Object segmentation and labeling by learning from examples

We propose a system that employs low-level image segmentation followed by color and two-dimensional (2-D) shape matching to automatically group those low-level segments into objects based on their similarity to a set of example object templates presented by the user. A hierarchical content tree data structure is used for each database image to store matching combinations of low-level regions as objects. The system automatically initializes the content tree with only "elementary nodes" representing homogeneous low-level regions. The "learning" phase refers to labeling of combinations of low-level regions that have resulted in successful color and/or 2-D shape matches with the example template(s). These combinations are labeled as "object nodes" in the hierarchical content tree. Once learning is performed, the speed of second-time retrieval of learned objects in the database increases significantly. The learning step can be performed off-line provided that example objects are given in the form of user interest profiles. Experimental results are presented to demonstrate the effectiveness of the proposed system with hierarchical content tree representation and learning by color and 2-D shape matching on collections of car and face images.     

Spectral properties of diesel fuel droplets

Absorption spectra of several types of diesel fuel are studied experimentally. Index of refraction of these fuels is calculated using subtractive Kramers-Krönig analysis. The ageing process of fuels is simulated by prolonged boiling. Radiative properties of diesel fuel droplets are calculated using the Mie theory and a simplified approach, based on approximations of absorption and scattering efficiency factors. It is pointed out that the accuracy of the simplified approach is sufficient for practical applications in the visible and infrared ranges, for various types of diesel fuel, and for droplet radii in the range from 5 to 50 μm. The monodisperse approximation is shown to be applicable for the analysis of infrared radiative properties of realistic polydisperse diesel fuel sprays.     

An evaluation of biological and abiotic controls for grapevine powdery mildew. 2. Vineyard trials

Grapevine powdery mildew ( Erysiphe necator) affects grape yield and fruit quality worldwide. Managers of conventional vineyards rely mainly on synthetic fungicides and sulfur to control powdery mildew, while in organic vineyards sulfur is the main control agent, often in rotation with canola-based oils, bicarbonates and biological control agents. The efficacy of those materials has not been evaluated critically under field conditions in Australia. Accordingly, a range of materials showing most promise in previous greenhouse trials (Crisp et al. 2006 Australian Journal of Grape and Wine Research 12 , pp. 192–202) were assessed via field trials in commercial vineyards. Applications of either milk or whey (alone, or mixed with a canola oil-based product), as well as applications of potassium bicarbonate (commercial formulation), all reduced the severity of powdery mildew compared with untreated vines. Eight applications of a 1:10 dilution of milk, 45 g/L whey powder or programs comprising rotations of potassium bicarbonate plus oil and whey, applied at 10–14 day intervals, reduced the severity of powdery mildew to levels not significantly different from that on vines sprayed with sulfur (wettable powder, 3–6 g/L). However, the relative control of powdery mildew by the test materials in field trials was dependent on the susceptibility of the grapevine cultivar and the extent of spray coverage achieved. In vineyards where highly susceptible cultivars were planted, and spray coverage was compromised, the resultant control of powdery mildew was reduced; and sometimes to commercially unacceptable levels.     

High-resolution photonics-based interference suppression filter with wide passband

A new topology for a photonic signal processor, which overcomes the basic recursive frequency response problem that limits the passband range, is presented. The structure is based on a new multiple-wavelength offset-cavity structure that is cascaded with a series of unbalanced delay line structures. This not only can synthesize a very narrow notch response with good shape factor but also permits a multifold extension of the free spectral range (FSR) and passband width. Results on the interference mitigation filter demonstrate a stopband of 1% of center frequency and a fourfold increase in the FSR and passband width, while also having a very small shape factor, in excellent agreement with predictions.     

