Quantization Errors of fGn and fBm Signals

In this Letter, we show that under the assumption of high resolution, the quantization errors of fGn and fBm signals with uniform quantizer can be treated as uncorrelated white noises

Sustainable Urban Living Environment Through Prefabricated Buildings

The endless destruction of the natural environment and man's unbridled demand for natural resources has led to the spread of a series of environmental problems such as global warming, air pollution and land desertification. These environmental problems not only greatly restrict the development of the economy and society, but also threaten the living environment of human beings. With the change in people's living standards, people's demand for the urban living environment is also updated, and the sustainable development of the urban living environment is also a very important issue today. Some studies show that the energy consumption of building construction accounts for 35%-40% of the total energy consumption, and the high energy consumption of the construction industry is a global problem. The trend in urban construction today is to use prefabricated buildings, also known as assembly buildings, to promote the upgrading and transformation of the construction industry. Energy-saving and emission-reducing prefabricated assembly buildings will also be a trend in the industry in the future, and in the long run, the energy-saving rate of assembled apartments will be evaluated so that the degree of efficiency can be fed back to the relevant authorities to judge whether the assembled homes meet the requirements of sustainable development

LayerDiffusion: Layered Controlled Image Editing with Diffusion Models

Text-guided image editing has recently experienced rapid development. However, simultaneously performing multiple editing actions on a single image, such as background replacement and specific subject attribute changes, while maintaining consistency between the subject and the background remains challenging. In this paper, we propose LayerDiffusion, a semantic-based layered controlled image editing method. Our method enables non-rigid editing and attribute modification of specific subjects while preserving their unique characteristics and seamlessly integrating them into new backgrounds. We leverage a large-scale text-to-image model and employ a layered controlled optimization strategy combined with layered diffusion training. During the diffusion process, an iterative guidance strategy is used to generate a final image that aligns with the textual description. Experimental results demonstrate the effectiveness of our method in generating highly coherent images that closely align with the given textual description. The edited images maintain a high similarity to the features of the input image and surpass the performance of current leading image editing methods. LayerDiffusion opens up new possibilities for controllable image editing.Comment: 17 pages, 14 figure

Ground-VIO: Monocular Visual-Inertial Odometry with Online Calibration of Camera-Ground Geometric Parameters

Monocular visual-inertial odometry (VIO) is a low-cost solution to provide high-accuracy, low-drifting pose estimation. However, it has been meeting challenges in vehicular scenarios due to limited dynamics and lack of stable features. In this paper, we propose Ground-VIO, which utilizes ground features and the specific camera-ground geometry to enhance monocular VIO performance in realistic road environments. In the method, the camera-ground geometry is modeled with vehicle-centered parameters and integrated into an optimization-based VIO framework. These parameters could be calibrated online and simultaneously improve the odometry accuracy by providing stable scale-awareness. Besides, a specially designed visual front-end is developed to stably extract and track ground features via the inverse perspective mapping (IPM) technique. Both simulation tests and real-world experiments are conducted to verify the effectiveness of the proposed method. The results show that our implementation could dramatically improve monocular VIO accuracy in vehicular scenarios, achieving comparable or even better performance than state-of-art stereo VIO solutions. The system could also be used for the auto-calibration of IPM which is widely used in vehicle perception. A toolkit for ground feature processing, together with the experimental datasets, would be made open-source (https://github.com/GREAT-WHU/gv_tools)