JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION
Year:2017 | Volume:5 | Issue:4|Papers Count:7

This paper presents system-level design and implementation of an ultra-wide tunable, high precision, fast locking, low phase noise, and low power portable fractional-N frequency synthesizer. The output frequency of the proposed design is ranged from 54 MHz to 6.8 GHz. The VCO cores cover frequencies from 3.4 GHz to 6.8 GHz. The programmable output dividers allow generation of the lower frequencies. The output power is tunable between -4dBm and+5dBm. It can generate a wide range, high precision, and linear frequency sweep. The sweep rate, frequency step, and frequency range are tunable. The new frequency tuning algorithm, named Yas algorithm, is proposed to improve frequency precision of the synthesizer. To demonstrate the efficiency of the Yas algorithm, the results of MATLAB simulations and experimental measurements are presented. The output phase noise is -95.55 dBc/Hz at 1 KHz offset from 3 GHz. The experimental measurement results demonstrate that the implemented frequency synthesizer can be used for applications, such as oscillator of spectrum analyzer, automatic test equipment, FMCW radars, high-performance clock source for high speed data converter, satellite communications, and measurement systems.

The ubiquity of the Internet and social networks have turned question and answer communities into an environment suitable for users to ask their questions about anything or to share their knowledge by providing answers to other users’ questions. These communities designed for knowledge-sharing aim to improve user knowledge, making it imperative to have a mechanism that can evaluate users’ knowledge level or in other words "to find experts". Experts are people who are highly talented in a specific field like technology, languages, cooking and etc, and we focused on specific fields in programming languages. There is a need for expert-finding algorithms in social networks or any other knowledge sharing environment like question and answer communities. So companies that looking for hiring programmers can easily find people suit their needs. There are various content analysis and link analysis methods for expert-finding in social networks. Experts can be identified by their behaviors in forums and their relationships in proportion to the questions and answers they share. Therefore, analyzing social networks can provide us with appropriate information. This paper aims to challenge four algorithms by applying them to our dataset and analyze the results in order to compare the algorithms. The algorithms suitable for expert finding has been found and ranked. Based on the results and tests, it is concluded that the Z-score algorithm has a better performance than others. The outcome of this article for enthusiasts is top algorithms for expert finding in question-answer communities. In this paper, first, we will start by introducing the problem of expert finding problem the related works will be presented then algorithms and dataset are introduced and finally results are illustrated.

The tiny and low-cost sensors cannot simultaneously sense more than one channel since they do not have high-speed Analog-to-Digital-Convertors (ADCs) and high-power batteries. It is a critical problem when they are used for multi-channel sensing in cognitive sensor networks (CSNs). One solution for this problem is that the sensors sense various channels at different sensing periods. Due to the energy limitation in these scenarios, the lifetime maximization will become an important issue. In this paper, maximizing the lifetime of a CSN is investigated by selecting both the cooperative sensors and their detector threshold, such that the desired detection performance constraints are satisfied. This is a NP-complete problem, and obtaining the optimum solution needs exhaustive search with exponential complexity order. Here we have proposed two convex-based optimization algorithms with low order of complexity. First algorithm applies the known instantaneous Signal-to-Noise-Ratio (SNR) and obtains the proper detector thresholds by solving an equation for every channel. Investigation the effect of detector thresholds on the energy consumption, the false alarm probability and the detection probability shows that we can minimize the detector thresholds such that the detection constraints are met. In the second algorithm in order to reduce the needed time for obtaining answers, the Bisection method is proposed for determining detector thresholds. Because knowing the instantaneous SNR is difficult, we have investigated the performance of the second algorithm by average value of SNR. Simulation results show that the proposed algorithms improve the performance of the network in case of lifetime and energy consumption.

