Precise foreground detection algorithm using motion estimation, minima and maxima inside the foreground object

In this paper the precise foreground mask is obtained in a complex environment by applying simple and effective methods on a video sequence consisting of multi-colour and multiple foreground object environment. To detect moving objects we use a simple algorithm based on block-based motion estimation, which requires less computational time. To obtain a full and improved mask of the moving object, we use an opening-and-closing-by- reconstruction mechanism to identify the minima and maxima inside the foreground object by applying a set of morphological operations. This further enhances the outlines of foreground objects at various stages of image processing. Therefore, the algorithm does not require the knowledge of the background image. That is why it can be used in real world video sequences to detect the foreground in cases where we do not have a background model in advance. The comparative performance results demonstrate the effectiveness of the proposed algorithm.The Institute of Management Sciences Peshawar (http://imsciences.edu.pk/) through Higher Education Commission Islamabad, Pakistan (http://hec.gov.pk/)

Calibration of Free-Space Radiometric Partial Discharge Measurements

The present study addresses the calibration of four types of partial discharge (PD) emulators used in the development of a PD Wireless Sensor Network (WSN). Three PD emulators have been constructed: a floating-electrode emulator, and two internal PD emulators. Both DC and AC high-voltage power supplies are used to initiate PD, which is measured using concurrent free-space radiometry (FSR) and a galvanic contact method based on the IEC 60270 standard. The emulators have been measured and simulated, and a good agreement has been found for the radiated fields. A new method of estimating the absolute PD activity level from radiometric measurements is proposed

3D Direction of Arrival Estimation:An Innovative Deep Neural Network Approach

The recent integration of neural networks into the domain of direction of arrival estimation marks a promising frontier in the landscape of next-generation wireless communications. Our paper meticulously delves into the architecture of the proposed deep convolutional neural network (DCNN), presenting a novel framework designed to streamline the classification process within the output layer. Operating on correlation matrices created by signals received by a 4 × 4 planar antenna array, our DCNN predicts angles of arrival in 3D space. We assess the model’s performance in scenarios involving the simultaneous reception of signals, employing the mean absolute error as a metric to gauge prediction errors in the angle domain. The simulation results affirm the superior performance of the proposed deep learning-based scheme. The model’s robustness is rigorously examined across various validation cases, providing conclusive evidence of its potential in real-world applications.</p