Precise Alignment Research Articles

Generation of pseudo-nondiffracting symmetrical Layer beams using cylindrical lenses

The pseudo-nondiffracting behavior of optical caustic beams makes them potentially useful in precise component alignment at CERN. This paper introduces an innovative method for generating one and two-dimensional symmetrical Airy-like beams, known as Layer beams, utilizing a specific configuration of plano-convex cylindrical lenses. Simulations and experimental results have validated this method, demonstrating its potential for scalable and cost-effective beam production with tunable properties, making it practical for a wide range of scientific applications.

Precise wavelength alignment of second-harmonic generation in thin-film lithium niobate resonators

Precise wavelength alignment of second-harmonic generation in thin-film lithium niobate resonators

Transport-of-intensity differential phase contrast imaging: defying weak object approximation and matched-illumination condition

Quantitative phase imaging (QPI) has emerged as a promising label-free imaging technique with growing importance in biomedical research, optical metrology, materials science, and other fields. Partially coherent illumination provides resolution twice that of the coherent diffraction limit, along with improved robustness and signal-to-noise ratio, making it an increasingly significant area of study in QPI. Partially coherent QPI, represented by differential phase contrast (DPC), linearizes the phase-to-intensity transfer process under the weak object approximation (WOA). However, the nonlinear errors caused by WOA in DPC can lead to phase underestimation. Additionally, DPC requires strict matching of the illumination numerical aperture (NA) to ensure the complete transmission of low-frequency information. This necessitates precise alignment of the optical system and limits the flexible use of objective and illumination. In this study, the applicability of the WOA under different coherence parameters is explored, and a method to defy WOA by reducing the illumination NA is proposed. The proposed method uses the transport-of-intensity equation through an additional defocused intensity image to recover the lost low-frequency information due to illumination mismatch, without requiring any iterative procedure. This method overcomes the limitations of DPC being unable to recover large phase objects and does not require the strict illumination matching conditions. The accurate quantitative morphological characterization of customized artifact and microlens arrays that do not satisfy WOA under non-matched-illumination conditions demonstrated the precise quantitative capability of the proposed method and its excellent performance in the field of measurement. Meanwhile, the phase retrieval of tongue slices and oral epithelial cells demonstrated its application potential in the biomedical field. The ability to accurately recover phase under a concise and implementable optical setup makes it a promising solution for widespread application in various label-free imaging domains.

A Blockchain and Zero Knowledge Proof Based Data Security Transaction Method in Distributed Computing

In distributed computing, data trading mechanisms are essential for ensuring the sharing of data across multiple computing nodes. Nevertheless, they currently encounter considerable obstacles, including low accuracy in matching trading parties, ensuring fairness in transactions, and safeguarding data privacy throughout the trading process. In order to address these issues, we put forward a data trading security scheme based on zero-knowledge proofs and smart contracts. In the phase of preparing the security parameters, the objective is to reduce the complexity of generating non-interactive zero-knowledge proofs and to enhance the efficiency of data trading. In the pre-trading phase, we devise attribute atomic matching smart contracts based on precise data property alignment, with the objective of achieving fine-grained matching of data attributes between trading parties. In the trading execution phase, lightweight cryptographic algorithms based on elliptic curve cryptography (ECC) and non-interactive zero-knowledge proofs are employed for the dual encryption of trading data and the generation of attribute proof contracts, thus ensuring the security and privacy of the data. The results of experiments conducted on the Ethereum platform in an industrial IoT scenario demonstrate that our scheme maintains stable and low-cost consumption while ensuring accuracy in matching and privacy protection.

Supercontinuum-tailoring multicolor imaging reveals spatiotemporal dynamics of heterogeneous tumor evolution

Tumor heterogeneity and tumor evolution contribute to cancer treatment failure. To understand how selective pressures drive heterogeneous tumor evolution, it would be useful to image multiple important components and tumor subclones in vivo. We propose a supercontinuum-tailoring two-photon microscope (SCT-TPM) and realize simultaneous observation of nine fluorophores with a single light beam, breaking through the ‘color barrier’ of intravital two-photon fluorescence imaging. It achieves excitation multiplexing only by modulating the phase of fiber supercontinuum (SC), allowing to capture rapid events of multiple targets with maintaining precise spatial alignment. We employ SCT-TPM to visualize the spatiotemporal dynamics of heterogeneous tumor evolution under host immune surveillance, particularly the behaviors and interactions of six tumor subclones, immune cells and vascular network, and thus infer the trajectories of tumor progression and clonal competition. SCT-TPM opens up the possibility of tumor lineage tracking and mechanism exploration in living biological systems.

