Wide-field-of-view optical detectors for deep ultraviolet light communication using all-inorganic CsPbBr3 perovskite nanocrystals.

Optical wireless communication (OWC) links suffer from strict requirements of pointing, acquisition, and tracking (PAT) between the transmitter and receiver. Extending the narrow field-of-view (FoV) of conventional light-focusing elements at the receiver side can relax the PAT requirements. Herein, we use all-inorganic CsPbBr3 nanocrystals (NCs) to extend various optical concentrators' FOV to 60°, regardless of the original FOV values of the concentrators. Given the robustness of UV light against communication channel misalignment, the used CsPbBr3 NCs provide another advantage of converting transmitted UVC light into a green color that matches the peak absorption of the widely available Si-based detectors. We evaluated the feasibility of the reported wide FoV optical detectors by including them in deep UV OWC systems, deploying non-return-to-zero on-off keying (NRZ-OOK) and orthogonal-frequency division multiplexing (OFDM) modulation schemes. The NRZ-OOK and OFDM schemes exhibit stable communication over the 60° FoV, providing data transmission rates of 100 Mb/s and 71.6 Mb/s, respectively, a unique capability to the reported design.

Submarine optical fiber communication provides an unrealized deep-sea observation network.

Oceans are crucial to human survival, providing natural resources and most of the global oxygen supply, and are responsible for a large portion of worldwide economic development. Although it is widely considered a silent world, the sea is filled with natural sounds generated by marine life and geological processes. Man-made underwater sounds, such as active sonars, maritime traffic, and offshore oil and mineral exploration, have significantly affected underwater soundscapes and species. In this work, we report on a joint optical fiber-based communication and sensing technology aiming to reduce noise pollution in the sea while providing connectivity simultaneously with a variety of underwater applications. The designed multifunctional fiber-based system enables two-way data transfer, monitoring marine life and ship movement near the deployed fiber at the sea bottom and sensing temperature. The deployed fiber is equally harnessed to transfer energy that the internet of underwater things (IoUTs) devices can harvest. The reported approach significantly reduces the costs and effects of monitoring marine ecosystems while ensuring data transfer and ocean monitoring applications and providing continuous power for submerged IoUT devices.

Simultaneous distributed acoustic sensing and communication over a two-mode fiber.

We designed and tested a distributed acoustic sensing (DAS) that co-exists with optical communication over a two-mode fiber (TMF). In particular, we excited both linearly polarized (LP) modes, LP01 and LP11a, using a photonic lantern for simultaneous information signal transmission while collecting the backscattered Rayleigh light at the near end of the fiber to detect vibrations from a predetermined source. While transmitting data using on-off keying (OOK) or orthogonal frequency-division multiplexing (OFDM) modulation schemes, the optical fiber DAS offers high signal-to-noise ratio (SNR) values that are always larger than the minimum acceptable 2 dB SNR. In addition, as a proof-of-concept experiment, we report parallel sensing and OFDM transmission achieving a data rate of up to 4.2 Gb/s with a bit error rate (BER) of 3.2 × 10-3.

CNN-Aided Optical Fiber Distributed Acoustic Sensing for Early Detection of Red Palm Weevil: A Field Experiment.

Red palm weevil (RPW) is a harmful pest that destroys many date, coconut, and oil palm plantations worldwide. It is not difficult to apply curative methods to trees infested with RPW; however, the early detection of RPW remains a major challenge, especially on large farms. In a controlled environment and an outdoor farm, we report on the integration of optical fiber distributed acoustic sensing (DAS) and machine learning (ML) for the early detection of true weevil larvae less than three weeks old. Specifically, temporal and spectral data recorded with the DAS system and processed by applying a 100-800 Hz filter are used to train convolutional neural network (CNN) models, which distinguish between "infested" and "healthy" signals with a classification accuracy of ∼97%. In addition, a strict ML-based classification approach is introduced to improve the false alarm performance metric of the system by ∼20%. In a controlled environment experiment, we find that the highest infestation alarm count of infested and healthy trees to be 1131 and 22, respectively, highlighting our system's ability to distinguish between the infested and healthy trees. On an outdoor farm, in contrast, the acoustic noise produced by wind is a major source of false alarm generation in our system. The best performance of our sensor is obtained when wind speeds are less than 9 mph. In a representative experiment, when wind speeds are less than 9 mph outdoor, the highest infestation alarm count of infested and healthy trees are recorded to be 1622 and 94, respectively.

