Natural resonance-based feature extraction with reduced aspect sensitivity for electromagnetic target classification

This paper presents a model-based electromagnetic feature extraction technique that makes use of time-frequency analysis to extract natural resonance-related target features from scattered signals. In this technique, the discrete auto-Wigner distribution of a given signal is processed to obtain a partitioned energy density vector with a significantly reduced sensitivity to aspect angle. Each partition of this vector contains, in the approximate sense, spectral distribution of the signal energy confined to a particular subinterval of time. Selection of sufficiently late-time partitions provides target features with a markedly increased target discrimination capacity. The potential of the suggested technique and the practical issues in its implementation are demonstrated by applying it to realistic target classification problems with very encouraging results.     

Spatially eigen-weighted Hausdorff distances for human face recognition

Hausdorff distance is an efficient measure of the similarity of two point sets. In this paper, we propose a new spatially weighted Hausdorff distance measure for human face recognition. The weighting function used in the Hausdorff distance measure is based on an eigenface, which has a large value at locations of importance facial features and can reflect the face structure more effectively. Two modified Hausdorff distances, namely, “spatially eigen-weighted Hausdorff distance” (SEWHD) and “spatially eigen-weighted ‘doubly’ Hausdorff distance” (SEW2HD) are proposed, which incorporate the information about the location of important facial features such as eyes, mouth, and face contour so that distances at those regions will be emphasized. Experimental results based on a combination of the ORL, MIT, and Yale face databases show that SEW2HD can achieve recognition rates of 83%, 90% and 92% for the first one, the first three and the first five likely matched faces, respectively, while the corresponding recognition rates of SEWHD are 80%, 83% and 88%, respectively.     

Recognition of dynamic hand gestures

This paper is concerned with the problem of recognition of dynamic hand gestures. We have considered gestures which are sequences of distinct hand poses. In these gestures hand poses can undergo motion and discrete changes. However, continuous deformations of the hand shapes are not permitted. We have developed a recognition engine which can reliably recognize these gestures despite individual variations. The engine also has the ability to detect start and end of gesture sequences in an automated fashion. The recognition strategy uses a combination of static shape recognition (performed using contour discriminant analysis), Kalman filter based hand tracking and a HMM based temporal characterization scheme. The system is fairly robust to background clutter and uses skin color for static shape recognition and tracking. A real time implementation on standard hardware is developed. Experimental results establish the effectiveness of the approach.     

Age estimation using a hierarchical classifier based on global and local facial features

The research related to age estimation using face images has become increasingly important, due to the fact it has a variety of potentially useful applications. An age estimation system is generally composed of aging feature extraction and feature classification; both of which are important in order to improve the performance. For the aging feature extraction, the hybrid features, which are a combination of global and local features, have received a great deal of attention, because this method can compensate for defects found in individual global and local features. As for feature classification, the hierarchical classifier, which is composed of an age group classification (e.g. the class of less than 20 years old, the class of 20-39 years old, etc.) and a detailed age estimation (e.g. 17, 23 years old, etc.), provide a much better performance than other methods. However, both the hybrid features and hierarchical classifier methods have only been studied independently and no research combining them has yet been conducted in the previous works. Consequently, we propose a new age estimation method using a hierarchical classifier method based on both global and local facial features. Our research is novel in the following three ways, compared to the previous works. Firstly, age estimation accuracy is greatly improved through a combination of the proposed hybrid features and the hierarchical classifier. Secondly, new local feature extraction methods are proposed in order to improve the performance of the hybrid features. The wrinkle feature is extracted using a set of region specific Gabor filters, each of which is designed based on the regional direction of the wrinkles, and the skin feature is extracted using a local binary pattern (LBP), capable of extracting the detailed textures of skin. Thirdly, the improved hierarchical classifier is based on a support vector machine (SVM) and a support vector regression (SVR). To reduce the error propagation of the hierarchical classifier, each age group classifier is designed so that the age range to be estimated is overlapped by consideration of false acceptance error (FAE) and false rejection error (FRE) of each classifier. The experimental results showed that the performance of the proposed method was superior to that of the previous methods when using the BERC, PAL and FG-Net aging databases.     

On the computation of the robust viability kernel for switched systems

This paper presents an algorithm of computing the robust viability kernel for discrete-time switched systems with arbitrary switching rule and bounded disturbance based on the robust one-step set and Pontryagin difference. The algorithm can be implemented by performing the relevant set computations using polyhedral algebra and computational geometry software when switched systems are linear. In addition, we propose a method of computing inner approximation of the robust viability kernel for switched systems based on the set theory. The convergence of the iterative algorithm is proved using a null controllable set. Finally, two examples are provided to show the effectiveness of the proposed method.     

