Recently, substantial development is observed in the area of Internet of Vehicles owing to the application of wireless communication technologies. Majority of these technologies are based on radio frequency (RF); however, RF spectra are overly congested and regulated, and hence, insufficient to support massive data traffic in the future. In recent times, optical camera communication (OCC) that uses a light-emitting diode (LED) as a transmitter and a camera as a receiver has been deemed an excellent solution for future intelligent transportation systems. As a communication medium, OCC mostly uses visible light, the spectrum of which is vast, completely free, and unregulated. The current outdoor environment is heavily crammed with LED infrastructures, and most vehicles have built-in cameras, rendering OCC immensely promising. OCC is highly secured, supports mobility, and can achieve an excellent bit-error rate. However, the data rate obtained using OCC is not as high as that obtained using other RF-based systems; therefore, its reliability in fast-changing channels is still under research. This review article discusses the applications of the OCC system in vehicle-to-vehicle and vehicle-to-infrastructure (or vice versa) networks; to the best of our knowledge, this is the first extensive review dedicated to the above topic. Herein, we provide a general overview of OCC standardization in IEEE and ISO in recent years. Then, we explain the general principles of OCC, including channel characteristics, region of interest signaling, and modulation schemes particularly considered in vehicular communications. Additionally, we present a comprehensive overview of the effects of mobility, noise, and interference in OCC. Finally, the challenges and future opportunities in OCC are outlined.

Optical Camera Communication (OCC), which utilizes the unlicensed visible light spectrum, is regarded as a key enabler for future sixth-generation wireless technologies. In OCC, the communication performance, specifically the maximum achievable distance and bit-error rate (BER), relies on the pixel values captured by the image sensor. However, spatial and temporal intensity variations across pixels in rolling shutter-based OCC systems present a significant challenge for data decoding. This study investigates a low-illumination indoor environment where overlapping dark and bright intensity histograms are heavily impacted by noise, further complicating the data extraction process. To address this issue, this paper proposes UOCC, a U-Net-based OCC architecture for image segmentation, designed to extract data under extremely low-light conditions in intensity-constrained scenarios by framing data pattern detection as pixel classification tasks. We further incorporate bit-plane slicing-based pre-processing and test-time augmentation-based post-processing to enhance the reliability of the model. Experimental results show that the proposed UOCC method achieves an excellent BER of 5 × 10⁻⁴ and a 97.20% intersection-over-union score after 50 epochs, significantly outperforming Otsu thresholding (10⁻¹) and both second- and third-order polynomial fitting methods (3 × 10⁻²) in this context. This performance is achieved by reducing noise through the exclusion of the lowest-order bit-plane of test images, which significantly enhances system reliability. Furthermore, the proposed method presents substantial potential for future applications, including non-line-of-sight and underwater communication environments.

Embedding optical camera communication (OCC) commercially as a favorable complement of radio-frequency technology has led to the desire for an intelligent receiver system that is eligible to communicate with an accurate light-emitting diode (LED) transmitter. To shed light on this issue, a novel scheme for detecting and recognizing data transmitting LEDs has been elucidated in this paper. Since the optically modulated signal is captured wirelessly by a camera that plays the role of the receiver for the OCC technology, the process to detect LED region and retrieval of exact information from the image sensor is required to be intelligent enough to achieve a low bit error rate (BER) and high data rate to ensure reliable optical communication within limited computational abilities of the most used commercial cameras such as those in smartphones, vehicles, and mobile robots. In the proposed scheme, we have designed an intelligent camera receiver system that is capable of separating accurate data transmitting LED regions removing other unwanted LED regions employing a support vector machine (SVM) classifier along with a convolutional neural network (CNN) in the camera receiver. CNN is used to detect every LED region from the image frame and then essential features are extracted to feed into an SVM classifier for further accurate classification. The receiver operating characteristic curve and other key performance parameters of the classifier have been analyzed broadly to evaluate the performance, justify the assistance of the SVM classifier in recognizing the accurate LED region, and decode data with low BER. To investigate communication performances, BER analysis, data rate, and inter-symbol interference have been elaborately demonstrated for the proposed intelligent receiver. In addition, BER against distance and BER against data rate have also been exhibited to validate the effectiveness of our proposed scheme comparing with only CNN and only SVM classifier based receivers individually. Experimental results have ensured the robustness and applicability of the proposed scheme both in the static and mobile scenarios.

