Multiple Targets Recognition for Highly-Compressed Color Images in a Joint Transform Correlator

In this paper, we are proposing a compression-based multiple color target detection for practical near real-time optical pattern recognition applications. By reducing the size of the color images to its utmost compression, the speed and the storage of the system are greatly increased. We have used the powerful Fringe-adjusted joint transform correlation technique to successfully detect compression-based multiple targets in colored images. The colored image is decomposed into three fundamental color components images (Red, Green, Blue) and they are separately processed by three-channel correlators. The outputs of the three channels are then combined into a single correlation output. To eliminate the false alarms and zero-order terms due to multiple desired and undesired targets in a scene, we have used the reference shifted phase-encoded and the reference phase-encoded techniques. The performance of the proposed compression-based technique is assessed through many computer simulation tests for images polluted by strong additive Gaussian and Salt & Pepper noises as well as reference occluded images. The robustness of the scheme is demonstrated for severely compressed images (up to 94% ratio), strong noise densities (up to 0.5), and large reference occlusion images (up to 75%).

Estimation of detection threshold in multiple ship target situations with HF ground wave radar

A credible method of calculating the detection threshold is presented for the multiple target situations, which appear frequently in the lower Doppler velocity region during the surveillance of sea with HF ground wave radar. This method defines a whole-peak-outlier elimination （WPOE） criterion, which is based on in-peak-samples correlation of each target echo spectra, to trim off the target signals and abnormal disturbances with great amplitude from the complex spectra. Therefore, cleaned background noise samples are obtained to improve the accuracy and reliability of noise level estimation. When the background noise is nonhomogeneous, the detection samples are limited and often occupied heavily with outliers. In this case, the problem that the detection threshold is overvalued can be solved. In applications on experimental data, it is verified that this method can reduce the miss alarm rate of signal detection effectively in multiple target situations as well as make the adaptability of the detector better.

A multi-frame track-before-detect algorithm based on root label clustering for multiple targets

In this paper,a novel multi-frame track-before-detect algorithm is proposed,which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase.A criterion of track extrapolation is used to construct state transition set,root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space,then measurement plots of multi-frame are divided into several clusters,and finally multi-frame track-before-detect algorithm is implemented in each cluster.The computational complexity can be reduced by employing the proposed algorithm.Simulation results show that the proposed algorithm can accurately detect multiple targets in close proximity and reduce the number of false tracks.

Rapid identification of diarrheagenic Escherichia coli based on barcoded magnetic bead hybridization

Diarrhea,as a global public health problem,causes a large number of infections and deaths every year.Although Escherichia coli(E.coli)is one of the normal flo ra microorganisms in the human intestinal tract,it has five pathogenic bacteria types that can cause human diarrhea,known as diarrheagenic E.coli.When people are infected,rapid and accurate diagnosis,along with timely treatment,are especially important.Here,we introduce a new method to identify and analyze a large number of pathogenic strains in E.coli by multiplex PCR and barcoded magnetic bead hybridization.Results show that the detection sensitivities of enterohemorrhagic E.coli,enterotoxigenic E.coli,enteropathogenic E.coli,enteroinvasive E.coli and enteroaggregative E.coli were 1.3×10^3 CFU/mL,2×10^4 CFU/mL,4×10^4 CFU/mL,7.2×10^4 CFU/mL and 1.7 CFU/mL respectively.This method has strong specificity and high sensitivity and detects multiple target sequences in one experiment.Compared with other methods,BMB array has great application potential.