VehSense: Slippery Road Detection Using Smartphones

This paper investigates a new application of vehicular sensing: detecting and reporting the slippery road conditions. We describe a system and associated algorithm to monitor vehicle skidding events using smartphones and OBD-II (On board Diagnostics) adaptors. This system, which we call the VehSense, gathers data from smartphone inertial sensors and vehicle wheel speed sensors, and processes the data to monitor slippery road conditions in real-time. Specifically, two speed readings are collected: 1) ground speed, which is estimated by vehicle acceleration and rotation, and 2) wheel speed, which is retrieved from the OBD-II interface. The mismatch between these two speeds is used to infer a skidding event. Without tapping into vehicle manufactures' proprietary data (e.g., antilock braking system), VehSense is compatible with most of the passenger vehicles, and thus can be easily deployed. We evaluate our system on snow-covered roads at Buffalo, and show that it can detect vehicle skidding effectively.Comment: 2017 IEEE 85th Vehicular Technology Conference (VTC2017-Spring

Road Grade Estimation Using Crowd-Sourced Smartphone Data

Estimates of road grade/slope can add another dimension of information to existing 2D digital road maps. Integration of road grade information will widen the scope of digital map's applications, which is primarily used for navigation, by enabling driving safety and efficiency applications such as Advanced Driver Assistance Systems (ADAS), eco-driving, etc. The huge scale and dynamic nature of road networks make sensing road grade a challenging task. Traditional methods oftentimes suffer from limited scalability and update frequency, as well as poor sensing accuracy. To overcome these problems, we propose a cost-effective and scalable road grade estimation framework using sensor data from smartphones. Based on our understanding of the error characteristics of smartphone sensors, we intelligently combine data from accelerometer, gyroscope and vehicle speed data from OBD-II/smartphone's GPS to estimate road grade. To improve accuracy and robustness of the system, the estimations of road grade from multiple sources/vehicles are crowd-sourced to compensate for the effects of varying quality of sensor data from different sources. Extensive experimental evaluation on a test route of ~9km demonstrates the superior performance of our proposed method, achieving $5\times$ improvement on road grade estimation accuracy over baselines, with 90\% of errors below 0.3$^\circ$.Comment: Proceedings of 19th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN'20

CyberGuarder: a virtualization security assurance architecture for green cloud computing

Cloud Computing, Green Computing, Virtualization, Virtual Security Appliance, Security Isolation

Kruppel-Like Factor 4-Dependent Staufen1-Mediated mRNA Decay Regulates Cortical Neurogenesis

Kruppel-like factor 4 (Klf4) is a zinc-finger-containing protein that plays a critical role in diverse cellular physiology. While most of these functions attribute to its role as a transcription factor, it is postulated that Klf4 may play a role other than transcriptional regulation. Here we demonstrate that Klf4 loss in neural progenitor cells (NPCs) leads to increased neurogenesis and reduced self-renewal in mice. In addition, Klf4 interacts with RNA-binding protein Staufen1 (Stau1) and RNA helicase Ddx5/17. They function together as a complex to maintain NPC self-renewal. We report that Klf4 promotes Stau1 recruitment to the 3′-untranslated region of neurogenesis-associated mRNAs, increasing Stau1-mediated mRNA decay (SMD) of these transcripts. Stau1 depletion abrogated SMD of target mRNAs and rescued neurogenesis defects in Klf4-overexpressing NPCs. Furthermore, Ddx5/17 knockdown significantly blocked Klf4-mediated mRNA degradation. Our results highlight a novel molecular mechanism underlying stability of neurogenesis-associated mRNAs controlled by the Klf4/Ddx5/17/Stau1 axis during mammalian corticogenesis

Graph theoretical analysis of functional network for comprehension of sign language

This work was supported by grants from the National Natural Science Foundation of China (NSFC: 31571158, 31170969) and National Key Basic Research Program of China (2014CB846102), and a grant from the National Institutes of Health (R01 DC010997). We thank Yong He and Roel Willems for providing insightful comments to this study and Amie Fairs for proofreading the manuscript. No conflict of interest is declared.Peer reviewedPostprin

Application of improved YOLOv7-based sugarcane stem node recognition algorithm in complex environments

IntroductionSugarcane stem node detection is one of the key functions of a small intelligent sugarcane harvesting robot, but the accuracy of sugarcane stem node detection is severely degraded in complex field environments when the sugarcane is in the shadow of confusing backgrounds and other objects.MethodsTo address the problem of low accuracy of sugarcane arise node detection in complex environments, this paper proposes an improved sugarcane stem node detection model based on YOLOv7. First, the SimAM (A Simple Parameter-Free Attention Module for Convolutional Neural Networks) attention mechanism is added to solve the problem of feature loss due to the loss of image global context information in the convolution process, which improves the detection accuracy of the model in the case of image blurring; Second, the Deformable convolution Network is used to replace some of the traditional convolution layers in the original YOLOv7. Finally, a new bounding box regression loss function WIoU Loss is introduced to solve the problem of unbalanced sample quality, improve the model robustness and generalization ability, and accelerate the convergence speed of the network.ResultsThe experimental results show that the mAP of the improved algorithm model is 94.53% and the F1 value is 92.41, which are 3.43% and 2.21 respectively compared with the YOLOv7 model, and compared with the mAP of the SOTA method which is 94.1%, an improvement of 0.43% is achieved, which effectively improves the detection performance of the target detection model.DiscussionThis study provides a theoretical basis and technical support for the development of a small intelligent sugarcane harvesting robot, and may also provide a reference for the detection of other types of crops in similar environments