Optical Encoding Research Articles

Asymmetric Pairing of Cholesteric Liquid Crystal Droplets for Programmable Photonic Cross-Communication.

The photonic cross-communication between photonic droplets has provided complex color patterns through multiple reflections, potentially serving as novel optical codes. However, the cross-communication is mostly restricted to symmetric pairs of identical droplets. Here, a design rule is reported for the asymmetric pairing of two distinct droplets to provide bright color patterns through strong cross-communication and enrich a variety of optical codes. Cholesteric liquid crystal (CLC) droplets with different stopband positions and sizes are paired. The brightness of corresponding color patterns is maximized when the pairs are selected to effectively guide light along the double reflection path by stopbands of two droplets. The experimental results are in good agreement with a geometric model where the blueshift of stopbands is better described by the angles of refraction rather than reflection. The model predicts the effectiveness of pairing quantitatively, which serves as a design rule for programming the asymmetric photonic cross-communication. Moreover, three distinct droplets can be paired in triangular arrays, where all three cross-communication paths yield bright color patterns when three droplets are selected to simultaneously satisfy the rule. It is believed that asymmetric pairing of distinct CLC droplets opens new opportunities for programmable optical encoding in security and anti-counterfeiting applications.

Sub-micron microparticles with tunable fluorescence emission obtained via co-self-assembly of amidoximed polymeric ligands and lanthanide ions.

Lanthanide coordinating polymeric microparticles have witnessed increasing research interests during the past decades due to their versatile morphology and tunable fluorescent properties. Herein, we have synthesized an amidoximed block copolymer containing aromatic backbone and pendent amidoxime as well as carboxyl groups, which has been employed as the ligand to sensitize the intrinsic fluorescence emission of lanthanide ions of Tb3+ and Eu3+. Furthermore, the lanthanide coordinating polymeric microparticles showing tunable green and red emission fluorescence have been prepared via the emulsion confinement co-self-assembly of amidoximed polymeric ligands with Tb3+ and Eu3+. It is found that both the fluorescence emission and sizes of obtained fluorescent microparticles can be easily modulated in a wide range by tuning concentration of polymers and lanthanide ions, as well as emulsion evaporation temperature. Thanks to their tunable sizes (250-900nm), fluorescence emission as well as presence of surface active functional groups, the present fluorescent microparticles would find potential applications in in-vitro detection, optical encoding and devices.

MAS-Net OCT: a deep-learning-based speckle-free multiple aperture synthetic optical coherence tomography.

High-resolution spectral domain optical coherence tomography (SD-OCT) is a vital clinical technique that suffers from the inherent compromise between transverse resolution and depth of focus (DOF). Meanwhile, speckle noise worsens OCT imaging resolving power and restricts potential resolution-enhancement techniques. Multiple aperture synthetic (MAS) OCT transmits light signals and records sample echoes along a synthetic aperture to extend DOF, acquired by time-encoding or optical path length encoding. In this work, a deep-learning-based multiple aperture synthetic OCT termed MAS-Net OCT, which integrated a speckle-free model based on self-supervised learning, was proposed. MAS-Net was trained on datasets generated by the MAS OCT system. Here we performed experiments on homemade microparticle samples and various biological tissues. Results demonstrated that the proposed MAS-Net OCT could effectively improve the transverse resolution in a large imaging depth as well as reduced most speckle noise.

Multimodal dynamic and unclonable anti-counterfeiting using robust diamond microparticles on heterogeneous substrate

The growing prevalence of counterfeit products worldwide poses serious threats to economic security and human health. Developing advanced anti-counterfeiting materials with physical unclonable functions offers an attractive defense strategy. Here, we report multimodal, dynamic and unclonable anti-counterfeiting labels based on diamond microparticles containing silicon-vacancy centers. These chaotic microparticles are heterogeneously grown on silicon substrate by chemical vapor deposition, facilitating low-cost scalable fabrication. The intrinsically unclonable functions are introduced by the randomized features of each particle. The highly stable signals of photoluminescence from silicon-vacancy centers and light scattering from diamond microparticles can enable high-capacity optical encoding. Moreover, time-dependent encoding is achieved by modulating photoluminescence signals of silicon-vacancy centers via air oxidation. Exploiting the robustness of diamond, the developed labels exhibit ultrahigh stability in extreme application scenarios, including harsh chemical environments, high temperature, mechanical abrasion, and ultraviolet irradiation. Hence, our proposed system can be practically applied immediately as anti-counterfeiting labels in diverse fields.

Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning

Based on orbital angular momentum (OAM) properties of Laguerre-Gaussian beams LG(p,ℓ), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on an intensity profile generated by a coherent superposition of two OAM-carrying Laguerre-Gaussian modes and a machine learning detection method. In the encoding process, the intensity profile for data encoding is generated based on the selection of p and ℓ indices, while the decoding process is performed using a support vector machine (SVM) algorithm. Two different decoding models based on an SVM algorithm are tested to verify the robustness of the optical encoding model, finding a BER =10-9 for 10.2 dB of signal-to-noise ratio in one of the SVM models.

Rapid all-in-focus imaging via physical neural network optical encoding

Lightfield phase modulation has become an effective implementation for extending depth-of-field (DOF) of computational imaging. However, correct reconstruction of tiny details and color configuration with a fast capture is still challenging. Here, we demonstrate a high-dynamic all-focus imaging based on a proposed paradigm of constructing diffractive network encoding and electronic network decoding (DE-ED) conformation. With an efficient collaboration of physical diffractive layers and electronic convolutional layers, this learning-based model demonstrates significantly enhanced generalization and data-fitting capabilities. In experiments, the proposed method exhibits an obvious superiority in high-dynamic adaptation, autofocusing, and denoise compared with conventional methods. Specifically, an ultrahigh capture frame rate with ST <1/3000 s can be precisely adapted with only ∼5.6 mm diameter of imaging aperture under natural illumination. Several videos for proposed high-dynamic reconstruction show this method's time-efficiency and consistency. These results highlight some unique advantages of the constructed hybrid opto-electronic network model based on data-driven end-to-end learning for next-generation innovative computational imaging.

All-Optical Reconfigurable Excitonic Charge States in Monolayer MoS2.

Excitons are quasi-particles composed of electron-hole pairs through Coulomb interaction. Due to the atomic-thin thickness, they are tightly bound in monolayer transition metal dichalcogenides (TMDs) and dominate their optical properties. The capability to manipulate the excitonic behavior can significantly influence the photon emission or carrier transport performance of TMD-based devices. However, on-demand and region-selective manipulation of the excitonic states in a reversible manner remains challenging so far. Herein, harnessing the coordinated effect of femtosecond-laser-driven atomic defect generation, interfacial electron transfer, and surface molecular desorption/adsorption, we develop an all-optical approach to manipulate the charge states of excitons in monolayer molybdenum disulfide (MoS2). Through steering the laser beam, we demonstrate reconfigurable optical encoding of the excitonic charge states (between neutral and negative states) on a single MoS2 flake. Our technique can be extended to other TMDs materials, which will guide the design of all-optical and reconfigurable TMD-based optoelectronic and nanophotonic devices.

Optical Ciphering Scheme for Cancellable Speaker Identification System

Most current security and authentication systems are based on personal biometrics. The security problem is a major issue in the field of biometric systems. This is due to the use in databases of the original biometrics. Then biometrics will forever be lost if these databases are attacked. Protecting privacy is the most important goal of cancelable biometrics. In order to protect privacy, therefore, cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification databases. One methodology to achieve non-invertibility is the employment of non-invertible transforms. This work suggests an encryption process for cancellable speaker identification using a hybrid encryption system. This system includes the 3D Jigsaw transforms and Fractional Fourier Transform (FrFT). The proposed scheme is compared with the optical Double Random Phase Encoding (DRPE) encryption process. The evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure, authoritative, and feasible. The encryption and cancelability effects are good and reveal good performance. Also, it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.

Feature and texture distillation via neural network training

Encoded-diffraction hybrid systems—optical encoding and simple electronic decoding—offers feature distillation via model training. Additionally, the most faithfully reconstructed images are not the ones that are best classified. We parametrize our results with singular value decomposition (SVD) entropy, a proxy for image complexity.

Coexistence of quantum spin and valley hall effect in two-dimensional dielectric photonic crystals