An efficient space data management is imperative in guaranteeing the best performance with a fair distribution of next generation satellite networks. Therefore, one of the major challenges to implement this kind of future satellite networks is evaluation any untrusted error for the best quality of service (QoS). In this regard, bit error rate (BER) criteria based on the type of space channel which it can be defined prefect or imperfect state between one or more satellites and terrestrial infrastructures seems to be an important subject for space communication. In this paper, the authors provide a bandwidth sharing algorithm for a proposed future heterogeneous satellite networks. This structure can have many satellites in different orbits beside terrestrial equipment having many antennas. In this paper to evaluate this system model, the coverage probability and space capacity based on input parameters such as path loss and signal to noise ratio (SNR) has been analyzed. Also, the bit error rate for a Multi-Input-Multi-Output (MIMO) satellite network based on imperfect channel estimation is simulated based on quadrature amplitude modulation (QAM) and quadrature phase shift keying (QPSK) digital modulations which input parameters are error rate due to imperfect channel estimation and the number of antennas. Finally, two digital modulation compared together based on error rate changes.

With rapid development of information technology (IT) and further deepening of informationization construction, more and more enterprises have realized the strategic value of IT and made great investments in it. However, during the IT implementation process, decision-making, adaptation degree, and IT performance are often inferior to the anticipation. The assimilation of technology can be defined by the extent to which the use of Information technology spreads across organizational processes and becomes routinized in activities. Capabilities of information technology play crucial role in an ever-changing environment and are considered as one of the most important sources for enterprises while enterprises should acquire some effective capabilities so that they can effectively deploy and utilize information technology. But companies need to increase their Absorbency of IT to accept it for effective use of IT, since the acquisition and use of information technology is simply not feasible and any information technology does not create value. The purpose of this investigation is to represent a model to measure absorbency of information technology in small and medium-sized enterprises. To do so, dimensions of the "absorbency of information technology" was determined through exploratory factor analysis in a survey research and confirmatory factor analysis was used to confirm model validity. Findings show that three dimensions are related to absorbency of information technology including the capability for innovative technology, Inside-Out IT capability and IT management capability, among which the capability for innovative technology has the highest correlation with the concept of absorbency of information technology.

Impulse noise removal from images is one of the most important concerns in digital image processing. Noise must be removed in a way that the main and important information of image is kept. Traditionally, the median filter has been the best way to deal with impulse noise; however, the image quality obtained in high noise density is not desirable. The aim of this paper is to propose an algorithm in order to improve the performance of adaptive median filter to remove high density impulse noise from digital images. The proposed method consists of two main stages of noise detection and noise removal. In the first stage, noise detection includes two global and local phases and in the second stage, noise removal is also done based on a two-phase algorithm. Global noise detection is done by a pixel classification approach in each block of the image and local noise detection is performed by automatically determining two threshold values in each block. In the noise removal stage only noise pixels detected from the first stage of the algorithm are processed by estimating noise density and applying adaptive median filter on noise-free pixels in the neighborhood. Comparing experimental results obtained on standard images with other proposed methods proves the success of the proposed algorithm.

The model in this study was created to apply a new and functional model for prediction of IT maintenance cost of system downtime by using artificial intelligence method in contrast of other research techniques in IT cost measuring. The estimation is based on the measuring of IT services availability. In this model, ANFIS was developed for quantifying Information Technology Generated Services (ITGS) perceptible by business users. In addition, it was used to forecasting IT cost related to system maintenance that it can help managers for future and constructive decision. This model has been applied and tested prediction technique by ANFIS in MATALB fuzzy toolbox by previous large volume of data gathering from IT cost factors, ITGS, and associative cost in order to building pattern, tuning and training this model well. First of all, the model was fully developed, stabilized, and passed through intensive training with large volume of data collected in one of the organization in Iran. In first phase, it can be possible to feed a specific period of data into the model to determine the quantity of services (ITGS), and in second phase, their related maintenance cost can be predicted. ANFIS learning technique predicted maintenance cost of measured services availability which it was totally provided with first quantifying services in a specific time period. Having an operational mechanism for measuring and quantifying information technology services tangible by users for estimating their costs is contributed to practical accurate investment. Some components have been considered and measured in the field of system maintenance. The main objective of this study is prediction the amount of investment for maintenance of entire ITGS by extraction and considering the factors affecting the software maintenance cost help to estimate the cost and reduce it by controlling the factors.