Online Optical Axis Parallelism Measurement Method for Continuous Zoom Camera Based on High-Precision Spot Center Positioning Algorithm

Ensuring precise alignment of the optical axis is critical for achieving high-quality imaging in continuous zoom cameras. However, existing methods for measuring optical axis parallelism often lack accuracy and fail to assess parallelism across the entire focal range. This study introduces an online measurement method designed to address these limitations by incorporating two enhancements. First, image processing methodologies enable sub-pixel-level extraction of the spot center, achieved through improved morphological processing and the incorporation of an edge tracing algorithm. Second, measurement software developed using Qt Creator can output real-time data on optical axis parallelism across the full focal range post-measurement. This software features a multi-threaded architecture that facilitates the concurrent execution of image acquisition, data processing, and serial communication. Experimental results derived from simulations and real data indicate that the maximum average error in extracting the center of the spot is 0.13 pixels. The proposed system provides critical data for optical axis calibration during camera adjustment and inspection.

Water-to-air unmanned aerial vehicle (UAV) based rolling shutter optical camera communication (OCC) system with gated recurrent unit neural network (GRU-NN)

Underwater sensors and autonomous underwater vehicles (AUVs) are widely adopted in oceanic research activities. As the number of underwater sensors and AUVs is growing quickly, the bandwidth requirements are increasing accordingly. In this work, we put forward and demonstrate a large field-of-view (FOV) water-to-air unmanned aerial vehicle (UAV) based optical camera communication (OCC) system with gated recurrent unit neural network (GRU-NN) for the first time to the best of our knowledge. As the UAVs are embedded with complementary-metal-oxide-semiconductor (CMOS) cameras, there is no need to install OCC receivers (Rxs), reducing the deployment cost. Moreover, the large photo-sensitive area of the CMOS camera can support a large FOV OCC transmission without the need for precise optical alignment. Here, by utilizing the column matrix identification during the rolling shutter pattern decoding in the CMOS image sensor, the scintillation caused by water turbulence can be reduced. Besides, in the outdoor and windy environment, the UAV will experience significant movement caused by the wind making it very difficult to capture stable OCC frames in the CMOS image sensor. Here, we propose and demonstrate utilizing GRU-NN, which is a special realization of the recurrent neural network (RNN) with memory cells capable of learning the time-domain dependent signals. It is shown that the GRU-NN can learn effectively from successive image frames in time-domain and produce correct prediction even under the windy and unstable UAV flying environment. Experimental results reveal that the proposed GRU-NN can outperform the previous pixel-per-symbol labeling neural network (PPS-NN), and also can significantly reduce the computation time when compared with long-short-term-memory-neural-network (LSTM-NN). The proposed system can decode 4-level pulse-amplitude-modulation (PAM4) rolling shutter OCC patterns at data rates of 5.4 kbit/s and 3.0 kbit/s under clear and cloudy water, respectively, fulfilling the pre-forward error correction bit-error rate (pre-FEC BER = 3.8 × 10−3). We also demonstrate that the UAV based OCC system can support data rates of 5.4 kbit/s, 4.2 kbit/s, and 3.0 kbit/s at distances of 2.2 m, 3.2 m, and 4.2 m, respectively, at outdoor and windy environments.

