Polycystic Ovarian Syndrome Detection by Using Two-Stage Image Denoising

The requirement of imaging methods in the medical field is vivid. If the capturing devices are not sophisticated, the acquired images will have a significant amount of noise. These noises are hazardous and cannot be entertained. Polycystic Ovarian Syndrome (PCOS) caused the state of affairs in girls if not diagnosed and look after early stages. Tran's epithelial duct ultrasound machine could be a non-invasive technique of imaging the human ovary to show salient options necessary for PCOS identification. Numbers of follicles and their sizes area unit the most options that characterize ovarian pictures. Hence, PCOS is diagnosed by investigating the numbers of follicles and measurement their sizes manually. conflict in medical aid is essentially created by technical advances in modalities that resulted from fruitful interactions among the essential science, bioscience, and manufacturer. Hence, PCOS is diagnosed by investigating the numbers of follicles and measurement their sizes manually. This paper attempts to identify the noise & try to generate a noise-free image by evaluation of noise properties. The noise pattern information thus provides an upper hand in the second stage filtering with specific filters i.e. fuzzified. A median filter for salt and pepper noise; and an adaptive wiener filter for Gaussian noise. 46.2%, 15.1%, and 12.4% improvement in MSE for salt & pepper, Gaussian, and speckle noise as compared to best existing methods.

A Simplified Algorithm for Count Rate Processing in Radiation Monitors That Address Statistical Fluctuations and Spurious Counts

While processing the signals from radiation detectors, for finding the true mean-count-rate, algorithms with hybrid pulse collection methodology have been proposed and used over the years. An algorithm based on this technique with a new methodology of adoption and implementation including spurious rejection is proposed here. It enables a specified and controllable error when the mean-count-rate remains within certain predefined limits from its true value. Effort is made to optimize the response time of prediction at low count rates preserving the optimum possible relative-standard-deviation (RSD). Chi-squared test is utilized for verifying the counting system to check if the observed fluctuations are consistent with the expected statistical fluctuations. A C-program code has been developed to test the algorithm. An observed set of detector outputs are given as input to the program and the corresponding Output is analyzed. A comparative study between the proposed method and floating-mean method is presented for the same set of observations. A typical short-lived high voltage (HV) induced spurious noise pattern is fed as input to the program verifying limited-spurious rejection capability of the algorithm. An embedded C program was written for microcontroller implementation of the algorithm. Case-study of a neutron roentgen equivalent man (REM) counter is presented for evaluating response time for various ranges of operation with calculation of RSD at these ranges. This general-purpose algorithm can enhance the read-out accuracy of radiation monitors used for radiation safety applications.

Adaptive data augmentation for supervised learning over missing data

Real-world data is dirty, which causes serious problems in (supervised) machine learning (ML). The widely used practice in such scenario is to first repair the labeled source (a.k.a. train) data using rule-, statistical- or ML-based methods and then use the "repaired" source to train an ML model. During production, unlabeled target (a.k.a. test) data will also be repaired, and is then fed in the trained ML model for prediction. However, this process often causes a performance degradation when the source and target datasets are dirty with different noise patterns , which is common in practice. In this paper, we propose an adaptive data augmentation approach, for handling missing data in supervised ML. The approach extracts noise patterns from target data, and adapts the source data with the extracted target noise patterns while still preserving supervision signals in the source. Then, it patches the ML model by retraining it on the adapted data, in order to better serve the target. To effectively support adaptive data augmentation, we propose a novel generative adversarial network (GAN) based framework, called DAGAN, which works in an unsupervised fashion. DAGAN consists of two connected GAN networks. The first GAN learns the noise pattern from the target, for target mask generation. The second GAN uses the learned target mask to augment the source data, for source data adaptation. The augmented source data is used to retrain the ML model. Extensive experiments show that our method significantly improves the ML model performance and is more robust than the state-of-the-art missing data imputation solutions for handling datasets with different missing value patterns.

