Enhancing the quality metric of protein microarray image

The novel method of improving the quality metric of protein microarray image presented in this paper reduces impulse noise by using an adaptive median filter that employs the switching scheme based on local statistics characters; and achieves the impulse detection by using the difference between the standard deviation of the pixels within the filter window and the current pixel of concern. It also uses a top-hat filter to correct the background variation. In order to decrease time consumption, the top-hat filter core is cross structure. The experimental results showed that, for a protein microarray image contaminated by impulse noise and with slow background variation, the new method can significantly increase the signal-to-noise ratio, correct the trends in the background, and enhance the flatness of the background and the consistency of the signal intensity.     

Adaptive Contour Model for Real-Time Foreground Detection

A multiscale foreground detection method was developed to segment moving objects from a stationary background. The algorithm is based on a fixed-mesh-based contour model, which starts at the bounding box of the difference map between an input image and its background and ends at a final contour.An adaptive algorithm was developed to calculate an appropriate energy threshold to control the contours to identify the foreground silhouettes. Experiments show that this method more successfully ignores the negative influence of image noise to obtain an accurate foreground map than other foreground detection algorithms. Most shadow pixels are also eliminated by this method.     

Active Noise Control for Vehicle Exhaust Noise Reduction

An active noise control (ANC) method was developed for exhaust noise reduction for medium-duty diesel trucks. A modified variable step size least mean squares (LMS) algorithm was used for the controller ina variable environment that considered the vehicle‘s acceleration characteristics. The variable step size time-based synchronized filtered-x LMS method (SFX-TB) used an adaptive algorithm that was more efficient than the conventional flltered-x LMS algorithm. The simulation and the experimental tests show that the control trackability and stability provided by the algorithm during acceleration enable the ANC system to effectively reduce the vehicle exhaust noise.     

Low Complexity MMSE-SQRD Signal Detection Based on Iteration

Aiming at the problem of high computational complexity of Vertical-BLAST(V-BLAST) algorithm in Multiple-Input Multiple-Output-Orthogonal Frequency Division Multiplexing(MIMO-OFDM) system signal detection, this paper first uses Sorted QR Decomposition(SQRD) iterative operation instead of matrix inversion to reduce the computational complexity of the algorithm, and then considering that the algorithm is greatly affected by noise, Minimum Mean Square Error(MMSE) criterion is used to weaken the noise effect. At the same time, in order to reduce the noise and computational complexity, MMSE and SQRD are combined, which can not only reduce the noise and computational complexity, but also obtain the sub-optimal detection order, thus improving the detection performance of the MIMO-OFDM system. Finally, the numerical simulation of the MMSE-SQRD detection algorithm is carried out. The results show that the Eb/No of MMSE-SQRD algorithm is 2 dB greater than that of the MMSE algorithm and the computational complexity is O(N_T~3) under the conditions that N_T =N_R=2 and the BER is 10~(–2). The detection algorithm satisfies the demand of short wave and wideband wireless communication.     

Robust Speech Recognition Using a Harmonic Model

Automatic speech recognition under conditions of a noisy environment remains a challenging problem. Traditionally, methods focused on noise structure, such as spectral subtraction, have been employed to address this problem, and thus the performance of such methods depends on the accuracy in noise estimation. In this paper, an alternative method, using a harmonic-based spectral reconstruction algorithm, is proposed for the enhancement of robust automatic speech recognition. Neither noise estimation nor noise-model training are required in the proposed approach. A spectral subtraction integrated autocorrelation function is proposed to determine the pitch for the harmonic model. Recognition results show that the harmonic-based spectral reconstruction approach outperforms spectral subtraction in the middle- and lowsignal noise ratio (SNR) ranges. The advantage of the proposed method is more manifest for non-stationary noise, as the algorithm does not require an assumption of stationary noise.     

Chaotic system for the detection of periodic signals under the background of strong noise

We propose a method to study the chaotic system for the detection of periodic signals in the presence of strong background noise.The numerical experiments indicate that the chaotic system constructed from the modified Duffing-Holmes equation is sensitive to the weak periodic signal mixed with noise,and it has certain imunity to noise.The signal to noise ratio for the system can reach to about -91dB.     