The location and transmission of light is the core of modern photonic integrated device, and the proposal of topological photonics provides a new way of implementing optical manipulation. Topological photonic structures based on the quantum spin hall effect or quantum valley hall effect have the properties of immunity to defects and suppress backscattering, so they play a key role in designing novel low-loss photonic devices. In this work, we design a two-dimensional dielectric photonic crystal with time-reversal symmetry to achieve the coexistence of the quantum spin hall effect and the quantum valley hall effect in a photonic crystal. The design can be likened to an electronic system in which two pairs of Kramers simplex pairs are constructed to achieve a quadruple simplex pair in a photonic crystal. First, based on the method of shrinking and expanding the silicon pillars arranged in the honeycomb structure, the quadruple degeneracy point at the &lt;i&gt;Γ&lt;/i&gt; point of the first Brillouin zone is opened, and the corresponding topologically trivial or non-trivial photonic band gap is formed,thereby realizing quantum spin hall effect. The expanded honeycomb lattice evolves into a Kagome structure, and then positive and negative perturbations are added to the Kagome lattice, breaking the spatial inversion symmetry of the Photonic crystal. When mirror symmetry is broken, different chiral photonic crystals can be created,leading the degeneracy point of the non-equivalent valleys &lt;i&gt;K&lt;/i&gt; and &lt;i&gt;K'&lt;/i&gt; in the Brillouin zone to be opened and a complete band gap to appear, thus realizing the Quantum valley hall effect. In the common band gap, topologically protected edge states are induced by nontrivial valley Chern number at the interface between two photonic crystals with opposite chirality. The numerical calculations show that unidirectional transport and bending-immune topological boundary states can be realized at the interface composed of topologically trivial (non-trivial) and positively (negatively) perturbed photonic crystals. Finally, a four-channel system based on the coexistence of the two effects is designed, The system is a novel electromagnetic wave router that can be selectively controlled by pseudospin degree of freedom or valley degree of freedom. This system provides a potential method for realizing the optical encoding and robust signal transmission, thereby providing greater flexibility for manipulating electromagnetic waves.

A novel cancellable biometric recognition system based on Rubik’s cube technique for cyber-security applications

ObjectiveTen years ago, biometrics became an efficient tool of security and authentication enforcement. In a classical access system, a user can easily change password if it is compromised. However, user’s biometrics are limited and unique, and if a user biometric trait is compromised, it will be impossible to change it. Hence, cancellable biometric systems depend on the transformation of data or a biometric feature into a new format, so that users can replace their biometric templates in the same or a different system. In this paper, an efficient cancellable biometric recognition system is presented. In this system, both signal-and feature-level transformations are used to generate the cancellable templates MethodsA novel hybrid encryption framework based on the Rubik’s cube technique is presented to allow efficient encryption of face, iris, and fingerprint biometric templates. This framework begins with extraction of biometric features using the Scale-Invariant Feature Transform (SIFT). After that, the resulting biometric features are encrypted with the optical Double Random Phase Encoding (DRPE) algorithm. Then, the resulting encrypted biometric features are further encrypted with the chaotic Baker map permutation, Advanced Encryption Standard (AES), or Ron's Code (RC6) algorithm in the Cipher Feed Back (CFB) mode of operation. Finally, the resulting encrypted biometrics are additionally encrypted with the Rubik’s cube technique. The chaotic, RC6, or AES encrypted biometrics are utilized on the faces of the Rubik’s cube. From the concepts of image encryption, the DRPE, RC6, and the AES algorithms introduce a degree of diffusion, whilst the chaotic Baker map adds a degree of permutation. Moreover, the Rubik’s cube technique adds more permutation to the encrypted biometrics, simultaneously. The quality evaluation of the proposed cancellable biometric system reveals good performance. ResultsThe simulation results prove that the suggested hybrid encryption framework is reliable, and it presents recommended security and robustness levels for its utilization for building efficient cancellable biometric systems compared to the traditional techniques.

Optically functionalization of 3D surfaces via voxel-based hierarchically micro structuralizing, a study case: Patterned optical ID

Optics is one of the most important applications of functional surfaces enabled by micro-/Nano-structuring. Mass production of optical functional surfaces mainly relies on replication technologies. In optical applications, structural consistency is essential for the precise control of functions such as micro-mirror/lens arrays. A big challenge from the design phase when fabricating functional surfaces is the geometrical constraint, e.g. patterning orient-consistent micromirrors on three-dimensional surfaces. This present paper demonstrates a voxel-based method to indirectly pattern consistently oriented microstructures onto 3D surfaces by creating a hierarchical structure, consisting of the substrate level of a “Minecraft”-like voxel structure and the feature level of micro features machined on the voxels. As a study case, RSA905 injection moulding inserts with 3D functional surfaces for optical encoding are fabricated with diamond machining: a spherical and a sinusoidal sample with a data matrix as an optical ID on each. The encoding is based on the optical contrast generated by the surfaces' directional reflection, which is led by the bevel surfaces of the micro ridge arrays. With the quantified evaluation of the resultant surface quality and functionality, the feasibility of the “Minecraft” method is validated, the surface quality is correlated with its complexity and the process optimization routine is determined. Moreover, by over 15,000 injection moulding cycles, the method is also proved suitable for batch production of 3D functional surfaces by micro injection moulding.

