Classification of Flying Insects with high performance using improved DTW algorithm based on hidden Markov model

ABSTRACT Insects play significant role in the human life. And insects pollinate major food crops consumed in the world. Insect pests consume and destroy major crops in the world. Hence to have control over the disease and pests, researches are going on in the area of entomology using chemical, biological and mechanical approaches. The data relevant to the flying insects often changes over time, and classification of such data is a central issue. And such time series mining tasks along with classification is critical nowadays. Most time series data mining algorithms use similarity search and hence time taken for similarity search is the bottleneck and it does not produce accurate results and also produces very poor performance. In this paper, a novel classification method that is based on the dynamic time warping (DTW) algorithm is proposed. The dynamic time warping algorithm is deterministic and lacks in modeling stochastic signals. The dynamic time warping (DTW) algorithm is improved by implementing a nonlinear median filtering (NMF). Recognition accuracy of conventional DTW algorithms is less than that of the hidden Markov model (HMM) by same voice activity detection (VAD) and noise-reduction. With running spectrum filtering (RSF) and dynamic range adjustment (DRA). NMF seek the median distance of every reference of time series data and the recognition accuracy is much improved. In this research work, optical sensors are used to record the sound of insect flight, with invariance to interference from ambient sounds. The implementation of our tool includes two parts, an optical sensor to record the "sound" of insect flight, and a software that leverages on the sensor information, to automatically detect and identify flying insects.

Image filtering technology in the application of B type ultrasonic diagnosis instrument / 中国医学装备

Objective:Due to the inherent characteristics of medical ultrasound images, the study of B-type ultrasonic medical image filtering technique, you can make the image quality greatly improved.Methods: through tools such as Matlab, for b-mode ultrasonic image Adaptive mean filter, median filter, median filter, wavelet transform, anisotropic diffusion equation filtering;Results: compares the common b-mode ultrasonic filtering algorithms and research.Conclusion:all the filtering method for image signal is enhanced to a certain extent, suppression for noise, too. Mean square error reflects filtering image noise, while attention to detail, keep, for medical ultrasonic images in the hope that is in between the two to achieve an optimal balance.

Technology of filtering in medical image processing / 医疗卫生装备

All sorts of medical imaging is filtered by image processing toolbox so as to wipe off all sorts of signal noise jamming and increase the SNR,which offers simple and convenient methods for the imperceptibility observing and analyzing.