Progress in Research on Insect Olfactory Perception of Habitat Odor Molecules

A highly sensitive olfactory system allows insects to precisely identify and position volatile compounds from different sources in their habitats,and plays a crucial role in their foraging,mating,and oviposition activities.During evolution,insects have successfully developed a large and complex olfactory system to adapt to heterogeneous environments,enabling the maintenance of inset population.A comprehensive examination of the olfactory system of insects may therefore yield novel insights into the development of innovative pest control and prevention strategies,as well as the study of olfactory mechanisms in vertebrates and even humans.This paper outlines the current state of research into the signal transduction mechanism by which insects perceive the olfactory molecules of their habitats.The aim of this review is to provide a reference point for future studies into the olfactory perception mechanism and its potential applications in pest management.

Radon CLF:A Novel Approach for Skew Detection Using Radon Transform

In the digital world,a wide range of handwritten and printed documents should be converted to digital format using a variety of tools,including mobile phones and scanners.Unfortunately,this is not an optimal procedure,and the entire document image might be degraded.Imperfect conversion effects due to noise,motion blur,and skew distortion can lead to significant impact on the accuracy and effectiveness of document image segmentation and analysis in Optical Character Recognition(OCR)systems.In Document Image Analysis Systems(DIAS),skew estimation of images is a crucial step.In this paper,a novel,fast,and reliable skew detection algorithm based on the Radon Transform and Curve Length Fitness Function(CLF),so-called Radon CLF,was proposed.The Radon CLF model aims to take advantage of the properties of Radon spaces.The Radon CLF explores the dominating angle more effectively for a 1D signal than it does for a 2D input image due to an innovative fitness function formulation for a projected signal of the Radon space.Several significant performance indicators,including Mean Square Error(MSE),Mean Absolute Error(MAE),Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Measure(SSIM),Accuracy,and run-time,were taken into consideration when assessing the performance of our model.In addition,a new dataset named DSI5000 was constructed to assess the accuracy of the CLF model.Both two-dimensional image signal and the Radon space have been used in our simulations to compare the noise effect.Obtained results show that the proposed method is more effective than other approaches already in use,with an accuracy of roughly 99.87%and a run-time of 0.048(s).The introduced model is far more accurate and timeefficient than current approaches in detecting image skew.