The use of autonomous underwater vehicles (AUVs), to potentially carry out underwater exploration missions, is limited due to insufficient onboard battery and data storage capacity. To overcome this problem, underwater docking stations are used to provide the facility of underwater charging and data transfer for AUVs. These docking stations are designed to be installed in the dynamic ocean environment, where the turbidity and low-light conditions are key challenges to hinder the successful docking operation. The vision guidance algorithms based on active or passive markers are typically used to precisely guide the AUV towards the docking station. In this paper, we propose a vision-based guidance method, using lock-in detection, to mitigate the effect of turbidity, and to reject the unwanted light sources or noisy luminaries, simultaneously. The lock-in detection method locks on the blinking frequency of light beacons located at the docking station and successfully vanishes the effect of unwanted light at other frequencies. The proposed method uses two light beacons, emitting at a fixed frequency, installed at the simulated docking station and a single CMOS camera. Proof-of-the-concept experiments are performed to show the validity of the proposed approach. The obtained results show that our method is capable of recognizing the light beacons at different turbidity levels, and it can efficiently reject the unwanted light without using separate image processing for this step of the vision-based guidance algorithm. The effectiveness of the proposed method is validated by calculating the true positive rate of the detection method at each turbidity level.
In recent years, artificial intelligence has achieved unprecedented development, and deep learning, represented by neural networks, plays an important role. After the emergence of large-scale pre-trained models with trillions of parameters, the model performance is significantly improved while the burden of computational resources and energy consumption of hardware devices are also increased simultaneously, thus limiting its application in more practical scenarios. Compared with neural networks implemented based on electronic devices, those implemented based on optical devices are called optical neural networks, which have unique properties to overcome the dilemma above. One of the most representative works of optical neural networks these years is the diffractive deep neural network (D2NN). In this paper, the research progress of D2NNs is summarized in four aspects: basic theory, further analysis, improvement, and application. Besides, it is analyzed that the common defect of D2NNs from simulation to physical fabrication, and corresponding theoretical improvement method is also proposed. Meanwhile, to further reduce the impact due to the gap between simulation and physical implementation, and to enhance the robustness of the model, the D2NN training method based on generative adversarial network (GAN) is proposed. The D2NN combines optical transmission with deep learning to achieve complex pattern recognition tasks in the optical domain at the speed of light. It is believed that under continuous research, the D2NN can play a greater role in optical communications and other fields.
Fiber optic shape sensing has a great potential for diverse medical and industrial applications to measure curvatures and even shapes. Featuring small footprint, strong immunity to radiation and high flexibility integration, fiber optic shape sensing opens up a new era in the fields of position tracking, human wearable devices, catheter navigation, bending detection and deformation monitoring. This paper focuses on a branch of fiber optic shape sensing techniques, with an emphasis on shape sensing based on fiber Bragg gratings (FBGs). Key technologies of shape sensing based on FBG are introduced in detail together with a critical view of its evolutionary trend. In addition, the major problems that exist in FBG shape sensing have been discussed in the end.
In modern optical communication systems, signal light polarization state control technology can not only realize highspeed and large-capacity communication, but is also an indispensable part in the design of photonic integrated circuits. So far, the polarization extinction ratio (PER) is the main factor limiting the bandwidth of polarization control devices and it causes additional polarization loss in the whole communication system. Additionally, with the development of optical wireless communication technology in free space and underwater environment, the research on high-performance polarization control devices that suitable for visible light band has not been widely concerned. In this paper, a design concept for cross-talk free polarization control devices based on coupled mode theory has been illustrated, and several devices (including polarization splitters, polarization converters) have been given. Moreover, the design scheme and technical difficulties of polarization control devices in blue and green light band have also been evaluated.
With the development of artificial intelligence technology, such as artificial neural networks, the increasing demand for computing drives the upgrading of computing accelerators. It’s known that the semiconductor process is approaching physical limits and the Von Neumann architecture of storage-computing separation affects the computing efficiency, which both lead to the gradual failure of electronic devices to meet application requirements. Optical neural networks (ONNs) can take full advantage of high speed, high bandwidth, high parallelism, and low power consumption of optical transmission to overcome the deficiencies of electronic devices. In this paper, we summarize and analyze previous researches on optical neural networks according to different physical implementations. And we conclude that most studies apply the characteristics of special materials to realize the dense matrix multiplication and nonlinear activation function of ONNs. Less research focuses on the nonlinear characteristics inherent in the optical signal transmission to realize important components of traditional neural networks. ONNs show great potentials in analog computing and information processing, such as marine in-situ imaging and optical receiver of underwater optical communication. And ONN is possible to be a new generation of neural network accelerator. But the large-scale application of ONNs requires more studies in optical implementation of nonlinear activation function and loss function, and accuracy improvement of optical computing.
A simple and low cost, integrated, fully passive optical network of free-space optics (FSO) and indoor visible light communication (VLC) technique, which can be used for both solid-state lighting and last-mile access network, is proposed in this paper. As a proof of a concept, a non-return-to-zero on-off keying (NRZ-OOK) modulation scheme for transmission over an integrated fully passive optical link of 7-m FSO, 2-m plastic optical fiber and 30-cm VLC was demonstrated by achieving 1.4-Gbps data rate with a bit error rate of 2.6 ×10−3. The phosphor film diverges the blue laser beam to a white light spot covering a radian angle up to approximately 120° with Commission Internationale de l'Eclairage CIE of (0.3439, 0.3541), which is very close to the perfect white area of CIE 1931 chromaticity coordinates (0.3333, 0.3333). Additionally, the generated white-light exhibits low correlated color temperature (CCT) of 5056 K and a high color rendering index (CRI) of 91. The proposed technique will find a wide range of applications in integrated, fully passive optical networks of free-space optics and indoor visible light communication.
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