Optics and Photonics for Information Processing XVIII

SLMs use PA-LCoS microdisplays for phase-only modulation, known for high resolution and small pixel size. However, phenomena like cross-talk and out-of-plane reorientation of the liquid crystal director degrade pixelated SLM device performance. As novel applications require higher resolutions and smaller pixel sizes, a numerical workflow is applied to analyse different parameters of the microdisplay, such as fill factor, pixel size, external voltage, LC director influence, and light interaction. Specifically, the analysis focuses on the impact of high-frequency binary phase gratings on radiometric and polarimetric response, setting up a framework for future microdisplays with higher resolutions and smaller pixel sizes.

Traditional microscopes face resolution constraints, and advanced techniques pose difficulties in studying multiple cells simultaneously. Additionally, exploring the dynamics of living cells within their natural organismic environment often demands sophisticated and costly equipment, which may not be affordable for every laboratory. Our approach utilizes computational techniques, such as super-resolution radial fluctuations, on sequences of tissue images. Through various adjustments, we have enhanced resolution, reduced background noise, and amplified signals for subcellular structures. Despite starting with unclear images, our refined methods ensure clarity in depicting organelle structures and dynamics in the final outcomes. These tools are now accessible for research across diverse settings.

A microscope design was developed using 3D printing and liquid crystals to achieve variable polarization configurations and semi-automatic processes without the need for mechanical movement of polarizers and retarders. The device includes a polariscope composed of electronically controlled liquid crystals and a compliant mechanism for precise micrometric movements. Stepper motors and software control these movements, and the process of acquiring images requires connecting a sensor to a Raspberry Pi. The microscope's lens and extension tube assembly magnify the image 600 times. The study compares two polarization techniques: Stokes and ellipsometric polarization. Additionally, it analyzes pyroxylin crystals and meiotic spindles of maturing porcine oocytes. The ellipsometric technique is more effective in detecting low retards, as indicated by the results. This prototype has the potential to reduce the cost of in vitro fertilization in assisted reproduction laboratories, with a focus on animals of high genetic value.

This paper presents the development of a lidar-based object classification system with the use of machine learning and signal processing. The proposal employs Support Vector Machines (SVM) to classify objects and terrain with the help of a self-made lidar which scans an area in the same way as a picture is taken. This project involves the processing of data to generate a point cloud that lets us visualize the scans taken by the lidar. Additionally two datasets were built by taking multiple scans of different terrains and objects with a cylindrical shape. This paper shows experimental results of machine learning models built around lidar acquired data and small datasets, it also shows point cloud visualizations and signal processing techniques to filter data.

This work presents a novel fiber optic Mach-Zehnder interferometer (MZI) sensor which can measure curvature due to its structure. This structure employs a 2-section MZI filter, constructed from SMF-28 fiber using the core-offset technique. The source in the 1480-1600 nm range shows an intermodal energy interference between the core and cladding and exhibits six spacing notches for sensing applications. The sensibility obtained was of 0.00018 nm/uW when curvature is applied. On the other hand, by including a thin film based on Zinc Oxide (ZnO) and another with Aluminum doped zinc oxide (AZO) in the sensing arrangement, an increase in the sensitivity was detected with a value of 0.00015 nm/uW and 0.00016 nm/uW, respectively. This sensor can be used for applications in various fields, such as environmental monitoring, engineering, and process control.

In the last years many efforts have been invested in the development of augmented reality devices. Depending on the application different constraints need to be faced. Providing full color see-through augmented reality on eyeglasses with customized ophthalmic correction, and valid for a wide range of use cases, is one of the most challenging and ambitious applications. We are within a European Project aiming to this goal. One of the key components in this eyewear is the holographic lens mirror (HLM), acting as the beam combiner responsible for the see-through capability. Preliminary results will be given in this work.

