Communication Systems Performance at mm and THz as a Function of a Rain Rate Probability Density Function Model.

6G is already being planned and will employ much higher frequencies, leading to a revolutionary era in communication between people as well as things. It is well known that weather, especially rain, can cause increased attenuation of signal transmission for higher frequencies. The standard methods for evaluating the effect of rain on symbol error rate are based on long-term averaging. These methods are inaccurate, which results in an inefficient system design. This is critical regarding bandwidth scarcity and energy consumption and requires a more significant margin of effort to cope with the imprecision. Recently, we have developed a new and more precise method for calculating communication system performance in case of rain, using the probability density function of rain rate. For high rain rate (above 10 mm/h), for a typical set of parameters, our method shows the symbol error rate in this range to be higher by orders of magnitude than that found by ITU standard methods. Our model also indicates that sensing and measuring the rain rate probability is important in order to provide the required bit error rate to the users. This will enable the design of more efficient systems, enabling design of an adaptive system that will adjust itself to rain conditions in such a way that performance will be improved. To the best knowledge of the authors, this novel analysis is unique. It can constitute a more efficient performance metric for the new era of 6G communication and prevent disruption due to incorrect system design.

Propagation of structured light through tissue-mimicking phantoms.

Optical interrogation of tissues is broadly considered in biomedical applications. Nevertheless, light scattering by tissue limits the resolution and accuracy achieved when investigating sub-surface tissue features. Light carrying optical angular momentum or complex polarization profiles, offers different propagation characteristics through scattering media compared to light with unstructured beam profiles. Here we discuss the behaviour of structured light scattered by tissue-mimicking phantoms. We study the spatial and the polarization profile of the scattered modes as a function of a range of optical parameters of the phantoms, with varying scattering and absorption coefficients and of different lengths. These results show the non-trivial trade-off between the advantages of structured light profiles and mode broadening, stimulating further investigations in this direction.

Optimization of FSO quantum backscatter communication.

In this paper, the free-space optics (FSO) quantum backscatter communication (QBC), inspired by the quantum illumination concept, is described. In this method, the transmitter generates entangled photon pairs. The signal photon is transmitted in the direction of the modulated retro reflector (MRR) or the tag antenna, and the idler photon is moved to the receiver. The MRR communicates by modulating the received photon and retro-reflecting it back to the receiver. QBC could provide performance improvement in comparison to conventional backscatter communications. In this work, a mathematical model of the FSO QBC systems is derived. In an FSO system, the pointing direction jitter is a stochastic process that reduces the communication performance. In this paper, optimization of FSO QBC performance is proposed to minimize the error probability. This could be done by adapting the telescope gain to jitter a standard deviation amplitude.

Direct detection receiver for vortex beam.

We present the theory of a direct detection receiver for vortex beams for an optical wireless communication system. The proposed receiver has an array of annular detectors, which enables analysis of power distribution of the vortex beam. We give a detailed description, numerical optimization, and different options for its design. One possible application of this receiver could be high security communication systems, and another could be intra data center communication. Using a given set of parameters, we find that a seven-ring symmetrically spaced detector is comparable to a three-ring detector with an optimized ring area.

1550 nm modulating retroreflector based on coated nanoparticles for free-space optical communication.

Nowadays, there is a renaissance in the field of space exploration. Current and future missions depend on astronauts and a swarm of robots for reconnaissance. In order to reduce the power consumption, weight, and size of the robots, an asymmetric communication system may be used. This is achieved by installing modulating retroreflectors (MRRs) on one side of the link and an interrogating laser on the other side. In this paper, we theoretically study an innovative device that can serve as an MRR in the infrared range of the spectrum. The device is based on a ferroelectric PZT thin film containing TiO2 coated Ag nanoparticles, which exhibit strong plasmonic resonance in the infrared range. After intensive analyses, which included calculations and simulations, we were able to design the device to operate at the 1550 nm wavelength. This is of great importance since the design of devices operating at 1550 nm as this wavelength is a mature technology widely used in free-space optics. Hence, this MRR can serve in asymmetric communication links relying on 1550 nm transmissions, which are also eye-safe. To the best of our knowledge, this is the first time coated metal nanoparticles have been proposed to modulate light in the infrared region. The performance of this device is unique, reaching a 17.5 dB modulation contrast with only a ±2 V operating voltage. This modulator may also be used for terrestrial communication such as fiber optics and optical interconnects in future data centers.

Tunable electro-optic filter based on metal-ferroelectric nanocomposite for VLC.

