Search Results (56)

Optical wireless communication (OWC) has emerged as a complementary technology to conventional radio frequency (RF)-based communication systems, particularly in scenarios requiring low electromagnetic interference, enhanced security, and efficient spectrum utilization. Within various OWC approaches, optical camera communication (OCC) has attracted increasing attention due to its ability to utilize commercially available image sensors as receivers. This paper presents a 2D multiple-input–multiple-output (MIMO) OCC system based on light-emitting diode (LED) arrays for reliable communication in mobile environments. The proposed system employs on–off keying (OOK) modulation, which supports both rolling shutter and global shutter cameras. To improve decoding reliability under mobility conditions, a deep learning-based decoding model is introduced to enhance LED state detection compared with conventional zero-crossing approaches. In addition, a sequence number-based synchronization is implemented to compensate for frame rate variation and packet missing in a real-time environment. Besides that, by applying YOLOv13 for light source detection and tracking, we can achieve 98% accuracy at 3 m/s velocity. Experimental results show reliable communication performance at transmission distances of up to 22 m under various mobility conditions. Furthermore, the proposed system is validated through real-time environmental data transmission using temperature and humidity sensors with 20 links. The results indicate that the proposed scheme provides stable and reliable OCC performance for mobility Internet of Things (IoT) applications. Full article

In Optical Camera Communication (OCC), precise localization of LED arrays under complex tilt conditions is a core challenge for reliable decoding. This paper proposes an OCC reception scheme for RGB-LED arrays that integrates YOLO-OBB rotated object detection with two-stage geometric correction. The system first employs a YOLOv8n-OBB model to extract a quadrilateral region of interest that tightly encloses the LED array boundary. This effectively suppresses background interference caused by superimposed perspective tilt and in-plane rotation. A coarse-to-fine two-stage correction framework is then applied. The first stage rapidly eliminates the dominant perspective distortion based on the detected bounding-box corners. The second stage performs a refined correction using the actual LED center positions. Two homography matrices are cascaded into a combined transformation, achieving two-stage correction accuracy through a single coordinate mapping. In the corrected image, K-Means clustering constructs a 16 × 16 LED topological grid. A locking strategy is adopted so that subsequent frames skip repeated LED detection and clustering. The steady-state per-frame processing time is reduced to approximately 78.9 ms. Experiments covered 16 cross-combinations of vertical tilt from 0° to 45° (0°, 15°, 30°, 45°) and in-plane rotation from 0° to 40° (0°, 15°, 30°, 40°). The uncorrected scheme and the horizontal-box scheme experienced severe bit errors or complete failure under complicated distortion. The proposed scheme maintained error-free transmission under all 16 tested conditions. The ratios of opposite sides of the corrected LED grid remained stable between 0.997 and 1.004. The system simultaneously achieves high reliability and low-latency real-time processing under complex geometric distortions. Full article

This research significantly improves the link reliability and robustness of optical camera communications (OCC) by leveraging deep learning for light source modulation filtering, reflection filtering, and precise light source localisation. By using image sensors as receivers in OCC, data transmission is not only enabled, but other applications are also facilitated, such as detecting objects and humans, making OCC highly attractive in healthcare, intelligent transport systems, and indoor positioning. However, the position of the desired signal in the received image frame must be tracked in dynamic scenarios (i.e., nonstationary applications), in order to maintain the communication link. Moreover, as sixth-generation (6G) wireless networks envision highly autonomous systems that rely on seamless integration of communication and sensing, deep learning is key to enabling robust and adaptive light source localisation and sensing in OCC, which enables vision-based autonomy in dynamic environments. It should be noted that a deep learning-based approach provides more accuracy even when there are multiple noise sources in the environment, reflections, and complex backgrounds, and under mobility conditions, in which traditional light source detection/tracking methods are not effective. Hence this study investigates the use of a deep learning-based approach by analysing the detection accuracy under different configurations and unseen images. The results obtained demonstrate consistently high detection performance with average precision (at an intersection-over-union threshold of 0.70 of 0.84 to 0.97. These results pave the way for autonomous receivers that will be able to select signals intelligently and decode them. Full article