Outcomes of tocilizumab therapy in severe or critical COVID-19 patients: A retrospective cohort, single-centre study.

OBJECTIVES: To assess the effectiveness and safety of tocilizumab, a humanised anti-interleukin-6 receptor antibody, in the treatment of critical or severe coronavirus disease 2019 (COVID-19) patients. METHODS: This was a retrospective cohort study of severe or critical COVID-19 patients (≥18 years) admitted to one hospital in Kuwait. Fifty-one patients received intravenous tocilizumab, while 78 patients received the standard of care at the same hospital. Both groups were compared for clinical improvement and in-hospital mortality. RESULTS: The tocilizumab (TCZ) group had a significantly lower 28-day in-hospital mortality rate than the standard-of care-group (21.6% vs. 42.3% respectively; p = 0.015). Fifty-five per cent of patients in the TCZ group clinically improved vs. 11.5% in the standard-of-care group (p < 0.001). Using Cox-proportional regression analysis, TCZ treatment was associated with a reduced risk of mortality (adjusted hazard ratio 0.25; 95% CI: 0.11-0.61) and increased likelihood of clinical improvement (adjusted hazard ratio 4.94; 95% CI: 2.03-12.0), compared to the standard of care. The median C-reactive protein, D-dimer, procalcitonin, lactate dehydrogenase and ferritin levels in the tocilizumab group decreased significantly over the 14 days of follow-up. Secondary infections occurred in 19.6% of the TCZ group, and in 20.5% of the standard-of-care group, with no statistical significance (p = 0.900). CONCLUSION: Tocilizumab was significantly associated with better survival and greater clinical improvement in severe or critical COVID-19 patients.

Wide-field-of-view optical detectors using fused fiber-optic tapers.

Photodetectors used in wireless applications suffer from a trade-off between their response speeds and their active areas, which limits the received signal-to-noise ratio (SNR). Conventional light-focusing elements used to improve the SNR narrow the field of view (FOV). Herein, we demonstrate a versatile imaging light-focusing element featuring a wide FOV and high optical gain using fused fiber-optic tapers. To verify the practicality of the proposed design, we demonstrated and tested a wide-FOV optical detector for optical wireless communication that can be used for wavelengths ranging from the visible-light band to the near infrared. The proposed detector offers improvements over luminescent wide-FOV detectors, including higher efficiency, a broader modulation bandwidth, and indefinite stability.

Towards Detecting Red Palm Weevil Using Machine Learning and Fiber Optic Distributed Acoustic Sensing.

Red palm weevil (RPW) is a detrimental pest, which has wiped out many palm tree farms worldwide. Early detection of RPW is challenging, especially in large-scale farms. Here, we introduce the combination of machine learning and fiber optic distributed acoustic sensing (DAS) techniques as a solution for the early detection of RPW in vast farms. Within the laboratory environment, we reconstructed the conditions of a farm that includes an infested tree with ∼12 day old weevil larvae and another healthy tree. Meanwhile, some noise sources are introduced, including wind and bird sounds around the trees. After training with the experimental time- and frequency-domain data provided by the fiber optic DAS system, a fully-connected artificial neural network (ANN) and a convolutional neural network (CNN) can efficiently recognize the healthy and infested trees with high classification accuracy values (99.9% by ANN with temporal data and 99.7% by CNN with spectral data, in reasonable noise conditions). This work paves the way for deploying the high efficiency and cost-effective fiber optic DAS to monitor RPW in open-air and large-scale farms containing thousands of trees.

