Journal of Information Systems and Telecommunication
Year:2019 | Volume:7 | Issue:1|Papers Count:7

A closed-form solution for target localization based on the realistic distance-dependent noises in illuminator of opportunity passive radar and the reduction method of the bias which exists in the two-stage weighted least squares (2SWLS) method is proposed. 2SWLS is a classic method for time-of-arrival (TOA) and frequency-of-arrival (FOA) localization problem and has a couple of improved solutions over the years. The 2SWLS and its improved solutions have great localization performances in their established location scenarios on the basis of two approximations that setting the noise to a constant and ignoring the high-order terms of TOA and FOA measurement noises. It is these two approximations that lead to a sub-optimal solution with bias. The bias of 2SWLS has a significant influence on the target localization in illuminator of opportunity passive radar that has lower measurement accuracy and higher noises than active radar. Therefore, this paper starts by taking into consideration of the realistic distance-dependent characteristics of TOA/-FOA noises and improving 2SWLS method. Then, the bias of the improved 2SWLS method is analyzed and a bias-reduced solution based on weighted least squares (WLS) is developed. Numerical simulations demonstrate that, compared to the existing improved solutions of the 2SWLS, the proposed method effectively reduces the bias and achieves higher localization accuracy.

IEEE 802. 11e is standardized to enhance real-time multimedia applications’ Quality of Service. This standard introduces four access categories for different types of applications. Each access category has four adjustable parameters: Arbitrary Inter-Frame Space Number, minimum Size of Contention Window, maximum size of Contention Window, and a Transmission Opportunity limit. A Transmission Opportunity limit is the time interval, in which a wireless station can transmit a number of frames consecutively, without releasing the channel and any further contention with other wireless stations. Transmission Opportunity improves network throughput as well as service differentiation. Proper Transmission Opportunity adjustment can lead to better bandwidth utilization and Quality of Service provisioning. This paper studies the dynamic adjustment of Transmission Opportunity in IEEE 802. 11e using a game-theory based approach called Game Theory Based Dynamic Transmission Opportunity. Based on the proposed method, each wireless node chooses its appropriate Transmission Opportunity according to its queue length and media access delay. Simulation results indicate that the proposed approach improves channel utilization, while preserving efficiency in WLANs and minimizing selfishness behaviors of stations in a distributed environment.

Traditional methods of summarization were very costly and time-consuming. This led to the emergence of automatic methods for text summarization. Extractive summarization is an automatic method for generating summary by identifying the most important sentences of a text. In this paper, two innovative approaches are presented for summarizing the Farsi texts. In these methods, using a combination of deep learning and statistical methods (TFIDF), we cluster the concepts of the text and, based on the importance of the concepts in each sentence, we derive the sentences that have the most conceptual burden. In these methods, we have attempted to address the weaknesses of representation in repetition-based statistical methods by exploiting the unsupervised extraction of association between vocabulary through deep learning. In the first unsupervised method, without using any hand-crafted features, we achieved state-of-the-art results on the Pasokh single-document corpus as compared to the best supervised Farsi methods. In order to have a better understanding of the results, we have evaluated the human summaries generated by the contributing authors of the Pasokh corpus as a measure of the success rate of the proposed methods. In terms of recall, these have achieved favorable results. In the second method, by giving the coefficient of title effect and its increase, the average ROUGE-2 values increased to 0. 4% on the Pasokh single-document corpus compared to the first method and the average ROUGE-1 values increased to 3% on the Khabir news corpus.

Microarray data using small samples and thousands of genes provides a difficult challenge for researchers. Utilizing gene selection helps to select the most relevant genes from original dataset with the purpose of dimensionality reduction of microarray data as well as increasing the prediction performance. In this paper, a new gene selection method based on community detection technique and ranking the best genes, is proposed. In order to select the best genes, Symmetric Uncertainty calculates the similarity between two genes, and between gene and its class label. In the first phase, this leads to representation of search space in form of graph. In the second phase, the proposed graph is divided into several clusters, using community detection algorithm. Finally, after ranking the genes, the ones with maximum ranks are selected as the best genes. This approach is a supervised/unsupervised filter-based gene selection method, which not only minimizes the redundancy between genes, but also maximizes the relevance of genes and their class labels. Performance of the proposed method is compared with twelve well-known unsupervised/supervised gene selection approaches over twelve microarray datasets using four classifiers including SVM, DT, NB and k-NN. The results illustrate the advantages of the proposed approach.

One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to femto-cell users, considering the limited resources in radio access networks. The basis for our proposed scheme is to introduce a self-optimization model based on neighbouring relations. Using this model, we can create the possibility of controlling resources and neighbouring parameters without the need of human manipulation and only based on the network‘ s intelligence. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process in a way that on the uplink, the sent data by users is to be analyzed in self-optimization engine. The experimental results show that despite the tremendous volume of the analyzed data – which is hundreds of times bigger than usual methods – it is possible to improve the KPIs, such as throughput, up to 30 percent by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25 percent when the number of parameters is too high Moreover, numerical results indicate the superiority of using support vector machine (SVM) learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15-percent increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme.

In today‘ s world, most of companies are trying to survive in a competitive environment which has been increased in recent years. This competition has raised the customer power to select desired products and services among different suppliers and providers. So the importance of customer satisfaction and loyalty has been increased dramatically for companies and businesses. This is more important for distributor companies which deal with a lot of customers in a B2B market. Mobile-CRM has emerged new opportunities on customer satisfaction and loyalty. However, implementing a Mobile-CRM system is a complicated large project that affects all aspects of an organization and needs a huge investment which increases the risk of failure. To avoid this risk, the assessing of company‘ s E-Readiness before starting main project is necessary but the vital question is that how companies can assess their E-Readiness level for implementing a M-CRM system and improve it. Since there was not introduced before a suitable model to help companies for achieving this, in this research we have investigated different models and selected VERDICT as a suitable model for assessing the E-Readiness of a company willing to implement a Mobile-CRM system. A large distributor company is the case study of this research. The research is conducted based on a descriptive-survey method using questionnaire tools for extracting the experts‘ opinion and determining the company‘ s E-Readiness level. The results show that the level of E-Readiness of the case company for implementing Mobile-CRM, is in an acceptable situation based on all four main factors including Management, Information Technology, People, and Process. Additionally, the VERDICT model is recommended to those distribution companies that are planning to implement Mobile-CRM system to help them to prevent the risk of project failure.

This paper proposes two algorithms for Voice Activity Detection (VAD) based on sparse representation in spectro-temporal domain. Spectral-temporal components which, in addition to the frequency and time dimensions, have two other dimensions of the scale and rate. Scale means spectral modulation and the rate means temporal modulation. On the other hand, using sparse representation in learning dictionaries of speech and noise, separate the speech and noise segment to be better separated. The first algorithm was made using two-dimensional STRF (Spectro-Temporal Response Field) space based on sparse representation. Dictionaries with different atomic sizes and two dictionary learning methods: NMF (non-negative matrix factorization) and the K-SVD (k-means clustering method), were investigated in this approach. This algorithm revealed good results at high SNRs (signal-to-noise ratio). The second algorithm, whose approach is more complicated, suggests a speech detector using the sparse representation in four-dimensional STRF space. Due to the large volume of STRF's four-dimensional space, this space was divided into cubes, with dictionaries made for each cube separately by NMF (non-negative matrix factorization) learning algorithm. Simulation results were presented to illustrate the effectiveness of our new VAD algorithms. The results revealed that the achieved performance was 90. 11% and 91. 75% under-5 dB SNR in white and car noise respectively, outperforming most of the state-of-the-art VAD algorithms.