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Several recent high-performing intelligibility estimators of acoustically degraded speech signals employ temporal modulation analysis. In this paper, we investigate the utility of using both spectro-and temporal-modulation for estimating speech intelligibility. We modified a pre-existing speech intelligibility estimation scheme (STMI) that was inspired by human auditory spectro-temporal modulation analysis. We produced several variants of the modified STMI and assessed their intelligibility prediction accuracy, in comparison with several highperforming estimators. Among the estimators tested, one of the STMI variants and eSTOI performed consistently well on both noisy and reverberated speech. These results suggest that spectro-temporal modulation analysis is useful for certain degradation conditions such as modulated noise and reverberation. Index Terms: speech intelligibility, speech quality model, spectro-temporal modulation Auditory-spectral and modulation analysisIn the following subsections, we present two combined ASA and MA front-ends used in this paper. Maryland front-endA biologically faithful ASA and MA scheme was laid out in [9] [10] [8] (we will denote it as MFN short for Maryland front-

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Abestract Background: Observing, categorizing and counting various types of white blood cells in a blood sample is one of the most important steps in the treatment of various diseases. This study aimed to develop a fast and reliable system based on processing microscopic images of blood samples for classifying four types of white blood cells. Materials and Methods: The modified k-means clustering method was used to perform image segmentation. Furthermore, white blood cells were classified using a deep convolutional neural network with the help of data in the MISP database, a free database composed of microscopic blood sample images. Moreover, several regularization techniques such as dropout and image augmentation were applied to prevent overfitting of the network. Results: The classification accuracy of the neural network was found to be 99%, which is more successful than many earlier studies. In the segmentation section, the cross-reference index was 0.73. Conclusion: The results of this research show that processing the microscopic images of the blood sample can help develop rapid and reliable systems using different methods of image processing and machine learning.