Iet Optoelectronics最新文献

We provide a detailed description of depth-dependent effects that affect the propagation of light in underwater optical wireless communication links. Absorption and scattering properties of water are evaluated for realistic chlorophyll profiles versus depth for all the visible spectrum, with particular attention to the wavelengths corresponding to practically feasible laser sources. Results show which visible wavelength is the less affected by propagation loss in specific water conditions, thus being the most suitable to improve the performance of vertical links in underwater optical wireless communications. The developed model is useful also to optimise the distribution of optical channels in systems based on dense wavelength division multiplexing technology, according to the type of water and the depth reached in the vertical communication link.

The increasing scalability of artificial intelligence (AI) algorithms has a high demand for efficient computing architectures and physical platforms. Intelligent photonic computing with high parallelism, high speed, low latency, and multi-dimensional data modulation properties shows its potential for dealing with current challenges of energy consumption in both computing clouds and edge devices. The implementation of photonic devices and physical computing systems for AI computing was reviewed from free-space to on-chip platforms. Firstly, the mathematics and physics of AI algorithms including feedforward neural network, recurrent neural network, and spiking neural network were introduced. Secondly, principles of devices and applications of new architectures including 3D and 2D metasurfaces which are based on diffractive deep neural network and on-chip waveguide devices including Mach–Zehnder interferometers and microring resonators were summarised. Thirdly, two emerging fields of AI computing including reconfigurability and non-linearity were surveyed, which are important factors of achieving in situ training and backpropagation towards the path of general AI computing. Finally, mainstream intelligent photonic computing platforms were compared, and the challenges were outlook.

In this paper, we present a proof of concept for an indoor optical camera communication (OCC) system utilising a deep learning network to detect and identify humans wearing light-emitting diode (LED) strips. Specifically, we propose using the You Only Look Once (YOLO) version 8 object detection algorithm, which is built on convolutional neural networks (CNNs), to identify wearable LED transmitters in challenging scenarios such as low visibility, mobility and multiple users, followed by image processing to effectively decode the transmitted data. The red-green-blue (RGB) LED strip's colours (red, green, blue and white) serve as indicators of the user's status. By combining communication and monitoring functionalities, the LEDs facilitate not only the transmission of user data but also accurate detection, tracking and identification within the environment. This demonstrates the feasibility of utilising widely available devices like LED strips and cameras, commonly found in many buildings, with potential applications in high-risk environments where monitoring individuals' physical conditions is crucial. The obtained results indicate our system's effectiveness, as it achieved up to 100% success of reception (SoR) in a static experimental setup, 96.2% in a walking experimental setup with one user and showed no effectiveness with two users.

In recent years, there has been a surge in interest in indoor positioning systems that use visible light communication (VLC) technology combined with light-emitting diodes (LEDs). These systems have gained attention because of their ability to offer high bandwidth, precise localisation, and potential for wireless communication to extend into the visible light spectrum in the future, making VLC a notable candidate. Furthermore, the visible light spectrum proves advantageous in the industrial internet of things setting, as it does not offer electromagnetic interference as in radio frequency (RF) spectrum. This paper analyses a database made up of approximately 356 image samples obtained from a CMOS sensor. The database encompasses eight distinct classes, each demonstrating frequency (bit rate) variations ranging from 1 to 4.5 kHz in 500 Hz increments. The aim is to implement this database for classification applications as a first stage with several neural networks based on extreme learning machines (ELM) in various forms: (1) standard ELM, (2) regularised ELM, (3) weighted ELM in two configurations, and (4) multilayer ELM with 2 and 3 hidden layers. The findings of this study reveal that standard ELM is particularly promising, achieving more than 99% in accuracy and G-mean, while maintaining low computational complexity (measured in tenths of seconds) when compared to convolutional neural networks and multilayer perceptrons, which offer superior performance, however at the cost of significant computational demands.

The authors theoretically report a plasmonic photodetector based on graphene-black phosphorus heterostructure which is capable of operating from visible to mid-infrared (MIR) wavelengths. The combination of plasmonic nanostructure and graphene-black phosphorus heterostructure can significantly enhance the absorption, simulation results show that the proposed photodetector is capable of working from 400 nm to 3 μm with ultrahigh responsivity all exceeding 14,000 AW⁻¹ and large modulation bandwidth all over 327 GHz. In addition, by utilising the evident anisotropic characteristics of black phosphorus, the broadband plasmonic photodetector exhibits the angle-dependent responsivity which can be used to design the customised plasmonic photodetector. The authors believe that the proposed photodetector would provide the new approach to design the broadband and angle-dependent optoelectronic devices based on 2D materials.

