Wireless Interference Identification with Convolutional Neural Networks

The steadily growing use of license-free frequency bands requires reliable coexistence management for deterministic medium utilization. For interference mitigation, proper wireless interference identification (WII) is essential. In this work we propose the first WII approach based upon deep convolutional neural networks (CNNs). The CNN naively learns its features through self-optimization during an extensive data-driven GPU-based training process. We propose a CNN example which is based upon sensing snapshots with a limited duration of 12.8 {\mu}s and an acquisition bandwidth of 10 MHz. The CNN differs between 15 classes. They represent packet transmissions of IEEE 802.11 b/g, IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the 2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII approaches and has a classification accuracy greater than 95% for signal-to-noise ratio of at least -5 dB

A Software-Defined Channel Sounder for Industrial Environments with Fast Time Variance

Novel industrial wireless applications require wideband, real-time channel characterization due to complex multipath propagation. Rapid machine motion leads to fast time variance of the channel's reflective behavior, which must be captured for radio channel characterization. Additionally, inhomogeneous radio channels demand highly flexible measurements. Existing approaches for radio channel measurements either lack flexibility or wide-band, real-time performance with fast time variance. In this paper, we propose a correlative channel sounding approach utilizing a software-defined architecture. The approach enables real-time, wide-band measurements with fast time variance immune to active interference. The desired performance is validated with a demanding industrial application example.Comment: Submitted to the 15th International Symposium on Wireless Communication Systems (ISWCS 2018

Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning

In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems might decrease the state of coexistence. Therefore, a central coexistence management system is needed, which allocates conflict-free resources to wireless systems. To ensure a conflict-free resource utilization, it is useful to predict the prospective medium utilization before resources are allocated. This paper presents a self-learning concept, which is based on reinforcement learning. A simulative evaluation of reinforcement learning agents based on neural networks, called deep Q-networks and double deep Q-networks, was realized for exemplary and practically relevant coexistence scenarios. The evaluation of the double deep Q-network showed that a prediction accuracy of at least 98 % can be reached in all investigated scenarios.Comment: Submitted to the 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2018

Simulations on Consumer Tests: Systematic Evaluation of Tolerance Ranges by Model-Based Generation of Simulation Scenarios

Context: Since 2014 several modern cars were rated regarding the performances of their active safety systems at the European New Car Assessment Programme (EuroNCAP). Nowadays, consumer tests play a significant role for the OEM's series development with worldwide perspective, because a top rating is needed to underline the worthiness of active safety features from the customers' point of view. Furthermore, EuroNCAP already published their roadmap 2020 in which they outline further extensions in today's testing and rating procedures that will aggravate the current requirements addressed to those systems. Especially Autonomous Emergency Braking/Forward Collision Warning systems (AEB/FCW) are going to face a broader field of application as pedestrian detection or two-way traffic scenarios. Objective: This work focuses on the systematic generation of test scenarios concentrating on specific parameters that can vary within certain tolerance ranges like the lateral position of the vehicle-under-test (VUT) and its test velocity for example. It is of high interest to examine the effect of the tolerance ranges on the braking points in different test cases representing different trajectories and velocities because they will influence significantly a later scoring during the assessments and thus the safety abilities of the regarding car. Method: We present a formal model using a graph to represent the allowed variances based on the relevant points in time. Now, varying velocities of the VUT will be added to the model while the vehicle is approaching a target vehicle. The derived trajectories were used as test cases for a simulation environment. Selecting interesting test cases and processing them with the simulation environment, the influence on the system's performance of different test parameters will be investigated.Comment: 15 pages, 6 figures, Fahrerassistenzsysteme und Integrierte Sicherheit, VDI Berichte 2014, pp. 403-41

Grand Design and Flocculent Spirals in the Spitzer Survey of Stellar Structure in Galaxies (S4G)

Spiral arm properties of 46 galaxies in the Spitzer Survey of Stellar Structure in Galaxies (S4G) were measured at 3.6mu, where extinction is small and the old stars dominate. The sample includes flocculent, multiple arm, and grand design types with a wide range of Hubble and bar types. We find that most optically flocculent galaxies are also flocculent in the mid-IR because of star formation uncorrelated with stellar density waves, whereas multiple arm and grand design galaxies have underlying stellar waves. Arm-interarm contrasts increase from flocculent to multiple arm to grand design galaxies and with later Hubble types. Structure can be traced further out in the disk than in previous surveys. Some spirals peak at mid-radius while others continuously rise or fall, depending on Hubble and bar type. We find evidence for regular and symmetric modulations of the arm strength in NGC 4321. Bars tend to be long, high amplitude, and flat-profiled in early type spirals, with arm contrasts that decrease with radius beyond the end of the bar, and they tend to be short, low amplitude, and exponential-profiled in late Hubble types, with arm contrasts that are constant or increase with radius. Longer bars tend to have larger amplitudes and stronger arms.Comment: 31 pages, 14 figures, ApJ in pres

Internet addiction: a 21st century epidemic?

Internet addiction, while not yet officially codified within a psychopathological framework, is growing both in prevalence and within the public consciousness as a potentially problematic condition with many parallels to existing recognized disorders. The rapid and unfettered increase in the number of people accessing a relatively unrestricted internet substantially increases the possibility that those suffering with an underlying psychological comorbidity may be at serious risk of developing an addiction to the internet, lending further credence to this hitherto understudied condition. In this commentary, I outline my recommendations for improved diagnosis, study and prevention of internet addiction