Resource Allocation for Downlink Multi-Cell OFDMA Cognitive Radio Network Using Hungarian Method

This paper considers the problem of resource allocation for downlink part of an OFDM-based multi-cell cognitive radio network which consists of multiple secondary transmitters and receivers communicating simultaneously in the presence of multiple primary users. We present a new framework to maximize the total data throughput of secondary users by means of subchannel assignment, while ensuring interference leakage to PUs is below a threshold. In this framework, we first formulate the resource allocation problem as a nonlinear and non-convex optimization problem. Then we represent the problem as a maximum weighted matching in a bipartite graph and propose an iterative algorithm based on Hungarian method to solve it. The present contribution develops an efficient subchannel allocation algorithm that assigns subchannels to the secondary users without the perfect knowledge of fading channel gain between cognitive radio transmitter and primary receivers. The performance of the proposed subcarrier allocation algorithm is compared with a blind subchannel allocation as well as another scheme with the perfect knowledge of channel-state information. Simulation results reveal that a significant performance advantage can still be realized, even if the optimization at the secondary network is based on imperfect network information

A Novel Device-to-Device Discovery Scheme for Underlay Cellular Networks

Tremendous growing demand for high data rate services such as video, gaming and social networking in wireless cellular systems, attracted researchers' attention to focus on developing proximity services. In this regard, device-to-device (D2D) communications as a promising technology for future cellular systems, plays crucial rule. The key factor in D2D communication is providing efficient peer discovery mechanisms in ultra dense networks. In this paper, we propose a centralized D2D discovery scheme by employing a signaling algorithm to exchange D2D discovery messages between network entities. In this system, potential D2D pairs share uplink cellular users' resources with collision detection, to initiate a D2D links. Stochastic geometry is used to analyze system performance in terms of success probability of the transmitted signal and minimum required time slots for the proposed discovery scheme. Extensive simulations are used to evaluate the proposed system performance.Comment: Accepted for publication in 25'th Iranian Conference on Electrical Engineering (ICEE2017

Localization of Distributed Wireless Sensor Networks using Two Sage SDP Optimization

A wireless sensor network (WSN) may comprise a large distributed set of low cost, low power sensing nodes. In many applications, the location of sensors is a necessity to evaluate the sensed data and it is not energy and cost efficient to equip all sensors with global positioning systems such as GPS. In this paper, we focus on the localization of sensors in a WSN by solving an optimization problem. In WSN localization, some sensors (called anchors) are aware of their location. Then, the distance measurements between sensors and anchors locations are used to localize the whole sensors in the network. WSN localization is a non-convex optimization problem, however, relaxation techniques such as semi-definite programming (SDP) are used to relax the optimization. To solve the optimization problem, all constraints should be considered simultaneously and the solution complexity order is O(n2) where n is the number of sensors. The complexity of SDP prevents solving large size problems. Therefore, it would be beneficial to reduce the problem size in large and distributed WSNs. In this paper, we propose a two stage optimization to reduce the solution time, while provide better accuracy compared with original SDP method. We first select some sensors that have the maximum connection with anchors and perform the SDP localization. Then, we select some of these sensors as virtual anchors. By adding the virtual anchors, we add more reference points and decrease the number of constraints. We propose an algorithm to select and add virtual anchors so that the total solution complexity and time decrease considerably, while improving the localization accuracy

تحلیل اجزای محدود آزمون غیرمخرب دمانگاری ارتعاشی به کمک فرکانس تشدید عیب

Vibro-thermography is an emerging and promising technique that uses ultrasonic elastic waves as an excitation source to detect and evaluate surface and subsurface defects. Friction of the edges of defects, viscoelastic behavior and non-linear vibrations of the defect region are the main sources of heating and the temperature gradient that shows up synchronously with variations of non-linear elastic energy. The temperature gradient in the defect region can be imaged by an infrared camera in order to estimate the location and size of the defects. In this paper, the vibro-thermography is simulated in COMSOL Multiphysics software. Lamb waves are used to excite an aluminum plate containing a flat-bottomed hole. First, the resonance frequency of the defect is found by means of the theory of vibrations and also by finite element method (FEM). An algorithm that incorporates frequency analysis as a function of out-of-plane displacements is used to verify this frequency and the results are compared with the eigenfrequency analysis results. The agreement observed between the theoretical and numerical models is found to be very good. The plate is then excited by an amplitude modulated sine-burst at the local defect resonance (LDR) frequency and a frequency related to the thermal penetration depth. Thermal image processing is carried out on the thermal waves to obtain their amplitude and phase images. By considering a four-point algorithm, the location, size and geometry of the defect is estimated with good accuracy

Detection of foot-and-mouth disease virus and identification of serotypes in East Azerbaijan province of Iran

Three serotypes of foot-and-mouth disease virus (FMDV) including A, Asia1 and O, have been detected in Iran. Following the mass vaccination program which was implemented in all parts of the country in 2002, the number of outbreaks has been significantly reduced. Therefore, rapid detection of FMDV and its serotypes in clinical samples is essential for control of new outbreaks. In this study a reverse transcription-polymerase chain reaction (RT-PCR) was used for detection and serotyping of FMDV in clinical samples. Twelve tissue samples were collected from suspected outbreaks from East Azerbaijan province and tested by this method. Of 12 samples, 10 were found to be positive for FMDV. These samples were also tested in a multiplex-PCR for serotype identification. Four samples showed to be serotype O, 4 samples identified as serotype A and 2 samples detected to be serotype Asia 1. Multiplex-PCR products were sequenced and specificity of results was confirmed. Results indicate that in order to practice a good control measure, the RT-PCR and multiplex-PCR could be successfully used as a robust diagnostic method

No-reference video quality estimation based on machine learning for passive gaming video streaming applications

Recent years have seen increasing growth and popularity of gaming services, both interactive and passive. While interactive gaming video streaming applications have received much attention, passive gaming video streaming, in-spite of its huge success and growth in recent years, has seen much less interest from the research community. For the continued growth of such services in the future, it is imperative that the end user gaming quality of experience (QoE) is estimated so that it can be controlled and maximized to ensure user acceptance. Previous quality assessment studies have shown not so satisfactory performance of existing No-reference (NR) video quality assessment (VQA) metrics. Also, due to the inherent nature and different requirements of gaming video streaming applications, as well as the fact that gaming videos are perceived differently from non-gaming content (as they are usually computer generated and contain artificial/synthetic content), there is a need for application specific light-weight, no-reference gaming video quality prediction models. In this paper, we present two NR machine learning based quality estimation models for gaming video streaming, NR-GVSQI and NR-GVSQE, using NR features such as bitrate, resolution, blockiness, etc. We evaluate their performance on different gaming video datasets and show that the proposed models outperform the current state-of-the-art no-reference metrics, while also reaching a prediction accuracy comparable to the best known full reference metric

The complete mitochondrial genome of Linguatula serrata (tongue worm) isolated from a dog and phylogenetic analysis

The complete mitogenome of Linguatula serrata isolated from nasal cavity of a dog was characterized for the first time. The total size of the circular mitogenome was 15,328 bp consisting of 37 genes including 13 protein coding genes, 22 tRNA genes, two rRNA genes and two control regions. Phylogenetic tree was constructed based on 17 closely related species and their genetic relationship with Linguatula serrata was analysed