The contention-based DCF suffers from collision seriously under heavy load conditions. In this paper, a novel medium access control protocol called contention-polling duality coordination function (CPDCF) is proposed to enhance the DCF-based MAC protocol. A polling ACK mechanism is introduced to admit the designated station to transmit without performing any contention process. By eliminating collisions and preventing waste from idle backoff slots, the system capacity is increased effectively. A mathematical model is developed to analyze the saturation throughput of the CPDCF with various CP combinations. The performance in terms of fairness is studied as well. The model accuracy is verified via extensive simulations. Numerical results show that the well-managed CPDCF can achieve a significantly higher system capacity than the legacy DCF does. The CPDCF is also employed to enhance the performance of IEEE 802.11e EDCA. We conclude that the proposed CPDCF is quite feasible, effective, and can be easily integrated with the widespread DCF-based devices.

Accurate dietary assessment is essential for effective health management and disease prevention. However, conventional methods that rely on manual food logging and nutritional lookup are often time consuming and error prone. This study proposes an image-based nutritional advisory system that integrates multimodal deep learning to automate food classification, volume estimation, and dietary recommendation to address these limitations. The system employs a fine-tuned CLIP model for zero-shot food recognition, achieving high accuracy across diverse food categories, including unseen items. For volume measurement, a learning-based multi-view stereo (MVS) approach eliminates the need for specialized hardware, yielding reliable estimations with a mean absolute percentage error (MAPE) of 23.5% across standard food categories. Nutritional values are then calculated by referencing verified food composition databases. Furthermore, the system leverages a large language model (Llama 3) to generate personalized dietary advice tailored to individual health goals. The experimental results show that the system attains a top 1 classification accuracy of 91% on CNFOOD-241 and 80% on Food 101 and delivers high-quality recommendation texts with a BLEU-4 score of 45.13. These findings demonstrate the system’s potential as a practical and scalable tool for automated dietary management, offering improved precision, convenience, and user experience.