This paper proposed a novel weighted multidimensional scaling (MDS) estimator for estimating the position of a stationary emitter with sensor position uncertainties using time-difference-of-arrival measurements. The solution is closed form and unbiased. It is shown analytically to achieve the Cramer-Rao lower bound performance in small noise region. Simulation results show that the proposed estimator offers smaller bias and mean square error than the two-step weighted least square approach and traditional MDS estimator ignoring sensor position uncertainties at moderate noise level. Additionally, the computation complexities of them are comparable.

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The massive growth of online information obliged the availability of a thorough research in the domain of automatic text summarization within the Natural Language Processing (NLP) community. To reach this goal, different approaches should be integrated and collaborated. One of these approaches is the classification od documents. Therefore, the aim of this paper is to propose a successful framework for agricultural documents classification as a step forward for a language independent automatic summarization approach. The main target of our serial research is to propose a complete novel framework which not only responses to the question, but also gives the user an opportunity to find additional information that is related to the question. We implemented the proposed method. As a case study, the implemented method is applied on Arabic text in the agriculture field. The implemented approach succeeded in classifying the documents submitted by the user. The approach results have been evaluated using Recall, Precision and F-score measures.

In this paper, we suggested new algorithms to discriminate between eight analogue modulated signals (amplitude modulation (AM), frequency modulation (FM), double side band (DSB), lower side band (LSB), upper side band (USB), vestigial side band (VSB), combined (AM-FM) and carrier wave (CW)). The simulation results show that the overall recognition of the new algorithms over 97% when the signal to noise ratio (SNR)=0dB. These new algorithms not only achieve a better recognition rate, but also reduce the computational loads. 

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