Aperture masking observations for binary stars have been done with a 1.56-m telescope at Shanghai Astronomical Observatory during 2020-2024. In order to ensure ample light is available to restore the high resolution images even at short exposure times and reach the diffraction limit of the 1.56m telescope,we select some binary stars and reference stars nearby with magnitude 5-7 and angular distance 0.2-2arcsec in the WDS catalog as observation targets.This article achieves high-resolution restoration of binaries by a hybrid data processing method, including data reduction, a spatial domain method named ISA to suppress atmospheric turbulence, and OSEM to reduce image degradation caused by multi-aperture interference.The results show that this method can effectively obtain high-resolution images of binary stars, and the measured angular distance is basically consistent with the given value in WDS catalog.

The ongoing paradigm shift in healthcare towards personalized and precision medicine is posing a critical need for noninvasive imaging technology that can provide quantitative tissue and molecular information. Magnetic resonance signals from biological systems contain information from multiple molecules and multiple physical/biological processes (e.g., T1 relaxation, T2 relation, diffusion, perfusion, etc.). So, magnetic resonance imaging (MRI) is inherently a high-dimensional imaging technology that can acquire structural, functional and molecular information simultaneously. In practice, due to the curse of dimensionality, MRI experiments are often done in a low-dimensional setting to acquire biomarkers one at a time. Such a “divide-and-conquer” approach not only reduces data acquisition efficiency but also makes it difficult to obtain molecular information in high resolution. By synergistically integrating machine learning with sparse sampling, constrained image reconstruction and quantum simulation, we have successfully demonstrated ultrafast high-dimensional imaging of the brain. This talk will give an overview of this unprecedented omni imaging technology and show some exciting experimental results of brain function and diseases.

With 140+ petabytes of historical data holdings, 3.8 million square kilometers of daily multi-spectral collection, integration of Synthetic Aperture Radar and newly launching assets every quarter, the opportunities to develop insight from sense making technologies at Maxar are ever growing. During this discussion, we will cover the challenges of collecting, organizing, and exploiting multi source electro-optical remote sensing systems at scale using modern machine learning architectures and techniques to derive actionable insights.

.This study introduces innovative deep learning techniques for automated flood extent and depth estimation on roadways, addressing the challenges posed by recurrent nuisance flooding. Leveraging advancements in computing and ubiquitous sensing, the proposed methods offer cost-effective solutions compared to traditional monitoring approaches. To overcome the scarcity of annotated data, a novel pipeline is devised to automatically generate training data using car image datasets, simulating various floodwater levels. The developed model achieves impressive performance metrics, including an F1 score of 0.9 for flood extent estimation and 74.85% accuracy, with an F1 score of 0.74 for four water levels, when tested with real-world flooding images. This approach substantially reduces the time and labor associated with dataset labeling, presenting a promising avenue for image-based flood depth estimation.

Individual identification of sea turtles relies on manual and semi-automated methods, requiring high-quality images and analyzing the geometric morphology of facial and dorsal scales. Automatic semantic segmentation accurately delineates areas in images, enabling object detection and identification. We propose an AI model for sea turtle identification, integrating the Segment Anything Model for semantic segmentation. This approach, based on the geometric morphology of facial scales, shell, fins, and neck, achieves precise identification leveraging unique anatomical features. High precision results are supported by evaluation metrics, providing a quantitative measure of the model's accuracy.

Spatial optical mode sorter is a promising device that can significantly contribute to communication and imaging domains. However, currently available commercial devices fall short in effectively sorting large mode bases with high fidelity, particularly in the visible range. Traditionally, such sorting has relied on multi-plane light conversion (MPLC) setups, wherein the optical field undergoes iterative bouncing between a mirror and a spatial light modulator programmed with different phase profiles. This configuration can be also thought of as a diffractive optical neural network (DONN), where each phase mask acts like a single layer. In this work we treat the mode sorter as a DONN and instead of employing iterative algorithms to calculate optimal phase masks with conventional MPLC approaches, we propose training it with machine learning algorithms. We investigate various training methodologies, including backpropagation within simulation environments, hybrid approach that incorporate experimental data, and forward-forward training strategies.

