<p class="0abstract">This paper introduces a new approach named ‘Find My Parked Car’ to provide the user with the return way for his parked car location in the indoor garage of a multi-storey building where GPS is denied using the acquisition data obtained from the inertial sensors on smart devices such as smartphones. We build up a system that employs Pedestrian Dead-Reckoning Technique (PDR) to track the user inside the building. This approach harness the environment landmarks for floors separation and the PDR paths’ landmarks to enhance them by applying the Kalman Filter. Then we prunes the paths to remove the redundant parts of paths. Finally, we display the paths that guide the user back to his car via an android application. The feature of our approach is to help users returning back to parked car location based only on the available ubiquitous sensors in the new smartphones without any need to have any map or any additional infrastructure support.</p>

With the widespread use of videos in many fields of our lives, it becomes very important to develop new techniques for video denoising. Spatial video denoising using wavelet transform has been the focus of the current research, as it requires less computation and more suitable for real-time applications. Two specific techniques for spatial video denoising using wavelet transform are considered in this work: 2D Discrete Wavelet Transform (2D DWT) and 2D Dual Tree Complex Wavelet Transform (2D DTCWT). We performed an analytical analysis to investigate the performance of each of these techniques. From this analysis, we found out that each of these techniques has its advantages and disadvantages. The first technique gives less quality at high levels of noise but consumes less time, whereas the second gives high quality video while consuming a large amount of time. In this work, we introduce an intelligent denoising system that makes a tradeoff between the quality of the denoised video and the time required for denoising. The system first estimates the noise level in the video frame then chooses the proper denoising technique to apply on the frame. The simulation results show that the proposed system is more suitable for real-time applications where time is critical, while still giving high quality videos at low to moderate levels of noise.

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One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the Fuzzy C-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the Fuzzy C-Means (FCM) and the Wavelet Fuzzy C-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.