This article presents an ontology-driven Digital Twin (DT) for hotel front-desk operations that fuses two real-time data streams: (i) physiological and activity signals from wrist-worn wearables assigned to staff, and (ii) 3D people-positioning and occupancy events captured by reception-area cameras using a proprietary implementation of Optical Camera Communication (OCC). Building on a previously proposed front-desk ontology, the semantic model is extended with positional events, zone semantics, and wearable-derived workload indices to estimate queue state, staff workload, and service demand in real time. A vendor-agnostic, property-based REST API specifies the DT interface in terms of observable properties, including authentication and authorization, idempotent ingestion, timestamp conventions, version negotiation, integrity protection for signed webhooks, rate limiting and backoff, pagination and filtering, and privacy-preserving identifiers, enabling any compliant backend to implement the specification. The proposed layered architecture connects ingestion, spatial reasoning, and decision services to dashboards and key performance indicators (KPIs). This article details the positioning pipeline (calibration, normalized 3D coordinates, zone mapping, and confidence handling), the wearable workload pipeline, and an evaluation protocol covering localization error, zone classification, queue-length estimation, and workload accuracy. The results indicate that a spatially aware, ontology-based DT can support more balanced staff allocation and improved guest experience while remaining technology-agnostic and privacy-conscious. Full article

Precise positioning is critical for autonomous uncrewed aerial vehicle (UAV) navigation, especially in GNSS-denied environments where radio-based signals are unreliable. This study presents an optical camera communication (OCC)-based positioning method that enables real-time 3D coordinate estimation using aviation obstruction light-emitting diodes (LEDs) as optical transmitters and a UAV-mounted camera as the receiver. In the proposed system, absolute positional identifiers are encoded into color-shift-keying-modulated optical signals emitted by fixed LEDs and captured by the UAV camera. The UAV’s 3D position is estimated by integrating the decoded LED information with geometric constraints through the Perspective-n-Point algorithm, eliminating the need for satellite or RF-based localization infrastructure. A virtual offshore wind farm, developed in Unreal Engine, was used to experimentally evaluate the feasibility and accuracy of the method. Results demonstrate submeter localization precision over a 50,000 cm flight path, confirming the system’s capability for reliable, real-time positioning. These findings indicate that OCC-based positioning provides a cost-effective and robust alternative for UAV navigation in complex or communication-restricted environments. The offshore wind farm inspection scenario further highlights the method’s potential for industrial operation and maintenance tasks and underscores the promise of integrating optical wireless communication into autonomous UAV systems. Full article

Optical Wireless Communication (OWC) technologies are emerging as promising complements to radio-frequency systems, offering high bandwidth, spatial confinement, and license-free operation. Within this domain, Visible Light Communication (VLC) and Optical Camera Communication (OCC) represent two distinct paradigms with divergent performance and security profiles. While VLC leverages LED-photodiode links for high-speed data transfer, OCC exploits ubiquitous image sensors to decode modulated light patterns, enabling flexible but lower-rate communication. Despite their potential, both remain vulnerable to various attacks, including eavesdropping, jamming, spoofing, and privacy breaches. This work applies—and extends—the Risk Matrix (RM) methodology to systematically evaluate the security of VLC and OCC across reconnaissance, denial, and exploitation phases. Unlike prior literature, which treats VLC and OCC separately and under incompatible threat definitions, we introduce a unified, domain-specific risk framework that maps empirical channel behavior and attack feasibility into a common set of impact and likelihood indices. A normalized risk rank (NRR) is proposed to enable a direct, quantitative comparison of heterogeneous attacks and technologies under a shared reference scale. By quantifying risks for representative threats—including war driving, Denial of Service (DoS) attacks, preshared key cracking, and Evil Twin attacks—our analysis shows that neither VLC nor OCC is intrinsically more secure; rather, their vulnerabilities are context-dependent, shaped by physical constraints, receiver architectures, and deployment environments. VLC tends to concentrate confidentiality-driven exposure due to optical leakage paths, whereas OCC is more sensitive to availability-related degradation under adversarial load. Overall, the main contribution of this work is the first unified, standards-aligned, and empirically grounded risk-assessment framework capable of comparing VLC and OCC on a common security scale. The findings highlight the need for technology-aware security strategies in future OWC deployments and demonstrate how an adapted RM methodology can identify priority areas for mitigation, design, and resource allocation. Full article

