Analysis of impersonation attacks on systems using RF fingerprinting and low-end receivers

Recently, physical layer security commonly known as Radio Frequency (RF) fingerprinting has been proposed to provide an additional layer of security for wireless devices. A unique RF fingerprint can be used to establish the identity of a specific wireless device in order to prevent masquerading/impersonation attacks. In the literature, the performance of RF fingerprinting techniques is typically assessed using high-end (expensive) receiver hardware. However, in most practical situations receivers will not be high-end and will suffer from device specific impairments which affect the RF fingerprinting process. This paper evaluates the accuracy of RF fingerprinting employing low-end receivers. The vulnerability to an impersonation attack is assessed for a modulation-based RF fingerprinting system employing low-end commodity hardware (by legitimate and malicious users alike). Our results suggest that receiver impairment effectively decreases the success rate of impersonation attack on RF fingerprinting. In addition, the success rate of impersonation attack is receiver dependent.     

Development of the Acidity of Zirconia-Supported Niobia Catalysts

A series of NbO _x /ZrO₂ catalysts containing up to 2.67wt Nb (ca. 80 nominal surface coverage) was prepared by incipient wetness impregnation from niobium oxalate and oxalic acid solution. The structure of the catalysts was monitored by X-ray diffraction and Raman spectroscopy. The results indicated the presence of a surface Nb phase. No evidence for the formation of crystalline Nb₂O₅ species was found. The development of the acidity as a function of Nb loading was monitored by adsorption of a basic probe molecule followed by infrared spectroscopy. The results indicated the appearance of Brnsted acid sites for a threshold of Nb loading. The abundance of Brnsted acid sites correlated well with the isopropanol dehydration activity. The overall behavior was very similar to that reported earlier for the WO _x /ZrO₂ system.     

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.     

Mechanical properties of composites from sawdust and recycled plastics

Mechanical properties of wood plastic composites (WPCs) manufactured from sawdust and virgin and/or recycled plastics, namely high density polyethylene (HDPE) and polypropylene (PP), were studied. Sawdust was prepared from beech industrial sawdust by screening to the desired particle size and was mixed with different virgin or recycled plastics at 50% by weight fiber loading. The mixed materials were then compression molded into panels. Flexural and tensile properties and impact strength of the manufactured WPCs were determined according to the relevant standard specifications. Although composites containing PP (virgin and recycled) exhibited higher stiffness and strength than those made from HDPE (virgin and recycled), they had lower unnotched impact strengths. Mechanical properties of specimens containing recycled plastics (HDPE and PP) were statistically similar and comparable to those of composites made from virgin plastics. This was considered as a possibility to expand the use of recycled plastics in the manufacture of WPCs. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 3641–3645, 2006     

Preparation and characterization of cellulose acetate organic/inorganic hybrid films

A series of organic/inorganic hybrid (OIH) films were prepared using cellulose acetate (CA) as the organic component and tetraethyl orthosilicate (TEOS) as the inorganic component. The chemical, morphological, and mechanical properties of these films were evaluated with a variety of analytical techniques. The results of these evaluations showed that crosslinked CA OIH films were formed during the sol-gel reactions. The structure of OIH films was very sensitive to the CA/TEOS ratio and film formation conditions. All of the films with added TEOS were two phase on a molecular level, i.e., inorganic TEOS domains surrounded by a CA matrix. Under some film formation conditions the presence of TEOS, a nonsolvent for CA, led to solvent/nonsolvent phase separation on the micron scale. © 1995 John Wiley & Sons, Inc.     

Rheological and extrusion characteristics of glass fiber-reinforced polycarbonate

An experimental investigation of the rheological properties of glass fiber-reinforced polycarbonate melts and the extrusion of such compounds through capillary and slit dies is presented. The viscosity–shear rate function seems independent of instrument for cone-plate and capillary investigations. The presence of fibers increases the level of the viscosity. Normal stresses at fixed shear stress are also increased by the presence of fibers. The extrudate swell is decreased by the presence of fibers and surface roughness is increased. Fiber orientation increases and surface roughness decreases with increasing extrusion rate.     

Privacy-preserving biometric verification with outsourced correlation filter computation

Multimedia Tools and Applications - In traditional biometric verification systems, personal computer stores biometric database and performs verification process. Because of limited storage,...     

Color object segmentation and tracking using flexible statistical model and level-set

Multimedia Tools and Applications - This study presents an unsupervised novel algorithm for color image segmentation, object detection and tracking based on unsupervised learning step followed with...     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.