We are delighted to present this special issue of IET Optoelectronics on ‘Towards large-scale commercialisation of Space-division multiplexing (SDM) fibres’. This issue features contributions from five speakers at the ECOC 2023 workshop ‘What Will Eventually Trigger Large-Scale Commercialisation of SDM Fibres?’, held in Glasgow on 1 October 2023.

SDM is a promising technology to effectively deal with the unabated growth of data traffic in optical fibre networks. Over the past decade, extensive research on SDM optical fibres has resulted in the development of high-quality, multicore and few-mode fibres as well as other network elements that leverage the SDM concept. Whereas large-scale commercialisation of SDM fibres is essential to meet the ever-increasing demand for higher data rates, achieving practical deployment requires more than innovative fibre designs. It also necessitates system optimisation to reduce cost per bit and standardisation to ensure interoperability. The successful commercialisation of SDM fibres also hinges on the advancement of other network elements such as optical amplifiers, multiplexers, connectors and splices. This special issue explores the challenges and opportunities associated with SDM fibre commercialisation. It aims to identify potential triggers that could accelerate the adoption of SDM technology across various applications such as submarine optical cable systems, terrestrial fibre networks and data centre interconnects.

We hope this special issue provides readers with a comprehensive overview of the latest advancements in SDM technologies and valuable insights into their large-scale deployment, paving the way for significant upgrades in optical fibre communication systems in the years ahead.

A novel refractive index (RI) plasmonic sensor is designed and simulated, which is expected to make a significant advancement in gas sensing. The sensor is composed of silver, air-filled waveguide and a square shaped cavity. In the implemented cavity, 16 silver circular nanorods (CNs) are locally placed to enhance the sensing performance. Moreover, the cavity is filled with ultraviolet-curable fluoro-siloxane (UVCFS) in order to faithfully characterise methane gas concentrations. RI sensitivity (RIS) for Peak I is 1210.12 ± 4.62 nm/RIU and for Peak II is 969.64 ± 8.56 nm/RIU as well as figure of merit (FoM) of 20.00 ± 0.08 1/RIU at Peak I and 11.82 ± 0.10 1/RIU at Peak II, respectively. Furthermore, a significant methane gas sensitivity (S_Gas) is achieved: about 4 nm/% for Peak I and about 3 nm/% for Peak II which shows that it can well determine methane gas concentrations. The introduced plasmonic-based sensor brings potential applications in gas sensing due to its high sensitivity as well accuracy in detecting methane gas concentrations.

This paper proposes and presents the first experimental demonstration of a high-precision indoor 2D and 3D visible light positioning (VLP) system using an imaging multiple-input multiple-output (MIMO) configuration with supervised artificial neural network. The proposed system utilises four distributed transmitters and receiver with four photodiodes and an imaging optics. The experiments are conducted in a typical indoor environment with transmitter separations of 300 mm and a link distance of 1400 mm. The experimental results show 2D and 3D positioning accuracies of 3.7 and 51 mm, respectively. A simulation model is also developed for the VLP system to validate the experimental results. Further optimisation of the VLP system in the simulation platform leads to improved 2D and 3D positioning accuracies of 2 and 14.7 mm, respectively. The proposed system can be seamlessly integrated with existing lighting infrastructures and is also compatible with the MIMO visible light communication system, indicating the potential for practical implementation in integrated communications and positioning applications.

In the long-term, enabling data transmission over additional wavelength bands offered by the installed fibre infrastructure is not sufficient to satisfy the continuously increasing demand for capacity. In order to be prepared for future requests, space-division multiplexing (SDM) is moving into the focus of commercial equipment manufacturers and providers. Different options are pointed out and basic design features for new fibres are explained, in particular in view of multi-core fibres. In this regard, emphasis is put on detailing the importance of different geometrical parameters. Commercial deployment of SDM technology requires to bring cost down to a competitive level, which cannot be achieved with new fibres alone but requires the development of new kinds of components. Approaches to bring down cost for optical amplifiers, transceivers, and wavelength-selective switches are presented. Finally, an upgrade scenario for introducing SDM in commercial networks is discussed.

The authors demonstrate the use of highly temperature insensitive dense wavelength division multiplexing filters combined with optical reflectors and optical time domain reflectometry to uniquely identify specific optical fibres beyond the optical splitter in an optical access passive optical network (PON). The very low wavelength shift (∼0.1 pm/°C) of the filters over the full industrial temperature range facilitates the future field deployment of these components in the PON without the requirement for complex and expensive wavelength tracking at the optical line terminal.