This work introduces a methodology for characterizing stellar objects using the photometric data from the J-PLUS mission's third data release, which does not include direct astrophysical parameters such as effective temperature, stellar radius, and luminosity. By cross-matching this data with the GAIA space mission's third release, we were able to enhance the J-PLUS catalog with these essential parameters. We then employed supervised machine learning techniques, specifically the XGBoost and Random Forest algorithms, to estimate the effective temperature, stellar radius, and luminosity for stars not catalogued by J-PLUS. This method provides a significant improvement in the characterization of the 8,013,455 stars observed in the J-PLUS mission and illustrates the potential of combining different astronomical data sources with advanced analytical techniques for stellar classification.

Hands appear frequently in the field of view in egocentric vision. Hand segmentation is an important and challenging computer vision task with applications in human-computer interaction and assistive technologies. Optimization techniques were introduced to the state-of-the-art model, RefineNet to improve performance. The improved deep convolutional neural network (RefinePix-Net) was trained using egohands dataset. This model performed better when tested with publicly available hands datasets outperforming the baseline model across two metrics mean intersection over union (mIoU) and mean precision (mPrec). This improved model can be applied in object and activity recognition and robotics.

Using Graph Convolutional Networks (GCNs) to classify and identify segmented images into superpixels is a growing field of study. These algorithms represent the content of images by creating a graph where superpixels act as nodes. Monitoring with GCNs provides valuable insights into the geographic distribution, population density, migration, survival, reproduction, and growth of sea turtles. The selection of GCN is based on the unique characteristics of sea turtle morphology and patterns. This algorithm offers high precision and accuracy, achieved through image segmentation and leveraging turtle morphology. Additionally, it offers a significant improvement in sea turtle identification compared to other algorithms, such as Hotspotter. Our new algorithm serves as a versatile tool to aid in monitoring and protecting these species, contributing to our understanding of marine life.

This talk introduces the design and analysis of a passive and fully optical on-chip direction-finding architecture, which can operate effectively in broad or narrow detection scenarios. The proposed system is based on an equally spaced linear array of M grating couplers that steer incoming waves toward a passive and non-unitary photonic processor. This performs a processing operation from which the incident angles of the incoming waves are discretely determined at the N outputs of the photonic processor. The detection functionalities are enhanced by introducing some tracking functions implemented in a post-processing stage at the photonic unit output. The benefits and disadvantages of each tracking function are illustrated. Lastly, an approximation to reduce the photonic unit to a unitary one is discussed, rendering a compact device that operates only on a narrow detection range.

A multiple view fringe projection profilometer can be constructed using several camera-projector pairs at different viewpoints. Alternatively, the profilometer can be constructed using flat mirrors to create virtual instances of a single camera-projector pair. In this work, the advantages of using several camera-projector pairs and mirrors are analyzed. A profilometer with three camera-projector pairs is compared with an equivalent profilometer composed of a single camera-projector pair and two mirrors. The performance of both profilometers is analyzed experimentally by performing full object reconstruction, considering system calibration simplicity and object reconstruction accuracy. This work provides practical insights for designing profilometers specialized in full object reconstruction.

Digital backplane liquid crystal on silicon devices (LCoS) are widely used in wavefront engineering applications. A thorough study of the relation between the different control parameters and their effect on the retardance modulation is lacking though. These control parameters, which are the low and high levels of the voltage signal together with the grey level addressed, in relation with the retardance modulation produced are systematically explored across the whole visible spectrum. This methodology provides an interpolating approach to calculate the retardance values across the multidimensional control space once properly sampled. The results presented are backed by the experimental measurements provided.

