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وبلاگ امیرحسین حاجی حسینی

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وبلاگ امیرحسین حاجی حسینی

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۱۵ مطلب با کلمه‌ی کلیدی «امیرحسین حاجی حسینی گزستانی» ثبت شده است

Distributed Diffusion Based Spectrum Sensing for Cognitive Radio Sensor Networks Considering Link Failure

Amirhosein Hajihoseini Gazestani and Seyed Ali Ghorashi

Abstract:

Wireless sensor networks (WSNs) typically use license free industrial, scientific and medical (ISM) frequency bands and by increasing the demand for different usages of WSNs, it is anticipated that this band gets saturated. One solution to deal with this spectrum scarcity in WSNs is utilizing the cognitive radio concept, called cognitive radio sensor networks (CRSNs). One of the most critical challenges in CRSNs is spectrum sensing, which can be implemented by centralized, cluster based or distributed methods. In CRSNs, distributed methods have better spectrum sensing performance due to their fast adaptation to the network changes. Also they have lower power consumption level, which is critical in CRSNs. In this paper, we propose a novel distributed diffusion based spectrum sensing method for CRSNs that improves the robustness of the spectrum sensing method against link failure and network topology changes. It also increases the algorithm’s convergence rate while its accuracy is acceptable. We prove the proposed method convergence and calculate its mean square error, considering link failure assumption. Simulation results confirm that the proposed method improves the convergence rate compared with conventional distributed methods, and using the proposed method increases the convergence rate and needs less communications to make a decision about absence or presence of the primary user.

link of paper

۰ نظر موافقین ۰ مخالفین ۰ ۰۵ شهریور ۹۷ ، ۰۸:۳۳
Introduction and Patent Analysis of Signal Processing for Big Data
Mohammad Eslami, Amirhosein Hajihoseini Gazestani, Seyed Ali Ghorashi
Abstract: 
Big data is rapidly considered in different scientific domains, industries and business methods. Considering the concept of Internet of Things, big data is generated by everything around us continuously, and therefore, dealing with big data and its challenges are important and requires new thinking strategies and also techniques. Signal processing is one of the solutions that is utilized with big data in most scientific fields. This paper gives a brief introductory preview of the subjects included in this area and describes some of challenges and tactics. In order to show the rapid growth of attentions in signal processing for big data, a statistical analysis on corresponding patents is considered as well.
 
۰ نظر موافقین ۰ مخالفین ۰ ۰۱ شهریور ۹۷ ، ۰۸:۲۹

Image dataset for Persian Road Surface Markings

Seyed Hamid Safavi, Mohammad Eslami, Aliasghar Sharifi, Amirhosein Hajihoseini, Mohammadreza Riahi, Maryam Rekabi, Sadaf Sarrafan, Rahman Zarnoosheh, Ehsan Khodapanah Aghdam, Sahar Barzegari Banadkoki, Seyed Mohammad Seyedin Navadeh, Farah Torkamani-Azar

Abstract:

Self-driving and autonomous cars are hot emerging technologies which can provide enormous impact in the near future. Since an important component of autonomous cars is vision processing, the increasing interest for self-driving cars has motivated researchers to collect different relative image datasets. Hence, we collect a comprehensive dataset about the road surface markings which are available in Iran. In addition, we evaluate the conventional recognition rate. In this paper, we present a novel and extensive dataset for Persian Road Surface Markings (PRSM) with ground truth labels. We also hope that it will be useful as a Persian benchmark dataset for researchers in this field. The dataset consists of over 68,000 labeled images of road markings in 18 popular classes. It also contains road surface markings under various daylight conditions. Our dataset with
further details is available online at: http://display.sbu.ac.ir/databases.

link of paper

۰ نظر موافقین ۰ مخالفین ۰ ۱۲ ارديبهشت ۹۷ ، ۲۳:۰۸

شاخه دانشجویی IEEE دانشکده مهندسی برق دانشگاه شهید بهشتی برگزار می کند:

چهارشنبه پنجم و بیست و نهم اردیبهشت، آمفی تئاتر دانشکده مهندسی برق دانشگاه شهید بهشتی

۰ نظر موافقین ۰ مخالفین ۰ ۲۷ فروردين ۹۷ ، ۱۵:۴۳

A Fingerprint method for Indoor Localization using Autoencoder based Deep Extreme Learning Machine

Zahra Ezzati Khatab, Amirhosein Hajihoseini and Seyed Ali Ghorashi

Abstract:

