Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation

In this paper, we address panoramic semantic segmentation which is under-explored due to two critical challenges: (1) image distortions and object deformations on panoramas; (2) lack of semantic annotations in the 360-degree imagery. To tackle these problems, first, we propose the upgraded Transformer for Panoramic Semantic Segmentation, i.e., Trans4PASS+, equipped with Deformable Patch Embedding (DPE) and Deformable MLP (DMLPv2) modules for handling object deformations and image distortions whenever (before or after adaptation) and wherever (shallow or deep levels). Second, we enhance the Mutual Prototypical Adaptation (MPA) strategy via pseudo-label rectification for unsupervised domain adaptive panoramic segmentation. Third, aside from Pinhole-to-Panoramic (Pin2Pan) adaptation, we create a new dataset (SynPASS) with 9,080 panoramic images, facilitating Synthetic-to-Real (Syn2Real) adaptation scheme in 360-degree imagery. Extensive experiments are conducted, which cover indoor and outdoor scenarios, and each of them is investigated with Pin2Pan and Syn2Real regimens. Trans4PASS+ achieves state-of-the-art performances on four domain adaptive panoramic semantic segmentation benchmarks. Code is available at https://github.com/jamycheung/Trans4PASS.Comment: Extended version of CVPR 2022 paper arXiv:2203.01452. Code is available at https://github.com/jamycheung/Trans4PAS

Behind every domain there is a shift: adapting distortion-aware vision transformers for panoramic semantic segmentation

In this paper, we address panoramic semantic segmentation which is under-explored due to two critical challenges: (1) image distortions and object deformations on panoramas; (2) lack of semantic annotations in the 360∘ imagery. To tackle these problems, first, we propose the upgraded Transformer for Panoramic Semantic Segmentation, ie, Trans4PASS+, equipped with Deformable Patch Embedding (DPE) and Deformable MLP (DMLPv2) modules for handling object deformations and image distortions whenever (before or after adaptation) and wherever (shallow or deep levels). Second, we enhance the Mutual Prototypical Adaptation (MPA) strategy via pseudo-label rectification for unsupervised domain adaptive panoramic segmentation. Third, aside from Pinhole-to-Panoramic ( Pin2Pan ) adaptation, we create a new dataset (SynPASS) with 9,080 panoramic images, facilitating Synthetic-to-Real ( Syn2Real ) adaptation scheme in 360∘ imagery. Extensive experiments are conducted, which cover indoor and outdoor scenarios, and each of them is investigated with Pin2Pan and Syn2Real regimens. Trans4PASS+ achieves state-of-the-art performances on four domain adaptive panoramic semantic segmentation benchmarks. Code is available at https://github.com/jamycheung/Trans4PASS

Global characteristics and drivers of sodium and aluminum concentrations in freshly fallen plant litter

Plant litter is not only the major component of terrestrial ecosystem net productivity, the decomposition of which is also an important process for the returns of elements, including sodium (Na) and aluminum (Al), which can be beneficial or toxic for plant growth. However, to date, the global characteristics and driving factors of Na and Al concentrations in freshly fallen litter still remain elusive. Here, we evaluated the concentrations and drivers of litter Na and Al with 491 observations extracted from 116 publications across the globe. Results showed that (1) the average concentrations of Na in leaf, branch, root, stem, bark, and reproductive tissue (flowers and fruits) litter were 0.989, 0.891, 1.820, 0.500, 1.390, and 0.500 g/kg, respectively, and the concentrations of Al in leaf, branch, and root were 0.424, 0.200 and 1.540 g/kg, respectively. (2) mycorrhizal association significantly affected litter Na and Al concentration. The highest concentration of Na was found in litter from trees associated with both arbuscular mycorrhizal fungi (AM) and ectomycorrhizal fungi (ECM), followed by litter from trees with AM and ECM. Lifeform, taxonomic, and leaf form had significant impacts on the concentration of Na and Al in plant litter of different tissues. (3) leaf litter Na concentration was mainly driven by mycorrhizal association, leaf form and soil phosphorus concentration, while leaf litter Al concentration was mainly controlled by mycorrhizal association, leaf form, and precipitation in the wettest month. Overall, our study clearly assessed the global patterns and influencing factors of litter Na and Al concentrations, which may help us to better understand their roles in the associated biogeochemical cycles in forest ecosystem

