Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots

Safety is paramount for mobile robotic platforms such as self-driving cars and unmanned aerial vehicles. This work is devoted to a task that is indispensable for safety yet was largely overlooked in the past -- detecting obstacles that are of very thin structures, such as wires, cables and tree branches. This is a challenging problem, as thin objects can be problematic for active sensors such as lidar and sonar and even for stereo cameras. In this work, we propose to use video sequences for thin obstacle detection. We represent obstacles with edges in the video frames, and reconstruct them in 3D using efficient edge-based visual odometry techniques. We provide both a monocular camera solution and a stereo camera solution. The former incorporates Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter enjoys a novel, purely vision-based solution. Experiments demonstrated that the proposed methods are fast and able to detect thin obstacles robustly and accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio

Experimental investigation on the flexural behavior of concrete reinforced by various types of steel fibers

The benefit of steel fiber on the mechanical behaviors of concrete has been well accepted. The flexural behavior of steel fiber reinforced concrete (SFRC) is complicated which depends on many factors, such as matrix properties, fiber material properties, fiber geometries, fiber volume contents, and interface properties. Thus, the investigations on the flexural behavior of SFRC are needed to be expanded. In this study, the effects of fiber type with varying shapes and aspect ratios on the flexural performance of SFRC were investigated. Five steel fibers were adopted in this study: milled fiber (M), corrugated fiber (C) and three hooked fibers with aspect radios of 45 (HA), 55 (HB), and 65 (HC). Two volume fractions (0.4% and 1.0%) of steel fiber and two compressive strengths (normal and high strengths) of matrix were considered. The load-deflection curves, energy absorption capacity and equivalent flexural strength were discussed. The results show that the flexural behavior of SFRC beams reinforced by 1.0% fibers is significantly higher than that of the beams reinforced by 0.4% fibers. Hooked fiber reinforced beams performed the best flexural load-deflection response compared to the beams reinforced by milled fiber and corrugated fiber reinforced, and exhibited an increasing trend of flexural performance as the fiber aspect ratio increased. The differences between specimens with different fibers for high strength matrix are more obvious compared to the normal strength matrix

A Computational Method Based on the Integration of Heterogeneous Networks for Predicting Disease-Gene Associations

The identification of disease-causing genes is a fundamental challenge in human health and of great importance in improving medical care, and provides a better understanding of gene functions. Recent computational approaches based on the interactions among human proteins and disease similarities have shown their power in tackling the issue. In this paper, a novel systematic and global method that integrates two heterogeneous networks for prioritizing candidate disease-causing genes is provided, based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein interactions. In this method, the association score function between a query disease and a candidate gene is defined as the weighted sum of all the association scores between similar diseases and neighbouring genes. Moreover, the topological correlation of these two heterogeneous networks can be incorporated into the definition of the score function, and finally an iterative algorithm is designed for this issue. This method was tested with 10-fold cross-validation on all 1,126 diseases that have at least a known causal gene, and it ranked the correct gene as one of the top ten in 622 of all the 1,428 cases, significantly outperforming a state-of-the-art method called PRINCE. The results brought about by this method were applied to study three multi-factorial disorders: breast cancer, Alzheimer disease and diabetes mellitus type 2, and some suggestions of novel causal genes and candidate disease-causing subnetworks were provided for further investigation

AutoCharge: Automatically Charge Smartphones Using a Light Beam

Abstract Smartphone charging imposes a big burden to users because they often have to recharge their smartphones every day or even multiple times per day. In this paper we try to answer the following question: can smartphones get automatically charged without requiring explicit effort from users? To this end, we propose a new approach, called AutoCharge, to explore the feasibility of automatic smartphone charging. The AutoCharge approach automatically locates a smartphone on a desk and charges it in a transparent matter from the user. This is achieved by two techniques. First, we leverage solar charging technique but use it in indoor spaces, to remotely charge a smartphone using a light beam without a wire. Second, we employ an image-processingbased technique to detect and track smartphones on a desk for automatic smartphone charging. As a result, AutoCharge is able to largely reduce users' efforts in smartphone charging and significantly improve the user experience. We have designed and implemented a prototype system of the AutoCharge approach. We report the design details of the light charger and the smartphone detection and tracking system. Experimental results show that our prototype is able to detect the presence of a smartphone within seconds and charge it as fast as existing wired chargers, demonstrating the feasibility of automatic smartphone charging

Experiencing and handling the diversity in data density and environmental locality in an indoor positioning service

ABSTRACT Diversity in training data density and environment locality is intrinsic in the real-world deployment of indoor localization systems and has a major impact on the performance of existing localization approaches. In this paper, through micro-benchmarks, we find that fingerprint-based approaches are preferable in scenarios where a dense database is available; while model-based approaches are the method of choice in the case of sparse data. It should be noted, however, that practical situations are complex. A single deployment often features both sparse and dense sampled areas. Furthermore, the internal layout affects the propagation of radio signals and exhibits environmental impacts. A certain number of measurement samples may be sufficient for one part of the building, but entirely insufficient for another. Thus, finding the right indoor localization algorithm for a given large-scale deployment is challenging, if not impossible; there is no one-size-fits-all indoor localization approach. Realizing the fundamental fact that the quality of the location database capturing the actual radio map dictates localization accuracy, in this paper, we propose Modellet, an algorithmic approach that optimally approximates the actual radio map by unifying modelbased and fingerprint-based approaches. Modellet represents the radio map using a fingerprint-cloud that incorporates both measured real fingerprints and virtual fingerprints, which are computed from models with a local support, based on the key concept of the supporting set. We evaluate Modellet with data collected from an office building as well as 13 large-scale deployment venues (shopping malls and airports), located across China, U.S., and Germany. Comparing Modellet with two representative baseline approaches, RADAR and EZPerfect, demonstrates that Modellet effectively adapts to different data densities and environmental conditions, substantially outperforming existing approaches

The ranks of known disease-causing or susceptibility genes for three cases on the whole genome.

<p>In <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024171#pone-0024171-t001" target="_blank">Table 1</a>, both the known disease-causing genes and the susceptibility genes for three cases of Breast Cancer, Alzheimer Disease and Diabetes Mellitus Type 2 are listed, altogether with the corresponding rank in the whole genome.</p