This technical paper proposes a new waveform combining the low-rate and high-rate data streams to detect the region-of-interest signal in a high-mobility environment using optical camera communication. The proposed technique augments the bit rate of the low-rate stream; consequently, the link setup time is reduced and the requirement of low frame rate camera is eliminated. Additionally, both the low-rate and high-rate data streams in the proposed bi-level pulse position modulation are decoded with a unique adaptive thresholding mechanism with a high frame rate camera. We also propose a vehicle localization scheme to assist the drivers in maintaining a safe following distance that can significantly reduce the frequency of accidents. Moreover, two neural networks are proposed to detect the light-emitting diodes (LEDs) for localization and communication, and to estimate the road curvature from different rear LED shapes of the forwarding vehicle, respectively. The system is implemented, and its performance is analyzed in Python 3.7. The implementation results show that the proposed system is able to achieve 75% localization accuracy, a 150 bps low-rate stream, and a 600 bps high-rate stream over a range of 25 m with a commercial 30 fps camera.

Low-resolution analog-to-digital converters (ADCs) in massive multiple-input multiple-output (m-MIMO) systems offer significant throughput increase for wireless communication. The low-resolution ADC part limits the performance of the transceiver, especially estimating the channels from highly quantized measurements. Mapping from quantized received signals to channel is very challenging. Since the channel response has the characteristic of sparsity, we have leveraged a fast and flexible denoising convolutional neural network (FFDNet) based channel estimation scheme for the m-MIMO system with one-bit ADCs treating the channel matrix as a 2D natural image in this letter. FFDNet can effectively learn from sufficiently large training datasets and is exploited to estimate the channel in the m-MIMO system equipped with one-bit ADCs. We have investigated the exhibited performance of the FFDNet based channel estimation through simulation results. A significantly reduced normalized mean square error has been achieved in our proposed scheme.

Low back pain (LBP) is common among adolescent cricketers, often due to repetitive lumbar stress. This study investigated LBP among 450 adolescent cricketers in Dhaka City, Bangladesh, considering a range of factors, including sociodemographic characteristics, game-related activities, preventive practices, and LBP-related history. Various ML algorithms applied to LBP severity classification included K-Nearest Neighbors, Random Forest, Logistic Regression, and Support Vector Machine (SVM). LBP severity was categorized into three classes as no pain, mild pain, and moderate pain because there was an insufficient amount of data for the severe pain category. The SVM using the sigmoid kernel of the models considered gave the best performance as it produced the best performance metrics of test accuracy (87.6%), precision (90%), recall (87.6%), and F1-score (87.1%). In addition, regression analysis was also applied to identify the predictors of LBP. Key correlates included female gender (adjusted odds ratio [AOR] = 2.52), higher educational attainment (e.g., undergraduate: AOR = 5.38), elevated family income (e.g., > 60,000 BDT: AOR = 4.36), longer weekly practice duration (>20 hours: higher prevalence of 81.7%), inconsistent warm-up (often/sometimes: AOR = 12.48-14.07) and cool-down practices (sometimes: AOR = 2.86), and prior LBP history (AOR = 6.92), all significantly associated with increased LBP risk (p < 0.05). The findings show the importance of early intervention and prevention protocols for minimizing LBP occurrence among junior cricket players. In short, this work demonstrates the effectiveness of ML and regression models for ascertaining sports injury patterns of risk, data-informed prevention and management protocols, and providing a foundation for future studies on this subject. Limitations include the exclusion of a severe pain category due to insufficient data, which reduces the model's capacity to triage urgent cases requiring immediate intervention.