Decoding brain responses to pixelized images in the primary visual cortex: implications for visual cortical prostheses

Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized images, and how to obtain the specific brain responses to different pixelized images in the primary visual cortex（the implant area） is still unknown. We conducted a functional magnetic resonance imaging experiment on normal human participants to investigate the brain activation patterns in response to 18 different pixelized images. There were 100 voxels in the brain activation pattern that were selected from the primary visual cortex, and voxel size was 4 mm × 4 mm × 4 mm. Multi-voxel pattern analysis was used to test if these 18 different brain activation patterns were specific. We chose a Linear Support Vector Machine（LSVM） as the classifier in this study. The results showed that the classification accuracies of different brain activation patterns were significantly above chance level, which suggests that the classifier can successfully distinguish the brain activation patterns. Our results suggest that the specific brain activation patterns to different pixelized images can be obtained in the primary visual cortex using a 4 mm × 4 mm × 4 mm voxel size and a 100-voxel pattern.

Responses of some landscape trees to the drought and high temperature events during 2006 and 2007 in Yamaguchi,Japan

Extreme weather events were analyzed based on the meteorological data from the year of 1967 to 2007 for Yamaguchi, Japan. The responses from landscape trees were also investigated mainly by the analysis of image pixel and spectral reflectance. Results show that after the dry, hot and windy summer in 2007, many landscape trees in Yamaguchi City tended to respond the extreme weather events by reducing their leaf surface area and receiving less radiation energy. Premature leaf discoloration or defoliation appeared on some landscape tree species and leaf necrosis occurred on tip and margin of many Kousa dogwood (Cornus kousa) trees at unfavorable sites. Described by image pixel analysis method, the leaf necrotic area percentage (LNAP) of sampled dogwood trees averaged 41.6% and the sampled Sasanqua camellia (Camelia sasanqua) tree also showed fewer flowers in flower season of 2007 than that in 2006. By differential analysis of partial discolored crown, it presented a logistic differential equation of crown color for sweet gum (Liquidambar styraciflua) trees. It suggested that the persistent higher temperature and lower precipitation could be injurious to the sensitive landscape trees at poor sites, even in relative humid area like Yamaguchi.

Responses of some landscape trees to the drought and high temperature events during 2006 and 2007 in Yamaguchi, Japan

Extreme weather events were analyzed based on the meteorological data from the year of 1967 to 2007 for Yamaguchi, Japan. The responses from landscape trees were also investigated mainly by the analysis of image pixel and spectral reflectance, Results show that after the dry, hot and windy summer in 2007, many landscape trees in Yamaguchi City tended to respond the extreme weather events by re- ducing their leaf surface area and receiving less radiation energy. Premature leaf discoloration or defoliation appeared on some landscape tree species and leaf necrosis occurred on tip and margin of many Kousa dogwood （Comus kousa） trees at unfavorable sites. Described by image pixel analysis method, the leaf necrotic area percentage （LNAP） of sampled dogwood trees averaged 41.6% and the sampled Sasanqua camellia （Camelia sasanqua） tree also showed fewer flowers in flower season of 2007 than that in 2006. By differential analysis of partial discolored crown, it presented a logistic differential equation of crown color for sweet gum （Liquidambar styraciflua） trees. It suggested that the persistent higher temperature and lower precipitation could be injurious to the sensitive landscape trees at poor sites, even in relative humid area like Yamaguchi.

Single pixel imaging based on semi-continuous wavelet transform

Single pixel imaging is a novel imaging technique,and it becomes a focus of research in recent years due to its advantages such as high lateral resolution and high robustness to noise.Imaging speed is one of the critical shortcomings,which limits the further development and applications of this technique.In this paper,we focus on the issues of imaging efficiency of a single pixel imaging system.We propose semi-continuous wavelet transform(SCWT)protocol and introduce the protocol into the single pixel imaging system.The proposed protocol is something between continuous wavelet transform and discrete wavelet transform,which allows the usage of those smooth(usually non-orthogonal,and they have advantages in representing smooth signals compressively,which can improve the imaging speed of single pixel imaging)wavelets and with limited numbers of measurements.The proposed imaging scheme is studied,and verified by simulations and experiments.Furthermore,a comparison between our proposed scheme and existing imaging schemes are given.According to the results,the proposed SCWT scheme is proved to be effective in reconstructing a image compressively.

