In this paper, a new Smartphone sensor based algorithm is proposed to detect accurate distance estimation. The algorithm consists of two phases; the first phase is for detecting the peaks from the Smartphone accelerometer sensor. The other one is for detecting the step length which varies from step to step. The proposed algorithm is tested and implemented in real environment and it showed promising results. Unlike the conventional approaches, the error of the proposed algorithm is fixed and is not affected by the long distance.

Indoor positioning and tracking services are garnering more attention. Recently, several state-of-the-art localization techniques have been proposed that use radio maps or the sensors readily available on smartphones. This paper presents a localization system called Indoor Localization using Physical maps and smartphone Sensors (ILPS), which is based on a building blueprint database and smartphone sensors. The blueprint database and access points (APs) provide a number of reference points that can be used to acquire the initial position and adjust the user position each time a reference point is detected. The proposed method is implemented on a smartphone and tested in real indoor environments. The experiments with ILPS demonstrate that using a static blueprint will avoid the costly database updates that are usually required in other approaches due to signal attenuation. Furthermore, ILPS performs better than existing work in term of accuracy and effectiveness for indoor localization.

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One of the most difficult problems analysts and decision-makers may face is how to improve the forecasting and predicting of financial time series. However, several efforts were made to develop more accurate and reliable forecasting methods. The main purpose of this study is to use technical analysis methods to forecast Jordanian insurance companies and accordingly examine their performance during the COVID-19 pandemic. Several experiments were conducted on the daily stock prices of ten insurance companies, collected by the Amman Stock Exchange, to evaluate the selected technical analysis methods. The experimental results show that the non-parametric Exponential Decay Weighted Average (EDWA) has higher forecasting capabilities than some of the more popular forecasting strategies, such as Simple Moving Average, Weighted Moving Average, and Exponential Smoothing. As a result, we show that using EDWA to forecast the share price of insurance companies in Jordan is good practice. From a technical analysis perspective, our research also shows that the pandemic had different effects on different Jordanian insurance companies.