A fast method for fuzzy neural modeling and refinement

In the identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse of dimensionality, which makes it difficult to retain a large number of system inputs or to consider a large number of fuzzy sets. Moreover, due to the correlations, not all possible network inputs or regression vectors in the network are necessary and adding them simply increases the model complexity and deteriorates the network generalisation performance. In this paper, the problem is solved by first proposing a fast algorithm for selection of network terms, and then introducing a refinement procedure to tackle the correlation issue. Simulation results show the efficacy of the method.

Targeted Impersonation As A Tool For The Detection Of Biometric System Vulnerabilities

This paper argues that biometric verification evaluations can obscure vulnerabilities that increase the chances that an attacker could be falsely accepted. This can occur because existing evaluations implicitly assume that an imposter claiming a false identity would claim a random identity rather than consciously selecting a target to impersonate. This paper shows how an attacker can select a target with a similar biometric signature in order to increase their chances of false acceptance. It demonstrates this effect using a publicly available iris recognition algorithm. The evaluation shows that the system can be vulnerable to attackers targeting subjects who are enrolled with a smaller section of iris due to occlusion. The evaluation shows how the traditional DET curve analysis conceals this vulnerability. As a result, traditional analysis underestimates the importance of an existing score normalisation method for addressing occlusion. The paper concludes by evaluating how the targeted false acceptance rate increases with the number of available targets. Consistent with a previous investigation of targeted face verification performance, the experiment shows that the false acceptance rate can be modelled using the traditional FAR measure with an additional term that is proportional to the logarithm of the number of available targets.

Privacy-Protected Facial Biometric Verification via Fuzzy Forest Learning

Although visual surveillance has emerged as an effective technolody for public security, privacy has become an issue of great concern in the transmission and distribution of surveillance videos. For example, personal facial images should not be browsed without permission. To cope with this issue, face image scrambling has emerged as a simple solution for privacyrelated applications. Consequently, online facial biometric verification needs to be carried out in the scrambled domain thus bringing a new challenge to face classification. In this paper, we investigate face verification issues in the scrambled domain and propose a novel scheme to handle this challenge. In our proposed method, to make feature extraction from scrambled face images robust, a biased random subspace sampling scheme is applied to construct fuzzy decision trees from randomly selected features, and fuzzy forest decision using fuzzy memberships is then obtained from combining all fuzzy tree decisions. In our experiment, we first estimated the optimal parameters for the construction of the random forest, and then applied the optimized model to the benchmark tests using three publically available face datasets. The experimental results validated that our proposed scheme can robustly cope with the challenging tests in the scrambled domain, and achieved an improved accuracy over all tests, making our method a promising candidate for the emerging privacy-related facial biometric applications.

Modeling the Rossiter-McLaughlin Effect: Impact of the Convective Center-to-limb Variations in the Stellar Photosphere

bservations of the Rossiter–McLaughlin (RM) effect provide information on star–planet alignments, which can inform planetary migration and evolution theories. Here, we go beyond the classical RM modeling and explore the impact of a convective blueshift that varies across the stellar disk and non-Gaussian stellar photospheric profiles. We simulated an aligned hot Jupiter with a four-day orbit about a Sun-like star and injected center-to-limb velocity (and profile shape) variations based on radiative 3D magnetohydrodynamic simulations of solar surface convection. The residuals between our modeling and classical RM modeling were dependent on the intrinsic profile width and v sin i; the amplitude of the residuals increased with increasing v sin i and with decreasing intrinsic profile width. For slowly rotating stars the center-to-limb convective variation dominated the residuals (with amplitudes of 10 s of cm s−1 to ~1 m s−1); however, for faster rotating stars the dominant residual signature was due a non-Gaussian intrinsic profile (with amplitudes from 0.5 to 9 m s−1). When the impact factor was 0, neglecting to account for the convective center-to-limb variation led to an uncertainty in the obliquity of ~10°–20°, even though the true v sin i was known. Additionally, neglecting to properly model an asymmetric intrinsic profile had a greater impact for more rapidly rotating stars (e.g., v sin i = 6 km s−1) and caused systematic errors on the order of ~20° in the measured obliquities. Hence, neglecting the impact of stellar surface convection may bias star–planet alignment measurements and consequently theories on planetary migration and evolution.

Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis

This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distri- bution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.