Wide Bandgap Donor can Offer High‐Efficiency LED Indoor Organic Photovoltaic with Indium‐Doped Zinc Oxide Electron Transport Layer

Indoor organic photovoltaic (OPV) cells offer a compelling solution for powering diverse electronic devices integrated into the Internet of Things (IoT) network. They are prized for their robust power conversion efficiency (PCE), mechanical resilience, and ultra‐thin nature. The recent surge in inverted‐structure OPVs reflects their enhanced stability over conventional designs. Despite the advantage, their adaptation for indoor light utilization remains underexplored. Optimal selection of an electron transport layer (ETL) with precise energy band alignment is critical in this system. Herein, an inverted‐structured OPV is fabricated utilizing PBDB‐T as the wide bandgap donor, with a focus on enhancing its PCE under 1000 lx LED illumination through the doping of the zinc oxide‐ (ZnO‐) based ETL with indium (In). The results indicate that the device utilizing undoped ZnO as the ETL achieves a peak PCE of 9.42% under these specified conditions. Conversely, the OPV utilizing In‐doped ZnO as the ETL achieves a significantly higher PCE of 29.78% with 5 at% In, indicates the usefulness of ETL doping by In. This may be caused by the tuning of energy band alignment, improvement in electron mobility, and reduction in surface roughness of ZnO by In doping.

Robust localization of shear connectors in accelerated bridge construction with neural radiance field

Accelerated bridge construction (ABC) demands precise alignment of prefabricated members to prevent assembly failure. Conventional methods struggle to localize shear connectors from point cloud data (PCD) generated by structure-from-motion due to its sparsity. This paper introduces a robust method for shear connector localization using PCD generated by a neural radiance field and a three-step narrowing-down algorithm. The PCD exhibits densely populated points for small connectors, allowing the algorithm to pinpoint their locations accurately. The method successfully identified all 72 shear connectors in a mock-up prefabricated girder, with an average error of 10 mm, demonstrating its potential for assessing constructability in ABC projects. Future research may integrate deep learning-based segmentation techniques to enhance efficiency and adaptability in complex geometries and non-standard bridge designs.

LRRTM2 controls presynapse nano-organization and AMPA receptor sub-positioning through Neurexin-binding interface

Synapses are organized into nanocolumns that control synaptic transmission efficacy through precise alignment of postsynaptic neurotransmitter receptors and presynaptic release sites. Recent evidence show that Leucine-Rich Repeat Transmembrane protein LRRTM2, highly enriched and confined at synapses, interacts with Neurexins through its C-terminal cap, but the role of this binding interface has not been explored in synapse formation and function. Here, we develop a conditional knock-out mouse model (cKO) to address the molecular mechanisms of LRRTM2 regulation, and its role in synapse organization and function. We show that LRRTM2 cKO specifically impairs excitatory synapse formation and function in mice. Surface expression, synaptic clustering, and membrane dynamics of LRRTM2 are tightly controlled by selective motifs in the C-terminal domain. Conversely, the N-terminal domain controls presynapse nano-organization and postsynapse AMPAR sub-positioning and stabilization through the recently identified Neurexin-binding interface. Thus, we identify LRRTM2 as a central organizer of pre- and post- excitatory synapse nanostructure through interaction with presynaptic Neurexins.

Decreasing propagation rate of interfacial debonding between a single carbon fiber and epoxy matrix under cyclic loading

The interfacial debonding of a single carbon fiber transversely embedded in a dumbbell-shaped epoxy sample was generated under cyclic loading, and images were captured using synchrotron radiation X-ray computed tomography. A fatigue testing machine driven by a piezoelectric actuator placed along the beamline for in situ observation was developed for precise alignment. Interfacial debonding was initially observed under a static tensile load and was confirmed to be almost of the same length at both ends of the carbon fiber, implying negligible bending deformation due to inclination. Cyclic loads were then applied to the sample to capture the progressive debonding. The propagation rate of the interfacial debonding decreased as the number of cycles increased. Another sample with a single carbon fiber aligned parallel to the loading direction was prepared following a single-fiber fragmentation test. Interfacial debonding was clearly observed around the fiber breakage. Cyclic loads were also applied to this sample; however, no progression of the interfacial debonding was evident. Degradation of the interfacial strength between the carbon fiber and epoxy matrix was not confirmed under cyclic loading within the elastic deformation range.

Impact of Scleral Lenses on Visual Acuity and Ocular Aberrations in Corneal Ectasia: A Comprehensive Review.