Mental Representations and Facial Impressions of Muslim Men in Japan From 2015 to 2017

There is prejudice against Muslims in many nations, including Japan. This prejudice would be related to biased mental representations of Muslim faces. Moreover, in 2015, the increased news coverage linking Muslims to terrorism in Japan would have enhanced such negative mental representations. In the present study, Japanese participants were asked to imagine Muslim men, and from two faces with a random noise pattern added, participants were instructed to choose the face they imagined to be more Muslim. Typical Muslim facial representations were visualized in 2015, 2016, and 2017 by averaging all selected noise patterns using reverse correlation. The visualized representations were evaluated using the dimensions of warmth, competence, and basic emotions. The results showed that the warmth scores for the visualized facial representation were lower in 2015 than in 2017, whereas competence scores did not differ between the representations in 2015, 2016, and 2017. “Angry” and “disgusted” scores for the facial representation in 2015 were higher than those in 2017, whereas “happy” scores in 2015 were lower than those in 2017. The decreased “angry” score and increased “happy” score predicted an increase in the impression of warmth from 2015 to 2017.

Investigation on clusters stability in DYNAMAP’s monitoring network during Covid-19 outbreak

From March 23rd to May 3rd 2020, Italy underwent a complete lockdown in the attempt to contain the spread of the pandemic due to Covid-19 outbreak. During this period, a new kind of environment has been experienced in all cities, resulting in an abatement of traffic noise levels. Consequently, due to the prohibition of all non-essential activities, traffic noise dynamics changed as well. In this paper, we analyse the data recorded from the permanent noise monitoring network installed in the pilot area of the city of Milan, Italy. The results show how, besides a dramatic reduction of the noise levels (about 6 dB on average), also the noise pattern was profoundly changed. This is particularly important in the framework of DYNAMAP, a statistically based European project able to predict traffic noise over an extended area based on the noise recorded by limited number of monitoring stations. The change of the traffic dynamics, resulting in different noise patterns of the normalized hourly median profiles for each sensor, pose some limitations about the use of such predicting tool during extraordinary situations such as that experienced during a lockdown.

Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

Stripe noise is a common and unwelcome noise pattern in various thermal infrared (TIR) image data including conventional TIR images and remote sensing TIR spectral images. Most existing stripe noise removal (destriping) methods are often difficult to keep a good and robust efficacy in dealing with the real-life complex noise cases. In this paper, based on the intrinsic spectral properties of TIR images and stripe noise, we propose a novel two-stage transform domain destriping method called Fourier domain anomaly detection and spectral fusion (ADSF). Considering the principal frequencies polluted by stripe noise as outliers in the statistical spectrum of TIR images, our naive idea is first to detect the potential anomalies and then correct them effectively in the Fourier domain to reconstruct a desired destriping result. More specifically, anomaly detection for stripe frequencies is achieved through a regional comparison between the original spectrum and the expected spectrum that statistically follows a generalized Laplacian regression model, and then an anomaly weight map is generated accordingly. In the correction stage, we propose a guidance-image-based spectrum fusion strategy, which integrates the original spectrum and the spectrum of a guidance image via the anomaly weight map. The final reconstruction result not only has no stripe noise but also maintains image structures and details well. Extensive real experiments are performed on conventional TIR images and remote sensing spectral images, respectively. The qualitative and quantitative assessment results demonstrate the superior effectiveness and strong robustness of the proposed method.

Pengembangan True Random Number Generator berbasis Citra menggunakan Algoritme Kaotis

The security of most cryptographic systems depends on key generation using a nondeterministic RNG. PRNG generates a random numbers with repeatable patterns over a period of time and can be predicted if the initial conditions and algorithms are known. TRNG extracts entropy from physical sources to generate random numbers. However, most of these systems have relatively high cost, complexity, and difficulty levels. If the camera is directed to a random scene, the resulting random number can be assumed to be random. However, the weakness of a digital camera as a source of random numbers lies in the resulting refractive pattern. The raw data without further processing can have a fixed noise pattern. By applying digital image processing and chaotic algorithms, digital cameras can be used to generate true random numbers. In this research, for preprocessing image data used method of floyd-steinberg algorithm. To solve the problem of several consecutive black or white pixels appearing in the processed image area, the arnold-cat map algorithm is used while the XOR operation is used to combine the data and generate the true random number. NIST statistical tests, scatter and histrogram analyzes show the use of this method can produce truly random numbers