Finding point correspondence using local similarity and global constraint under insignificant scaling and roll

Establishing point correspondences is an important research topic in computer vision. Proposed is an algorithm using local similarity and global constraint to obtain point correspondence. The point correspondences are obtained by comparing their associated colour codes, which are computed by image gradients, and using spatial relationships among neighbouring feature points. The approach assumes insignificant scaling and roll, and is suitable for real-time applications     

Low-Dose X-ray CT Reconstruction via Dictionary Learning

Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures.     

Design of low-loss tapered waveguides using the telescope structure compensation

A low-loss tapered waveguide is achieved by telescope structure compensation. The configuration of this design is similar to the Galilean telescope based on bulk geometrical optics. We numerically calculate the transmission efficiency in the use of the beam propagation method (BPM) and the finite-difference time-domain method. The BPM simulation results reveal that the normalized transmission efficiency is more than 95%, even if the tapered angle is as large as 10/spl deg/.     

Incoherent bistatic scattering from the sea surface at L-band

A bistatic electromagnetic wave scattering model for the sea surface is developed to examine its wind dependence property over a wide range of incident angles along the specular direction. This is done by combining an existing scattering model with a sea spectrum recently reported in the literature. In general, electromagnetic wave scattering from a rough surface is dependent on the Fourier transform of the nth power of its height correlation function which can be computed numerically from the surface spectrum. This transform relation indicates that scattering is sensitive not only to the surface spectrum but also to its convoluted properties. Generally, surface scattering is sensitive only to a portion of the surface correlation measured from the origin. The size of this portion is a function of three variables (the incident angle, the surface height standard deviation, and the exploring wavelength) and the rate of decay of the correlation function. The decay rate near the origin of the sea surface correlation is very small, so much so that at L-band this portion is too wide for a two-term approximation of the correlation function. This is true in spite of the fact that the sea surface has a very large rms height. Thus, a scattering model based on geometric optics is generally not applicable at L-band especially at large angles of incidence. An additional finding is that in specular scattering wind dependence is stronger at larger angles of incidence for incident angles between 0 and 70° over the wind speed range of 4 m/s-20 m/s     

大尺寸TFT-LCD电视的彩色和图像增强

最近几年,薄膜晶体管液晶显示器(TFT-LCD)技术取得了快速而显著的进步,其性能超过了PDP、CRT以及投影显示。毫无疑问,TFT-LCD已经成为高清电视应用的最佳实现方案。本文将介绍进一步增强大尺寸LCD电视的彩色和图像性能的技术。     

Artificial neural networks and statistical modeling for electronic stress prediction using thermal profiling

Electronic components are constantly under stress due to factors such as signal density, temperature, humidity, and high current and voltage. Relatively little research has emphasized stress-level prediction under voltage stress. The purpose of this paper was to develop an electronic thermal profile model for stress-level prediction utilizing neural network (NN) and statistical approaches, such as multivariate regression models. Electronic components were removed from boards, subjected to different levels of stress, then replaced. An infrared camera was then used to capture information about component temperature changes over time under normal operating and stress conditions. Statistical analysis of the captured images suggests a strong correlation between thermal profiles and voltage stress levels. Artificial neural network (ANN) and statistical approaches were used to model temperature change profiles for components that had been stressed at different levels, and their predictive ability was compared. Separate data sets were used for model development and model verification. ANN prediction rates were around 70%, compared to 30% for the statistical approach. Experiments were also conducted to evaluate the robustness of the ANN model to the presence of noise in the data. Results suggested that the ANN model was able to accommodate the presence of noise. Various backpropagation (BP) learning algorithms were also evaluated and yielded similar average error rates. A 3-2-1 ANN topology performed better than 3-3-1 or 3-2-2-1 topologies, perhaps because the 3-2-1 topology has a higher data sample to nodes ratio than the other topologies.     

Ag and Cu migration phenomena on wire-bonding

After storing a plastic packaged sample at 250°C for 588 h, the Au plus 1% Pd wire composition was found to be changed. The Ag and Cu atoms can migrate from the wedge bond through the wire surface and arrive at the ball bond. At the same time, Ag and Cu atoms diffuse into the gold wire itself and form a layer type structure. The atom migration phenomena are due to three driving forces: diffusion, alloy formation, and Galvanic effect. The obtained diffusion rate constant is in the order of 10⁻¹² cm square per sec, which corresponds to an activation energy of 0.7∼0.76 eV. The phases sequence formed by diffusion is inconsistent with the equilibrium Ag-Au-Cu phase diagram which indicates that the present diffusion layer has reached thermodynamic equilibrium.     

Adaptive parallel interference cancellation for CDMA systems—A weight selection and filtering scheme

Parallel interference cancellation (PIC) is a well-known multiuser detection algorithm in direct-sequence code-division multiple-access (DS-CDMA) systems. It is typically implemented with a multi-stage architecture. One problem associated with the PIC is that unreliable interference cancellation may occur in the early stages and the system performance may be degraded. Thus, the partial PIC detector was developed to control the cancellation level by use of interference cancellation factors. Partial PIC can be implemented with an adaptive form, in which optimal weights are derived using the least mean square (LMS) algorithm. In this paper, we propose an algorithm improving the conventional adaptive partial PIC. The main idea is to reduce the number of active weights in the LMS algorithm, and to perform weight post-filtering such that the resultant excess mean square error can be reduced. We also analyze the performance of the proposed algorithm and derive the bit error rate of the second stage output. Simulation results verify that the proposed algorithm outperforms the conventional partial PIC, and derived analytical results are accurate.     

Application of an auto-tuning neuron to sliding mode control

This paper presents a control strategy that incorporates an auto-tuning neuron into the sliding mode control (SMC) in order to eliminate the high control activity and chattering due to the SMC. The main difference between the auto-tuning neuron and the general one is that a modified hyperbolic tangent function with adjustable parameters is employed. In this proposed control structure, an auto-tuning neuron is then used as the neural controller without any connection weights.. The control law will be switched from the sliding control to the neural control, when the state trajectory of system enters in some boundary layer. In this way, the chattering phenomenon will not occur. The results of numerical simulations are provided to show the control performance of our proposed method.