scholarly journals Enhancing Edge Attack Strategy via an OWA Operator-Based Ensemble Design in Real-World Networks

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 830
Author(s):  
Yuan Feng ◽  
Baoan Ren ◽  
Chengyi Zeng ◽  
Yuyuan Yang ◽  
Hongfu Liu
Keyword(s):  
Real World ◽  
Similarity Index ◽  
Structural Similarity ◽  
Experimental Results ◽  
Weighted Averaging ◽  
Owa Operator ◽  
Structural Similarity Index ◽  
Ordered Weighted Averaging ◽  
Long Time ◽  
Attack Strategy

Network disintegration has been an important research hotspot in complex networks for a long time. From the perspective of node attack, researchers have devoted to this field and carried out numerous works. In contrast, the research on edge attack strategy is insufficient. This paper comprehensively evaluates the disintegration effect of each structural similarity index when they are applied to the weighted-edge attacks model. Experimental results show that the edge attack strategy based on a single similarity index will appear limited stability and adaptability. Thus, motivated by obtaining a stable disintegration effect, this paper designs an edge attack strategy based on the ordered weighted averaging (OWA) operator. Through final experimental results, we found that the edge attack strategy proposed in this paper not only achieves a more stable disintegration effect on eight real-world networks, but also significantly improves the disintegration effect when applied on a single network in comparison with the original similarity index.

scholarly journals Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder

2021 ◽  
Vol 11 (6) ◽  
pp. 2873
Author(s):  
Dong Hwan Kim ◽  
Woo Jin Ahn ◽  
Myo Taeg Lim ◽  
Tae Koo Kang ◽  
Dong Won Kim
Keyword(s):  
Real World ◽  
Similarity Index ◽  
Structural Similarity ◽  
Superior Performance ◽  
Structural Similarity Index ◽  
Atmospheric Scattering ◽  
Rain Removal ◽  
Robust Network ◽  
Computer Vision Applications ◽  
Convolutional Encoder

Removing haze or rain is one of the difficult problems in computer vision applications. On real-world road images, haze and rain often occur together, but traditional methods cannot solve this imaging problem. To address rain and haze problems simultaneously, we present a robust network-based framework consisting of three steps: image decomposition using guided filters, a frequency-based haze and rain removal network (FHRR-Net), and image restoration based on an atmospheric scattering model using predicted transmission maps and predicted rain-removed images. We demonstrate FHRR-Net’s capabilities with synthesized and real-world road images. Experimental results show that our trained framework has superior performance on synthesized and real-world road test images compared with state-of-the-art methods. We use PSNR (peak signal-to-noise) and SSIM (structural similarity index) indicators to evaluate our model quantitatively, showing that our methods have the highest PSNR and SSIM values. Furthermore, we demonstrate through experiments that our method is useful in real-world vision applications.

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

scholarly journals A Study of OWA Operators Learned in Convolutional Neural Networks

2021 ◽  
Vol 11 (16) ◽  
pp. 7195
Author(s):  
Iris Dominguez-Catena ◽  
Daniel Paternain ◽  
Mikel Galar
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Practical Method ◽  
Local Information ◽  
Weighted Averaging ◽  
Global Information ◽  
Owa Operator ◽  
Owa Operators ◽  
Ordered Weighted Averaging

Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the exponential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.

scholarly journals A method for decision making with the OWA operator

2012 ◽  
Vol 9 (1) ◽  
pp. 357-380 ◽  
Author(s):  
José Merigó ◽  
Anna Gil-Lafuente
Keyword(s):  
Decision Making ◽  
Distance Measures ◽  
Weighted Averaging ◽  
Owa Operator ◽  
Minimum Level ◽  
Ordered Weighted Averaging ◽  
Special Cases ◽  
Wide Range ◽  
Decision Making Problem ◽  
Parameterized Family

A new method for decision making that uses the ordered weighted averaging (OWA) operator in the aggregation of the information is presented. It is used a concept that it is known in the literature as the index of maximum and minimum level (IMAM). This index is based on distance measures and other techniques that are useful for decision making. By using the OWA operator in the IMAM, we form a new aggregation operator that we call the ordered weighted averaging index of maximum and minimum level (OWAIMAM) operator. The main advantage is that it provides a parameterized family of aggregation operators between the minimum and the maximum and a wide range of special cases. Then, the decision maker may take decisions according to his degree of optimism and considering ideals in the decision process. A further extension of this approach is presented by using hybrid averages and Choquet integrals. We also develop an application of the new approach in a multi-person decision-making problem regarding the selection of strategies.

