SPEEDING-UP RE-SAMPLED ALGORITHM IN RAY CASTING VOLUME RENDERING OF MEDICAL IMAGES

Ray casting algorithm can obtain a better quality image in volume rendering, however, it exists some problems, such as powerful computing capacity and slow rendering speed. How to improve the re-sampled speed is a key to speed up the ray casting algorithm. An algorithm is introduced to reduce matrix computation by matrix transformation characteristics of re-sampling points in a two coordinate system. The projection of 3-D datasets on image plane is adopted to reduce the number of rays. Utilizing boundary box technique avoids the sampling in empty voxel. By extending the Bresenham algorithm to three dimensions, each re-sampling point is calculated. Experimental results show that a two to three-fold improvement in rendering speed using the optimized algorithm, and the similar image quality to traditional algorithm can be achieved. The optimized algorithm can produce the required quality images, thus reducing the total operations and speeding up the volume rendering.

An improved particle filtering algorithm based on observation inversion optimal sampling

According to the effective sampling of particles and the particles impoverishment caused by re-sampling in particle filter,an improved particle filtering algorithm based on observation inversion optimal sampling was proposed. Firstly,virtual observations were generated from the latest observation,and two sampling strategies were presented. Then,the previous time particles were sampled by utilizing the function inversion relationship between observation and system state. Finally,the current time particles were generated on the basis of the previous time particles and the system one-step state transition model. By the above method,sampling particles can make full use of the latest observation information and the priori modeling information,so that they further approximate the true state. The theoretical analysis and experimental results show that the new algorithm filtering accuracy and real-time outperform obviously the standard particle filter,the extended Kalman particle filter and the unscented particle filter.

Estimating index of population trend by re-sampling techniques （jackknife and bootstrap） and its application to the life table study of rice leaf roller, Cnaphalocrocis medinalis （Lepidoptera： Pyralidae）

Taking a published natural population life table of rice leaf roller, Cnaphalocrocis medinalis （Lepidoptera： Pyralidae）, as an example, we estimated the population trend index, I, via re-sampling methods （jackknife and bootstrap）, determined its statistical properties and illustrated the application of these methods in determining the control effectiveness of bio- agents and chemical insecticides. Depending on the simulation outputs, the smoothed distribution pattern of the estimates of I by delete-1 jackknife is visually distinguishable from the normal density, but the smoothed pattern produced by delete-d jackknife, and logarithm- transformed smoothed patterns produced by both empirical and parametric bootstraps, matched well the corresponding normal density. Thus, the estimates of I produced by delete-1 jackknife were not used to determine the suppressive effect of wasps and insecticides. The 95% percent confidence intervals or the narrowest 95 percentiles and Z-test criterion were employed to compare the effectiveness of Trichogrammajaponicum Ashmead and insecticides （powder, 1.5% mevinphos ＋ 3% alpha-hexachloro cyclohexane） against the rice leaf roller based on the estimates of I produced by delete-d jackknife and bootstrap techniques. At a = 0.05 level, there were statistical differences between wasp treatment and control, and between wasp and insecticide treatments, if the normality is ensured, or by the narrowest 95 percentiles. However, there is still no difference between insecticide treatment and control. By Z-test criterion, wasp treatment is better than control and insecticide treatment with P-value 〈 0.01. Insecticide treatment is similar to control with P-value 〉 0.2 indicating that 95% confidence intervals procedure is more conservative. Although similar conclusions may be drawn by re-sampling techniques, such as the delta method, about the suppressive effect of trichogramma and insecticides, the normality of the estimates can be checked and guaranteed, and the correlation among sequential life stages of rice leaf roller is also considered in the estimation. Judged by the P-values from Z-test, the delta method is more conservative.

Testing conditional independence with data missing at random

It is known that conditional independence is a quite basic assumption in many fields of statistics. How to test its validity is of great importance and has been extensively studied by the literature. Nevertheless, all of the existing methods focus on the case that data are fully observed, but none of them seems having taken into account of the scenario when missing data are present. Motivated by this, this paper develops two testing statistics to handle such a situation relying on the idea of inverse probability weighted and augmented inverse probability weighted techniques. The asymptotic distributions of the proposed statistics are also derived under the null hypothesis. The simulation studies indicate that both testing statistics perform well in terms of size and power.

