7,283 research outputs found
Gyrokinetic analysis and simulation of pedestals, to identify the culprits for energy losses using fingerprints
Fusion performance in tokamaks hinges critically on the efficacy of the Edge
Transport Barrier (ETB) at suppressing energy losses. The new concept of
fingerprints is introduced to identify the instabilities that cause the
transport losses in the ETB of many of today's experiments, from widely posited
candidates. Analysis of the Gyrokinetic-Maxwell equations, and gyrokinetic
simulations of experiments, find that each mode type produces characteristic
ratios of transport in the various channels: density, heat and impurities.
This, together with experimental observations of transport in some channel, or,
of the relative size of the driving sources of channels, can identify or
determine the dominant modes causing energy transport. In multiple ELMy H-mode
cases that are examined, these fingerprints indicate that MHD-like modes are
apparently not the dominant agent of energy transport; rather, this role is
played by Micro-Tearing Modes (MTM) and Electron Temperature Gradient (ETG)
modes, and in addition, possibly Ion Temperature Gradient (ITG)/Trapped
Electron Modes (ITG/TEM) on JET. MHD-like modes may dominate the electron
particle losses. Fluctuation frequency can also be an important means of
identification, and is often closely related to the transport fingerprint. The
analytical arguments unify and explain previously disparate experimental
observations on multiple devices, including DIII-D, JET and ASDEX-U, and
detailed simulations of two DIII-D ETBs also demonstrate and corroborate this
An Implementation Approach and Performance Analysis of Image Sensor Based Multilateral Indoor Localization and Navigation System
Optical camera communication (OCC) exhibits considerable importance nowadays
in various indoor camera based services such as smart home and robot-based
automation. An android smart phone camera that is mounted on a mobile robot
(MR) offers a uniform communication distance when the camera remains at the
same level that can reduce the communication error rate. Indoor mobile robot
navigation (MRN) is considered to be a promising OCC application in which the
white light emitting diodes (LEDs) and an MR camera are used as transmitters
and receiver respectively. Positioning is a key issue in MRN systems in terms
of accuracy, data rate, and distance. We propose an indoor navigation and
positioning combined algorithm and further evaluate its performance. An android
application is developed to support data acquisition from multiple simultaneous
transmitter links. Experimentally, we received data from four links which are
required to ensure a higher positioning accuracy
Android Malware Family Classification Based on Resource Consumption over Time
The vast majority of today's mobile malware targets Android devices. This has
pushed the research effort in Android malware analysis in the last years. An
important task of malware analysis is the classification of malware samples
into known families. Static malware analysis is known to fall short against
techniques that change static characteristics of the malware (e.g. code
obfuscation), while dynamic analysis has proven effective against such
techniques. To the best of our knowledge, the most notable work on Android
malware family classification purely based on dynamic analysis is DroidScribe.
With respect to DroidScribe, our approach is easier to reproduce. Our
methodology only employs publicly available tools, does not require any
modification to the emulated environment or Android OS, and can collect data
from physical devices. The latter is a key factor, since modern mobile malware
can detect the emulated environment and hide their malicious behavior. Our
approach relies on resource consumption metrics available from the proc file
system. Features are extracted through detrended fluctuation analysis and
correlation. Finally, a SVM is employed to classify malware into families. We
provide an experimental evaluation on malware samples from the Drebin dataset,
where we obtain a classification accuracy of 82%, proving that our methodology
achieves an accuracy comparable to that of DroidScribe. Furthermore, we make
the software we developed publicly available, to ease the reproducibility of
our results.Comment: Extended Versio
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