Microstrip stepped impedance resonator bandpass filter with an extended optimal rejection bandwidth

Bandpass filters with an optimal rejection bandwidth are designed using parallel-coupled stepped impedance resonators (SIRs). The fundamental (f/sub o/) and higher order resonant harmonics of an SIR are analyzed against the length ratio of the high-Z and low-Z segments. It is found that an optimal length ratio can be obtained for each high-Z to low-Z impedance ratio to maximize the upper rejection bandwidth. A tapped-line input/output structure is exploited to create two extra transmission zeros in the stopband. The singly loaded Q(Q/sub si/) of a tapped SIR is derived. With the aid of Q/sub si/, the two zeros can be independently tuned over a wide frequency range. When the positions of the two zeros are purposely located at the two leading higher order harmonics, the upper rejection band can be greatly extended. Chebyshev bandpass filters with spurious resonances up to 4.4f/sub o/, 6.5f/sub o/, and 8.2f/sub o/ are fabricated and measured to demonstrate the idea.     

Classification of audio radar signals using radial basis function neural networks

Radial basis function (RBF) neural networks are used to classify real-life audio radar signals that are collected by a ground surveillance radar mounted on a tank. Currently, a human operator is required to operate the radar system to discern among signals bouncing off tanks, vehicles, planes, and so on. The objective of this project is to investigate the possibility of using a neural network to perform this target recognition task, with the aim of reducing the number of personnel required in a tank. Different signal classification methods in the neural net literature are considered. The first method employs a linear autoregressive (AR) model to extract linear features of the audio data, and then perform classification on these features, i.e, the AR coefficients. AR coefficient estimations based on least squares and higher order statistics are considered in this study. The second approach uses nonlinear predictors to model the audio data and then classifies the signals according to the prediction errors. The real-life audio radar data set used here was collected by an AN/PPS-15 ground surveillance radar and consists of 13 different target classes, which include men marching, a man walking, airplanes, a man crawling, and boats, etc. It is found that each classification method has some classes which are difficult to classify. Overall, the AR feature extraction approach is most effective and has a correct classification rate of 88% for the training data and 67% for data not used for training.     

Analysis of the mechanism of die soldering in aluminum die casting

A mechanism of soldering of an aluminum alloy die casting to a steel die is proposed. A soldering critical temperature is postulated, at which iron begins to react with aluminum to form an aluminum-rich liquid phase and solid intermetallic compounds. The liquid joins the die with the casting upon solidification. The critical temperature is determined by the elements in both the casting alloy and the die material and is equal to the solidus temperature of the resulting alloy. The critical temperature is used to predict the onset of die soldering, and the local liquid fraction is related to the soldering tendency. Experiments have been carried out to validate the concept and to determine the critical temperature for die soldering in an iron-aluminum system. Thermodynamic calculations are used to determine the critical temperature and soldering tendency for the cases of pure aluminum and a 380 alloy in a steel mold. Factors affecting the soldering tendency are discussed, and methods for reducing die soldering are suggested.     

Mechanical spectroscopy in commercial Fe–6 wt.% Si alloys between 400 and 1000 K

Mechanical spectroscopy, neutron diffraction and differential scanning calorimetry (DSC) were performed on commercial Fe–6 wt.% Si alloy after quenching from high temperature. The damping spectrum shows a peak at around 800 K and an associated modulus defect. The modulus shows an increase during the second and subsequent heating runs. In addition, an anomaly in the modulus behavior has been found at around 400 K. Different thermal treatments allows to obtain two different recovery degrees of the quenched-in defects. The influence of the recovery degree on the 800 K internal friction peak and on the anelastic modulus has been evaluated and confirm the validity of the grain boundary mechanism associated to this peak. Experimental results are discussed on the basis of recovery and ordering processes.     

Wavelength-tunable 40-GHz picosecond harmonically mode-locked fiber laser source

We demonstrate a novel 40-GHz mode-locked fiber laser that utilizes a single active device to provide both gain and mode-locking. The laser produces pulses as short as 2.2 ps, is tunable over a 27-nm band centered at 1553 nm, and exhibits long-term stability without cavity-length feedback control. The pulse train at 1556 nm was used in a 40-Gb/s transmission experiment over 45 km with a low 0.4-dB power penalty.     

Extractive Properties of Synergistic Mixtures of Dipicolinic Acid Diamides and Chlorinated Cobalt Dicarbollide

Extraction of Cs, Sr, Eu, U, Np, and Am with mixtures of dipicolinic acid diamides with chlorinated cobalt dicarbollide was studied. A synergistic effect was revealed.