In this paper, an alternative approach in operational modal analysis is presented, utilizing image PROCESSING technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the TIME domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement TIME histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

In traditional speech PROCESSING, feature extraction and classification were conducted as separate steps. The advent of deep neural networks has enabled methods that simultaneously model the relationship between acoustic and phonetic characteristics of speech while classifying it directly from the raw waveform. The first convolutional layer in these networks acts as a filter bank. To enhance interpretability and reduce the number of parameters, researchers have explored the use of parametric filters, with the SincNet architecture being a notable advancement. In SincNet's initial convolutional layer, rectangular bandpass filters are learned instead of fully trainable filters. This approach allows for modeling with fewer parameters, thereby improving the network's convergence speed and accuracy. Analyzing the learned filter bank also provides valuable insights into the model's performance. The reduction in parameters, along with increased accuracy and interpretability, has led to the adoption of various parametric filters and deep architectures across diverse speech PROCESSING applications. This paper introduces different types of parametric filters and discusses their integration into various deep architectures. Additionally, it examines the specific applications in speech PROCESSING where these filters have proven effective.

سابقه و هدف: واریکوسل از جمله عوامل ناباروری در مردان است. این بیماری، با تغییر در دما و درناژ وریدهای بیضوی موجب اختلال در اسپرماتوژنز و کاهش باروری می گردد. هر چند واریکوسلکتومی، درمان اینگونه بیماران می باشد اما امروزه روشهای کمک باروری و آماده کردن به روشهای مختلف از جمله Sperm PROCESSING و انجام swin up کمک قابل توجهی به این بیماران جهت بارور شدن می نماید.مواد و روشها: این مطالعه به روش مداخله ای در 155 نفر در سال 83 در مرکز ناباروری دانشگاه علوم پزشکی بابل انجام شد. پارامترهای اسپرم افراد واریکوسل قبل و بعد از PROCESSING مورد بررسی قرار گرفت.یافته ها: میانگین سن بیماران 31.8 سال بود. حرکت اسپرم در Grade III و Grade IV قبل از Sperm PROCESSING بترتیب 53.9% و 12.9% بود که بعد از آماده سازی، بترتیب به 78.8% و 56.8% افزایش یافت. Total motility در گروه کمتر از 40 درصد حرکت اسپرم، از 19 نفر به 87 نفر یعنی بیش از 80 درصد افزایش یافت.نتیجه گیری: نتایج این مطالعه نشان داد که در این نوع بیماران می توان به آماده سازی و جمع آوری اسپرمهای مناسب، شانس باروری را افزایش داد.