Outdoor travel-aids using bird-eye view virtual camera

This paper presents a travel-aid system for sight-impaired pedestrians in outdoor environment.Unlike many existing travel-aid systems that rely on stereo-vision,the proposed system aims to detect generic obstacles in a cluttered road environment by using just the single camera attached to user's body.To achieve this goal,the original image is re-sampled and mapped to a bird-eye view virtual plane,on which background edges are sub-sampled while obstacle edges are oversampled.Morphology filters with connected component analysis are used to enhance obstacle edges as edge-blobs with larger size,whereas sparse edges from background are filtered out.Based on the identified obstacles,safe walking areas are estimated by calculating a polar edge histogram.The algorithm is tested in different sidewalk scenes with complex pavements,and its efficiency has been confirmed.

Continuously Variable Bandwidth Sharp FIR Filters with Low Complexity

In software defined radio (SDR), sharp filters of different bandwidth are required to fine tune the desired channel. This requires different computational resources and large number of filter coefficients. This paper proposes a continuously variable bandwidth sharp finite impulse response (FIR) filter with low distortion and low complexity. For this, a fixed length FIR filter is used with two arbitrary sampling rate converters. This system can be used for both the continuous increase as well as decrease of the effective bandwidth of a filter. The low complexity and sharpness are achieved by using the frequency-response masking (FRM) approach for the design of the fixed length FIR filter. The sharp transition width leads to maximum rejection to channel interference in SDR.

Sample Size Affect Ethnobotanical Index Values: Bootstrap as a Remedial Approach

Ethnobotanical indices are widely used to quantify cultural importance of plants in social studies. This study aims to show users of ethnobotanical indices the effect of sample variation and what methodological approach can be used to circumvent the problems related to sample variation. The methods used are to write an algorithm and used to simulate different sample sizes from which four ethnobotanic indices selected for the present study were estimated. Results showed the instability of the ethnobotanical indices under variations in the size of informants. It proposes bootstrapping as a statistical aid tool to remove the sample size effect in quantitative ethnobotany. For the indices used in the present study 1000 re-samplings eliminated the effect of sample size on the value of the indices. Researchers will have to take this new approach into account in order to calculate more precise ethnobotanical indices in order to better appreciate the cultural importance of plants.

A Classification Method of Imbalanced Data Base on PSO Algorithm

In order to improve the performance of the resampling algorithm in imbalanced data learning and the optimal combination of the weight coefficients of the base classifiers in the ensemble classification algorithm,a new algorithm based on particle swarm optimization(PSO)is proposed.In traditional sampling method,sampling rate is often set artificially.This would make the final classification can’t get a optimal solution.By using the particle swarm optimization algorithm,the sampling rate of the boundary samples and the safety samples are optimized to obtain the optimal over sampling rate self-adaptivly,the weekness of setting sampling rate of traditional method would be overcome.

Track-before-detect for Infrared Maneuvering Dim Multi-target via MM-PHD

In this paper, we present a novel and efficient track-before-detect （TBD） algorithm based on multiple-model probability hypothesis density （MM-PHD） for tracking infrared maneuvering dim multi-target. Firstly, the standard sequential Monte Carlo probability hypothesis density （SMC-PHD） TBD-based algorithm is introduced and sequentially improved by the adaptive process noise and the importance re-sampling on particle likelihood, which result in the improvement in the algorithm robustness and convergence speed. Secondly, backward recursion of SMC-PHD is derived in order to ameliorate the tracking performance especially at the time of the multi-target arising. Finally, SMC-PHD is extended with multiple-model to track maneuvering dim multi-target. Extensive experiments have proved the efficiency of the presented algorithm in tracking infrared maneuvering dim multi-target, which produces better performance in track detection and tracking than other TBD-based algorithms including SMC-PHD, multiple-model particle filter （MM-PF）, histogram probability multi-hypothesis tracking （H-PMHT） and Viterbi-like.