Edge detection of range images using genetic neural networks

Due to the complexity and asymmetrical illumination, the images of object are difficult to be effectively segmented by some routine method. In this paper, a kind of edge detection method based on image features and genetic algorithms neural network for range images was proposed. Fully considering the essential difference between an edge point and a noise point, some characteristic parameters were extracted from range maps as the input nodes of the network in the algorithm. Firstly, a genetic neural network was designed and implemented. The neural network is trained by genetic algorithm, and then genetic neural network algorithm is combined with the virtue of global optimization of genetic algorithm and the virtue of parallel computation of neural network, so that this algorithm is of good global property. The experimental results show that this method can get much faster and more accurate detection results than the classical differential algorithm, and has better antinoise performance.     

Robust monocular SLAM towards motion disturbance

The standard extended Kalman filter-based simultaneously localization and mapping （EKF-SLAM） algorithm has a drawback that it could not handle the sudden motion caused by the motion disturbance. This prevents the SLAM system from real applications. Many techniques have been developed to make the system more robust to the motion disturbance. In this paper, we propose a robust monocular SLAM algorithm. First, when the motion model-based system failed to track the features, a KLT tracker will be activated for each feature. Second, the KLT tracked features are used to update the camera states. Third, the difference between the camera states and the predictions is used to adjust the input motion noise. Finally, we do the standard EKF-SLAM with the new input motion noise. In order to make the system more reliable, a joint compatibility branch and bound algorithm are used to check the outliers, and an IEKF filter is used to make the motion estimation smoother when the camera encounters sudden movement. The experiments are done on an image sequence caught by a shaking hand-held camera, which show that the proposed method is very robust to large motion disturbance.     

Denoising of hyperspectral imagery by cubic smoothing spline in the wavelet domain

The acquired hyperspectral images （HSIs） are inherently attected by noise wlm Dano-varylng level, which cannot be removed easily by current approaches. In this study, a new denoising method is proposed for removing such kind of noise by smoothing spectral signals in the transformed multi- scale domain. Specifically, the proposed method includes three procedures： 1 ） applying a discrete wavelet transform （DWT） to each band; 2） performing cubic spline smoothing on each noisy coeffi- cient vector along the spectral axis; 3 ） reconstructing each band by an inverse DWT. In order to adapt to the band-varying noise statistics of HSIs, the noise covariance is estimated to control the smoothing degree at different spectra｜ positions. Generalized cross validation （GCV） is employed to choose the smoothing parameter during the optimization. The experimental results on simulated and real HSIs demonstrate that the proposed method can be well adapted to band-varying noise statistics of noisy HSIs and also can well preserve the spectral and spatial features.     

A Method to Recognize Caption Area in MPEG Compressed Video

An efficient method to recognize caption area in MPEG compressed video was presented, by making use of the contrast of l-frame to distinguish caption area with background. We define texture energy, intensity of boundary. distance of background, and texture correlation to recognize caption area and caption frame. The benefit of only analyzing l-frame is that we can make use of DCT coefficients directly without losing information. We have experimented with our algorithm, and the result of experiment indicates that the performance of the algorithm is efficient.     

Realization of magnetic signal acquisition scheme for banknotes

In order to solve the problem of low signal-to-noise ratio(about 15 d B) in magnetic signal acquisition of banknotes, a new method of magnetic signal acquisition and processing is proposed taking RMB as an example. In this method, weak signa detection is performed to reduce the noise accompanied with the signal. Seven orders of Chebyshev(Ⅰ) filter and the anti-jamming technology are used in the PCB layout, and grounding modes are introduced to reduce the noise of the amplitude waveform. The proposed method reduce the final output noise by 2/3 and the sig nal-to-noise ratio is increased to 24 d B. The experimental results show that the magnetic signal of RMB banknotes are acquired by the circuit stability, which provides an important guarantee for the improvements of the anti-counterfeit and discrimination for banknotes performance.     