Efficient modeling of novel uniplanar left-handed metamaterials

This paper presents an efficient. modeling technique for the derivation of the dispersion characteristics of novel uniplanar metallodielectric periodic structures. The analysis is based on the method of moments and an interpolation scheme, which significantly accelerates the computations. Triangular basis functions are used that allow for modeling of arbitrary shaped metallic elements. Based on this method, novel uniplanar left-handed (LH) metamaterials are proposed. Variations of the split rectangular-loop element printed on grounded dielectric substrate are demonstrated to possess LH propagation properties. Full-wave dispersion curves are presented. Based on the dual transmission-line concept, we study the distribution of the modal fields And the variation of series capacitance and shunt inductance for all the proposed elements. A verification of the left-handedness is presented by means of full-wave simulation of finite uniplanar arrays using commercial software (HFSS). The cell dimensions are a small fraction of the wavelength (approximately lambda/24) so that the structures can he considered as a homogeneous effective medium. The structures are simple, readily scalable to higher frequencies, and compatible with low-cost fabrication techniques.

Low-cost digital signature architecture suitable for radio frequency identification tags

Continuing achievements in hardware technology are bringing ubiquitous computing closer to reality. The notion of a connected, interactive and autonomous environment is common to all sensor networks, biosystems and radio frequency identification (RFID) devices, and the emergence of significant deployments and sophisticated applications can be expected. However, as more information is collected and transmitted, security issues will become vital for such a fully connected environment. In this study the authors consider adding security features to low-cost devices such as RFID tags. In particular, the authors consider the implementation of a digital signature architecture that can be used for device authentication, to prevent tag cloning, and for data authentication to prevent transmission forgery. The scheme is built around the signature variant of the cryptoGPS identification scheme and the SHA-1 hash function. When implemented on 130 nm CMOS the full design uses 7494 gates and consumes 4.72 mu W of power, making it smaller and more power efficient than previous low-cost digital signature designs. The study also presents a low-cost SHA-1 hardware architecture which is the smallest standardised hash function design to date.

Modelling and Control of Shape Memory Alloy Actuators by using Preisach Model, Genetic Algorithm and Fuzzy Logic

Shapememoryalloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications in aeronautics, surgical tools, robotics and so on. Nonlinearity hysteresis effects existing in SMA actuators present a problem in the motion control of these smart actuators. This paper investigates the control problem of SMA actuators in both simulation and experiment. In the simulation, the numerical Preisachmodel with geometrical interpretation is used for hysteresis modeling of SMA actuators. This model is then incorporated in a closed loop PID control strategy. The optimal values of PID parameters are determined by using geneticalgorithm to minimize the mean squared error between desired output displacement and simulated output. However, the control performance is not good compared with the simulation results when these parameters are applied to the real SMA control since the system is disturbed by unknown factors and changes in the surrounding environment of the system. A further automated readjustment of the PID parameters using fuzzylogic is proposed for compensating the limitation. To demonstrate the effectiveness of the proposed controller, real time control experiment results are presented.

Phenomenology and system engineering of micro- and nano-antenna FPA sensors for detection of concealed weapons and improvised explosive devices

The ability of millimetre wave and terahertz systems to penetrate clothing is well known. The fact that the transmission of clothing and the reflectivity of the body vary as a function of frequency is less so. Several instruments have now been developed to exploit this capability. The choice of operating frequency, however, has often been associated with the maturity and the cost of the enabling technology rather than a sound systems engineering approach. Top level user and systems requirements have been derived to inform the development of design concepts. Emerging micro and nano technology concepts have been reviewed and we have demonstrated how these can be evaluated against these requirements by simulation using OpenFx. Openfx is an open source suite of 3D tools for modeling, animation and visualization which has been modified for use at millimeter waves. © 2012 SPIE.

Terahertz detection of illegal objects

This paper describes the main parameters - contrast, spatial resolution, and thermal sensitivity - which define the performance of any stand-off imaging system. The origin of the signature for both metal and dielectric objects hidden under clothing in the frequency range from 100 GHz to 500 GHz is discussed. At 100 GHz the signature is dominated by reflection whilst at 500 GHz it is dominated by emission. A 94-GHz-passive millimetre-wave imaging system has been designed and fabricated to image objects under clothing. This imager is based on a Schmidt camera folded using polarisation techniques.