We present an encryption system consisting of an electrically tunable metasurface and a matched detector for secure encryption of grayscale images in the 8 – 12 μm wavelength range. In the proposed scheme, the encrypted image corresponds to the spatially varying thermal intensity of the metasurface as captured by its matched detector. Using examples of single and multi-image encryption, we show that the optical properties of either the metasurface or matched detector alone do not reveal any meaningful information about the encrypted image, thereby validating the security of the proposed scheme. The electrical tunability of the metasurface provides an additional layer of security as the image can only be retrieved by operating it at a predefined voltage level. Our results provide intriguing possibilities for the development of compact and secure object tagging and anti-counterfeiting applications in the infrared.

Disparity refinement is a post-processing step in stereo vision that retrieves unknown disparity values caused by pixel occlusions or estimation errors. This step is crucial for improving depth estimation accuracy and reducing artifacts. In this work, we propose an iterative method based on genetic optimization to perform disparity refinement for stereo vision. The estimation of unknown disparity values is formulated as an optimization problem, where a fitness function is optimized by minimizing a trade-off between disparity variations and point correspondence errors. The proposed method achieves accurate refined disparity maps for stereo depth estimation. Computer simulation results are presented and discussed in terms of objective performance measures. Additionally, the results are compared with those obtained using a well-known existing method.

Conventional fringe projection profilometers utilize cameras and projectors in the visible spectrum. Nevertheless, some applications require profilometers with a complementary thermal camera for the infrared spectrum. Since the point cloud is computed from pixel correspondences between the visible camera-projector pair, the texture in the visible spectrum is obtained by direct association of color from each image pixel to its corresponding point in the cloud. Unfortunately, the texture from the thermal camera is not straightforward because of the inexistence of pixel-point correspondences. In this paper, the distorted pinhole model is employed to determine the texture of the reconstructed object using thermal images. The theoretical principles are reviewed, and an experimental verification is conducted using a visible-thermal fringe projection profilometer. This work provides a helpful framework for three-dimensional data fusion for advanced multi-modal profilometers.

Binocular vision is an effective technique for depth estimation in outdoor applications. However, the performance of this technique can be affected by low-contrasted images captured in a scattering medium. This paper presents a binocular vision-based method for image dehazing and depth estimation in a scattering medium. First, the disparity map of the scene is computed from preprocessed binocular images. Next, the atmospheric parameters of the medium and scene depth are determined with a proposed statistical estimation method. Finally, undegraded images of the scene are obtained using an atmospheric optics restoration method. The theoretical foundations of the proposed approach are reviewed, and an experimental validation is presented utilizing a laboratory platform.

This paper presents the development of an autonomous navigation system for Unmanned Aerial Vehicles (UAVs) using visual reference. The proposal employs a Convolutional Neural Network (CNN) to classify traffic signal images, enabling UAVs to navigate in dynamically evolving environments. This research involves the configuration of the Robot Operating System (ROS) for UAV communication, the implementation of a specialized CNN for image classification, and the integration of this network into the navigation system. Additionally, algorithms will be presented for image acquisition and UAV manipulation based on CNN outputs. We present experimental results that were specially designed to demonstrate the efficiency of the proposal, in order to validate the analysis and implementation.

This work presents the development of an autonomous mapping and navigation system tailored for a holonomic robot operating in unknown environments, leveraging LiDAR technology. The core of this research lies in the integration of a sophisticated Simultaneous Localization and Mapping (SLAM) algorithm with real-time LiDAR data processing, enabling the robot to generate maps while identifying its location within these maps accurately. The study involves the design and implementation of an omnidirectional mobility mechanism, the selection and calibration of LiDAR, and the implementation of efficient path-planning algorithms that consider the dynamic nature of unknown environments. We present experimental setups to demonstrate the system’s capability to navigate and map with high precision demonstrating the potential applications in areas such as autonomous exploration, search and rescue, and indoor navigation.