Improved hand tracking algorithm in video sequences for intelligent rehabilitation

Intelligent rehabilitation system is an active research topic. It is motivated by the increased number of limb disabled patients. Human motion tracking is the key technology of intelligent rehabilitation system, because the movement of limb disabled patients needs to be localized and learned so that any undesired motion behavior can be corrected in order to reach an expectation. This paper introduces a realtime tracking system of human hand motion, specifically intent to be used for home rehabilitation. Vision sensor (camera) is employed in this system to track the hand movement, and the improved Camshift algorithm and Kalman filter are used to implement dynamic hand tracking in the video. CAMSHIFT algorithm is able to track any kind of target colors by building a histogram distribution of the H channel in HSV color space from the region of interests selected by users at the initial stage. Kalman filter is able to predict hand location in one image frame based on its location data detected in the previous frame. The experimental results show that this system can track 2D hand motion and has acceptable accuracy by using the two algorithms properly. The new algorithm proposed in this paper can not only deal with the skin color interference problems, but also deal well with the track of complex background.     

Time-Based Dynamic Trust Model Using Ant Colony Algorithm

The trust in distributed environment is uncertain, which is variation for various factors. This paper introduces TDTM, a model for time-based dynamic trust. Every entity in the distribute environment is endowed with a trust-vector, which figures the trust intensity between this entity and the others. The trust intensity is dynamic due to the time and the inter-operation between two entities, a method is proposed to quantify this change based on the mind of ant colony algorithm and then an algorithm for the transfer of trust relation is also proposed. Furthermore, this paper analyses the influence to the trust intensity among all entities that is aroused by the change of trust intensity between the two entities, and presents an algorithm to resolve the problem. Finally, we show the process of the trusts＇ change that is aroused by the time＇s lapse and the inter-operation through an instance.     

AdaBoost for Improved Voice-Band Signal Classification

A good voice-band signal classification can not only enable the safe application of speech ceding techniques, the implementation of a Digital Signal Interpolation （DSI） system, but also facilitate network administration and planning by providing accurate voice-band traffic analysis. A new method is proposed to detect and classify the presence of various voice-band signals on the General Switched Telephone Network （GSTN）. The method uses a combination of simple base classifiers through the AdaBoost algorithm. The conventional classification features for voice- band data classification are combined and optimized by the AdaBoost algorithm and spectral subtraction method. Experiments show the simpleness, effectiveness, efficiency and flexibility of the method.     

Ability to detect weak effective seismic signals by utilizing chaotic vibrator system

Taking the advantage of CVS (Chaotic Vibrator System) sensitivity of large-scale periodic phase-state response to quasi-periodic or periodic signals,a series of numerical experiments were made to understand the ability of CVS to detect weak effective seismic signals in the common-shot seismic record distorted by strong stochastic noise. The re-sults demonstrate that the large-scale periodic phase-states of CVS are correlated with the signal composition of the quasi-periodic wavelet sequence constructing from horizontal moveout of seismic events,noise strength and the noise distortion de-gree to signal. For the same kind of events,the higher the noise distortion degree is,the lower the detectable SNR can be reached by CVS. For seismic data with the same noise distortion degree,the closer the scanning seismic velocity (the trial moveout ve-locity) approaches to the accurate velocity,the higher the detectable SNR can be reached by CVS. More-over,the truncating scanning velocities form an asymmetric belt,which indirectly makes CVS achieve a large-scale periodic phase-state and then the ratio of wavelet distortion coefficients in events can be a biggish variable scope.     

A Bayesian based quasi-analytical algorithm for retrieval of inherent optical properties from ocean color

