Low-cost, high-efficiency quasi-planar array of waveguide-fed circularly polarized microstrip antennas

A low-cost quasiplanar Ku-band array of circularly polarized microstrip antennas benefiting from a low-loss waveguide feed network is demonstrated (patent pending). The 32 elements of the array which are arranged in a 2-by-16 configuration are subdivided into four two-by-four subarrays. To maintain feed losses and thus the overall noise temperature at a minimum, the subarrays are excited using a one-by-four corporate feed network of hollow metallic waveguides. This network is composed of E-plane components such as Tee-junctions and bends and is manufactured out of only two metallic pieces that accommodate the feed network in the form of milled grooves. Because of insensitivity of the exploited E-plane components to air gaps or slight misalignments, the pieces are secured together with only four screws without welding, braising, or conducting adhesives. Owing to a low-loss foam substrate, the array elements show high circular polarization gain of 9 dBic and wide relative bandwidth of 4%. To achieve circular polarization, use is made of circular patches with two nearly perpendicular perturbations. Using sequential rotation of the elements along with quadrature phase shifting, the axial ratio of the array is reduced to 1 dB over 4% of bandwidth. The measured circular polarization gain of the array amounts to 23 dBic with an aperture efficiency of 63% in the Ku-band of frequencies. The achieved efficiency, which is higher than the reported efficiency for comparable planar arrays with microstrip feed networks, can be credited to the low losses of only 0.2 dB in its waveguide corporate feed. The paper also presents measurement results for an arrangement of two inclined single arrays mounted in parallel. This configuration which has a measured circular polarization gain of 25.7 dBic with an axial ratio of 1 dB is desirable for mobile low-profile antenna systems.     

Online learning of task-driven object-based visual attention control

We propose a biologically-motivated computational model for learning task-driven and object-based visual attention control in interactive environments. In this model, top-down attention is learned interactively and is used to search for a desired object in the scene through biasing the bottom-up attention in order to form a need-based and object-driven state representation of the environment. Our model consists of three layers. First, in the early visual processing layer, most salient location of a scene is derived using the biased saliency-based bottom-up model of visual attention. Then a cognitive component in the higher visual processing layer performs an application specific operation like object recognition at the focus of attention. From this information, a state is derived in the decision making and learning layer. Top-down attention is learned by the U-TREE algorithm which successively grows an object-based binary tree. Internal nodes in this tree check the existence of a specific object in the scene by biasing the early vision and the object recognition parts. Its leaves point to states in the action value table. Motor actions are associated with the leaves. After performing a motor action, the agent receives a reinforcement signal from the critic. This signal is alternately used for modifying the tree or updating the action selection policy. The proposed model is evaluated on visual navigation tasks, where obtained results lend support to the applicability and usefulness of the developed method for robotics.     

Investigation of the properties of nickel-boron/nano-diamond composite coatings upon pre/post heat treatment

The study has explored the physical and chemical properties of nickel-boron/nano-diamond composite coatings upon pre/post heat treatment. The nano-composite coatings were produced by electroless plating. In fact, the effects of using the nano-diamond additives (0 g l⁻¹, 0.1 g l⁻¹, 0.3 g l⁻¹, 0.5 g l⁻¹, 0.7 g l⁻¹) were investigated in pre/post heat treatment. According to the results, the micro-hardness of the nickel-boron/nano-diamond composite coating produced in the bath containing 0.5 g l⁻¹ nano-diamond additive reached to 1005 HV 0.05 upon post-heat treatment. Also, the corrosion resistance of the nickel-boron/nano-diamond composite coatings was modified by using the nano-additive or heat treatment. But, adding nano-diamond particles reduced the ductility and wear resistance of the nickel-boron coatings. We also found, modification of the properties (ductility and wear resistance) of the composite coatings was achieved by using the heat treatment.     

Invariance analysis of modified C2 features: case study—handwritten digit recognition

Humans are very efficient in recognizing alphanumeric characters, even in the presence of significant image distortions. Recent advances in visual neuroscience have led to a solid model of object and shape recognition in the visual ventral stream which competes with the state-of-the-art computer vision systems on some standard recognition tasks. A modification of this model is also proposed by adding more biologically inspired properties such as sparsification of features, lateral inhibition and feature localization to enhance its performance. In this study, we show that using features proposed by the modified model results in higher handwritten digit recognition rates compared with the original model over English and Farsi handwritten digit datasets. Our analyses also demonstrate higher invariance of the modified model to various image distortions.     

Robust Handwritten Character Recognition with Features Inspired by Visual Ventral Stream

This paper focuses on the applicability of the features inspired by the visual ventral stream for handwritten character recognition. A set of scale and translation invariant C2 features are first extracted from all images in the dataset. Three standard classifiers kNN, ANN and SVM are then trained over a training set and then compared over a separate test set. In order to achieve higher recognition rate, a two stage classifier was designed with different preprocessing in the second stage. Experiments performed to validate the method on the well-known MNIST database, standard Farsi digits and characters, exhibit high recognition rates and compete with some of the best existing approaches. Moreover an analysis is conducted to evaluate the robustness of this approach to orientation, scale and translation distortions.     

Numerical study of heat transfer and water flow velocities in microchannels with electrical double layers

The effect of the electrical double layer near the solid-liquid interface on the parameters of three-dimensional heat transfer and water-flow pressure drop in the rectangular microchannel is numerically studied. The Patankar’s finite volume method is used to numerically solve the continuity, momentum, and energy conservation equations.     

Rapid calculation of the Green's function in a rectangular enclosure with application to conductor loaded cavity resonators

A new technique for rapid calculation of the Green's functions in a rectangular cavity is presented. The method is based on a best polynomial approximation in three dimensions, which is implemented through a fast cosine transform. Generating the required samples for polynomial modeling is greatly accelerated through Ewald summation technique. To validate the efficiency of the resulting Chebyshev series for the potential Green's functions, a surface integral-equation (SIE) formulation is used to compute the resonant frequency of conductor loaded cavity resonators. The new scheme is proved to be considerably faster than Ewald transform in filling the method of moments (MoM) matrix. A SIE with the MoM can now be efficiently used for electromagnetic analysis and optimization of conductor or dielectric loaded resonators and filters with rectangular enclosures.     

Salient object detection: A survey

Computational Visual Media - Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many...     

Cost-sensitive learning of top-down modulation for attentional control

A biologically-inspired model of visual attention known as basic saliency model is biased for object detection. It is possible to make this model faster by inhibiting computation of features or scales, which are less important for detection of an object. To this end, we revise this model by implementing a new scale-wise surround inhibition. Each feature channel and scale is associated with a weight and a processing cost. Then a global optimization algorithm is used to find a weight vector with maximum detection rate and minimum processing cost. This allows achieving maximum object detection rate for real time tasks when maximum processing time is limited. A heuristic is also proposed for learning top-down spatial attention control to further limit the saliency computation. Comparing over five objects, our approach has 85.4 and 92.2% average detection rates with and without cost, respectively, which are above 80% of the basic saliency model. Our approach has 33.3 average processing cost compared with 52 processing cost of the basic model. We achieved lower average hit numbers compared with NVT but slightly higher than VOCUS attentional systems.