Corneal ectasias, including keratoconus (KC), pellucid marginal degeneration (PMD), and post-LASIK ectasia, poses significant visual rehabilitation challenges due to the resultant irregular astigmatism, myopia, and higher-order aberrations (HOAs). These conditions often resist traditional corrective methods, necessitating advanced optical solutions. Scleral lenses (SLs) have emerged as a primary non-surgical option for managing these complex corneal irregularities. SLs form a smooth optical interface by forming a tear-filled chamber between the lens and the cornea, effectively mitigating HOAs and improving both high-contrast and low-contrast visual acuity (VA). This review evaluates the efficacy of SLs in enhancing VA and reducing aberrations in patients with corneal ectasia. It also explores the technological advancements in SLs, such as profilometry and wavefront-guided systems, which enable more precise and customized lens fittings by accurately mapping the eye's surface and addressing specific visual aberrations. The current body of evidence demonstrates that custom SLs significantly improve visual outcomes across various ectatic conditions, offering superior performance compared to conventional correction methods. However, challenges such as the complexity of fitting and the need for precise alignment remain. Ongoing innovations in SL technology and customization are likely to further enhance their clinical utility, solidifying their role as an indispensable tool in the management of corneal ectasias.

Improving neural-network-based prediction models for misalignment in off-axis three-mirror anastigmat telescopes

The performance of a telescope system heavily relies on the precise alignment of the mirrors. The off-axis three-mirror anastigmat (TMA) telescope presents unique challenges due to its complex optical design. Each optical element within the off-axis TMA telescope is inherently introduced with theoretical eccentricity and tilt. Furthermore, the incorporation of freeform surfaces and other optical elements with intricate surface features typically leads to low initial alignment accuracy of the optical path. With this low initial alignment accuracy and the noise of the measurements, the prediction of the misalignment of the telescope is getting harder. A fully connected neural network architecture is proposed as a misalignment calculation method for an off-axis TMA telescope system with a freeform surface. Random training data is created by using optical design software. The sensitivity of the mirrors to the wavefront error is quantified and incorporated into the loss function of the neural network to improve prediction accuracy. Adding the noisy measurement samples to the training data creates a noise-immune neural network model. Simulation results show that our model can successfully predict misalignment of the mirrors with noisy measurement values.

Semiconductor Nanoporous Anodic Alumina Photonic Crystals as a Model Photoelectrocatalytic Platform for Solar Light‐Driven Reactions

In this study, nanoporous anodic alumina distributed‐Bragg reflectors (NAA–DBRs) functionalized with tungsten trioxide (WO3) are used as prototype photoelectrocatalysts (PEC) for harnessing the slow photon effect to maximize photon‐to‐electron conversion efficiency under UV–visible–NIR illumination. NAA–DBR structures are structurally engineered by anodization, where their characteristic photonic stopband is precisely tuned along specific positions of the UV–visible spectrum. Subsequent atomic layer deposition is employed to coat the inner surface of these porous structures with WO3 semiconductor layers. Upon the application of overpotential bias, these platforms reveal excellent electron–hole pair separation to boost photoelectrocatalytic reactions. Photoelectrochemical degradation of methylene blue is used as a model reaction to elucidate enhancements associated with structural and optoelectronic arrangements. Notably, precise spectral alignment between the photonic stopband's red edge and the absorbance band of methylene blue enhances the degradation performance through the slow photon effect. Applying an overpotential bias further improves the photodegradation performance through efficient charge separation. These systems outperform comparable structures in this model reaction, achieving a maximum kinetic rate of 13.7 ± 2.0 h−1. The findings create new opportunities to develop high‐performing PEC technologies harnessing light–matter interactions.

ChatGeoAI: Enabling Geospatial Analysis for Public through Natural Language, with Large Language Models

Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis.

Intelligent Dance Motion Evaluation: An Evaluation Method Based on Keyframe Acquisition According to Musical Beat Features.