By growing the demand for location based services in indoor environments in recent years, fingerprint based indoor localization has attracted many researchers' interest. The fingerprint localization method works based on received signal strength (RSS) in Wireless Sensor Networks (WSNs). This methods uses RSS measurements from available transmitter sensors, which are collected by a smart phone with internal sensors. In this paper, we propose a novel algorithm that takes the advantages of deep learning, extreme learning machine (ELM) and high level extracted features by autoencoder, to improve the localization performance in the feature extraction and the classification. Furthermore, as the fingerprint database needs to be updated (due to the dynamic nature of environment), we also increase the number of training data, in order to improve the localization performance, gradually. Simulation results indicate that the proposed method provides a significant improvement in localization performance, by using high level extracted features by autoencoder, and increasing the number of training data.
۰ نظر موافقین ۰ مخالفین ۰ ۱۷ دی ۹۶ ، ۱۰:۲۲

شاخه دانشجویی IEEE دانشکده مهندسی برق دانشگاه شهید بهشتی برگزار می کند:

 پوستر

پنجشنبه بیست و هفتم مهر و چهار آبان، آمفی تئاتر دانشکده مهندسی برق دانشگاه شهید بهشتی

۰ نظر موافقین ۰ مخالفین ۰ ۰۷ مهر ۹۶ ، ۱۰:۲۸

شاخه دانشجویی IEEE دانشکده مهندسی برق دانشگاه شهید بهشتی برگزار می کند:

پوستر

پنجشنبه سیزدهم و بیستم مهرماه، آمفی تئاتر دانشکده مهندسی برق دانشگاه شهید بهشتی

۰ نظر موافقین ۰ مخالفین ۰ ۰۶ مهر ۹۶ ، ۱۲:۰۶

پژوهشکده فضای مجازی دانشگاه شهید بهشتی با همکاری شاخه دانشجویی IEEE برگزار می کند:

پوستر

۰ نظر موافقین ۰ مخالفین ۰ ۰۶ مهر ۹۶ ، ۱۱:۵۹

Distributed Spectrum Sensing for Cognitive Radio Sensor Networks using Diffusion Adaptation

Amirhosein Hajihoseini, Seyed Ali Ghorashi

Abstract:

Cognitive radio is a practical solution for spectrum scarcity. In cognitive networks, unlicensed (secondary) users should sense the spectrum before any usage, to make sure that the licensed (primary) users do not use the spectrum at that time. Due to the importance of spectrum sensing in cognitive networks, this should be fast and reliable, particularly in networks with communication link failure, which leads to network topology change. Decentralized decision making algorithms are known as a promising technique to provide reliability, scalability and adaptation, especially in sensor networks. In this paper, we propose a distributed diffusion based method in which, secondary users (sensors) cooperate to improve the performance of spectrum sensing. The proposed method provides a significant improvement in convergence rate and reliability. Simulation results indicate that the proposed algorithm shows an acceptable performance and converges twice faster than recently proposed consensus based spectrum sensing algorithms in the literature, and is almost insensitive to communication link failure.

link of paper

۰ نظر موافقین ۰ مخالفین ۰ ۱۰ مرداد ۹۶ ، ۱۰:۳۶

Decentralized Consensus Based Target Localization in Wireless Sensor Networks

Amirhosein Hajihoseini Gazestani, Reza Shahbazian, Seyed Ali Ghorashi

Abstract:

Target localization is an attractive subject for modern systems that utilize different types of distributed sensors for location based services such as navigation, public transport, retail services and so on. Target localization could be performed in both centralized and decentralized manner. Due to drawbacks of centralized systems such as security and reliability issues, decentralized systems are become more desirable. In this paper, we introduce a new decentralized and cooperative target localization algorithm for wireless sensor networks. In cooperative consensus based localization, each sensor knows its own location and estimates the targets position using the ranging techniques such as received signal strength. Then, all nodes cooperate with their neighbours and share their information to reach a consensus on targets location. In our proposed algorithm, we weight the received information of neighbour nodes according to their estimated distance toward the target node. Simulation results confirm that our proposed algorithm is faster, less sensitive to targets location and improves the localization accuracy by 85% in comparison with distributed Gauss–Newton algorithm.

link of paper

۰ نظر موافقین ۰ مخالفین ۰ ۰۹ مرداد ۹۶ ، ۱۷:۲۸