Background and Aim: Angular cheilitis is a common condition among dental patients. However, it often responds poorly to antifungal treatment, and the use of corticosteroids can lead to lesion recurrence or medication resistance. This study aimed to assess the frequency of Staphylococcus aureus in patients who visited the Oral and Maxillofacial Medicine Department at Yazd Dental Faculty in 2017. Methods: This case- control study included 163 participants, with 65 patients diagnosed with Angular cheilitis in the case group and 98 healthy individuals in the control group. Swabs were collected and inoculated on blood agar and Sabouraud dextrose agar. The cultures were then examined for the presence of Staphylococcus aureus and Candida albicans. Data were analyzed using SPSS software, employing Chi- square and t-tests, with a significance level set at  p < 0.05. Results: The study included 98 healthy individuals and 65 patients with Angular cheilitis. The results indicated that Staphylococcus aureus was not detected in either the patient or control group (p < 0.9). A higher prevalence of Angular cheilitis was observed among female patients. Additionally, there was a direct correlation between the presence of Angular cheilitis and Candida albicans. Conclusion: The findings suggest that Staphylococcus aureus is not a contributing factor in Angular cheilitis lesions. Therefore, antibiotic treatment for refractory cases should be postponed until microbial cultures confirm bacterial involvement.

This paper presents a new method for blind two-channel speech sources separation without the need for prior knowledge about speech sources. In the proposed method, by weighting the mixture signal spectrum based on the location of the speech sources in terms of distance to the microphone, the speech sources are separated. Therefore, by forming an Angular spectrum by generalized cross-correlation function, the speech sources in the mixture signal are localized. First, by creating an Angular spectrogram by generalized cross-correlation function, the speech sources in the mixture signal are localized. Then according to the location of the sources, the amplitude of the mixture signal spectrum is weighted. By multiplying the weighted spectrum by the values obtained from the Angular spectrograms, a binary mask is constructed for each source. By applying the binary mask to the amplitude of the mixture signal spectrum, the speech sources are separated. This method is evaluated on SiSEC database and the measurement tools and criteria contained in this database are used for evaluation. The results show that the proposed method is comparable in terms of the criteria available in the database to the competing ones, has lower computational complexity.

Objective: To obtain abnormalities in quantitative Electroencephalography (QEEG) and to observe connectivity between electrodes in children with Asperger disorder.Method: In this study, spectrogram criteria and coherence values are used as a tool for evaluating QEEG in 15 children with Asperger disorder (10 boys and 5 girls aged between 6 to 11 years old) and in 11 control children (7 boys and 4 girls with the same age range).Results: The evaluation of QEEG using statistical analysis and spectrogram criteria demonstrates that the relaxed eye-opened condition in gamma frequency band (34-44Hz) has the best distinction level of 96.2% using spectrogram. The children with Asperger disorder had significant lower spectrogram criteria values (p<0.01) at Fp1 electrode and lower values (p<0.05) at Fp2 and T6 electrodes. Coherence values at 171 pairs of EEG electrodes indicate that the connectivity at (T4, P4), (T4, Cz), (T4, C4) electrode pairs and (T4, O1) had significant differences (p<0.01) in the two groups in the gamma band.Conclusions: It is shown that gamma frequency band can discriminate 96.2% of the two groups using the spectrogram criteria. The results demonstrate that there are more abnormalities in the prefrontal and right temporal lobes using spectrogram criteria and there are more abnormalities in the connectivity of right temporal lobe with the other lobes in the gamma frequency band.

The abstract should include the One of the most exciting topics for researchers over the past few years is detecting underwater acoustic noises. Meanwhile, the complicated nature of the ocean makes this task very challenging. Also, making signals formatted data compatible with machine learning approaches needs much knowledge in signal processing for feature detection. This paper proposed a method to overcome these challenges, which extracts features with Convolutional Neural Network (CNN) and Mel-spectrogram (converting signal data to images). This method needless knowledge in signal processing and more knowledge in machine learning; because using CNNs find the hidden pattern and knowledge of the data automatically. The proposed approach detected the presence of the ships and categorized them into different kinds of them with 99% accuracy that is a noticeable improvement considering state of the art. The performed CNN models consist of 2 CNN layers for feature extraction and a Dense layer for classification the underwater ship noises.

Recently permutation multimedia ciphers were broken in a chosen-plaintext scenario. That attack models a very resourceful adversary which may not always be the case. To show insecurity of these ciphers, we present a cipher-text only attack on speech permutation ciphers. We show inherent redundancies of speech can pave the path for a successful cipher-text only attack. To that end, regularities of speech are extracted in time and frequency using short time Fourier transform. We show that spectrograms of cipher-texts are in fact scrambled puzzles. Then, different techniques including estimation, image processing, and graph theory are fused together in order to create and solve these puzzles. Conducted tests show that the proposed method achieves accuracy of 87: 8% and intelligibility of 92: 9%. These scores are 50: 9% and 34: 6%, respectively, higher than scores of previous method. Finally a novel method, based on moving spectrogram distance, is proposed that can give accurate estimation of segment length of the scrambler system.