The emerging technology of visible light communications (VLC) will provide a new modality of communication. This technology uses illumination lighting to carry information. We propose to add a smart capability to mitigate interferences from unwanted light sources. This is achieved by adaptively filtering interference light using a tunable filter to block interferences dynamically. In this Letter, we present an innovative concept for a tunable notch filter based on ferroelectric thin films embedded with noble metal nanoparticles. The adaptivity of the filter is achieved by controlling the external applied voltage. This voltage creates an electric field that changes the refractive index of the host film through the linear electro-optic effect. Moreover, the fundamental characteristics of the filter are determined by the layer's parameters, such as film thickness, nanoparticles concentration and geometry, and the material of both the host thin film and nanoparticles. We study the tunability of lead zirconate titanate (PZT) embedded with Ag nanoparticles that reaches approximately 50 nm, between 530 and 590 nm. Moreover, we showed that a PZT notch filter embedded with Ag nanoshells has its stop band shifted to shorter wavelengths. These tunable filters can be used as mode selectors inside a laser resonator, spatial light filters for imaging and communication both for VLC and infrared communication.

Statistics of remote roll angle measurement.

Remote measurement of object orientation is used in various scientific fields, such as robotics, optics, and biology (e.g., optical tweezers). Roll angle is one of the three angles that describe the orientation of an object in space. A common method to measure the roll angle is based on analyzing the polarization of the backreflection of a beam. The accuracy of the measurement is degraded by low signal-to-noise ratio (SNR). The low SNR is the result of the large distance between the measurement device and the object, or due to the small backreflection cross section. We perform a laboratory experiment and derive a mathematical model for the probability density function of the measured roll angle and its expectation value. This model makes it possible to calculate the accuracy of the roll angle measurement at low SNRs. Experiments and theoretical analysis using our model were performed and good agreement between the two approaches has been found.

Evaluation of the estimation accuracy of polarization-based roll angle measurement.

The orientation of remote objects can be easily measured optically and very accurately using optical technologies. In this paper, we address the accuracy of a roll angle estimation technique that is based on the estimation of the polarization of a retroreflected beam and is applied in a long-range measurement task. Over long transmission ranges, the optical power decreases significantly leading to severe SNR deterioration. As a result, the measurement accuracy decreases. In this study, the estimation is carried out using a maximum-likelihood estimator and its performance is evaluated using the analytical Cramer-Rao bound.

Imaging inside highly scattering media using hybrid deep learning and analytical algorithm.

Imaging through highly scattering media is a challenging problem with numerous applications in biomedical and remote-sensing fields. Existing methods that use analytical or deep learning tools are limited by simplified forward models or a requirement for prior physical knowledge, resulting in blurry images or a need for large training databases. To address these limitations, we propose a hybrid scheme called Hybrid-DOT that combines analytically derived image estimates with a deep learning network. Our analysis demonstrates that Hybrid-DOT outperforms a state-of-the-art ToF-DOT algorithm by improving the PSNR ratio by 4.6 dB and reducing the resolution by a factor of 2.5. Furthermore, when compared to a deep learning stand-alone model, Hybrid-DOT achieves a 0.8 dB increase in PSNR, 1.5 times the resolution, and a significant reduction in the required dataset size (factor of 1.6-3). The proposed model remains effective at higher depths, providing similar improvements for up to 160 mean-free paths.

Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming.

Diffuse optical tomography (DOT) is a non-invasive method for detecting breast cancer; however, it struggles to produce high-quality images due to the complexity of scattered light and the limitations of traditional image reconstruction algorithms. These algorithms can be affected by boundary conditions and have a low imaging accuracy, a shallow imaging depth, a long computation time, and a high signal-to-noise ratio. However, machine learning can potentially improve the performance of DOT by being better equipped to solve inverse problems, perform regression, classify medical images, and reconstruct biomedical images. In this study, we utilized a machine learning model called "XGBoost" to detect tumors in inhomogeneous breasts and applied a post-processing technique based on genetic programming to improve accuracy. The proposed algorithm was tested using simulated DOT measurements from complex inhomogeneous breasts and evaluated using the cosine similarity metrics and root mean square error loss. The results showed that the use of XGBoost and genetic programming in DOT could lead to more accurate and non-invasive detection of tumors in inhomogeneous breasts compared to traditional methods, with the reconstructed breasts having an average cosine similarity of more than 0.97 ± 0.07 and average root mean square error of around 0.1270 ± 0.0031 compared to the ground truth.

Feasibility of retroreflective transdermal optical wireless communication.

