An RF-Isolated Real-Time Multipath Testbed for Performance Analysis of WLANs

Real-time performance evaluation of wireless local area networks (WLANs) is an extremely challenging topic. The major drawback of real-time performance analysis in actual network installations is a lack of repeatability due to uncontrollable interference and propagation complexities. These are caused by unpredictable variations in the interference scenarios and statistical behavior of the wireless propagation channel. This underscores the need for a Radio Frequency (RF) test platform that provides isolation from interfering sources while simulating a real-time wireless channel, thereby creating a realistic and controllable radio propagation test environment. Such an RF-isolated testbed is necessary to enable an empirical yet repeatable evaluation of the effects of the wireless channel on WLAN performance. In this thesis, a testbed is developed that enables real-time laboratory performance evaluation of WLANs. This testbed utilizes an RF-isolated test system, Azimuthâ„¢ Systems 801W, for isolation from external interfering sources such as cordless phones and microwave ovens and a real-time multipath channel simulator, Elektrobit PROPSimâ„¢ C8, for wireless channel emulation. A software protocol analyzer, WildPackets Airopeek NX, is used to capture data packets in the testbed from which statistical data characterizing performance such as data rate and Received Signal Strength (RSS) are collected. The relationship between the wireless channel and WLAN performance, under controlled propagation and interference conditions, is analyzed using this RF-isolated multipath testbed. Average throughput and instantaneous throughput variation of IEEE 802.11b and 802.11g WLANs operating in four different channels - a constant channel and IEEE 802.11 Task Group n (TGn) Channel Models A, B, and C - are examined. Practical models describing the average throughput as a function of the average received power and throughput variation as a function of the average throughput under different propagation conditions are presented. Comprehensive throughput models that incorporate throughput variation are proposed for the four channels using Weibull and Gaussian probability distributions. These models provide a means for realistic simulation of throughput for a specific channel at an average received power. Also proposed is a metric to describe the normalized throughput capacity of WLANs for comparative performance evaluation

Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes

The success of deep learning in computer vision is based on availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation and object detection models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment real images with virtual objects. This allows us to create realistic composite images which exhibit both realistic background appearance and a large number of complex object arrangements. In contrast to modeling complete 3D environments, our augmentation approach requires only a few user interactions in combination with 3D shapes of the target object. Through extensive experimentation, we conclude the right set of parameters to produce augmented data which can maximally enhance the performance of instance segmentation models. Further, we demonstrate the utility of our approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenes. We test the models trained on our augmented data on the KITTI 2015 dataset, which we have annotated with pixel-accurate ground truth, and on Cityscapes dataset. Our experiments demonstrate that models trained on augmented imagery generalize better than those trained on synthetic data or models trained on limited amount of annotated real data

Server-side performance evaluation of NDN

International audienceNDN is a promising protocol that can help to reduce congestion at Internet scale by putting content at the center of communications instead of hosts, and by providing each node with a caching capability. NDN can also natively authenticate transmitted content with a mechanism similar to website certificates that allows clients to assess the original provider. But this security feature comes at a high cost, as it relies heavily on asymmetric cryptography which affects server performance when NDN Data are generated. This is particularly critical for many services dealing with real-time data (VOIP, live streaming, etc.), but current tools are not adapted for a realistic server-side performance evaluation of NDN traffic generation when digital signature is used. We propose a new tool, NDNperf, to perform this evaluation and show that creating NDN packets is a major bottleneck of application performances. On our testbed, 14 server cores only generate ∼400 Mbps of new NDN Data with default packet settings. We propose and evaluate practical solutions to improve the performance of server-side NDN Data generation leading to significant gains

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)