Low storage space for compressive sensing: semi-tensor product approach

Abstract Random measurement matrices play a critical role in successful recovery with the compressive sensing (CS) framework. However, due to its randomly generated elements, these matrices require massive amounts of storage space to implement a random matrix in CS applications. To effectively reduce the storage space of the random measurement matrix for CS, we propose a random sampling approach for the CS framework based on the semi-tensor product (STP). The proposed approach generates a random measurement matrix, where the dimensions of the random measurement matrix are reduced to a quarter (or 1/16, 1/64, and even 1/256) of the number of dimensions, which are used for conventional CS. We then estimate the values of the sparse vector with a modified iteratively re-weighted least-squares (IRLS) algorithm. The results of numerical simulations showed that the proposed approach can reduce the storage space of a random matrix to at least a quarter while maintaining quality of reconstruction. All results confirmed that the proposed approach significantly influences the physical implementation of the CS in images, especially on embedded system and field programmable gate array (FPGA), where storage is limited

Intelligent Mining of Urban Ventilated Corridor Based on Digital Surface Model under the Guidance of K-Means

With the acceleration of urbanization, climate problems affecting human health and safe operation of cities have intensified, such as heat island effect, haze, and acid rain. Using high-resolution remote sensing mapping image data to design scientific and efficient algorithms to excavate and plan urban ventilation corridors and improve urban ventilation environment is an effective way to solve these problems. In this paper, we use unmanned aerial vehicle (UAV) tilt photography technology to obtain high-precision remote sensing image digital elevation model (DEM) and digital surface model (DSM) data, count the city’s dominant wind direction in each season using long-term meteorological data, and use building height to calculate the dominant wind direction. The projection algorithm calculates the windward area density of this dominant direction. Under the guidance of K-means, the binarized windward area density map is used to determine each area and boundary of potential ventilation corridors within the threshold range, and the length and angle of each area’s fitted elliptical long axis are calculated to extract the ventilation corridors that meet the criteria. On the basis of high-precision stereo remote sensing data from UAV, the paper uses image classification, segmentation, fitting, and fusion algorithms to intelligently mine potential urban ventilation corridors, and the effectiveness of the proposed method is demonstrated through a case study in Zhuji City, Zhejiang Province

Research on Multiple-Axis Contour Error Suppression Method Based on Composite Layered Control

With the widespread application of multi-axis machining in the industrial manufacturing, aerospace, and military equipment sectors, the demand for machining ultra-precision components has been steadily increasing. Contour errors directly impact the quality of machined parts. In conventional multi-axis motion control systems based on cross-coupling, it is conventionally assumed that all individual axes are of equal significance during machine processing. However, in practical machining scenarios, diverse machining trajectories and accuracy requirements give rise to distinct control necessities for each axis. This complication leads to challenges in ensuring a consistent single-plane contour, thereby constraining the elevation of the overall contour accuracy. To address this issue, this study proposes a multi-axis contour error suppression method based on composite hierarchical control. The approach advocated in this paper initially ensures the precision of single-axis position control through the development of an advanced S-shaped function-based sliding-mode disturbance observer. Building on this foundation, the three-dimensional spatial contour is segregated into upper and lower layers. Subsequently, dedicated fuzzy PID cross-coupling controllers are devised for each layer. The experimental outcomes substantiate that in comparison to conventional cross-coupling control methods, the method introduced in this study, rooted in composite hierarchical control, not only guarantees the accuracy of single-plane contours but also further enhances the overall contour precision

Riding towards a sustainable future: an evaluation of bike sharing’s environmental benefits in Xiamen Island, China