Motion perception is crucial in competitive sports like dance, basketball, and diving. However, evaluations in these sports heavily rely on professionals, posing two main challenges: subjective assessments are uncertain and can be influenced by experience, making it hard to guarantee timeliness and accuracy, and increasing labor costs with multi-expert voting. While video analysis methods have alleviated some pressure, challenges remain in extracting key points/frames from videos and constructing a suitable, quantifiable evaluation method that aligns with the static-dynamic nature of movements for accurate assessment. Therefore, this study proposes an innovative intelligent evaluation method aimed at enhancing the accuracy and processing speed of complex video analysis tasks. Firstly, by constructing a keyframe extraction method based on musical beat detection, coupled with prior knowledge, the beat detection is optimized through a perceptually weighted window to accurately extract keyframes that are highly correlated with dance movement changes. Secondly, OpenPose is employed to detect human joint points in the keyframes, quantifying human movements into a series of numerically expressed nodes and their relationships (i.e., pose descriptions). Combined with the positions of keyframes in the time sequence, a standard pose description sequence is formed, serving as the foundational data for subsequent quantitative evaluations. Lastly, an Action Sequence Evaluation method (ASCS) is established based on all action features within a single action frame to precisely assess the overall performance of individual actions. Furthermore, drawing inspiration from the Rouge-L evaluation method in natural language processing, a Similarity Measure Approach based on Contextual Relationships (SMACR) is constructed, focusing on evaluating the coherence of actions. By integrating ASCS and SMACR, a comprehensive evaluation of dancers is conducted from both the static and dynamic dimensions. During the method validation phase, the research team judiciously selected 12 representative samples from the popular dance game Just Dance, meticulously classifying them according to the complexity of dance moves and physical exertion levels. The experimental results demonstrate the outstanding performance of the constructed automated evaluation method. Specifically, this method not only achieves the precise assessments of dance movements at the individual keyframe level but also significantly enhances the evaluation of action coherence and completeness through the innovative SMACR. Across all 12 test samples, the method accurately selects 2 to 5 keyframes per second from the videos, reducing the computational load to 4.1-10.3% compared to traditional full-frame matching methods, while the overall evaluation accuracy only slightly decreases by 3%, fully demonstrating the method's combination of efficiency and precision. Through precise musical beat alignment, efficient keyframe extraction, and the introduction of intelligent dance motion analysis technology, this study significantly improves upon the subjectivity and inefficiency of traditional manual evaluations, enhancing the scientificity and accuracy of assessments. It provides robust tool support for fields such as dance education and competition evaluations, showcasing broad application prospects.

Enhancing surgical occlusion setting in orthognathic surgery planning using mixed reality technology: a comparative study

ObjectivesOrthognathic surgery necessitates precise occlusal alignment during surgical planning, traditionally achieved through manual alignment of physical dental models as the recognized gold standard. This study aims to evaluate the efficacy of mixed reality technology in enhancing surgical occlusion setting compared to traditional physical alignment and an established virtual method, addressing the research question: Can mixed reality technology improve the accuracy and efficiency of occlusion setting in orthognathic surgery planning?Materials & methodsThis experimental study compared the surgical occlusion settings of 30 orthognathic cases using three methods: a new virtual method with mixed reality technology, the traditional gold standard of physical alignment, and an established virtual occlusion method using the IPS Case Designer (KLS Martin SE & Co. KG, Tuttlingen, Germany).ResultsResults indicated that surgical occlusions set with mixed reality technology were comparable to the conventional method in terms of maxillary movement and occlusal relationship. Differences observed were within the inter-observer variability of the gold standard. Both virtual methods tended to position the maxilla more anteriorly, resulting in fewer occlusal contacts. However, virtual occlusion demonstrated clinical applicability, achieving an average of 11 occlusal contacts with a bilaterally symmetrical distribution along the dental arch.ConclusionsThe mixed reality environment provides an intuitive and flexible experience for setting surgical occlusion, eliminating the need for costly 3D-printed physical models or the automatic calculations required by other virtual occlusion methods, thereby offering maximum freedom.Clinical relevanceAs a novel form of virtual occlusion, it presents a comprehensive tool that contributes to a timely and cost-effective full digital workflow of orthognathic surgery planning.

