A Novel And Fast Feature Based Motion estimation ALgorithm Through extraction Of Background And Object

This paper presents a novel and fast Feature Based Motion Estimation algorithm which is developed for typical video-phone scenario. In essence it combines the technique of object extraction with traditional block based motion estimation methods by estimating the background and extracting the moving object continuously in the first stage, then performs a block based motion estimation on the extracted. Simulation of the algorithm with full search as core shows that the estimation time can be reduced by as much as 50%, while the MSE and PSNR remain almost the same as the full search results.published_or_final_versio

Enhanced Bandwidth Based Handover Decision Making Algorithm For Small Cell Wireless Networks

In small cell wireless networks, fast and precise vertical handover decision making algorithms are needed to minimize the handover failures and unnecessary handovers, especially in high-speed scenario. In small cell wireless networks such as WLAN and 5G, shorter traveling time is anticipated for a fast-moving user traversing the cell coverage. This results in frequent handovers. It leads to poor user experience and wastage of network resources. To overcome this problem, this paper proposed a new handover decision making algorithm that integrates the traveling distance prediction technique with the bandwidth based handover algorithm. The simulation results show that the proposed algorithm has successfully reduced the number of unnecessary handovers and handover failure in small cell wireless networks

PCA Based Robust Motion Data Recovery.

Human motion tracking is a prevalent technique in many fields. A common difficulty encountered in motion tracking is the corrupted data is caused by detachment of markers in 3D motion data or occlusion in 2D tracking data. Most methods for missing markers problem may quickly become ineffective when gaps exist in the trajectories of multiple markers for an extended duration. In this paper, we propose the principal component eigenspace based gap filling methods that leverage a training sample set for estimation. The proposed method is especially beneficial in the scenario of motion data with less predictable or repeated movement patterns, and that of even missing entire frames within an interval of a sequence. To highlight algorithm robustness, we perform algorithms on twenty test samples for comparison. The experimental results show that our methods are numerical stable and fast to work

Velocity Dealiased Spectral Estimators of Range Migrating Targets using a Single Low-PRF Wideband Waveform

Wideband radars are promising systems that may provide numerous advantages, like simultaneous detection of slow and fast moving targets, high range-velocity resolution classification, and electronic countermeasures. Unfortunately, classical processing algorithms are challenged by the range-migration phenomenon that occurs then for fast moving targets. We propose a new approach where the range migration is used rather as an asset to retrieve information about target velocitiesand, subsequently, to obtain a velocity dealiased mode. More specifically three new complex spectral estimators are devised in case of a single low-PRF (pulse repetition frequency) wideband waveform. The new estimation schemes enable one to decrease the level of sidelobes that arise at ambiguous velocities and, thus, to enhance the discrimination capability of the radar. Synthetic data and experimental data are used to assess the performance of the proposed estimators

Dynamic FOV visible light communications receiver for dense optical networks

This study explores how the field-of-view (FOV) of a visible light communications (VLCs) receiver can be manipulated to realise the best signal-to-noise ratio (SNR) while supporting device mobility and optimal access point (AP) selection. The authors propose a dynamic FOV receiver that changes its aperture according to receiver velocity, location, and device orientation. The D-FOV technique is evaluated through modelling, analysis, and experimentation in an indoor environment comprised of 15 VLC APs. The proposed approach is also realised as an algorithm that is studied through analysis and simulation. The results of the study indicate the efficacy of the approach including a 3X increase in predicted SNR over static FOV approaches based on measured received signal strength in the testbed. Additionally, the collected data reveal that D-FOV increases effectiveness in the presence of noise. Finally, the study describes the tradeoffs among the number of VLC sources, FOV, user device velocity, and SNR as a performance metric.Accepted manuscrip