Transfer alignment of master and slave systems plays a key role in the inertial navigation accuracy of the marine cooperative vehicles. Accuracy enhancement of misalignment angle and orientation estimation is the main purpose of the transfer alignment. Velocity and orientation matching is a well-known method for transfer alignment. However, in many applications, there are no velocity measurements of both the master and slave systems due to weight, dimensional and technological limitations of accurate speed sensors, such as Doppler Velocity Loggers (DVL). Angular velocity configuration is a suitable solution for transfer alignment in this situation. However, the orientation error cannot be estimated in this configuration. Taking this drawback into account, a new configuration based on using the integral of Angular velocity in addition to Angular velocity measurement is presented for transfer alignment in the current research. Furthermore, appropriate abilities are considered to estimate the dynamic misalignment angle, orientation error and also measurement errors of the slave gyroscope. Two linear and non-linear observation models are developed for the transfer alignment configuration. The simulation results reveal the appropriate performance of the proposed configuration for marine application, especially when there are no accurate velocity measurements. Based on the simulation results, the performance of the non-linear observation model is better than linear ones in dynamic misalignment angle estimation. Moreover, it can be inferred from the orientation error estimation that rich data in high-maneuvered motion is necessary for required estimation accuracy. Additionally, 200 runs of Monte-Carlo simulation are developed and the estimation RMSE are presented.

Objective: to evaluate the brain signals in children with autism disorder in many different conditions of quantitative Electroencephalography (qEEG) recordings in order to highlight abnormalities and to characterize this group.Method: In this study, spectrogram was used as a tool for evaluating qEEG in 15 children with autism disorders (13 boys and 2 girls aged between 6 to 11 years old) and in 11 normal children (7 boys and 4 girts with the same age range). Signals of the two groups were recorded in nine conditions. Results: The recorded signals with the relaxed eye-opened condition in alpha band, those recorded with looking at a stranger's picture condition in beta band, and the ones obtained with children looking at inverted stranger's picture in the same beta band show the best discrimination of 92.3%, 88,9% and 88.9%respectively using spectrogram. Conclusion: Among the several different EEG recordings, the relaxed eye-opened condition in alpha band had been the best condition for discriminating the two groups using spectrogram. More abnormalities were observed in the prefrontal lobe and the left brain hemisphere in children with autism disorders.

Diffusion Tensor Imaging (DTI) is a common method for the investigation of brain white matter. In this method, it is assumed that diffusion of water molecules is Gaussian and so, it fails in fiber crossings where this assumption does not hold. High Angular Resolution Diffusion Imaging (HARDI) allows more accurate investigation of microstructures of the brain white matter; it can present fiber crossing in each voxel. HARDI contains complex orientation information of the fibers.Therefore, registration of these images is more complicated than the scalar images. In this paper, we propose a HARDI registration algorithm based on the feature vectors that are extracted from the Orientation Distribution Functions (ODFs) in each voxel. Hammer similarity measure is used to match the feature vectors and thin-plate spline (TPS) based registration is used for spatial registration of the skeleton and its neighbors. A re-orientation strategy is utilized to re-orient the ODFs after spatial registration. Finally, we evaluate our method based on the differences in principal diffusion direction and we will show that utilizing the skeleton as landmark in the registration results in accurate alignment of HARDI data.

Automatic Speaker Verification (ASV) systems have proven to bevulnerable to various types of presentation attacks, among whichLogical Access attacks are manufactured using voiceconversion and text-to-speech methods. In recent years, there has beenloads of work concentrating on synthetic speech detection, and with the arrival of deep learning-based methods and their success in various computer science fields, they have been a prevailing tool for this very task too. Most of the deep neural network-based techniques forsynthetic speech detection have employed the acoustic features basedon Short-Term Fourier Transform (STFT), which are extracted from theraw audio signal. However, lately, it has been discovered that the usageof Constant Q Transform's (CQT) spectrogram can be a beneficialasset both for performance improvement and processing power andtime reduction of a deep learning-based synthetic speech detection. In this work, we compare the usage of the CQT spectrogram and some most utilized STFT-based acoustic features. As lateral objectives, we consider improving the model's performance as much as we can using methods such as self-attention and one-class learning. Also, short-duration synthetic speech detection has been one of the lateral goals too. Finally, we see that the CQT spectrogram-based model not only outperforms the STFT-based acoustic feature extraction methods but also reduces the processing time and resources for detecting genuine speech from fake. Also, the CQT spectrogram-based model places wellamong the best works done on the LA subset of the ASVspoof 2019 dataset, especially in terms of Equal Error Rate.