An Adaptive and Automatic Power Supply Distribution System with Active Landmarks for Autonomous Mobile Robots.

With the development of automation and intelligent technologies, the demand for autonomous mobile robots in the industry has surged to alleviate labor-intensive tasks and mitigate labor shortages. However, conventional industrial mobile robots' route-tracking algorithms typically rely on passive markers, leading to issues such as inflexibility in changing routes and high deployment costs. To address these challenges, this study proposes a novel approach utilizing active landmarks-battery-powered luminous landmarks that enable robots to recognize and adapt to flexible navigation requirements. However, the reliance on batteries necessitates frequent recharging, prompting the development of an automatic power supply system. This system integrates omnidirectional contact electrodes on mobile robots, allowing to recharge active landmarks without precise positional alignment. Despite these advancements, challenges such as the large size of electrodes and non-adaptive battery charging across landmarks persist, affecting system efficiency. To mitigate these issues, this research focuses on miniaturizing active landmarks and optimizing power distribution among landmarks. The experimental results of this study demonstrated the effectiveness of our automatic power supply method and the high accuracy of landmark detection. Our power distribution calculation method can adaptively manage energy distribution, improving the system's persistence by nearly three times. This study aims to enhance the practicality and efficiency of mobile robot remote control systems utilizing active landmarks by simplifying installation processes and extending operational durations with adaptive and automatic power supply distribution.

Digital versus manual axis marking for toric phakic intraocular lens alignment: a prospective randomized intraindividual trial.

This study compares visual and refractive outcomes and postoperative axis alignment for toric Implantable Collamer Lens (ICL) implantation in astigmatic myopia using manual versus digital marking techniques. Department of Ophthalmology, Medical University of Graz, Austria. Prospective randomized single-centered intraindividual comparison. Patients undergoing bilateral toric ICL implantation for myopia with astigmatism ≥ 0.5 diopters (D) were enrolled. Patients received both marking techniques and randomization was performed. Postoperative retroillumination photography assessed axis alignment, and visual and refractive parameters were evaluated. Duration of the surgeries was recorded. The study includes 20 patients and 40 eyes. Preoperative visual and refractive parameters showed no significant differences. Postoperatively, residual astigmatism (p=0.824) and spherical equivalent (p=0.309) were comparable. No notable differences between right and left eyes in terms of preoperative (p=0.371) and postoperative (p=0.824) astigmatism were observed. Although slight, corrected distance visual acuity (CDVA) increased in both groups postoperatively (p=0.381). Gain in CDVA was comparable between marking techniques (p=0.637). Safety and efficacy indices were comparable (p=0.991 and p=0.189, respectively). Postoperative axial misalignment was 2.8±3.1 degrees in the digital- and 4.4±5.1 degrees in the manual group (p=0.260). Vector analysis showed no significant differences between manual and digital marking. Duration remained uninfluenced by the marking technique (p=0.970) and side of surgery (p=0.682). In conclusion digital and manual marking techniques provided comparable results in axis alignment, surgical duration and visual and refractive outcomes. Both methods are viable for precise axis alignment, with digital marking offering a potential advantage in efficacy.

Multifunctional multifocal lens for simultaneous edge and polarisation imaging

ABSTRACT The method of inserting ultrathin devices in the Fourier plane of the 4F imaging system can achieve multifunctional imaging. However, such a method generally requires precise alignment in the system, which also makes the system more complex. Here, we present an imaging system that incorporates a multifocal Pancharatnam–Berry (PB) phase liquid crystal (LC) lens capable of achieving simultaneous edge and polarisation imaging. Without placing optical elements in the Fourier plane of the 4F system, the proposed imaging system replace a refracted lens in 4F system by multifocal LC lens, which integrates edge and polarisation imaging modes. The phase distribution of multifocal lens is obtained through manipulating the positions and polarisation states of four foci with holographic principles. Furthermore, the imaging system based on the proposed multifocal lens has been shown to be effective in simultaneously achieving polarisation and edge imaging with broadband characteristics. The proposed optical system effectively reduces the complexity of the imaging system, which is expected to be applied in the fields of biomedical imaging, optical interconnection and microscopic imaging.