201 research outputs found
Path Generation for Wheeled Robots Autonomous Navigation on Vegetated Terrain
Wheeled robot navigation has been widely used in urban environments, but
little research has been conducted on its navigation in wild vegetation.
External sensors (LiDAR, camera etc.) are often used to construct point cloud
map of the surrounding environment, however, the supporting rigid ground used
for travelling cannot be detected due to the occlusion of vegetation. This
often causes unsafe or not smooth path during planning process. To address the
drawback, we propose the PE-RRT* algorithm, which effectively combines a novel
support plane estimation method and sampling algorithm to generate real-time
feasible and safe path in vegetation environments. In order to accurately
estimate the support plane, we combine external perception and proprioception,
and use Multivariate Gaussian Processe Regression (MV-GPR) to estimate the
terrain at the sampling nodes. We build a physical experimental platform and
conduct experiments in different outdoor environments. Experimental results
show that our method has high safety, robustness and generalization
Eunomia: Enabling User-specified Fine-Grained Search in Symbolically Executing WebAssembly Binaries
Although existing techniques have proposed automated approaches to alleviate
the path explosion problem of symbolic execution, users still need to optimize
symbolic execution by applying various searching strategies carefully. As
existing approaches mainly support only coarse-grained global searching
strategies, they cannot efficiently traverse through complex code structures.
In this paper, we propose Eunomia, a symbolic execution technique that allows
users to specify local domain knowledge to enable fine-grained search. In
Eunomia, we design an expressive DSL, Aes, that lets users precisely pinpoint
local searching strategies to different parts of the target program. To further
optimize local searching strategies, we design an interval-based algorithm that
automatically isolates the context of variables for different local searching
strategies, avoiding conflicts between local searching strategies for the same
variable. We implement Eunomia as a symbolic execution platform targeting
WebAssembly, which enables us to analyze applications written in various
languages (like C and Go) but can be compiled into WebAssembly. To the best of
our knowledge, Eunomia is the first symbolic execution engine that supports the
full features of the WebAssembly runtime. We evaluate Eunomia with a dedicated
microbenchmark suite for symbolic execution and six real-world applications.
Our evaluation shows that Eunomia accelerates bug detection in real-world
applications by up to three orders of magnitude. According to the results of a
comprehensive user study, users can significantly improve the efficiency and
effectiveness of symbolic execution by writing a simple and intuitive Aes
script. Besides verifying six known real-world bugs, Eunomia also detected two
new zero-day bugs in a popular open-source project, Collections-C.Comment: Accepted by ACM SIGSOFT International Symposium on Software Testing
and Analysis (ISSTA) 202
Resonance modes of plasmonic nanorod metamaterials and their applications
Plasmonic nanorod metamaterials exhibit transversal and longitudinal resonance modes. It is found that the resonance intensity of the transversal modes (T-Modes) excited by the p- polarized wave is obviously larger than the intensity for the s- polarized wave at the wavelength of the transversal resonance, and the resonance intensity of the longitudinal modes (L-Modes) excited by the s- polarized wave is clearly larger than the intensity for the p- polarized wave at the longitudinal resonance wavelength, indicating a distinct polarization characteristics, which results from excitation of the different resonance modes of surface plasmons at different wavelengths. Moreover, the polarization behavior in near field regions for the different resonance modes has been demonstrated by the electric field distributions of the plasmonic nanorods based on FDTD simulation. In addition, the working wavelength of the polarizer can be tuned by the diameter and length of the silver nanorods in the visible spectral range, higher extinction ratios and lower insertion losses can be achieved based on the different resonance modes associated with the different polarizations. The polarizers will be a promising candidate for its potential applications in integration of nanophotonic devices
CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving
Contemporary deep-learning object detection methods for autonomous driving
usually assume prefixed categories of common traffic participants, such as
pedestrians and cars. Most existing detectors are unable to detect uncommon
objects and corner cases (e.g., a dog crossing a street), which may lead to
severe accidents in some situations, making the timeline for the real-world
application of reliable autonomous driving uncertain. One main reason that
impedes the development of truly reliably self-driving systems is the lack of
public datasets for evaluating the performance of object detectors on corner
cases. Hence, we introduce a challenging dataset named CODA that exposes this
critical problem of vision-based detectors. The dataset consists of 1500
carefully selected real-world driving scenes, each containing four object-level
corner cases (on average), spanning more than 30 object categories. On CODA,
the performance of standard object detectors trained on large-scale autonomous
driving datasets significantly drops to no more than 12.8% in mAR. Moreover, we
experiment with the state-of-the-art open-world object detector and find that
it also fails to reliably identify the novel objects in CODA, suggesting that a
robust perception system for autonomous driving is probably still far from
reach. We expect our CODA dataset to facilitate further research in reliable
detection for real-world autonomous driving. Our dataset will be released at
https://coda-dataset.github.io.Comment: ECCV 202
Rapid identification of early renal damage in asymptomatic hyperuricemia patients based on urine Raman spectroscopy and bioinformatics analysis
Objective: The issue of when to start treatment in patients with hyperuricemia (HUA) without gout and chronic kidney disease (CKD) is both important and controversial. In this study, Raman spectroscopy (RS) was used to analyze urine samples, and key genes expressed differentially CKD were identified using bioinformatics. The biological functions and regulatory pathways of these key genes were preliminarily analyzed, and the relationship between them as well as the heterogeneity of the urine components of HUA was evaluated. This study provides new ideas for the rapid evaluation of renal function in patients with HUA and CKD, while providing an important reference for the new treatment strategy of HUA disease.Methods: A physically examined population in 2021 was recruited as the research subjects. There were 10 cases with normal blood uric acid level and 31 cases with asymptomatic HUA diagnosis. The general clinical data were collected and the urine samples were analyzed by Raman spectroscopy. An identification model was also established by using the multidimensional multivariate method of orthogonal partial least squares discriminant analysis (OPLS-DA) model for statistical analysis of the data, key genes associated with CKD were identified using the Gene Expression Omnibus (GEO) database, and key biological pathways associated with renal function damage in CKD patients with HUA were analyzed.Results: The Raman spectra showed significant differences in the levels of uric acid (640 cm−1), urea, creatinine (1,608, 1,706 cm−1), proteins/amino acids (642, 828, 1,556, 1,585, 1,587, 1,596, 1,603, 1,615 cm−1), and ketone body (1,643 cm−1) (p < 0.05). The top 10 differentially expressed genes (DEGs) associated with CKD (ALB, MYC, IL10, FOS, TOP2A, PLG, REN, FGA, CCNA2, and BUB1) were identified. Compared with the differential peak positions analyzed by the OPLS-DA model, it was found that the peak positions of glutathione, tryptophan and tyrosine may be important markers for the diagnosis and progression of CKD.Conclusion: The progression of CKD was related to the expression of the ALB, MYC, IL10, PLG, REN, and FGA genes. Patients with HUA may have abnormalities in glutathione, tryptophan, tyrosine, and energy metabolism. The application of Raman spectroscopy to analyze urine samples and interpret the heterogeneity of the internal environment of asymptomatic HUA patients can be combined with the OPLS-DA model to mine the massive clinical and biochemical examination information on HUA patients. The results can also provide a reference for identifying the right time for intervention for uric acid as well as assist the early detection of changes in the internal environment of the body. Finally, this approach provides a useful technical supplement for exploring a low-cost, rapid evaluation and improving the timeliness of screening. Precise intervention of abnormal signal levels of internal environment and energy metabolism may be a potential way to delay renal injury in patients with HUA
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