Deeply Subwavelength Localization with Reverberation-Coded Aperture

Lightweight super-resolution multimode fiber imaging with regularized linear regression

Super-resolution multimode fiber imaging provides the means to image samples quickly with compact and flexible setups finding many applications from biology and medicine to material science and nanolithography. Typically, fiber-based imaging systems suffer from low spatial resolution and long measurement times. State-of-the-art computational approaches can achieve fast super-resolution imaging through a multimode fiber probe but currently rely on either per-sample optimised priors or large data sets with subsequent long training and image reconstruction times. This unfortunately hinders any real-time imaging applications. Here we present an ultimately fast non-iterative algorithm for compressive image reconstruction through a multimode fiber. The proposed approach helps to avoid many constraints by determining the prior of the target distribution from a simulated set and solving the under-determined inverse matrix problem with a mathematical closed-form solution. We have demonstrated theoretical and experimental evidence for enhanced image quality and sub-diffraction spatial resolution of the multimode fiber optical system.

Intelligent wireless power transfer via a 2-bit compact reconfigurable transmissive-metasurface-based router

With the rapid development of the Internet of Things, numerous devices have been deployed in complex environments for environmental monitoring and information transmission, which brings new power supply challenges. Wireless power transfer is a promising solution since it enables power delivery without cables, providing well-behaved flexibility for power supplies. Here we propose a compact wireless power transfer framework. The core components of the proposed framework include a plane-wave feeder and a transmissive 2-bit reconfigurable metasurface-based beam generator, which constitute a reconfigurable power router. The combined profile of the feeder and the beam generator is 0.8 wavelengths. In collaboration with a deep-learning-driven environment sensor, the router enables object detection and localization, and intelligent wireless power transfer to power-consuming targets, especially in dynamic multitarget environments. Experiments also show that the router is capable of simultaneous wireless power and information transfer. Due to the merits of low cost and compact size, the proposed framework may boost the commercialization of metasurface-based wireless power transfer routers.

Intelligent indoor metasurface robotics

Intelligent indoor robotics is expected to rapidly gain importance in crucial areas of our modern society such as at-home health care and factories. Yet, existing mobile robots are limited in their ability to perceive and respond to dynamically evolving complex indoor environments because of their inherently limited sensing and computing resources that are, moreover, traded off against their cruise time and payload. To address these formidable challenges, here we propose intelligent indoor metasurface robotics (I2MR), where all sensing and computing are relegated to a centralized robotic brain endowed with microwave perception; and I2MR's limbs (motorized vehicles, airborne drones, etc.) merely execute the wirelessly received instructions from the brain. The key aspect of our concept is the centralized use of a computation-enabled programmable metasurface that can flexibly mold microwave propagation in the indoor wireless environment, including a sensing and localization modality based on configurational diversity and a communication modality to establish a preferential high-capacity wireless link between the I2MR's brain and limbs. The metasurface-enhanced microwave perception is capable of realizing low-latency and high-resolution three-dimensional imaging of humans, even around corners and behind thick concrete walls, which is the basis for action decisions of the I2MR's brain. I2MR is thus endowed with real-time and full-context awareness of its operating indoor environment. We implement, experimentally, a proof-of-principle demonstration at ∼2.4 GHz, in which I2MR provides health-care assistance to a human inhabitant. The presented strategy opens a new avenue for the conception of smart and wirelessly networked indoor robotics.

Super-resolution multimode fiber imaging with an untrained neural network

Multimode fiber endoscopes provide extreme miniaturization of imaging components for minimally invasive deep tissue imaging. Typically, such fiber systems suffer from low spatial resolution and long measurement time. Fast super-resolution imaging through a multimode fiber has been achieved by using computational optimization algorithms with hand-picked priors. However, machine learning reconstruction approaches offer the promise of better priors, but require large training datasets and therefore long and unpractical pre-calibration time. Here we report a method of multimode fiber imaging based on unsupervised learning with untrained neural networks. The proposed approach solves the ill-posed inverse problem by not relying on any pre-training process. We have demonstrated both theoretically and experimentally that untrained neural networks enhance the imaging quality and provide sub-diffraction spatial resolution of the multimode fiber imaging system.

High-availability displacement sensing with multi-channel self mixing interferometry

Laser self-mixing is in principle a simple and robust general purpose interferometric method, with the additional expressivity which results from nonlinearity. However, it is rather sensitive to unwanted changes in target reflectivity, which often hinders applications with non-cooperative targets. Here we analyze experimentally a multi-channel sensor based on three independent self-mixing signals processed by a small neural network. We show that it provides high-availability motion sensing, robust not only to measurement noise but also to complete loss of signal in some channels. As a form of hybrid sensing based on nonlinear photonics and neural networks, it also opens perspectives for fully multimodal complex photonics sensing.

