Model quantization reduces the bit-width of weights and activations, improving memory efficiency and inference speed in diffusion models. However, achieving 4-bit quantization remains challenging. Existing methods, primarily based on integer quantization and post-training quantization fine-tuning, struggle with inconsistent performance. Inspired by the success of floating-point (FP) quantization in large language models, we explore low-bit FP quantization for diffusion models and identify key challenges: the failure of signed FP quantization to handle asymmetric activation distributions, the insufficient consideration of temporal complexity in the denoising process during fine-tuning, and the misalignment between fine-tuning loss and quantization error. To address these challenges, we propose the mixup-sign floating-point quantization (MSFP) framework, first introducing unsigned FP quantization in model quantization, along with timestep-aware LoRA (TALoRA) and denoising-factor loss alignment (DFA), which ensure precise and stable fine-tuning. Extensive experiments show that we are the first to achieve superior performance in 4-bit FP quantization for diffusion models, outperforming existing PTQ fine-tuning methods in 4-bit INT quantization.

In non-contact respiratory monitoring, reducing motion artifact and selecting the appropriate Region of Interest (ROI) pose significant challenges. Most motion artifact removal methods rely on signal periodicity assumptions, while respiratory signals usually are non-periodic in real-world scenarios. Existing automated ROI selection approaches are mostly primarily impacted by the texture of clothing, absence of chest landmarks, and obstruction of face. To improve the quality of respiratory signals, in this study, we propose a framework for automatic respiratory ROI selection based on video, namely, Optimizing Video-based Respiration Monitoring (OVRM), which consists of peak-trough adaptive motion artifact removal and characteristic-driven adaptive ROI selection. This motion artifact removal strategy removes motion artifacts by using a dynamic ratio-based judgment mechanism, and reconstructs signals using sinusoidal interpolation. The adaptive ROI method scores signals based on periodicity, similarity, smoothness, and energy, selecting the highest-scoring blocks as the ROIs to match respiratory signals efficiently. Experimental results, validated across four datasets, demonstrate that OVRM effectively reduces signal noise caused by subject movement and outperforms state-of-the-art non-contact respiratory monitoring algorithms. The dataset and code are publicly available at:https://github.com/zxx5058/OVRM.

The video-based non-contact respiration detection technology can be used in many application scenarios to unobtrusively and ubiquitously monitor the physical state of living beings, and various researchers are currently working on this technology. The optical flow method in tandem with crossover point method is rather effective for respiration rate extraction. However, each method has one disadvantage: 1) the redundant feature points in the traditional optical flow method increase the computational effort and reduce the estimation accuracy; and 2) the traditional crossover point method suffers from crossover points unrelated to breathing movements. For these two challenges, two optimization points are proposed in this work: 1) optimize feature point space by combining spatio-temporal information; and 2) use negative feedback design to adaptively remove crossovers that are not related to respiratory movements. The performance of the proposed algorithm is validated by the Large-scale Bedside Respiration Dataset for Intensive Care (LBRD-IC), which is established using the actual surveillance videos acquired from ICU wards. The validity of the above two optimization points is verified by the ablation experiments. The influential analysis of computation time and video resolution on the performance of the proposed algorithm demonstrates that the proposed algorithm can be deployed to various application terminals to monitor the respiration rate of living organisms in real-time and with high accuracy. In addition, field measurements in the ICU ward have shown that our algorithm can measure respiratory signals of the single patient and multiple patients when only one surveillance camera is present.

The video-based non-contact respiration detection technology can be used in many application scenarios to unobtrusively and ubiquitously monitor the physical state of living beings, and various researchers are currently working on this technology. The optical flow method in tandem with crossover point method is rather effective for respiration rate extraction. However, each method has one disadvantage: 1) the redundant feature points in the traditional optical flow method increase the computational effort and reduce the estimation accuracy; and 2) the traditional crossover point method suffers from crossover points unrelated to breathing movements. For these two challenges, two optimization points are proposed 1) optimize feature point space by combining spatio-temporal information; and 2) use negative feedback design to adaptively remove crossovers unrelated to respiratory movements. The performance of the proposed algorithm is validated by the Large-scale Bedside Respiration Dataset for Intensive Care (LBRD-IC), which is established using the actual surveillance videos acquired from ICU wards. In addition, field measurements in the ICU ward have shown that our algorithm can measure respiratory signals of the single patient and multiple patients when only one surveillance camera is present.

The application of unmanned aerial vehicle (UAV) to achieve precision irrigation in agriculture is a hot research topic in the industry. However, much spay and much leakage of pesticide are tricky for the current UAV-based irrigation methods to deal with. In this paper, we propose a new UAV-based irrigation system for precision agriculture. First, considering that different areas in the same farmland may have different pesticide shortage, a map preprocess strategy is introduced to divide the entire farmland into pieces. Second, we establish a UAV precision irrigation model and put forward an adaptive and fast dynamic ant colony optimization (AFD-ACO) algorithm to minimize the longest flight path with the lowest energy consumption and pesticide residues. In order to promote the efficiency and the optimization effect, we utilize the scent pervasion rule to make the global map preprocessed and the neighborhood adaptive search policy to accomplish planning work. Finally, comparing with other two ACO-based algorithms, the proposed algorithm is proved to be effective for the research problem, especially when the more pieces the farmland is divided, the better our solution performs.