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In this paper, an efficient geography registration method for INSAR coherent change detection is proposed. In the algorithm, considering Terra-SAR image, firstly we do geography registration on two images by oversampling the geography information in master image, then do coarse and fine registration. In coarse registration, find the range and azimuth shifts by calculating the cross correlation of...
This contribution introduces position estimation methods relying on observations of the received power and mean delay obtained in a wideband multi-link scenario. In particular, one- and two-step methods are introduced based on statistical models of the observed link parameters. The proposed methods are tested on data from a wideband measurement campaign. The results show that including observations...
Prediction intervals that provide estimated values as well as the corresponding reliability are applied to nonlinear time series forecast. However, constructing reliable prediction intervals for noisy time series is still a challenge. In this paper, a bootstrapping reservoir computing network ensemble (BRCNE) is proposed and a simultaneous training method based on Bayesian linear regression is developed...
The robustness and reusability of Intellectual Properties (IPs) is the key to the success of the modern System on chip (SoC) designs. Therefore, it is very important to implement a rigid IP qualification platform to ensure the quality of IPs in the SoC design flow. In this paper, we propose an automated IP qualification platform, which uses XML schema technique to describe the quality model and has...
This paper presents a novel multispectral remote sensing image change detection (CD) algorithm based on Markovian fusion. This new method intends to obtain the optimal change map (change detection result) by fusing information contained in each band. The optimal change map are modeled as Markov Random Fields (MRF) which takes into account not only the spectral information of multiple bands but also...
A new strategy of feature classification method for speaker recognition based on the grid-density clustering is presented. According to the concept of density-based and grid-distance-based distribution in the Mel-frequency cepstrum domain, the feature vectors of each speaker were self-adaptively classified into L clusters with less overlapped. With these convex and non-interwoven clusters, the Gaussian...
This paper introduces the working principle of a high accuracy current comparator. For reducing the error caused by the capacitive leakage current while testing current flows through the secondary winding, the secondary winding is coiled of coaxial cable. Cooperating with well shielding, changing the turns of secondary winding will not bring extra error any more. This paper also presents a new self-calibration...
Metric learning is a fundamental problem in computer vision. Different features and algorithms may tackle a problem from different angles, and thus often provide complementary information. In this paper, we propose a fusion algorithm which outputs enhanced metrics by combining multiple given metrics (similarity measures). Unlike traditional co-training style algorithms where multi-view features or...
Figures method is based on the lissajou graph of more fringe photoelectric signal. The waveform generator produce signal of simulation, the signal lissajou graphs and actual collection of signal lissajou graphs fitting to and comparison. Through the goodness-of-fit parameters of judgement, we can determine parameters of actual waveform. Then we can get the photoelectric signal to meet the equation...
It is well known that it is difficult for local stereo algorithms to obtain correct disparities at occluded regions and depth discontinuities. In order to increase matching accuracy, current algorithms always use some disparity refinement measures. An adaptive algorithm to refine disparities based on color and distances has got good results but still not satisfactory. This paper proposes a simple...
Diagnosis for configuration troubleshooting in femtocell networks is extremely important for end users and network operators. However, because the small-size femtocell only serves several users, the historical data are very scarce. The data scarcity makes traditional cellular troubleshooting solutions which require a large amount of historical data not applicable. In this paper, we propose a new framework...
There are two different people counting methods: (1) counting people across a detecting line in certain time duration and (2) estimating the total number of people in some region at certain time instance. This paper presents a new approach to count the number of people crossing a line of interest (LOI). First, the foreground object silhouettes are extracted described as blobs. Second, we generate...
This paper introduces a vision-based continuous sign language recognition (CSR) system. This CSR system can differentiate the signs in vocabulary and the non-signs. First, the continuous sign language is segmented into isolated sign segments. Then, the sign segment which can be interpreted by Product-HMMs (pHMM) is a sign, otherwise it is a non-sign. In the experiments, we test 40 signs from Taiwanese...
The Support Vector Machine method has a good learning and generalization ability. Unfortunately, there are no comprehensive theories to guide the parameter selection of the SVM, which largely limits its application. In order to get the optimal parameters automatically, researchers have tried a variety of methods. Using genetic algorithms to optimize parameters of an SVM Classifier has become one of...
It has been proved that it's helpful to improve brain electrical activity mode through effective exercise training. According to Gardner's multiple intelligences theory, after a long period of professional training, college students of different specialties may also have intellectual independence and EEG specificity. In this study, the author chose motor imagery EEG of college students specialized...
Identifying the subject's simple judging states from fMRI data is the basis of studying complex logical relationship and has great theoretical significance. In this paper, we study judging states from fMRI data in terms of logical recognition classifications. We found that the ROI (Regions of Interest) regions played an important role in visual recognition task and identified what ROI regions were...
In the presented work, standard and high-density electrocorticographic (ECoG) electrodes were used to record cortical field potentials in three human subjects during a hand posture task requiring the application of specific levels of force during grasping. We show two-class classification accuracies of up to 80% are obtained when classifying between two-finger pinch and whole-hand grasp hand postures...
This study examined the feasibility of decoding semantic information from human cortical activity. Four human subjects undergoing presurgical brain mapping and seizure foci localization participated in this study. Electrocorticographic (ECoG) signals were recorded while the subjects performed simple language tasks involving semantic information processing, such as a picture naming task where subjects...
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