In the pursuit of sustainable urbanization, Bike-Sharing Services (BSS) emerge as a pivotal instrument for promoting green, low-carbon transit. While BSS is often commended for its environmental benefits, we offer a more nuanced analysis that elucidates previously neglected aspects. Through the Dominant Travel Distance Model (DTDM), we evaluate the potential of BSS to replace other transportation modes for specific journey based on travel distance. Utilizing multiscale geographically weighted regression (MGWR), we illuminate the relationship between BSS’s environmental benefits and built-environment attributes. The life cycle analysis (LCA) quantifies greenhouse gas (GHG) emissions from production to operation, providing a deeper understanding of BSS’s environmental benefits. Notably, our study focuses on Xiamen Island, a Chinese “Type II large-sized city” (1–3 million population), contrasting with the predominantly studied “super large-sized cities” (over 10 million population). Our findings highlight: (1) A single BSS trip in Xiamen Island reduces GHG emissions by an average of 19.97 g CO2-eq, accumulating monthly savings of 144.477 t CO2-eq. (2) Areas in the southwest, northeast, and southeast of Xiamen Island, characterized by high population densities, register significant BSS environmental benefits. (3) At a global level, the stepwise regression model identifies five key built environment factors influencing BSS’s GHG mitigation. (4) Regionally, MGWR enhances model precision, indicating that these five factors function at diverse spatial scales, affecting BSS’s environmental benefits variably

A Miniaturized Piezoelectric MEMS Accelerometer with Polygon Topological Cantilever Structure

This work proposes a miniaturized piezoelectric MEMS accelerometer based on polygonal topology with an area of only 868 × 833 μm2. The device consists of six trapezoidal cantilever beams with shorter fixed sides. Meanwhile, a device with larger fixed sides is also designed for comparison. The theoretical and finite element models are established to analyze the effect of the beam′s effective stiffness on the output voltage and natural frequency. As the stiffness of the device decreases, the natural frequency of the device decreases while the output signal increases. The proposed polygonal topology with shorter fixed sides has higher voltage sensitivity than the larger fixed one based on finite element simulations. The piezoelectric accelerometers are fabricated using Cavity-SOI substrates with a core piezoelectric film of aluminum nitride (AlN) of about 928 nm. The fabricated piezoelectric MEMS accelerometers have good linearity (0.99996) at accelerations less than 2 g. The measured natural frequency of the accelerometer with shorter fixed sides is 98 kHz, and the sensitivity, resolution, and minimum detectable signal at 400 Hz are 1.553 mV/g, 1 mg, and 2 mg, respectively. Compared with the traditional trapezoidal cantilever with the same diaphragm area, its output voltage sensitivity is increased by 22.48%

Intelligent Labeling of Tumor Lesions Based on Positron Emission Tomography/Computed Tomography

Positron emission tomography/computed tomography (PET/CT) plays a vital role in diagnosing tumors. However, PET/CT imaging relies primarily on manual interpretation and labeling by medical professionals. An enormous workload will affect the training samples’ construction for deep learning. The labeling of tumor lesions in PET/CT images involves the intersection of computer graphics and medicine, such as registration, a fusion of medical images, and labeling of lesions. This paper extends the linear interpolation, enhances it in a specific area of the PET image, and uses the outer frame scaling of the PET/CT image and the least-squares residual affine method. The PET and CT images are subjected to wavelet transformation and then synthesized in proportion to form a PET/CT fusion image. According to the absorption of 18F-FDG (fluoro deoxy glucose) SUV in the PET image, the professionals randomly select a point in the focus area in the fusion image, and the system will automatically select the seed point of the focus area to delineate the tumor focus with the regional growth method. Finally, the focus delineated on the PET and CT fusion images is automatically mapped to CT images in the form of polygons, and rectangular segmentation and labeling are formed. This study took the actual PET/CT of patients with lymphatic cancer as an example. The semiautomatic labeling of the system and the manual labeling of imaging specialists were compared and verified. The recognition rate was 93.35%, and the misjudgment rate was 6.52%

Intelligent Mining of Urban Ventilation Corridors Based on High-Precision Oblique Photographic Images

With the advancement of urbanization and the impact of industrial pollution, the issue of urban ventilation has attracted increasing attention. Research on urban ventilation corridors is a hotspot in the field of urban planning. Traditional studies on ventilation corridors mostly focus on qualitative or simulated research on urban climate issues such as the intensified urban heat island effect, serious environmental pollution, and insufficient climate adaptability. Based on the high-precision urban remote sensing image data obtained by aeromagnetic oblique photography, this paper calculates the frontal area density of the city with reference to the urban wind statistics. Based on the existing urban patterns, template matching technology was used to automatically excavate urban ventilation corridors, which provides scientific and reasonable algorithmic support for the rapid construction of potential urban ventilation corridor paths. It also provides technical methods and decision basis for low-carbon urban planning, ecological planning and microclimate optimization design. This method was proved to be effective through experiments in Deqing city, Zhejiang Province, China