Detection and characterization of defects using GMR probes and artificial neural networks

This work presents an eddy-current testing system based on a giant magnetoresistive (GMR) sensing device. Non-destructive tests in aluminum plates are applied in order to extract information about possible defects: cracks, holes and other mechanical damages. Eddy-current testing (ECT) presents major benefits such as low cost, high checking speed, robustness and high sensitivity to large classes of defects. Coil based architecture probes or coil-magnetoresistive probes are usually used in ECT. In our application the GMR sensor is used to detect a magnetic field component parallel to a plate surface, when an excitation field perpendicular to the plate is imposed. A neural network processing architecture, including a multilayer perceptron and a competitive neural network, is used to classify defects using the output amplitude of the eddy-current probe (ECP) and its operation frequency. The crack detection, classification and estimation of the geometrical characteristics, for different classes of defects, are described in the paper.     

Wireless sensor node powered by aircraft specific thermoelectric energy harvesting

An energy autonomous wireless sensor system consisting of an energy harvesting power source, an energy management unit and a low power wireless sensor node is tested for aircraft applications. The autonomous power source combines aircraft specific outside temperature changes with a thermoelectric generator (TEG) and a heat storage unit. The temperature difference generated with the latter component artificially at the TEG is used to power the sensor node by thermoelectricity. Additionally, a high efficient low input voltage power management circuit is necessary to store the generated energy and to convert it to higher voltage levels needed to operate the sensor. The experimental data are compared with results from numerical simulation models to predict the energy conversion in the heat storage - TEG system. A new TEG prototype is tested and the energy output is improved by 14%. The power management storage capacitors are adapted to the available energy, thereby increasing storage voltage and conversion efficiency. Doing so, the efficiency of the complete system can be increased by around 50%.     

Pull-in-based μg-resolution accelerometer: Characterization and noise analysis

The pull-in time (t_pi) of electrostatically actuated parallel-plate microstructures enables the realization of a high-sensitivity accelerometer that uses time measurement as the transduction mechanism. The key feature is the existence of a metastable region that dominates pull-in behavior, thus making pull-in time very sensitive to external accelerations. Parallel-plate MEMS structures have been designed and fabricated using a SOI micromachining process (SOIMUMPS) for the implementation of the accelerometer. This paper presents the experimental characterization of the microdevices, validating the concept and the analytical models used. The accelerometer has a measured sensitivity of 0.25 μs/μg and a bandwidth that is directly related to the pull-in time, BW = 1/2t_pi ≈ 50 Hz. These specifications place this sensor between the state of the art accelerometers found both in the literature and commercially. More importantly, the resolution of the measurement method used is very high, making the mechanical-thermal noise the only factor limiting the resolution. The in-depth noise analysis to the system supports these conclusions. The total measured noise floor of 400 μg (100 μs) is mainly due to the contribution of the environmental noise, due to lack of isolation of the experimental setup from the building vibrations (estimated mechanical thermal noise of 2.8 μg/√Hz). The low requirements of the electronic readout circuit makes this an interesting approach for high-resolution accelerometers.     

The properties of lattice-shifted microcavity in photonic crystal slab and its applications for electro-optical sensor

In this paper, a micro electro-optic sensor structure and its sensing technique based on lattice-shifted resonant microcavity (H0-nanocavity) in a triangular lattice photonic crystals (PhCs) slab are presented. The H0-nanocavity is formed by only laterally shifting two adjacent holes outwards slightly in the opposite direction, which can realize a nanocavity with high quality factor (Q) value to meet the requirements of practical application. The electro-optic sensor is realized in hole-array based photonic crystal slab with triangular lattice air holes infiltrated with a nonlinear optical (NLO) polymer (n_poly = 1.6) in Silicon-on-Insulator (SOI) operating in the wavelength range from 1400 nm to 1600 nm. The simulation results of PhC electro-sensitive structure show that the optical properties of PhCs can be used to design sensing devices characterized by a high degree of compactness and good resolution. The properties of the sensor are analyzed and calculated using the plane-wave expansion (PWE) method and simulated using the finite-difference time-domain (FDTD) method. The simulation results display that the resonant wavelength of the mode localized in the microcavity shifts its spectral drop position following a linear behavior when a driving voltage ranging between 0.0 V and 3.2 V is applied, and the sensitivity of 31.90 nm/V is observed.     

A novel humid electronic nose combined with an electronic tongue for assessing deterioration of wine

We report herein the use of a combined system for the analysis of the spoilage of wine when in contact with air. The system consists of a potentiometric electronic tongue and a humid electronic nose. The potentiometric electronic tongue was built with thick-film serigraphic techniques using commercially available resistances and conductors for hybrid electronic circuits; i.e. Ag, Au, Cu, Ru, AgCl, and C. The humid electronic nose was designed in order to detect vapours that emanate from the wine and are apprehended by a moist environment. The humid nose was constructed using a piece of thin cloth sewn, damped with distilled water, forming five hollows of the right size to introduce the electrodes. In this particular case four electrodes were used for the humid electronic nose: a glass electrode, aluminium (Al), graphite and platinum (Pt) wires and an Ag-AgCl reference electrode. The humid electronic nose together with the potentiometric electronic tongue were used for the evaluation of the evolution in the course of time of wine samples. Additionally to the analysis performed by the tongue and nose, the spoilage of the wines was followed via a simple determination of the titratable (total) acidity.