Integrating quantum algorithms with machine and deep learning models has emerged as a promising method for addressing medical image classification challenges. This addition can increase speed and efficiency when performing complex computations. However, hybrid quantum model research, specifically on Quantum Convolutional Neural Networks (QCNNs) poses two main drawbacks: The location of the quantum convolutional layer before the model architecture and the lack of integration of the quantum layer inside the training process. These disadvantages reduce model robustness and method reproducibility. This study explores an approach encompassing the model architecture’s quantum layer to address these shortcomings; we present a comparative analysis between a hybrid quantum deep learning model and its classical counterpart for classifying skin cancer dermoscopic images. Model performance is evaluated through accuracy, recall, F1 Score, and the confusion matrix classification metrics. The findings demonstrate the advantages of weight adjustment of a quantum layer and the potential for advancing medical image analysis.

This paper presents the implementation of a method that can effectively predict the best-fitting colorization model based on the input image. Altogether, this pipeline can produce a model with a high level of accuracy for several datasets. We show experimental results that were specially designed to demonstrate the efficiency of the model, in order to validate the analysis and implementation.

A crucial task in facial expression recognition is the classification of facial features in captured images. This classification task is challenging because facial features dynamically change due to facial expressions. Additionally, the captured face images are often degraded by additive noise, nonuniform illumination, geometrical modifications, and partial occlusions, increasing the classification uncertainty. Several successful methods for facial landmark classification based on machine learning have been proposed. This work presents a comparative study of existing classification methods for facial landmarks in image sequences degraded by noise, nonuniform illumination, and partial occlusions. The performance of the classification methods considered in the study is quantified in terms of accuracy, precision, and sensitivity using face images from wellknown datasets and images captured in a laboratory experiment. The study aims to provide useful insights into the efficacy of existing facial landmark classification methods under challenging conditions.

This work introduces an AI-based approach for individual sea turtle identification using Photo-ID. Comparing a self-developed algorithm with the HotSpotter ID, a well-known method for semi-automated identification of individual sea turtles within populations, highlighting differences in image preprocessing and One-vs-Many matching evaluation. Tested on a diverse database of sea turtle images. The database images vary in environments, light conditions, qualities and resolutions, at different distances, and angles. The proposed Photo-ID method involves automatic detection of the turtle’s head using a modified YOLOv5 network, extraction of pixels to emphasize head scale patterns, and the ORB algorithm for identification. With standard quality images, the proposed algorithm achieves 98% accuracy, outperforming the HotSpotter’s 96%. This approach enhances the identification process, contributing to endangered species protection and addressing the challenges associated with the time-consuming nature of Photo-ID methods.

Hyperthermia is a cancer treatment that utilizes magnetic nanofluidics and magnetic induction. The experimental evaluation techniques used to validate heat transfer and changes in thermal diffusivity include the use of phantom models to simulate human tissue and infrared thermography. The present study applies phantom models with magnetic inserts, which pose challenges in determining temperature distribution due to their location and depth, affect thermal contrast. Processing thermograms is crucial for identifying areas of interest, which enables the development of new hyperthermia evaluation techniques.

Water consumption monitoring is a critical task to address for controlling water usage and reducing unnecessary water waste. In this sense, water supply companies and government agencies struggle to identify water leaks and perform data analytics for predictive decision-making due to a lack of real-time monitoring systems, inaccurate metering, and aging infrastructure. In this paper, we propose an Internet of Things (IoT) and machine learning (ML)-based solution for residential water consumption monitoring and leak detection. The IoT network was implemented using two smart water meters (SWMs) connected to a LoraWAN gateway (GW), which sends data to The Thing Network (TTN) and the Amazon Web Services (AWS) cloud platform for storage, management, processing, ML algorithm configuration, and visualization. The SWMs were connected to residences located in two zones of the city of Tijuana. Different machine learning algorithms were analyzed and compared using different metrics such as Accuracy, Recall, Precision, and F1 Score for leak detection. A data visualization interface was developed to visualize the data and perform predictive analysis.

When an intense laser interacts with a gas of atoms, high-order harmonics are generated. In the time domain, this radiation forms a train of extremely short light pulses, of the order of 100 attoseconds. Attosecond pulses allow the study of the dynamics of electrons in atoms and molecules, using pump-probe techniques. This presentation will highlight some of the key steps of the field of attosecond science.