Videokameras sind von größter Bedeutung für Anwendungen, wie das autonome Fahren. Die Erforschung der entsprechenden Probleme des maschinellen Sehens hat in den letzten Jahrzehnten enorme Fortschritte gemacht, jedoch bleiben einige Aspekte heute noch ungelöst. Beispiele hierfür sind reflektierende und texturlose Oberflächen oder große Bewegungen, bei denen herkömmliche lokale Methoden häufig scheitern. Weitere Herausforderungen sind niedrige Bildraten, Verdeckungen, große Verzerrungen und schwierige Lichtverhältnisse. In dieser Arbeit schlagen wir vor nicht-lokale Interaktionen zu modellieren, die semantische und kontextbezogene Informationen nutzen, um diese Herausforderungen zu meistern. Für die binokulare Stereo Schätzung schlagen wir zuallererst vor zusammenhängende Bereiche mit objektklassen-spezifischen Disparitäts Vorschlägen zu regularisieren, die wir mit inversen Grafik Techniken auf der Grundlage einer spärlichen Disparitätsschätzung und semantischen Segmentierung des Bildes erhalten. Die Disparitäts Vorschläge kodieren die Tatsache, dass die Gegenstände bestimmter Kategorien nicht willkürlich geformt sind, sondern typischerweise regelmäßige Strukturen aufweisen. Wir integrieren sie für die komplexe Objektklasse 'Auto' in Form eines nicht-lokalen Regularisierungsterm in ein Superpixel-basiertes grafisches Modell und zeigen die Vorteile vor allem in reflektierenden Bereichen. Zweitens nutzen wir für die 3D-Rekonstruktion die Tatsache, dass mit der Größe der rekonstruierten Fläche auch die Wahrscheinlichkeit steigt, Objekte von ähnlicher Art und Form in der Szene zu enthalten. Dies gilt besonders für Szenen im Freien, in denen Gebäude und Fahrzeuge oft vorkommen, die unter fehlender Textur oder Reflexionen leiden aber ähnlichkeit in der Form aufweisen. Wir nutzen diese ähnlichkeiten zur Lokalisierung von Objekten mit Detektoren und zur gemeinsamen Rekonstruktion indem ein volumetrisches Modell ihrer Form erlernt wird. Dies ermöglicht auftretendes Rauschen zu reduzieren, während fehlende Flächen vervollständigt werden, da Objekte ähnlicher Form von allen Beobachtungen der jeweiligen Kategorie profitieren. Die Evaluierung auf einem neuen, herausfordernden vorstädtischen Datensatz in Anbetracht von LIDAR-Entfernungsdaten zeigt die Vorteile der Modellierung von strukturellen Abhängigkeiten zwischen Objekten. Zuletzt, motiviert durch den Erfolg von Deep Learning Techniken bei der Mustererkennung, präsentieren wir eine Methode zum Erlernen von kontextbezogenen Merkmalen zur Lösung des optischen Flusses mittels diskreter Optimierung. Dazu stellen wir eine effiziente Methode vor um zusätzlich zu einem Lokalen Netzwerk ein Kontext-Netzwerk zu erlernen, das mit Hilfe von erweiterter Faltung auf Patches ein großes rezeptives Feld besitzt. Für das Feature Matching vergleichen wir mit schnellen GPU-Matrixmultiplikation jedes Pixel im Referenzbild mit jedem Pixel im Zielbild. Das aus dem Netzwerk resultierende Matching Kostenvolumen bildet den Datenterm für eine diskrete MAP Inferenz in einem paarweisen Markov Random Field. Eine umfangreiche Evaluierung zeigt die Relevanz des Kontextes für das Feature Matching