A micromachined flow shear-stress sensor based on thermal transfer principles

Microhot-film shear-stress sensors have been developed by using surface micromachining techniques. The sensor consists of a suspended silicon-nitride diaphragm located on top of a vacuum-sealed cavity. A heating and heat-sensing element, made of polycrystalline silicon material, resides on top of the diaphragm. The underlying vacuum cavity greatly reduces conductive heat loss to the substrate and therefore increases the sensitivity of the sensor. Testing of the sensor has been conducted in a wind tunnel under three operation modes-constant current, constant voltage, and constant temperature. Under the constant-temperature mode, a typical shear-stress sensor exhibits a time constant of 72 μs

BALF: Simple and Efficient Blur Aware Local Feature Detector

Local feature detection is a key ingredient of many image processing and computer vision applications, such as visual odometry and localization. Most existing algorithms focus on feature detection from a sharp image. They would thus have degraded performance once the image is blurred, which could happen easily under low-lighting conditions. To address this issue, we propose a simple yet both efficient and effective keypoint detection method that is able to accurately localize the salient keypoints in a blurred image. Our method takes advantages of a novel multi-layer perceptron (MLP) based architecture that significantly improve the detection repeatability for a blurred image. The network is also light-weight and able to run in real-time, which enables its deployment for time-constrained applications. Extensive experimental results demonstrate that our detector is able to improve the detection repeatability with blurred images, while keeping comparable performance as existing state-of-the-art detectors for sharp images

Analysis of bacterial and fungal community structure in replant strawberry rhizosphere soil with denaturing gradient gel electrophoresis

High quality DNA is the basis of analyzing bacterial and fungal community structure in replant strawberry rhizosphere soil with the method of denaturing gradient gel electrophoresis (DGGE). DNA of soil  microorganisms was extracted from the rhizosphere soil of strawberries planted in different replanted  years (0, two, six and seven), respectively, and crude DNA was purified after extraction. Three methods  were established to evaluate the effects of cetyl trimethylammonium bromide (CTAB),  polyvinylpolypyrolidone (PVPP), proteinase K and bacteriolytic enzymes on DNA extraction. DNA  fragments above 23 kb in size were isolated well by method 1 (1% CTAB, proteinase K, no PVPP, no  bacteriolytic enzyme) and method 3 (no CTAB, no proteinase K, 3% PVPP, bacteriolytic enzyme). Method 3 got the best yields 43.06 ìg/g, and A260/A280 and A260/A230 were 1.1623 and 0.8135, respectively,  which could ensure the veracity of subsequent DGGE analysis. Method 2 (3% CTAB, no proteinase K, no PVPP, no bacteriolytic enzyme) could not extract enough DNA to do the next PCR-DGGE analysis.  F341/R534 and FR1/FF390 primers were used to amplify the 16S rDNA V3 region of bacteria and 18S rDNA of fungi, and the expected fragments of 230 bp 16S rDNA V3 region and 390 bp 18S rDNA were amplified. The results of DGGE analysis showed that there were common and specific bacterial and fungal  communities in different replant soils of strawberry. There were 84 and 54% similarity of bacterial and  fungal communities between different replant soils. The numbers of both bacterial and fungal communities increased in the replant strawberry soil, they were positively correlated with the replant years. As the  number of replant years increased from two to seven years, while the ratio of bacteria/fungi was  decreased from 2.29 to 1.46 in the rhizosphere soils planted with strawberries.Key words: Rhizosphere soil, bacterial community, fungal community, replant strawberry, fruiting fields

TacIPC: Intersection- and Inversion-free FEM-based Elastomer Simulation For Optical Tactile Sensors

Tactile perception stands as a critical sensory modality for human interaction with the environment. Among various tactile sensor techniques, optical sensor-based approaches have gained traction, notably for producing high-resolution tactile images. This work explores gel elastomer deformation simulation through a physics-based approach. While previous works in this direction usually adopt the explicit material point method (MPM), which has certain limitations in force simulation and rendering, we adopt the finite element method (FEM) and address the challenges in penetration and mesh distortion with incremental potential contact (IPC) method. As a result, we present a simulator named TacIPC, which can ensure numerically stable simulations while accommodating direct rendering and friction modeling. To evaluate TacIPC, we conduct three tasks: pseudo-image quality assessment, deformed geometry estimation, and marker displacement prediction. These tasks show its superior efficacy in reducing the sim-to-real gap. Our method can also seamlessly integrate with existing simulators. More experiments and videos can be found in the supplementary materials and on the website: https://sites.google.com/view/tac-ipc

Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network

An evidence-weighted functional-linkage network of human genes reveals associations among diseases that share no known disease genes and have dissimilar phenotype

Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes

Object detection via inaccurate bounding boxes supervision has boosted a broad interest due to the expensive high-quality annotation data or the occasional inevitability of low annotation quality (\eg tiny objects). The previous works usually utilize multiple instance learning (MIL), which highly depends on category information, to select and refine a low-quality box. Those methods suffer from object drift, group prediction and part domination problems without exploring spatial information. In this paper, we heuristically propose a \textbf{Spatial Self-Distillation based Object Detector (SSD-Det)} to mine spatial information to refine the inaccurate box in a self-distillation fashion. SSD-Det utilizes a Spatial Position Self-Distillation \textbf{(SPSD)} module to exploit spatial information and an interactive structure to combine spatial information and category information, thus constructing a high-quality proposal bag. To further improve the selection procedure, a Spatial Identity Self-Distillation \textbf{(SISD)} module is introduced in SSD-Det to obtain spatial confidence to help select the best proposals. Experiments on MS-COCO and VOC datasets with noisy box annotation verify our method's effectiveness and achieve state-of-the-art performance. The code is available at https://github.com/ucas-vg/PointTinyBenchmark/tree/SSD-Det.Comment: accepted by ICCV 202

P2RBox: A Single Point is All You Need for Oriented Object Detection

Oriented object detection, a specialized subfield in computer vision, finds applications across diverse scenarios, excelling particularly when dealing with objects of arbitrary orientations. Conversely, point annotation, which treats objects as single points, offers a cost-effective alternative to rotated and horizontal bounding boxes but sacrifices performance due to the loss of size and orientation information. In this study, we introduce the P2RBox network, which leverages point annotations and a mask generator to create mask proposals, followed by filtration through our Inspector Module and Constrainer Module. This process selects high-quality masks, which are subsequently converted into rotated box annotations for training a fully supervised detector. Specifically, we've thoughtfully crafted an Inspector Module rooted in multi-instance learning principles to evaluate the semantic score of masks. We've also proposed a more robust mask quality assessment in conjunction with the Constrainer Module. Furthermore, we've introduced a Symmetry Axis Estimation (SAE) Module inspired by the spectral theorem for symmetric matrices to transform the top-performing mask proposal into rotated bounding boxes. P2RBox performs well with three fully supervised rotated object detectors: RetinaNet, Rotated FCOS, and Oriented R-CNN. By combining with Oriented R-CNN, P2RBox achieves 62.26% on DOTA-v1.0 test dataset. As far as we know, this is the first attempt at training an oriented object detector with point supervision