Dataset Information

Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS).

ABSTRACT: The correct identification of smartphones has various applications in the field of security or the fight against counterfeiting. As the level of sophistication in counterfeit electronics increases, detection procedures must become more accurate but also not destructive for the smartphone under testing. Some components of the smartphone are more likely to reveal their authenticity even without a physical inspection, since they are characterized by hardware fingerprints detectable by simply examining the data they provide. This is the case of MEMS (Micro Electro-Mechanical Systems) components like accelerometers and gyroscopes, where tiny differences and imprecisions in the manufacturing process determine unique patterns in the data output. In this paper, we present the experimental evaluation of the identification of smartphones through their built-in MEMS components. In our study, three different phones of the same model are subject to repeatable movements (composing a repeatable scenario) using an high precision robotic arm. The measurements from MEMS for each repeatable scenario are collected and analyzed. The identification algorithm is based on the extraction of the statistical features of the collected data for each scenario. The features are used in a support vector machine (SVM) classifier to identify the smartphone. The results of the evaluation are presented for different combinations of features and Inertial Measurement Unit (IMU) outputs, which show that detection accuracy of higher than 90% is achievable.

SUBMITTER: Baldini G

PROVIDER: S-EPMC4934244 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS).

Baldini Gianmarco G Steri Gary G Dimc Franc F Giuliani Raimondo R Kamnik Roman R

Sensors (Basel, Switzerland) 20160603 6

The correct identification of smartphones has various applications in the field of security or the fight against counterfeiting. As the level of sophistication in counterfeit electronics increases, detection procedures must become more accurate but also not destructive for the smartphone under testing. Some components of the smartphone are more likely to reveal their authenticity even without a physical inspection, since they are characterized by hardware fingerprints detectable by simply examin ...[more]

PMID: 27271630

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Project description:Left main (LM) coronary artery bifurcation stenting is a challenging topic due to the distinct anatomy and wall structure of LM. In this work, we investigated computationally and experimentally the mechanical performance of a novel everolimus-eluting stent (SYNERGY MEGATRON) purpose-built for interventions to large proximal coronary segments, including LM. MEGATRON stent has been purposefully designed to sustain its structural integrity at higher expansion diameters and to provide optimal lumen coverage. Four patient-specific LM geometries were 3D reconstructed and stented computationally with finite element analysis in a well-validated computational stent simulation platform under different homogeneous and heterogeneous plaque conditions. Four different everolimus-eluting stent designs (9-peak prototype MEGATRON, 10-peak prototype MEGATRON, 12-peak MEGATRON, and SYNERGY) were deployed computationally in all bifurcation geometries at three different diameters (i.e., 3.5, 4.5, and 5.0 mm). The stent designs were also expanded experimentally from 3.5 to 5.0 mm (blind analysis). Stent morphometric and biomechanical indices were calculated in the computational and experimental studies. In the computational studies the 12-peak MEGATRON exhibited significantly greater expansion, better scaffolding, smaller vessel prolapse, and greater radial strength (expressed as normalized hoop force) than the 9-peak MEGATRON, 10-peak MEGATRON, or SYNERGY (p < 0.05). Larger stent expansion diameters had significantly better radial strength and worse scaffolding than smaller stent diameters (p < 0.001). Computational stenting showed comparable scaffolding and radial strength with experimental stenting. 12-peak MEGATRON exhibited better mechanical performance than the 9-peak MEGATRON, 10-peak MEGATRON, or SYNERGY. Patient-specific computational LM stenting simulations can accurately reproduce experimental stent testing, providing an attractive framework for cost- and time-effective stent research and development.

Dataset Information

Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS).

Publications

Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS).

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