Reconfigurable design of a thermo-optically addressed liquid-crystal phase modulator by a neural network

We present a machine learning approach to program the light phase modulation function of an innovative thermo-optically addressed, liquid-crystal based, spatial light modulator (TOA-SLM). The designed neural network is trained with a little amount of experimental data and is enabled to efficiently generate prescribed low-order spatial phase distortions. These results demonstrate the potential of neural network-driven TOA-SLM technology for ultrabroadband and large aperture phase modulation, from adaptive optics to ultrafast pulse shaping.

Detection and localization of Goliath grouper using their low-frequency pulse sounds

The goal of this paper is to implement and deploy an automated detector and localization model to locate underwater marine organisms using their low-frequency pulse sounds. This model is based on time difference of arrival (TDOA) and uses a two-stage approach, first, to identify the sound and, second, to localize it. In the first stage, an adaptive matched filter (MF) is designed and implemented to detect and determine the timing of the sound pulses recorded by the hydrophones. The adaptive MF measures the signal and noise levels to determine an adaptive threshold for the pulse detection. In the second stage, the detected sound pulses are fed to a TDOA localization algorithm to compute the locations of the sound source. Despite the uncertainties stemming from various factors that might cause errors in position estimates, it is shown that the errors in source locations are within the dimensions of the array. Further, our method was applied to the localization of Goliath grouper pulse-like calls from a six-hydrophone array. It was revealed that the intrinsic error of the model was about 2 m for an array spanned over 50 m. This method can be used to automatically process large amount of acoustic data and provide a precise description of small scale movements of marine organisms that produce low-frequency sound pulses.

Towards passive non-line-of-sight acoustic localization around corners using uncontrolled random noise sources

Non-line-of-sight (NLoS) imaging is an important challenge in many fields ranging from autonomous vehicles and smart cities to defense applications. Several recent works in optics and acoustics tackle the challenge of imaging targets hidden from view (e.g. placed around a corner) by measuring time-of-flight information using active SONAR/LiDAR techniques, effectively mapping the Green functions (impulse responses) from several controlled sources to an array of detectors. Here, leveraging passive correlations-based imaging techniques (also termed 'acoustic daylight imaging'), we study the possibility of acoustic NLoS target localization around a corner without the use of controlled active sources. We demonstrate localization and tracking of a human subject hidden around a corner in a reverberating room using Green functions retrieved from correlations of broadband uncontrolled noise sources recorded by multiple detectors. Our results demonstrate that for NLoS localization controlled active sources can be replaced by passive detectors as long as a sufficiently broadband noise is present in the scene.

Reflectionless programmable signal routers

We demonstrate experimentally that reflectionless scattering modes (RSMs), a generalized version of coherent perfect absorption, can be functionalized to perform reflectionless programmable signal routing. We achieve versatile programmability both in terms of operating frequencies and routing functionality with negligible reflection upon in-coupling, which avoids unwanted signal power echoes in radio frequency or photonic networks. We report in situ observations of routing functionalities like wavelength demultiplexing, including cases where multichannel excitation requires adapted coherent input wavefronts. All experiments are performed in the microwave domain based on the same irregularly shaped cavity with strong modal overlap that is massively parametrized by a 304-element-programmable metasurface. RSMs in our highly overdamped multiresonance transport problem are fundamentally intriguing because the simple critical coupling picture for reflectionless excitation of isolated resonances fails spectacularly. We show in simulation that the distribution of damping rates of scattering singularities broadens under strong absorption so that weakly damped zeros can be tuned toward functionalized RSMs.

Fringe Detection and Displacement Sensing for Variable Optical Feedback-Based Self-Mixing Interferometry by Using Deep Neural Networks

Laser feedback-based self-mixing interferometry (SMI) is a promising technique for displacement sensing. However, commercial deployment of such sensors is being held back due to reduced performance in case of variable optical feedback which invariably happens due to optical speckle encountered when sensing the motion of non-cooperative remote target surfaces. In this work, deep neural networks have been trained under variable optical feedback conditions so that interferometric fringe detection and corresponding displacement measurement can be achieved. We have also proposed a method for automatic labelling of SMI fringes under variable optical feedback to facilitate the generation of a large training dataset. Specifically, we have trained two deep neural network models, namely Yolov5 and EfficientDet, and analysed the performance of these networks on various experimental SMI signals acquired by using different laser-diode-based sensors operating under different noise and speckle conditions. The performance has been quantified in terms of fringe detection accuracy, signal to noise ratio, depth of modulation, and execution time parameters. The impact of network architecture on real-time sensing is also discussed.