Sensing within the OTDR dead-zone using a two-mode fiber.

An optical time-domain reflectometer (OTDR) is incapable of providing sensing or diagnostic information within dead-zones. We use a two-mode fiber (TMF) and a photonic lantern to completely overcome the main OTDR's dead-zone originating from the front facet of optical fiber. This is achieved by injecting the optical pulses of the OTDR in the form of the fundamental ${{\rm LP}_{{01}}}$ mode and meanwhile collecting the Rayleigh signals associated with the higher-order modes. Using the reported TMF-based OTDR, we accurately sense the position and frequency of a vibration event located within the dead-zone as a proof-of-concept demonstration.

Identifying structured light modes in a desert environment using machine learning algorithms.

The unique orthogonal shapes of structured light beams have attracted researchers to use as information carriers. Structured light-based free space optical communication is subject to atmospheric propagation effects such as rain, fog, and rain, which complicate the mode demultiplexing process using conventional technology. In this context, we experimentally investigate the detection of Laguerre Gaussian and Hermite Gaussian beams under dust storm conditions using machine learning algorithms. Different algorithms are employed to detect various structured light encoding schemes including the use of a convolutional neural network (CNN), support vector machine, and k-nearest neighbor. We report an identification accuracy of 99% under a visibility level of 9 m. The CNN approach is further used to estimate the visibility range of a dusty communication channel.

Early detection of red palm weevil using distributed optical sensor.

Red palm weevil (RPW) poses a serious threat to the cultivation of date palms. It is considered to be the most destructive epidemic pest of palms, responsible for massive economic losses worldwide. Curative methods for RPW are not difficult to apply; however, the early detection of the pest remains a great challenge. Although several detection techniques have been implemented for the early detection of RPW, none of these methods have been proven to be reliable. Here, we use an optical-fiber-distributed acoustic sensor (DAS) as a paradigm shift technology for the early detection of RPW. Our sensitive sensor shows a detection of feeding sound produced by larvae as young as 12 days, in an infested tree. In comparison with existing, commonly-used technologies, this novel sensing technique represents a cost-effective and non-invasive alternative that could provide 24-7, real-time monitoring of 1,000 palm trees or even more. It could also monitor the temperature, an essential feature to control farm fires, another major problem for the cultivation of palm trees around the world.

Normalized differential method for improving the signal-to-noise ratio of a distributed acoustic sensor.

We experimentally introduce a normalized differential method to enhance the time domain signal-to-noise ratio (SNR) of an optical fiber distributed acoustic sensor (DAS). The reported method is calibrated against the typical differential method in noisy DAS systems, including those utilizing a relatively wide linewidth laser or few-mode fiber. In these two systems, the normalized differential method respectively identifies the position information of various vibration events with 1.7 dB and 0.53 dB SNR improvement. We further demonstrate the ability to locate positions along a fiber that are subjected to vibrations of frequencies higher than the theoretical maximum, but without determining these frequencies.

Fiber-based simultaneous mode and wavelength demultiplexer.

We theoretically design and analyze the performance of a fiber-based linearly polarized (LP) mode demultiplexer using a Fabry-Perot interferometer. The all-fiber geometry of the reported demultiplexer is obtained by writing fiber Bragg gratings (FBGs) in a few-mode fiber (FMF), such that the FBGs act as partial reflecting mirrors for the LP modes. We also demonstrate the ability to decompose a received optical signal into its individual LP components in wavelength-division multiplexing networks.

Fabrication and characterization of periodically patterned silica fiber structures for enhanced second-order nonlinearity: publisher's note.

This publisher's note amends the author list of [Opt. Express23, 8113 (2015)].

Mode-division-multiplexing of absorption-based fiber optical sensors.