The growing number of connected devices has strained traditional radio frequency wireless networks, driving interest in alternative technologies such as optical wireless communications (OWC). Among OWC solutions, optical camera communication (OCC) stands out as a cost-effective option because it leverages existing devices equipped with cameras, such as smartphones and security systems, without requiring specialized hardware. This paper proposes a novel deep learning-based detection and classification model designed to optimize the receiver’s performance in an OCC system utilizing color-shift keying (CSK) modulation. The receiver was experimentally validated using an $8\times 8$ LED matrix transmitter and a CMOS camera receiver, achieving reliable communication over distances ranging from 30 cm to 3 m under varying ambient conditions. The system employed CSK modulation to encode data into eight distinct color-based symbols transmitted at fixed frequencies. Captured image sequences of these transmissions were processed through a YOLOv8-based detection and classification framework, which achieved $98.4$% accuracy in symbol recognition. This high precision minimizes transmission errors, validating the robustness of the approach in real-world environments. The results highlight OCC’s potential for low-cost applications, where high-speed data transfer and long-range are unnecessary, such as Internet of Things connectivity and vehicle-to-vehicle communication. Future work will explore adaptive modulation and coding schemes as well as the integration of more advanced deep learning architectures to improve data rates and system scalability. Full article

Radio frequency (RF)-based wireless systems are broadly used in communication systems such as mobile networks, satellite links, and monitoring applications. These systems offer outstanding advantages over wired systems, particularly in terms of ease of installation. However, researchers are looking for safer alternatives as a result of worries about possible health problems connected to high-frequency radiofrequency transmission. Using the visible light spectrum is one promising approach; three cutting-edge technologies are emerging in this regard: Optical Camera Communication (OCC), Light Fidelity (Li-Fi), and Visible Light Communication (VLC). In this paper, we propose a Multiple-Input Multiple-Output (MIMO) modulation technology for Internet of Things (IoT) applications, utilizing an LED array and time-domain on-off keying (OOK). The proposed system is compatible with both rolling shutter and global shutter cameras, including commercially available models such as CCTV, webcams, and smart cameras, commonly deployed in buildings and industrial environments. Despite the compact size of the LED array, we demonstrate that, by optimizing parameters such as exposure time, camera focal length, and channel coding, our system can achieve up to 20 communication links over a 20 m distance with low bit error rate. Full article

Using the rolling-shutter mechanism to enhance the signal sampling frequency of Optical Camera Communication (OCC) is a low-cost solution, but its periodic sampling interruptions may cause signal loss, and existing solutions often compromise communication rate and distance. To address this, this paper proposes NoGap-RS, a no-gap sampling method, theoretically addressing the signal loss issue at longer distances from a perspective of CMOS exposure timing. Experiments show that NoGap-OOK, a OCC system based on NoGap-RS and On-Off key modulation, can achieve a communication rate of 6.41 Kbps at a distance of 3 m, with a BER of ${10}^{-5}$ under indoor artificial light. This paper further proposes TickRS, a time slot division method, innovatively addressing the overlap that occurs during consecutive-row exposures to further enhance communication rate. Experiments show that TickRS-CSK, a OCC system based on TickRS and Color-Shift Key, can achieve a communication rate of 20.09 Kbps at a distance of 3.6 m, with a BER of ${10}^{-2}$ under indoor natural light. Full article