There is an increasing demand for transdermal high-data-rate communication for use with in-body devices, such as pacemakers, smart prostheses, neural signals processors at the brain interface, and cameras acting as artificial eyes as well as for collecting signals generated within the human body. Prominent requirements of these communication systems include (1) wireless modality, (2) noise immunity and (3) ultra-low-power consumption for the in-body device. Today, the common wireless methods for transdermal communication are based on communication at radio frequencies, electrical induction, or acoustic waves. In this paper, we will explore another alternative to these methods--optical wireless communication (OWC)--for which modulated light carries the information. The main advantages of OWC in transdermal communication, by comparison to the other methods, are the high data rates and immunity to external interference availed, which combine to make it a promising technology for next-generation systems. In this paper, we present a mathematical model and experimental results of measurements from direct link and retroreflection link configurations with Gallus gallus domesticus derma as the transdermal channel. The main conclusion from this work is that an OWC link is an attractive communication solution in medical applications. For a modulating retroreflective link to become a competitive solution in comparison with a direct link, low-energy-consumption modulating retroreflectors should be developed.

Regression-based neural network for improving image reconstruction in diffuse optical tomography.

Diffuse optical tomography (DOT) is a non-invasive imaging technique utilizing multi-scattered light at visible and infrared wavelengths to detect anomalies in tissues. However, the DOT image reconstruction is based on solving the inverse problem, which requires massive calculations and time. In this article, for the first time, to the best of our knowledge, a simple, regression-based cascaded feed-forward deep learning neural network is derived to solve the inverse problem of DOT in compressed breast geometry. The predicted data is subsequently utilized to visualize the breast tissues and their anomalies. The dataset in this study is created using a Monte-Carlo algorithm, which simulates the light propagation in the compressed breast placed inside a parallel plate source-detector geometry (forward process). The simulated DL-DOT system's performance is evaluated using the Pearson correlation coefficient (R) and the Mean squared error (MSE) metrics. Although a comparatively smaller dataset (50 nos.) is used, our simulation results show that the developed feed-forward network algorithm to solve the inverse problem delivers an increment of ∼30% over the analytical solution approach, in terms of R. Furthermore, the proposed network's MSE outperforms that of the analytical solution's MSE by a large margin revealing the robustness of the network and the adaptability of the system for potential applications in medical settings.

Imaging through diffuse media using multi-mode vortex beams and deep learning.

Optical imaging through diffuse media is a challenging issue and has attracted applications in many fields such as biomedical imaging, non-destructive testing, and computer-assisted surgery. However, light interaction with diffuse media leads to multiple scattering of the photons in the angular and spatial domain, severely degrading the image reconstruction process. In this article, a novel method to image through diffuse media using multiple modes of vortex beams and a new deep learning network named "LGDiffNet" is derived. A proof-of-concept numerical simulation is conducted using this method, and the results are experimentally verified. In this technique, the multiple modes of Gaussian and Laguerre-Gaussian beams illuminate the displayed digits dataset number, and the beams are then propagated through the diffuser before being captured on the beam profiler. Furthermore, we investigated whether imaging through diffuse media using multiple modes of vortex beams instead of Gaussian beams improves the imaging system's imaging capability and enhances the network's reconstruction ability. Our results show that illuminating the diffuser using vortex beams and employing the "LGDiffNet" network provides enhanced image reconstruction compared to existing modalities. An enhancement of ~ 1 dB, in terms of PSNR, is achieved using this method when a highly scattering diffuser of grit 220 and width 2 mm (7.11 times the mean free path) is used. No additional optimizations or reference beams were used in the imaging system, revealing the robustness of the "LGDiffNet" network and the adaptability of the imaging system for practical applications in medical imaging.

Identification of coronary calcifications in optical coherence tomography imaging using deep learning.

Coronary calcifications are an obstacle for successful percutaneous treatment of coronary artery disease patients. The optimal method for delineating calcifications extent is coronary optical coherence tomography (OCT). To identify calcification on OCT and subsequently tailor the appropriate treatment, requires expertise in both image acquisition and interpretation. Image acquisition consists from system calibration, blood clearance by a contrast agent along with synchronization of the pullback process. Accurate interpretation demands careful review by the operator of a segment of 50-75 mm of the coronary vessel at steps of 5-10 frames per mm accounting for 375-540 images in each OCT run, which is time consuming and necessitates some expertise in OCT analysis. In this paper we developed a new deep learning algorithm to assist the physician to identify and quantify coronary calcifications promptly, efficiently and accurately. Our algorithm achieves an accuracy of 0.9903 ± 0.009 over the test set at size of 1500 frames and even managed to find calcifications that were not recognized manually by the physician. For the best knowledge of the authors our algorithm achieves high accuracy which was never achieved in the past.

OAM light propagation through tissue.

