A Novel Soft Clustering Method for Detection of Exudates

One of the earliest indications of diabetes consequence is Diabetic Retinopathy(DR),the main contributor to blindness worldwide.Recent studies have proposed that Exudates(EXs)are the hallmark of DR severity.The present study aims to accurately and automatically detect EXs that are difficult to detect in retinal images in the early stages.An improved Fusion of Histogram-Based Fuzzy C-Means Clustering(FHBFCM)by a New Weight Assignment Scheme(NWAS)and a set of four selected features from stages of pre-processing to evolve the detection method is proposed.The features of DR train the optimal parameter of FHBFCM for detecting EXs diseases through a stepwise enhancement method through the coarse segmentation stage.The histogram-based is applied to find the color intensity in each pixel and performed to accomplish Red,Green,and Blue(RGB)color information.This RGB color information is used as the initial cluster centers for creating the appropriate region and generating the homogeneous regions by Fuzzy C-Means(FCM).Afterward,the best expression of NWAS is used for the delicate detection stage.According to the experiment results,the proposed method successfully detects EXs on the retinal image datasets of DiaretDB0(Standard Diabetic Retinopathy Database Calibration level 0),DiaretDB1(Standard Diabetic Retinopathy Database Calibration level 1),and STARE(Structured Analysis of the Retina)with accuracy values of 96.12%,97.20%,and 93.22%,respectively.As a result,this study proposes a new approach for the early detection of EXs with competitive accuracy and the ability to outperform existing methods by improving the detection quality and perhaps significantly reducing the segmentation of false positives.

Individual Identification of Electronic Equipment Based on Electromagnetic Fingerprint Characteristics

With the rapid development of communication and computer,the individual identification technology of communication equipment has been brought to many application scenarios.The identification of the same type of electronic equipment is of considerable significance,whether it is the identification of friend or foe in military applications,identity determination,radio spectrum management in civil applications,equipment fault diagnosis,and so on.Because of the limited-expression ability of the traditional electromagnetic signal representation methods in the face of complex signals,a new method of individual identification of the same equipment of communication equipment based on deep learning is proposed.The contents of this paper include the following aspects:(1)Considering the shortcomings of deep learning in processing small sample data,this paper provides a universal and robust feature template for signal data.This paper constructs a relatively complete signal template library from multiple perspectives,such as time domain and transform domain features,combined with high-order statistical analysis.Based on the inspiration of the image texture feature,characteristics of amplitude histogram of signal and the signal amplitude co-occurrence matrix(SACM)are proposed in this paper.These signal features can be used as a signal fingerprint template for individual identification.(2)Considering the limitation of the recognition rate of a single classifier,using the integrated classifier has achieved better generalization ability.The final average accuracy of 5 NRF24LE1 modules is up to 98%and solved the problem of individual identification of the same equipment of communication equipment under the condition of the small sample,low signal-to-noise ratio.

Reversible data hiding based on histogram and prediction error for sharing secret data

With the advancement of communication technology,a large number of data are constantly transmitted through the internet for various purposes,which are prone to be illegally accessed by third parties.Therefore,securing such data is crucial to protect the transmitted information from falling into the wrong hands.Among data protection schemes,Secret Image Sharing is one of the most popular methods.It protects critical messages or data by embedding them in an image and sharing it with some users.Furthermore,it combines the security concepts in that private data are embedded into a cover image and then secured using the secret-sharing method.Despite its advantages,this method may produce noise,making the resulting stego file much different from its cover.Moreover,the size of private data that can be embedded is limited.This research works on these problems by utilizing prediction-error expansion and histogram-based approaches to embed the data.To recover the cover image,the SS method based on the Chinese remainder theorem is used.The experimental results indicate that this proposed method performs better than similar methods in several cover images and scenarios.