arXiv subjects

Dongbing Gu

Publications and source records attributed to Dongbing Gu.

6 recordsLinked to original sources

arXiv2019

FLAG: A Framework for FPGA-based LoAd Generation in Profinet Communication

Like other automated system technologies, PROFINET, a real-time Industrial Ethernet Standard has shown increasing level of integration into the present IT Infrastructure. Such vast use of PROFINET can expose the controllers and I/O devices to operate in critical failures when traffic goes unexpectedly higher than normal. Rigorous testing of the running devices then becomes essential and therefore, in this paper, we prototype and design an FPGA based load Generating solution called FLAG (FPGA-based LoAd Generator) for PROFINET based traffic at the desired load configurations such as, bits per second, the number and size of the packets with their Ethertypes and MAC addresses. We have employed, a Zynq-7000 FPGA as our implementation platform for the proposed FLAG framework. The system can easily be deployed and accessed via the web or command line interface for successful load generation. Extensive experiments have been conducted to verify the effectiveness of our proposed solution and the results confirm that the proposed framework is capable to generate precise load at Fast/Gigabit line rate with a defined number of packets.

cs.NI↗

arXiv2019

Profi-Load: An FPGA-Based Solution for Generating Network Load in Profinet Communication

Industrial automation has received a considerable attention in the last few years with the rise of Internet of Things (IoT). Specifically, industrial communication network technology such as Profinet has proved to be a major game changer for such automation. However, industrial automation devices often have to exhibit robustness to dynamically changing network conditions and thus, demand a rigorous testing environment to avoid any safety-critical failures. Hence, in this paper, we have proposed an FPGA-based novel framework called Profi-Load to generate Profinet traffic with specific intensities for a specified duration of time. The proposed Profi-Load intends to facilitate the performance testing of the industrial automated devices under various network conditions. By using the advantage of inherent hardware parallelism and re-configurable features of FPGA, Profi-Load is able to generate Profinet traffic efficiently. Moreover, it can be reconfigured on the fly as per the specific requirements. We have developed our proposed Profi-Load framework by employing the Xilinx-based NetJury device which belongs to Zynq-7000 FPGA family. A series of experiments have been conducted to evaluate the effectiveness of Profi-Load and it has been observed that Profi-Load is able to generate precise load at a constant rate for stringent timing requirements. Furthermore, a suitable Human Machine Interface (HMI) has also been developed for quick access to our framework. The HMI at the client side can directly communicate with the NetJury device and parameters such as, required load amount, number of packet(s) to be sent or desired time duration can be selected using the HMI.

cs.NI↗

arXiv2019

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks

Recently end-to-end unsupervised deep learning methods have achieved an effect beyond geometric methods for visual depth and ego-motion estimation tasks. These data-based learning methods perform more robustly and accurately in some of the challenging scenes. The encoder-decoder network has been widely used in the depth estimation and the RCNN has brought significant improvements in the ego-motion estimation. Furthermore, the latest use of Generative Adversarial Nets(GANs) in depth and ego-motion estimation has demonstrated that the estimation could be further improved by generating pictures in the game learning process. This paper proposes a novel unsupervised network system for visual depth and ego-motion estimation: Stacked Generative Adversarial Network(SGANVO). It consists of a stack of GAN layers, of which the lowest layer estimates the depth and ego-motion while the higher layers estimate the spatial features. It can also capture the temporal dynamic due to the use of a recurrent representation across the layers. See Fig.1 for details. We select the most commonly used KITTI [1] data set for evaluation. The evaluation results show that our proposed method can produce better or comparable results in depth and ego-motion estimation.

cs.RO↗

arXiv2018

UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning

We propose a novel monocular visual odometry (VO) system called UnDeepVO in this paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera and the depth of its view by using deep neural networks. There are two salient features of the proposed UnDeepVO: one is the unsupervised deep learning scheme, and the other is the absolute scale recovery. Specifically, we train UnDeepVO by using stereo image pairs to recover the scale but test it by using consecutive monocular images. Thus, UnDeepVO is a monocular system. The loss function defined for training the networks is based on spatial and temporal dense information. A system overview is shown in Fig. 1. The experiments on KITTI dataset show our UnDeepVO achieves good performance in terms of pose accuracy.

cs.CV↗

arXiv2016

Exploring Dynamic Environments Using Stochastic Search Strategies

In this paper, we conduct a literature review of laws of motion based on stochastic search strategies which are mainly focused on exploring highly dynamic environments. In this regard, stochastic search strategies represent an interesting alternative to cope with uncertainty and reduced perceptual capabilities. This study aims to present an introductory overview of research in terms of directional rules and searching methods mainly based on bio-inspired approaches. This study critically examines the role of animal searching behavior applied to random walk models using stochastic rules and kinesis or taxis. The aim of this study is to examine existing techniques and to select relevant work on random walks and analyze their actual contributions. In this regard, we cover a wide range of displacement events with an orientation mechanism given by a reactive behavior or a source-seeking behavior. Finally, we conclude with a discussion concerning the usefulness of using optimal foraging strategies as a reliable methodology.

cs.RO↗

arXiv2015

Towards a Standard Sampling Methodology on Online Social Networks: Collecting Global Trends on Twitter

One of the most significant current challenges in large-scale online social networks, is to establish a concise and coherent method able to collect and summarize data. Sampling the content of an Online Social Network (OSN) plays an important role as a knowledge discovery tool. It is becoming increasingly difficult to ignore the fact that current sampling methods must cope with a lack of a full sampling frame i.e., there is an imposed condition determined by a limited data access. In addition, another key aspect to take into account is the huge amount of data generated by users of social networking services. This type of conditions make especially difficult to develop sampling methods to collect truly reliable data. Therefore, we propose a low computational cost method for sampling emerging global trends on social networking services such as Twitter. The main purpose of this study, is to develop a methodology able to carry out an efficient collecting process via three random generators: Brownian, Illusion and Reservoir. These random generators will be combined with a Metropolis-Hastings Random Walk (MHRW) in order to improve the sampling process. We demonstrate the effectiveness of our approach by correctly providing a descriptive statistics of the collected data. In addition, we also sketch the collecting procedure on real-time carried out on Twitter. Finally, we conclude with a trend concentration graphical description and a formal convergence analysis to evaluate whether the sample of draws has attained an equilibrium state to get a rough estimate of the sample quality.

cs.SI↗