Localization within Hostile Indoor Environments for Emergency Responders

Recent advances in techniques to improve indoor localization accuracy for personnel and asset tracking challenges has enabled wide-spread adoption within the retail, manufacturing, and health care industries. Most currently deployed systems use distance estimates from known reference locations to localize a person or asset using geometric lateration techniques. The distances are determined using one of many radio frequency (RF) based ranging techniques. Unfortunately, such techniques are susceptible to interference and multipath propagation caused by obstructions within buildings. Because range inaccuracies from known locations can directly lead to incorrect position estimates, these systems often require careful upfront deployment design to account for site-specific interference sources. However, the upfront system deployment requirements necessary to achieve high positioning accuracy with RF-based ranging systems makes the use of such systems impractical, particularly for structures constructed of challenging materials or dense configurations. In this paper, we evaluate and compare the accuracy and precision of alternative RF-based devices within a range of indoor spaces composed of different materials and sizes. These spaces range from large open areas such as gymnasiums to confined engineering labs of traditional buildings as well as training buildings at the local Fire Department Training Facility. Our goal is to identify the impact of alternative RF-based systems on localization accuracy and precision specifically for first responders that are called upon to traverse structures composed of different materials and configurations. Consequently, in this study we have specifically chosen spaces that are likely to be encountered by firefighters during building fires or emergency medical responses. Moreover, many of these indoor spaces can be considered hostile using RF-based ranging techniques. We built prototype wearable localization edge devices designed for first responders and characterize both ranging and localization accuracy and precision using alternative transceivers including Bluetooth Low Energy, 433 MHz, 915 MHz, and ultra-wide band. Our results show that in hostile environments, using ultra-wide band transceivers for localization consistently outperforms the alternatives in terms of precision and accuracy.

Use of transmission and reflection complex time delays to reveal scattering matrix poles and zeros: Example of the ring graph

We identify the poles and zeros of the scattering matrix of a simple quantum graph by means of systematic measurement and analysis of Wigner, transmission, and reflection complex time delays. We examine the ring graph because it displays both shape and Feshbach resonances, the latter of which arises from an embedded eigenstate on the real frequency axis. Our analysis provides a unified understanding of the so-called shape, Feshbach, electromagnetically induced transparency, and Fano resonances on the basis of the distribution of poles and zeros of the scattering matrix in the complex frequency plane. It also provides a first-principles understanding of sharp resonant scattering features and associated large time delay in a variety of practical devices, including photonic microring resonators, microwave ring resonators, and mesoscopic ring-shaped conductor devices. Our analysis involves use of the reflection time difference, as well as a comprehensive use of complex time delay, to analyze experimental scattering data.

Meta-programmable analog differentiator

We present wave-based signal differentiation with unprecedented fidelity and flexibility by purposefully perturbing overmoded random scattering systems such that zeros of their scattering matrices lie exactly at the desired locations on the real frequency axis. Our technique overcomes limitations of hitherto existing approaches based on few-mode systems, both regarding their extreme vulnerability to fabrication inaccuracies or environmental perturbations and their inability to maintain high fidelity under in-situ adaptability. We demonstrate our technique experimentally by placing a programmable metasurface with hundreds of degrees of freedom inside a 3D disordered metallic box. Regarding the integrability of wave processors, such repurposing of existing enclosures is an enticing alternative to fabricating miniaturized devices. Our over-the-air differentiator can process in parallel multiple signals on distinct carriers and maintains high fidelity when reprogrammed to different carriers. We also perform programmable higher-order differentiation. Conceivable applications include segmentation or compression of communication or radar signals and machine vision.

Underwater Sound Source Localization Based on Passive Time-Reversal Mirror and Ray Theory

This study investigates the performance of a passive time-reversal mirror (TRM) combined with acoustic ray theory in localizing underwater sound sources with high frequencies (3-7 kHz). The TRM was installed on a floating buoy and comprised four hydrophones. The ray-tracing code BELLHOP was used to determine the transfer function between a sound source and a field point. The transfer function in the frequency domain obtained from BELLHOP was transformed into the time domain. The pressure field was then obtained by taking the convolution of the transfer function in the time domain with the time-reversed signals that were received by the hydrophones in the TRM. The location with the maximum pressure value was designated as the location of the source. The performance of the proposed methodology for source localization was tested in a towing tank and in the ocean. The aforementioned tests revealed that even when the distances between a source and the TRM were up to 1600 m, the distance deviations between estimated and actual source locations were mostly less than 2 m. Errors originated mainly from inaccurate depth estimation, and the literature indicates that they can be reduced by increasing the number of TRM elements and their apertures.