Action recognition based on discrete cosine transform by optical pixel-wise encoding

The framework provides a novel pipeline for action recognition. The action recognition task classifies the action label of the scene. High-speed cameras are commonly used to generate high frame-rate videos for capturing sufficient motion information. However, the data volume would be the bottleneck of the system. With the insight that the discrete cosine transform (DCT) of video signals reveals the motion information remarkably, instead of obtaining video data as with traditional cameras, the proposed method directly captures a DCT spectrum of video in a single shot through optical pixel-wise encoding. Considering that video signals are sparsely distributed in the DCT domain, a learning-based frequency selector is designed for pruning the trivial frequency channels of the spectrum. An opto-electronic neural network is designed for action recognition from a single coded spectrum. The optical encoder generates the DCT spectrum, and the rest of the network jointly optimizes the frequency selector and classification model simultaneously. Compared to conventional video-based action recognition methods, the proposed method achieves higher accuracy with less data, less communication bandwidth, and less computational burden. Both simulations and experiments demonstrate that the proposed method has superior action recognition performance. To the best of our knowledge, this is the first work investigating action recognition in the DCT domain.

Stable CsPbBr3 Achieved by Porphyrin–Thiol Surface Management and Their Dual‐Stimuli Responsive for Optical Encoding

AbstractSurface ligand management plays an essential role in improving the stability of perovskite quantum dots (QDs) and endowing them with novel property. Herein, a red fluorescence porphyrin‐thiol (named Por‐SH) is applied to adjust the surface and stimuli responsiveness of CsPbBr3 QDs. According to the theoretical and experimental results, Por‐SH can bind tightly to the exposed Pb of CsPbBr3 QDs, thus leading to the formation of Por‐SH‐QDs with an excellent resistance to water and UV light. Moreover, CsPbBr3 act as energy donors to improve the photoresponse of Por‐SH through energy transfer, especially when heated. Notably, the Por‐SH and QDs behave differently in terms of response to the temperature in the Por‐SH‐QDs system. Therefore, the Por‐SH‐QDs are capable of reversible stimuli–response property in the presence of heat/cool stimuli when excited by UV light. Finally, the disposable or reversible optical encoding label based on Por‐SH‐QDs and QDs is obtained by combining stability and the responsiveness to stimuli. Through the selection of suitable ligands, the surface of QDs is efficiently managed, which can not only mitigate instability but also endow perovskite QDs with the responsiveness to stimuli.

Cancelable biometric system for IoT applications based on optical double random phase encoding.

The security issue is essential in the Internet-of-Things (IoT) environment. Biometrics play an important role in securing the emerging IoT devices, especially IoT robots. Biometric identification is an interesting candidate to improve IoT usability and security. To access and control sensitive environments like IoT, passwords are not recommended for high security levels. Biometrics can be used instead, but more protection is needed to store original biometrics away from invaders. This paper presents a cancelable multimodal biometric recognition system based on encryption algorithms and watermarking. Both voice-print and facial images are used as individual biometrics. Double Random Phase Encoding (DRPE) and chaotic Baker map are utilized as encryption algorithms. Verification is performed by estimating the correlation between registered and tested models in their cancelable format. Simulation results give Equal Error Rate (EER) values close to zero and Area under the Receiver Operator Characteristic Curve (AROC) equal to one, which indicates the high performance of the proposed system in addition to the difficulty to invert cancelable templates. Moreover, reusability and diversity of biometric templates is guaranteed.

Encoding and decoding communications based on perfect vector optical vortex arrays

We proposed the perfect vector optical vortex arrays (PVOVAs) for encoding and decoding communications, including the 2 × 2, 1 × 4, 4 × 4, and 2 × 4 array forms, which were generated with the coaxial superposition of two vector optical vortex arrays with left- and right-handed circular polarization. The polarization order and state of each vector element could be modulated independently by adjusting the corresponding topological charges and additional phases, θn . By attaching code information to these vector elements with different states of polarization, the hexadecimal code elements, including 0 to F, were represented. Therefore, PVOVAs consisting of multiple vector elements can transmit encoding information and be decoded as original information in the receiving end. The school badge grayscale image, with a size of 64 × 64 pixels, was transmitted using the encoding and decoding communication via PVOVAs, and the received restored image had a reliable accuracy. This study proves PVOVAs have flexible spatial structure and controllable states of polarization, and it expands the application of vector optical fields in optical encoding and decoding communication.