Nowadays, underwater activities are becoming more and more important. As the number of underwater sensing devices grows rapidly, the amount of bandwidth needed also increases very quickly. Apart from underwater communication, direct communication across the water–air interface is also highly desirable. Air-to-water wireless transmission is crucial for sending control information or instructions from unmanned aerial vehicles (UAVs) or ground stations above the sea surface to autonomous underwater vehicles (AUVs). On the other hand, water-to-air wireless transmission is also required to transmit real-time information from AUVs or underwater sensor nodes to UAVs above the water surface. Previously, we successfully demonstrated a water-to-air optical camera-based OWC system, which is also known as optical camera communication (OCC). However, the reverse transmission (i.e., air-to-water) using OCC has not been analyzed. It is worth noting that in the water-to-air OCC system, since the camera is located in the air, the image of the light source is magnified due to diffraction. Hence, the pixel-per-symbol (PPS) decoding of the OCC pattern is easier. In the proposed air-to-water OCC system reported here, since the camera is located in the water, the image of the light source in the air will be diminished in size due to diffraction. Hence, the PPS decoding of the OCC pattern becomes more difficult. In this work, we propose and experimentally demonstrate a wide field-of-view (FOV) air-to-water OCC system using CUDA Deep-Neural-Network Long-Short-Term-Memory (CuDNNLSTM). Due to water turbulence and air turbulence affecting the AUV and UAV, a precise line-of-sight (LOS) between the AUV and the UAV is difficult to achieve. OCC can provide wide FOV without the need for precise optical alignment. Results revealed that the proposed air-to-water OCC system can support a transmission rate of 7.2 kbit/s through a still water surface, and 6.6 kbit/s through a wavy water surface; this satisfies the hard-decision forward error correction (HD-FEC) bit-error-rate (BER). Full article

Display field communication (DFC) is an emerging technology that enables seamless communication between electronic displays and cameras. It utilizes the frequency-domain characteristics of image frames to embed and transmit data, which are then decoded and interpreted by a camera. DFC offers a novel solution for screen-to-camera data communication, leveraging existing displays and camera infrastructures. This makes it a cost-effective and easily deployable solution. DFC can be applied in various fields, including secure data transfer, mobile payments, and interactive advertising, where data can be exchanged by simply pointing a camera at a screen. This article provides a comprehensive survey of DFC, highlighting significant milestones achieved in recent years and discussing future challenges in establishing a fully functional DFC system. We begin by introducing the broader topic of screen–camera communication (SCC), classifying it into visible and hidden SCC. DFC, a type of spectral-domain hidden SCC, is then explored in detail. Various DFC variants are introduced, with a focus on the physical layer. Finally, we present promising experimental results from our lab and outline further research directions and challenges. Full article

With the help of traffic lights and street cameras, optical camera communication (OCC) can be adopted in Internet of Vehicles (IoV) applications to realize communication between vehicles and roadside units. However, the encoded light emitted by these OCC transmitters (LED infrastructures on the roadside and/or LED-based headlamps embedded in cars) will generate stripe patterns in image frames captured by existing license-plate recognition systems, which seriously degrades the accuracy of the recognition. To this end, we propose and experimentally demonstrate a method that can reduce the interference of OCC stripes in the image frames captured by the license-plate recognition system. We introduce an innovative pipeline with an end-to-end image reconstruction module. This module learns the distribution of images without OCC stripes and provides high-quality license-plate images for recognition in OCC conditions. In order to solve the problem of insufficient data, we model the OCC strips as multiplicative noise and propose a method to synthesize a pairwise dataset under OCC using the existing license-plate dataset. Moreover, we also build a prototype to simulate real scenes of the OCC-based vehicle networks and collect data in such scenes. Overall, the proposed method can achieve a recognition performance of $81.58$% and $79.35$% on the synthesized dataset and that captured from real scenes, respectively, which is improved by about $31.18$% and $24.26$%, respectively, compared with the conventional method. Full article

