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18 results on '"Shams, Mohammadreza"'

1. Machine Learning-Driven Microwave Imaging for Soil Moisture Estimation near Leaky Pipe

Author

Ramezaninia, Mohammad, Shams, Mohammadreza, and Zoofaghari, Mohammad

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Signal Processing
Abstract

Characterizing soil moisture (SM) around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated by two different microwave imaging techniques based on spatio-temporal measurements at various frequency bands. The study investigates the performance of K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNN) algorithms for moisture classification based on these images, both before and after performing soil clutter reduction. We also explore the potentials of CNN and KNN for moisture estimation around the plant roots and in the presence of pebbles. The results demonstrate the more accurate moisture estimation using CNN when it is applied after clutter reduction considering back projection algorithm (BPA) as the imaging technique.

Published
2024

8. Real‐time vehicle detection using segmentation‐based detection network and trajectory prediction.

Author

Zarei, Nafiseh, Moallem, Payman, and Shams, Mohammadreza

Subjects
CITY traffic, IMAGE segmentation, OBJECT recognition (Computer vision), TRAFFIC monitoring, FORECASTING, DETECTORS
Abstract

The position of vehicles is determined using an algorithm that includes two stages of detection and prediction. The more the number of frames in which the detection network is used, the more accurate the detector is, and the more the prediction network is used, the algorithm is faster. Therefore, the algorithm is very flexible to achieve the required accuracy and speed. YOLO's base detection network is designed to be robust against vehicle scale changes. Also, feature maps are produced in the detector network, which contribute greatly to increasing the accuracy of the detector. In these maps, using differential images and a u‐net‐based module, image segmentation has been done into two classes: vehicle and background. To increase the accuracy of the recursive predictive network, vehicle manoeuvres are classified. For this purpose, the spatial and temporal information of the vehicles are considered simultaneously. This classifier is much more effective than classifiers that consider spatial and temporal information separately. The Highway and UA‐DETRAC datasets demonstrate the performance of the proposed algorithm in urban traffic monitoring systems. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Applying Sentiment and Social Network Analysis in User Modeling

Author

Shams, Mohammadreza, Saffar, Mohammadtaghi, Shakery, Azadeh, Faili, Heshaam, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Gelbukh, Alexander, editor

Published
2012
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16. Providing a novel algorithm in sddc’s storing based on fault tolerance and high availability

Author

Shams, Mohammadreza, Majma, Mohammadreza, Shams, Mohammadreza, and Majma, Mohammadreza

Abstract

Las cuestiones como el aumento de la velocidad de transmisión, la reducción de los costos de transmisión de datos, la seguridad de los datos y el tráfico de red son importantes y considerables en la transmisión o replicación de datos entre dos centros de datos definidos por software; Estos centros pueden lograr una mayor fiabilidad y disponibilidad y garantizar servicios sin escalas. En este trabajo, hemos propuesto un algoritmo para dividir y cifrar los datos que es producido por el servicio (SDDC) proveedores de software definido centro de datos sobre la base del tipo de importancia. Transferimos datos más importantes con RSA y menos importantes con algoritmos DES con encriptación, y también datos de cada proveedor de servicios sin cifrado, a diferentes partes de almacenamiento, simultáneamente. Los resultados de los experimentos muestran que el algoritmo propuesto reduce el tráfico de red en un 67%, el volumen de datos cifrados en un 55% y el costo de adquisición de ancho de banda en un 50%. Sin embargo, en algunos casos, el cifrado de datos requiere mucho tiempo.

Published
2017

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