Surface regulation by bifunctional BODIPY to fabricate stable CsPbBr3 for multi-layered optical anti-counterfeiting

Cesium lead halide perovskite quantum dots (QDs) have attracted immense attention for luminescent materials due to their narrow emission bands and color-tunable emission. However, indispensable surface ligands originating from ligand-assisted synthesis strategies severely deteriorate the stability and luminescence properties of QDs since these ligands have a highly dynamic binding. Herein, we used a green fluorescence BODIPY molecule containing thiol (named SH-BDP) to regulate the CsPbBr3 QDs surface by ligand regulation. Density functional theory calculations proved that the SH-BDP molecule could bind to the exposed Pb of CsPbBr3 QDs stronger than traditional ligands to form stable SH-BDP-QDs. Moreover, the SH-BDP fixed on the CsPbBr3 QDs surface can improve water and light resistance. It also served as a knob to tune their luminescence properties and the reversible thermal-stimuli response. Finally, the multi-response property of SH-BDP-QDs was realized under polar solvent or heat along with UV light. In addition, we used the SH-BDP-QDs to create various anti-counterfeiting labels; several luminous modes were achieved under different external stimuli, which improved the quality of the optical anti-counterfeiting labels and ensured information security. This work indicates the immense potential of surface ligand manipulation in improving the stability and multi-stimuli-responsive optical encoding of perovskite quantum dots.

Synthesis of stable core-shell perovskite based nano-heterostructures

Despite having exceptional optical and photoelectric properties, the application of organometal halide perovskites (OHP) is restricted due to the limited penetration depth of the UV excitation light and poor stability. Attempts have been made to make composite materials by mixing other materials such as upconversion nanoparticles (UCNP) with OHP. In contrast to linear absorption and emission of OHP, the nonlinear upconversion of UCNP offers numerous advantages such as deep penetration depth of the near-infrared (NIR) excitation light, minimal photodamage to biological tissues, and negligible background interference, which offer great potential in various applications such as multiplexed optical encoding, three-dimensional displays, super-resolution bioimaging, and effective solar spectrum conversion. However, it is challenging to synthesize hybrid OHP-UCNP nanocrystals due to the inherent difference in crystal structures of hexagonal phase UCNP and cubic phase OHP. In this work, we report OHP-UCNP heterostructured nanocrystals synthesized via growing cubic phase NaGdF4 UCNP over cubic phase CsPbBr3 OHP in a seed-mediated process based on a very small lattice mismatch and then converting cubic phase UCNP to hexagonal phase through heating. The juxtaposition of UCNP over OHP in a single nanocrystal facilitates efficient energy transfer from UCNP to OHP under NIR excitation and acts as a protective layer improving the stability. The stability is further enhanced by coating an inert UCNP shell on the OHP-UCNP nano-heterostructures with the same UCNP material earlier used in the heterostructures. The coating demonstrated greater stability under continuous UV exposure and in harsh environments such as high temperatures and polar solvents. These NIR excitable perovskite-UCNP nano-heterostructures with improved stability have great potential for use in new optoelectronic and biological applications.

Seeing through obstructions with diffractive cloaking

Unwanted camera obstruction can severely degrade captured images, including both scene occluders near the camera and partial occlusions of the camera cover glass. Such occlusions can cause catastrophic failures for various scene understanding tasks such as semantic segmentation, object detection, and depth estimation. Existing camera arrays capture multiple redundant views of a scene to see around thin occlusions. Such multi-camera systems effectively form a large synthetic aperture, which can suppress nearby occluders with a large defocus blur, but significantly increase the overall form factor of the imaging setup. In this work, we propose a monocular single-shot imaging approach that optically cloaks obstructions by emulating a large array. Instead of relying on different camera views, we learn a diffractive optical element (DOE) that performs depth-dependent optical encoding, scattering nearby occlusions while allowing paraxial wavefronts to be focused. We computationally reconstruct unobstructed images from these superposed measurements with a neural network that is trained jointly with the optical layer of the proposed imaging system. We assess the proposed method in simulation and with an experimental prototype, validating that the proposed computational camera is capable of recovering occluded scene information in the presence of severe camera obstruction.

All-Optical Reconfigurable Electronic Memory in a Graphene/SrTiO3 Heterostructure.

Direct optical data coding in an electronic device is meaningful for photonic technology. Herein, we report electronic memory in a graphene/SrTiO3 heterostructure, which presents the all-optical logic operation (encoding and decoding). The underlying physics have been elucidated in which the synergistic effect of surface localization with interface band bending is responsible for optical encoding and decoding in the electronic memory device of the graphene/SrTiO3 heterostructure. Further, we demonstrate a robust retention and synaptic-like processing of optical signals, which may lead to significant applications in neuromorphic imaging sensors.