We theoretically consider the possibility of using a few-mode fiber (FMF) and mode-division-multiplexing (MDM) to construct a quasi-distributed network of absorption-based fiber optical sensors. In this design, we utilize the low-attenuation fundamental linearly polarized (LP) mode for signal transmission, and a high-attenuation LP mode for absorption-based sensing. We develop a matrix formalism and use it to analyze the performance of such a MDM sensor network. We demonstrate that such a sensor network can indeed combine high sensitivity with large scale multiplexing, which is very difficult to achieve in traditional single-mode-fiber-based sensor networks.

Investigating the characteristics of TM-pass/TE-stop polarizer designed using plasmonic nanostructures.

Plasmonics-based polarizers are important for many photonic devices and applications. In this paper, we design and investigate the characteristics of a new TM-pass/TE-stop polarizer using silver nanograting of exponentially tapered slit sidewalls. Performance of the designed polarizer is determined through monitoring the modification of its insertion loss, return loss, extinction ratio, and far-field transform due to changing its structural parameters. We find that the structural parameters of the reported polarizer such as a slit sidewall tapering coefficient and slit opening widths have a significant impact on tuning the polarizer characteristics.

Fabrication and characterization of periodically patterned silica fiber structures for enhanced second-order nonlinearity.

We develop and characterize a UV ablation technique that can be used to pattern soft materials such as polymers and nonlinear molecules self-assembled over silica microstructures. Using this method, we fabricate a spatially periodic coating of nonlinear film over a thin silica fiber taper for second harmonic generation (SHG). Experimentally, we find that the second harmonic signal produced by the taper with periodic nonlinear coating is 15 times stronger than the same taper with uniform nonlinear coating, which suggests that quasi-phase-matching is at least partially achieved in the patterned nonlinear silica taper. The same technique can also be used to spatially pattern other types of functional nanomaterials over silica microstructures with curved surfaces, as demonstrated by deposition of gold nanoparticles in patterned structures.

Fluorescence lifetime based characterization of active and tunable plasmonic nanostructures.

We report a non-contact method that utilizes fluorescence lifetime (FL) to characterize morphological changes of a tunable plasmonic nanostructure with nanoscale accuracy. The key component of the plasmonic nanostructure is pH-responsive polyelectrolyte multilayers (PEMs), which serve as a dynamically tunable "spacer" layer that separates the plasmonic structure and the fluorescent materials. The validity of our method is confirmed through direct comparison with ellipsometry and atomic force microscopy (AFM) measurements. Applying the FL-based approach, we find that a monolayer polycation film responds to pH changes with significantly less hysteresis than a thicker multilayer film with polyelectrolytes of both charges. Additionally, we characterize an active and tunable plasmonic nanostructure composed of self-assembled fluorescent dye (Texas Red), pH-sensitive PEMs, and gold nanospheres adsorbed on the PEM surface. Our results point towards the possibility of using stimulus-sensitive polymers to construct active and tunable plasmonic nanodevices.

Impact of lithography on the fluorescence dynamics of self-assembled fluorophores.

Micro- and nano-patterned fluorescent materials are important for many photonic devices and applications. In this paper, we investigate the impact of three common lithographical techniques, deposition and removal of sacrificial masks, ultraviolet ablation, and focused ion beam milling, on self-assembled fluorophores. We find that different patterning techniques can dramatically change the fluorescence lifetime of the fluorophores and that the degree of modification depends on the patterning techniques.

High quality factor silica microspheres functionalized with self-assembled nanomaterials.

With extremely low material absorption and exceptional surface smoothness, silica-based optical resonators can achieve extremely high cavity quality (Q) factors. However, the intrinsic material limitations of silica (e.g., lack of second order nonlinearity) may limit the potential applications of silica-based high Q resonators. Here we report some results in utilizing layer-by-layer self-assembly to functionalize silica microspheres with nonlinear and plasmonic nanomaterials while maintaining Q factors as high as 10(7). We compare experimentally measured Q factors with theoretical estimates, and find good agreement.