The recently developed quasi-analytical algorithm （QAA） is a promising algorithm for deriving inherent optical properties from ocean color. Unlike the conventional semi-analytical algorithm, QAA does not need a priori knowledge of the spectral shape of chlorophyll absorption. However, several empirical relations, which may not be universally applicable and can result in low noise tolerance, are involved in QAA. In this study, the Bayesian inversion theory is introduced to improve the performance of QAA. In the estimation of total absorption coefficient at the reference wavelength, instead of empirical algorithms used in the QAA, the Bayesian approach is employed in combination with an optical model that uses separate parameters to account explicitly for the contribution of molecular and particle scatterings to remote sensing reflectance, a priori knowledge produced by the QAA, the Akaike＇s Bayesian information criterion （ABIC） for choosing the optimal regularization parameter, and genetic algorithms for global optimization. Coefficients at other wavelengths are then derived using an empirical estimate of particle backscattering spectral shape. When applied to a simulated dataset synthesized by IOCCG, the Bayesian algorithm outperforms QAA algorithm, especially in higher chlorophyll concentration waters. The root mean square errors （RMSEs） between the true and the derived α（440） and bb（440） are reduced from 0.918 and 0.039 m^-1 for QAA-555 to 0.367 and 0.023 m^-1 for Bayes-555, 0.205 and 0.007 m^-1 for QAA-640 to 0.092 and 0.005 m^-1 for Bayes-640, and 0.207 and 0.007 m^-1 for QAA-blending to 0.096 and 0.005 m^-1 for Bayes-blending. Results of noise sensitivity analysis show that the Bayesian algorithm is more robust than QAA.     

A Four-color Matching Method Combining Neural Networks with Genetic Algorithm

A brief review of color-matching technology and its application of printing RGB images by CMY or CMYK ink-jet printers is presented, followed by an explanation to the conventional approaches that are commonly used in color-matching. Then, a four-color matching method combining neural network with genetic algorithm is proposed. The initial weights and thresholds of the BP neural network for RGB-to-CMY color conversion are optimized by the new genetic algorithm based on evolutionarily stable strategy. The fourth component K is generated by using GCR (Gray Component Replacement) concept. Simulation experiments show that it is well behaved in both accuracy and generalization performance.     

Convergence of Recursive Identification for ARMAX Process with Increasing Variances

The autoregressive moving average exogenous (ARMAX) model is commonly adopted for describing linear stochastic systems driven by colored noise. The model is a finite mixture with the ARMA component and external inputs. In this paper we focus on a parameter estimate of the ARMAX model. Classical modeling methods are usually based on the assumption that the driven noise in the moving average (MA) part has bounded variances, while in the model considered here the variances of noise may increase by a power of log n. The plant parameters are identified by the recursive stochastic gradient algorithm. The diminishing excitation technique and some results of martingale difference theory are adopted in order to prove the convergence of the identification. Finally, some simulations are given to show the reliability of the theoretical results.     

Artificial Neural Network Maximum Power Point Tracker for Solar Electric Vehicle

This paper proposes an artificial neural network maximum power point tracker (MPPT) for solar electric vehicles. The MPPT is based on a highly efficient boost converter with insulated gate bipolar transistor (IGBT) power switch. The reference voltage for MPPT is obtained by artificial neural network (ANN) with gradient descent momentum algorithm. The tracking algorithm changes the duty-cycle of the converter so that the PV-module voltage equals the voltage corresponding to the MPPT at any given insolation, temperature, and load conditions. For fast response, the system is implemented using digital signal processor (DSP)The overall system stability is improved by including a proportional-integral-derivative (PID) controller, which is also used to match the reference and battery voltage levels. The controller, based on the information supplied by the ANN, generates the boost converter duty-cycle. The energy obtained is used to charge the lithium ion battery stack for the solar vehicle. The experimental and simulation results show that the proposed scheme is highly efficient.     

Shadow free texture acquisition of a large scale scene for city modeling

Texture acquisition of a large scale scene is one of the critical research areas in computer vision and can be used in other application areas such as computer graphics (CG),the intelligent transportation system (ITS) and the 3D geographic information system (GIS). Moreover,to acquire texture without noise (e. g.,a shadow,an obstacle body) is vital for such work. Although obstacles can be removed by using 3D geometric data,shadow elimination is still a difficult problem and strongly required for the CG and ITS community,especially for city modeling and simulation purposes. In this paper,we propose an automatic multiple image fusion technique and an efficient and simple shadow removing technique to retrieve high quality texture images of an urban area. The image fusion can be efficiently achieved by epipolar plane image (EPI) analysis,and the shadow elimination can be successfully carried out by an illumination-independent color clustering technique. The strength of this algorithm is that we can successfully fuse multiple images and eliminate shadows from the fused single image, especially in low dynamic range images,which have proven difficult using previous techniques.