The most widely used machine for precision sowing of cucumber and sorghum seeds is considered as the vacuum type. The performance and distribution accuracy of the vacuum type metering unit is of great importance. Therefore, the experiments at three pressures of 25, 35, 45 mbar and two levels of forward speed in the ranges of 3 to 4.5 km/h and 6 to 8.5 km/h were conducted and multiple planting, feeding quality and miss planting determined. In the meantime, a motion model is constructed using Kalman filter to track and draw the seed trajectories. Based on the analysis of seeds falling trajectories, it was found that there was a close relationship between seeds’ falling trajectories and uniformity of seeding. The optimum levels of vacuum pressure and forward speed for precision seeding were found to be 35 mbar, 3 to 4.5 km/h, and 35 mbar, 3 to 4.5 km/h for cucumber and sorghum seeds, respectively. Finally, it was perceived that with increase in forward speed at inappropriate vacuum pressure, percentage of deviated seeds from a straight movement was increased, which caused decrease of seed distribution uniformity.

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.

The weeds must be removed from the field due to their competition with principal crops to use water, nutrients, sunlight, etc. There are different methods to remove the weeds: mechanically, manually or chemically (applying herbicides). For farmers, applying herbicides is a usual way, but brings some concerns, from the point of environmental issues, due to equal application of chemicals all over fields, regardless the presence or absence of weed. For this reason, a machine vision system based on Video processing was proposed to recognize Secale cereale L. (as a weed) from potato plant (as principal crop) to make herbicide application more accurate. Nine hundred sixty five objects were recognized after taking Videos, pre-processing and segmentation. Fourteen features were extracted from each object. Using the hybrid artificial neural network-genetic algorithm, of 14 extracting features, only 6 features were selected as effective ones: average, the third moment, autocorrelation, correlation, dissimilarity, and entropy. Data were classified into two groups: training data (70% of the total data) and testing data (30% of the total data). The classification was performed using hybrid of artificial neural network-Bio-geography Based Optimization (BBO) algorithm. Performance of classification system was evaluated through analysis of confusion matrix and Receiver Operating Characteristic (ROC). Sensitivity, specificity, and accuracy were calculated using confusion matrix. The results showed that the sensitivity, accuracy and specificity of classification system reached to an acceptable level: 99. 49 %, 99. 65% and 98. 91%, respectively. Our conclusion is that it is possible to manufacture the machine vision system with mentioned aims that work as online.

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.

Introduction: Juvenile Myoclonic Epilepsy (JME) is a generalized epileptic syndrome. Age of onset is usually between 12 to 18 years. JME consists of myoclonic jerks, generalized tonic-clonic seizures (GTCs) and typical absence attacks. EEG shows characteristic changes in JME. Long term Video-electroencephalography monitoring (VEM) is a helpful diagnostic procedure in the diagnosis of patient with unclear history or EEG findings. In the current study, we aimed to evaluate the role of VEM in diagnosis of refractory epileptic patients.Materials and Methods: This study is retrospective and descriptive on patients of Epilepsy Monitoring Unit of Razavi Hospital, Mashhad, Iran between March 2011 and March 2012. Telephone interview was scheduled 6-18 months after discharge to evaluate results of VEM on the frequency of seizures, the therapeutic regimes and patients’ quality of life.Results: 24 cases with diagnosis of JME were chosen among 250 patients who were admitted with refractory epilepsy. Fourteen of them were female. The average age of patients was 24 years old and the average duration of the seizure attacks was 12.97 years. The mean frequency of GTCs was 2.76 attacks per month and after VEM and proper treatment, it decreased to 0.27 attacks per month.Conclusion: VEM is a helpful diagnostic procedure for evaluating of refractory JME epileptic patients.