Most of Optical Camera Communication (OCC) systems benefit from the rolling shutter mechanism of Complementary Metal-Oxide Semiconductor (CMOS) cameras to record the brightness evolution of the Light-Emitting Diode (LED) through dark and bright strips within images. While this technique enhances the maximum achievable data rate, the main difficulty lies in the demodulation of the signal extracted from images, subject to blooming effect. Thus, two main approaches were proposed to deal with this issue, using adaptive thresholds whose value evolves according to amplitude changes or detecting signal variations with the first-order derivative. As the second method is more robust, a new demodulation method based on the detection of the zeros of the first-order derivative of the extracted signal was proposed in this paper. Obtained results clearly show an improvement in the extracted signal demodulation compared to other methods, achieving a raw Bit Error Rate (BER) of 10⁻³ around 50 cm in a Line-Of-Sight scenario, and increasing the maximum communication distance by 43.5%, reaching 330 cm in the case of a Non-Line-Of-Sight transmission. Full article

In this study, we present a visible light communication (VLC) system that analyzes the performance of an optical camera communication (OCC) system, utilizing a mobile phone camera as the receiver and a computer monitor as the transmitter. By creating color channels in the form of a 4 × 4 matrix within a frame, we determine the parameters that affect the successful transmission of data packets. Factors such as the brightness or darkness of the test room, the light color of the lamp in the illuminated environment, the effects of daylight when the monitor is positioned in front of a window, and issues related to dead pixels and light bleed originating from the monitor’s production process have been considered to ensure accurate data transmission. In this context, we utilized the PyCharm, Pydroid, Python, Tkinter, and OpenCV platforms for programming the transmitter and receiver units. Through the application of image processing techniques, we mitigated the effects of daylight on communication performance, thereby proposing a superior system compared to standard VLC systems that incorporate photodiodes. Additionally, considering objectives such as the maximum number of channels and the maximum distance, we regulated the sizes of the channels, the distances between the channels, and the number of channels. The NumPy library, compatible with Python–Tkinter, was employed to determine the color levels and dimensions of the channels. We investigate the effects of RGB and HSV color spaces on the data transmission rate and communication distance. Furthermore, the impact of the distance between color channels on color detection performance is discussed in detail. Full article

Drones have been attracting significant attention in the field of agriculture. They can be used for various tasks such as spraying pesticides, monitoring pests, and assessing crop growth. Sensors are also widely used in agriculture to monitor environmental parameters such as soil moisture and temperature. Due to the high cost of communication infrastructure and radio-wave modules, the adoption of high-density sensing systems in agriculture is limited. To address this issue, we propose an agricultural sensor network system using drones and Optical Camera Communication (OCC). The idea is to transmit sensor data from LED panels mounted on sensor nodes and receive the data using a drone-mounted camera. This enables high-density sensing at low cost and can be deployed in areas with underdeveloped infrastructure and radio silence. We propose a trajectory control algorithm for the receiving drone to efficiently collect the sensor data. From computer simulations, we confirmed that the proposed algorithm reduces total flight time by $30\%$ compared to a shortest-path algorithm. We also conducted a preliminary experiment at a leaf mustard farm in Kamitonda-cho, Wakayama, Japan, to demonstrate the effectiveness of the proposed system. We collected 5178 images of LED panels with a drone-mounted camera to train YOLOv5 for object detection. With simple On–Off Keying (OOK) modulation, we achieved sufficiently low bit error rates (BERs) under ${10}^{-3}$ in the real-world environment. The experimental results show that the proposed system is applicable for drone-based sensor data collection in agriculture. Full article