Calibration-free speckle matrix imaging

Unknown speckle patterns can be used to image targets embedded in complex scattering media 100 times faster than previous techniques based on carefully calibrated illuminations.

Multipath Propagation of Acoustic Signal in a Swimming Pool-Source Localization Problem

This paper explores the problem of severe multipath propagation of underwater acoustic signals in a swimming pool. The problem appeared in a study that examined a system used to signal emergency situations (i.e., pre-drowning symptoms detected by a wearable device on a pool user's wrist) and locate the signal source. A swimming pool acoustic environment is characterized by the presence of large flat reflecting planes surrounding a small volume of water. The reflections are numerous and much stronger than in typical hydroacoustic applications. In this paper, we attempted to create a model of the swimming pool response, one that is suitable for simulation experiments with detection and localization of emergency signals. Then, we explore the possible remedies for the localization system, applied on the transmit side (waveform design) and on the receive side (receiver placement and signal processing). Finally, we present an algorithm for object localization, considering the possible reflections with a multi-hypothesis approach.

High-throughput volumetric adaptive optical imaging using compressed time-reversal matrix

Deep-tissue optical imaging suffers from the reduction of resolving power due to tissue-induced optical aberrations and multiple scattering noise. Reflection matrix approaches recording the maps of backscattered waves for all the possible orthogonal input channels have provided formidable solutions for removing severe aberrations and recovering the ideal diffraction-limited spatial resolution without relying on fluorescence labeling and guide stars. However, measuring the full input-output response of the tissue specimen is time-consuming, making the real-time image acquisition difficult. Here, we present the use of a time-reversal matrix, instead of the reflection matrix, for fast high-resolution volumetric imaging of a mouse brain. The time-reversal matrix reduces two-way problem to one-way problem, which effectively relieves the requirement for the coverage of input channels. Using a newly developed aberration correction algorithm designed for the time-reversal matrix, we demonstrated the correction of complex aberrations using as small as 2% of the complete basis while maintaining the image reconstruction fidelity comparable to the fully sampled reflection matrix. Due to nearly 100-fold reduction in the matrix recording time, we could achieve real-time aberration-correction imaging for a field of view of 40 × 40 µm2 (176 × 176 pixels) at a frame rate of 80 Hz. Furthermore, we demonstrated high-throughput volumetric adaptive optical imaging of a mouse brain by recording a volume of 128 × 128 × 125 µm3 (568 × 568 × 125 voxels) in 3.58 s, correcting tissue aberrations at each and every 1 µm depth section, and visualizing myelinated axons with a lateral resolution of 0.45 µm and an axial resolution of 2 µm.

Optimal Control of Coherent Light Scattering for Binary Decision Problems

Because of quantum noise fluctuations, the rate of error achievable in decision problems involving several possible configurations of a scattering system is subject to a fundamental limit known as the Helstrom bound. Here, we present a general framework to calculate and minimize this bound using coherent probe fields with tailored spatial distributions. As an example, we experimentally study a target located in between two disordered scattering media. We first show that the optimal field distribution can be directly identified using a general approach based on scattering matrix measurements. We then demonstrate that this optimal light field successfully probes the presence of the target with a number of photons that is reduced by more than 2 orders of magnitude as compared to unoptimized fields.

Fingerprint Feature Extraction for Indoor Localization

This paper proposes a fingerprint-based indoor localization method, named FPFE (fingerprint feature extraction), to locate a target device (TD) whose location is unknown. Bluetooth low energy (BLE) beacon nodes (BNs) are deployed in the localization area to emit beacon packets periodically. The received signal strength indication (RSSI) values of beacon packets sent by various BNs are measured at different reference points (RPs) and saved as RPs’ fingerprints in a database. For the purpose of localization, the TD also obtains its fingerprint by measuring the beacon packet RSSI values for various BNs. FPFE then applies either the autoencoder (AE) or principal component analysis (PCA) to extract fingerprint features. It then measures the similarity between the features of PRs and the TD with the Minkowski distance. Afterwards, k RPs associated with the k smallest Minkowski distances are selected to estimate the TD’s location. Experiments are conducted to evaluate the localization error of FPFE. The experimental results show that FPFE achieves an average error of 0.68 m, which is better than those of other related BLE fingerprint-based indoor localization methods.