The rapid development of the Internet of Things (IoT) has put great challenges on the next generation's communication technologies. Optical Camera Communication (OCC), with its advantages of the green principle, less electromagnetic interference, and harmonious integrations, can serve as a complementary, and sometimes viable alternative technology to mainstream Radio Frequency (RF)-based one. Designing efficient OCC platforms crucially involves accurate modelling of the OCC system and evaluating the efficiency of OCC-based techniques, which then stimulates related practical applications. This paper aims to provide a systematic perspective to understand OCC and to present design guidelines for OCC systems from theory to practice. First, the main parameters and analytical models for OCC basic components are presented to provide guidance on hardware parameter selection and system simulations. Next, typical implementation procedures and common performance evaluation criteria for an overall OCC system are provided. Then, detailed goal-oriented OCC enhancement solutions are summarized aiming to provide systematic guidelines for system performance enhancement. Finally, OCC designed examples are presented based on the results of our OCC experimental platform and some future research directions are discussed.

Large Language Models of the Transformer architecture display great promise in automated code error detection based on their strength in processing sequential data. Nevertheless, their efficacy could be further improved by addressing the inherent weakness in handling structural code dependencies. In response to this, we introduce a novel model that integrates the semantic comprehension power of RoBERTa with the structural learning strength of Graph Neural Networks. This model aims to detect the most common categories of programming faults in the form of runtime errors, index errors, and import/module errors. Experimental evaluation has demonstrated that the hybrid model, utilizing a proper fusion technique, outperforms other models in terms of accuracy and robustness. The introduced mechanism leads to numerical benefits, improving test accuracy by 1.75% over competitive baseline.

Atmospheric turbulence can significantly impact the effectiveness of light detection and ranging (LIDAR) in long-range detection. A technique for wavefront correction, which is based on the stochastic parallel gradient descent (SPGD) optimization algorithm, is proposed. The method integrates coherent detection theory with adaptive optics technology, effectively mitigating the adverse effects of turbulence. This work evaluates the suitability of the algorithm in coherent LIDAR through theoretical analysis and establishes the necessary theoretical relationships. Through numerical simulation, we assess its optimization ability for Strehl ratio (SR), bit error rate (BER), signal to noise ratio (SNR), and detection distance (DR). We also conduct a comprehensive analysis of the impact of the number of iterations of the algorithm affecting SR, SNR, and DR. This analysis provides robust data support for balancing the performance of the system. The results show that the corrected SR can reach 0.96, 0.88, and 0.75, the SNR can be improved by 7 dB, 16 dB, and 26 dB, and the DR can be improved by 8%, 17%, and 30% in gentle, moderate, and strong turbulence, respectively.