Master Frame Extraction of Fetal Cardiac Images Using B Mode Ultrasound Images

2022 ◽  
Vol 54 ◽  
pp. 51-64
Author(s):  
Sushma Tumkur Venugopal ◽  
Sriraam Natarajan ◽  
Megha P. Arakeri ◽  
Suresh Seshadri
Keyword(s):  
Mean Square Error ◽  
Hausdorff Distance ◽  
Cardiac Cycle ◽  
Fetal Heart ◽  
Fetal Echocardiography ◽  
Similarity Index ◽  
Structural Similarity ◽  
Ultrasound Images ◽  
Mean Square ◽  
Structural Similarity Index

Fetal Echocardiography is used for monitoring the fetal heart and for detection of Congenital Heart Disease (CHD). It is well known that fetal cardiac four chamber view has been widely used for preliminary examination for the detection of CHD. The end diastole frame is generally used for the analysis of the fetal cardiac chambers which is manually picked by the clinician during examination/screening. This method is subjected to intra and inter observer errors and also time consuming. The proposed study aims to automate this process by determining the frame, referred to as the Master frame from the cine loop sequences that can be used for the analysis of the fetal heart chambers instead of the clinically chosen diastole frame. The proposed framework determines the correlation between the reference (first) frame with the successive frames to identify one cardiac cycle. Then the Master frame is formed by superimposing all the frames belonging to one cardiac cycle. The master frame is then compared with the clinically chosen diastole frame in terms of fidelity metrics such as Dice coefficient, Hausdorff distance, mean square error and structural similarity index. The average value of the fidelity metrics considering the dataset used for this study 0.73 for Dice, 13.94 for Hausdorff distance, 0.99 for Structural Similarity Index and 0.035 for mean square error confirms the suitability of the proposed master frame extraction thereby avoiding manual intervention by the clinician. .

scholarly journals Cell-filtering-based multi-scale Shannon–Cosine wavelet denoising method for locust slice images

2019 ◽  
Vol 17 (05) ◽  
pp. 1950035 ◽  
Author(s):  
Shenghan Mei ◽  
Xiaochun Liu ◽  
Shuli Mei
Keyword(s):  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Similarity Index ◽  
Structural Similarity ◽  
Wavelet Denoising ◽  
Experimental Results ◽  
Transform Method ◽  
Denoising Method ◽  
Multi Scale ◽  
Piecewise Smoothness

The locust slice images have all the features such as strong self-similarity, piecewise smoothness and nonlinear texture structure. Multi-scale interpolation operator is an effective tool to describe such structures, but it cannot overcome the influence of noise on images. Therefore, this research designed the Shannon–Cosine wavelet which possesses all the excellent properties such as interpolation, smoothness, compact support and normalization, then constructing multi-scale wavelet interpolative operator, the operator can be applied to decompose and reconstruct the images adaptively. Combining the operator with the local filter operator (mean and median), a multi-scale Shannon–Cosine wavelet denoising algorithm based on cell filtering is constructed in this research. The algorithm overcomes the disadvantages of multi-scale interpolation wavelet, which is only suitable for describing smooth signals, and realizes multi-scale noise reduction of locust slice images. The experimental results show that the proposed method can keep all kinds of texture structures in the slice image of locust. In the experiments, the locust slice images with mixture noise of Gaussian and salt–pepper are taken as examples to compare the performances of the proposed method and other typical denoising methods. The experimental results show that the Peak Signal-To-Noise Ratio (PSNR) of the denoised images obtained by the proposed method is greater 27.3%, 24.6%, 2.94%, 22.9% than Weiner filter, wavelet transform method, median and average filtering, respectively; and the Structural Similarity Index (SSIM) for measuring image quality is greater 31.1%, 31.3%, 15.5%, 10.2% than other four methods, respectively. As the variance of Gaussian white noise increases from 0.02 to 0.1, the values of PSNR and SSIM obtained by the proposed method only decrease by 11.94% and 13.33%, respectively, which are much less than other 4 methods. This shows that the proposed method possesses stronger adaptability.

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