A major challenge in use of the optical spectrum for communication and imaging applications is the scattering of light as it passes through diffuse media. Recent studies indicate that light beams with orbital angular momentum (OAM) can penetrate deeper through diffuse media than simple Gaussian beams. To the best knowledge of the authors, in this paper we describe for the first time an experiment examining transmission of OAM beams through biological tissue with thickness of up to a few centimeters, and for OAM modes reaching up to 20. Our results indicate that OAM beams do indeed show a higher transmittance relative to Gaussian beams, and that the greater the OAM, the higher the transmittance also up to 20, Our results extend measured results to highly multi scattering media and indicate that at 2.6 cm tissue thickness for OAM of order 20, we measure nearly 30% more power in comparison to a Gaussian beam. In addition, we develop a mathematical model describing the improved permeability. This work shows that OAM beams can be a valuable contribution to optical wireless communication (OWC) for medical implants, optical biological imaging, as well as recent innovative applications of medical diagnosis.

Enhanced absorption of light by charged nanoparticles.

We found that various charged nanoparticles (NPs) can raise the attenuation of electromagnetic (EM) radiation over 30 times more efficiently during resonance in comparison to equivalent noncharged particles for a given set of parameters. A condition that indicates a state of resonance between the incident EM radiation and the NP surface excitations is mathematically derived. Our results shed light on the mechanism responsible for the strong absorption of light by such charged NPs. The outcome of this research could help to design a new generation of communication devices as well as a new technique for biological cell imaging.

Maximization of imaging resolution in optical wireless sensor/lab-on-chip/SoC networks with solar cells.

The availability of sophisticated and low-cost hardware on a single chip, for example, CMOS cameras, CPU, DSP, processors and communication transceivers, optics, microfluidics, and micromechanics, has fostered the development of system-on-chip (SoC) technology, such as lab-on-chip or wireless multimedia sensor networks (WMSNs). WMSNs are networks of wirelessly interconnected devices on a chip that are able to ubiquitously retrieve multimedia content such as video from the environment and transfer it to a central location for additional processing. In this paper, we study WMSNs that include an optical wireless communication transceiver that uses light to transmit the information. One of the primary challenges in SoC design is to attain adequate resources like energy harvesting using solar cells in addition to imaging and communication capabilities, all within stringent spatial limitations while maximizing system performances. There is an inevitable trade-off between enhancing the imaging resolution and the expense of reducing communication capacity and energy harvesting capabilities, on one hand, and increasing the communication or the solar cell size to the detriment of the imaging resolution, on the other hand. We study these trade-offs, derive a mathematical model to maximize the resolution of the imaging system, and present a numerical example that demonstrates maximum imaging resolution. Our results indicate that an eighth-order polynomial with only two constants provides the required area allocation between the different functionalities.

Sensing and communication trade-offs in picosatellite formation flying missions.

One of the primary challenges in all small satellite design is the attainment of adequate sensing and communication capabilities within the stringent spatial limitations. These can be defined in terms of surface area expenditure for the different payloads. There is an inevitable trade-off between enhancing the sensing capacity at the expense of reducing communication capabilities on the one hand and, on the other hand, increasing the communication capacity to the detriment of the sensing ability. Careful balancing of the conflicting demands is necessary to achieve acceptable performance levels. In this paper we study two intersatellite optical wireless communication scenarios: (i) a direct link between two satellites and (ii) a folded path link between a master satellite and a picosatellite equipped with a modulatable retroreflector. In the latter case the picosatellite does not have a laser transmitter and the data carrier is the retroreflected beam from the master satellite. The data rate, which is bounded by the sensing payload resolution, is derived using diffraction theory and Shannon capacity considerations. We develop a mathematical model to describe the interrelations between sensing and communication facilities in a picosatellite flight formation using optical technologies and demonstrate system performance trade-offs with a numerical example.

Minimization of outage probability of WiMAX link supported by laser link between a high-altitude platform and a satellite.

Various technologies for the implementation of a WiMAX (IEEE802.16) base station on board a high-altitude platform (HAP) are currently being researched. The network configuration under consideration includes a satellite, several HAPs, and subscribers on the ground. The WiMAX base station is positioned on the satellite and connects with the HAP via an analog RF over-laser communication (LC) link. The HAPs house a transparent transponder that converts the optic signal to a WiMAX RF signal and the reverse. The LC system consists of a laser transmitter and an optical receiver that need to be strictly aligned to achieve a line-of-sight link. However, mechanical vibration and electronic noise in the control system challenge the transmitter-receiver alignment and cause pointing errors. The outcome of pointing errors is fading of the received signal, which leads to impaired link performance. In this paper, we derive the value of laser transmitter gain that can minimize the outage probability of the WiMAX link. The results indicate that the optimum value of the laser transmitter gain is not a function of the pointing error statistics.