DIUN:Deeper Inception U-Network for Recovering Partial Pixelated Images

In our daily life,it is nothing strange to see pixelated images that are spoiled artificially to hide certain information for protecting privacy or pixelated deliberately to cover up bad behaviors even crimes.To prevent these phenomena and recover the true information from pixelated images,it is meaningful to research an effective reconstruction method for recovering pixelated images.This paper aims at recovering the artificial partial pixelated images via deep learning(DL).To abstract more abundant features and enhance the repair ability of DL model,we propose a new DL structure,called deeper inception U-Net,to act as the generator of a generative adversarial network.We combine the feature loss with structural similarity index measure loss as the context loss to minimize the distance between feature maps of clear images and the generated images,which helps to improve the quality of repair images.After obtaining inception features,we use fusion layer to adaptively learn featuresin each inception block.To evaluate the performance of our model,we introduce a new home dataset that contains 10174 clear home images with corresponding pixelated images.A series of experiments show that our model has ability to rebuild pixelated images.

CardioLabelNet:An uncertainty estimation using fuzzy for abnormalities detection in ECG

Electrocardiography(ECG)abnormalities are evaluated through several automatic detection methods.Primarily,real-world ECG data are digital signals those are stored in the form of images in hospitals.Also,the existing automated detection technique eliminates the cardiac pattern that is abnormal and it is difficult to multiple abnormalities at some instances.To address those issues in this paper conventional ECG image automated techniques CardioLabelNet model is proposed.The proposed model incorporates two stages for image abnormality detection.At first fuzzy membership is performed in the image for computation of uncertainty.In second stage,classification is performed for computation of abnormal activity.The proposed CardioLabelNet collect ECG image data set for the uncertainty estimation while taking the account of various image classes which includes the global and local entropy of image pixels.For each waveform,uncertainties are calculated on the basis of global entropy.The computation of uncertainty in the images is performed with the fuzzy membership function.The spatial likelihood estimation of a fuzzy weighted membership function is used to calculate local entropy.Upon completion of fuzzification,classification is performed for the detection of normal and abnormal patterns in the ECG signal images.Through integration of stacked architecture model classification is performed for ECG images.The proffered algorithm performance is calculated in terms of accuracy for segmentation,Dice similarity coefficient,and partition entropy.Additionally,classification parameters accuracy sensitivity,specificity,and AUC are evaluated.The proposed approach outperforms the existing methodology,according to the results of a comparative analysis.

Minimizing the effects of unmodulated light and uneven intensity profile on the holographic images reconstructed by pixelated spatial light modulators

A simple and effective approach is proposed to minimize the effect of unmodulated light and uneven intensity caused by the pixelated structure of the spatial light modulator in a holographic display. A more uniform image is produced by purposely shifting the holographic images of multiple reconstructed lights with different incident angles from the zero-diffraction-order and overlapping those selected different orders. The simulation and optical experimental results show that the influence of the zero-diffraction-order can be reduced, while keeping the good uniformity of the target images by this new approach.

Single Photon Compressive Imaging Based on Digital Grayscale Modulation Method

In single-pixel imaging or computational ghost imaging,the measurement matrix has a great impact on the performance of the imaging system,because it involves modulation of the optical signal and image reconstruction.The measurement matrix reported in the existing literatures is first binarized and then loaded onto the digital micro-mirror device(DMD)for optical modulation,that is,each pixel can only be modulated into on-off states.In this paper,we propose a digital grayscale modulation method for more efficient compressive sampling.On the basis of this,we demonstrate a single photon compressive imaging system.A control and counting circuit,based on field-programmable gate array(FPGA),is developed to control DMD to conduct digital grayscale modulation and count single-photon pulse output from the photomultiplier tube(PMT)simultaneously.The experimental results show that the imaging reconstruction quality can be improved by increasing the sparsity ratio properly and compressive sampling ratio(SR)of these gray-scale matrices.However,when the compressive SR and sparsity ratio are increased appropriately to a certain value,the reconstruction quality is usually saturated,and the imaging reconstruction quality of the digital grayscale modulation is better than that of binary modulation.

A novel 2-dimensional cosmic ray position detector based on a CsI(Na) pixel array and an ICCD camera

A novel 2-D cosmic ray position detector has been built and studied. It is integrated from a CsI（Na） crystal pixel array, an optical fiber array, an image intensifier and an ICCD camera. The 2-D positions of one cosmic ray track is determined by the location of a fired CsI（Na） pixel. The scintillation light of these 1.0× 1.0 mm CsI（Na） pixels is delivered to the image intensifier through fibers. The light information is recorded in the ICCD camera in the form of images, from which the 2-D positions can be reconstructed. The background noise and cosmic ray images have been studied. The study shows that the cosmic ray detection efficiency can reach up to 11.4%, while the false accept rate is less than 1%.