Zhen Gao

This paper addresses interference management of indoor Massive Multi-input Multi-output (MIMO) visible light communication (VLC) systems. We propose an Adaptive Multi-Layered Resource Optimization Framework (AMROF) integrating user grouping and non-orthogonal multiple access (NOMA) in VLC systems. This approach enhances capacity and data rates, specifically targeting high-density indoor environments. This approach dynamically allocates power to similar-condition users, while utilizing NOMA according to diverse channel characteristics, thereby significantly increasing spectral efficiency and overall system throughput. Additionally, a branch-and-bound LED selection strategy is employed to minimize system complexity and energy consumption. The proposed AMROF framework, integrated with the alternating direction method of multipliers (ADMM), provides a scalable optimization scheme. This approach effectively meets the real-time and interference-resilient requirements of multi-cell VLC systems.

Data compression with Huffman codes has been commonly employed to reduce the memory size required for emerging applications with large storage needs, such as Machine Learning (ML). However, memories at the nanoscale can suffer from errors, causing data corruption. This issue is even more severe for memories storing compressed data, because the error can propagate after decompression. This paper proposes an efficient error-resilient data compression scheme applicable to nanoscale memories by employing interleaved data storage and Huffman coding. Based on the evaluation results on protecting two compressed ML models, this scheme reduces the memory overhead by over 99% compared to conventional error protection schemes (such as parity or Hamming codes), while limiting the accuracy loss in the range of 0.26% to 3.27%.

Dielectric constant is a critical metric for saline concentration measurement, which is estimated in this study non-invasively using RFID technology. To ensure its robustness and dependability, the General Uncertainty Management (GUM) concepts are employed to evaluate the results. A new methodology investigates the relationship between changes in RFID tag capacitance and variations in inorganic salt concentrations. The proposed approach unites the Havriliak–Negami and slit cylindrical capacitance models through HN and NGDO optimization techniques and Genetic Algorithm for hybrid optimization. The enhanced method effectively analyzes multicomponent inorganic salt mixtures with exceptional accuracy, showing markedly reduced MRE and RMSE compared to values reported in the current literature. In capacitance predictions, the HN and NGDO model performs better than conventional methods, obtaining a mean absolute error (MAE) of 0.8% and a root mean square error (RMSE) of 0.25% predictive accuracy. Furthermore, the model maintains a good association coefficient (R2 > 0.998) at different salt concentrations. This development demonstrates RFID technology’s promise as a dependable instrument for precise and ecological monitoring, offering a strong framework for assessing uncertainty and assisting in the development of more efficient wastewater treatment procedures.