A comparative study of full‐duplex relaying schemes for low latency applications

Various sectors are likely to carry a set of emerging applications while targeting a reliable communication with low latency transmission. To address this issue, upon a spectrally‐efficient transmission, this paper investigates the performance of a 1 full‐dulpex relay system and considers for that purpose, 2 basic relaying schemes, namely, the symbol‐by‐symbol transmission, i.e., amplify‐and‐forward and the block‐by‐block transmission, i.e., selective decode‐and‐forward. The conducted analysis presents an exhaustive comparison, covering both schemes, over 2 different transmission modes, i.e., the noncombining mode where the best link, direct, or relay link is decoded and the signals combining mode, where direct and relay links are combined at the receiver side. While targeting latency purpose as a necessity, simulations show a refined results of performed comparisons and reveal that amplify‐and‐forward relaying scheme is more adapted to combining mode, whereas the selective decode‐and‐forward relaying scheme is more suitable for noncombining mode.     

An evaluation and ranking of evolutionary algorithms in segmenting abnormal masses in digital mammograms

Breast cancer is the most common cancer in women worldwide and the second main cause of cancer mortality after lung cancer. Up to now, there still no prevention nor early symptoms of breast cancer. Early detection can decrease significantly the mortality rate as the disease can be treated at an early stage. X-Ray is the current screening method that helps in detecting the most two common abnormalities of the breast, masses and micro-calcifications. However, interpreting mammograms is challenging in dense breasts as the abnormal masses and the normal glandular tissue of the breast have similar characteristics. Recently, the evolutionary algorithms have been widely used in image segmentation. In this paper, we evaluate and compare the performance of six most used evolutionary algorithms, invasive weed optimization (IWO), genetic algorithm (GA), particle swarm optimization (PSO), electromagnetism-like optimization (EMO), ant colony optimization (ACO), and artificial bee colony (ABC) in terms of clustering abnormal masses in the breast, particularly dense and extremely dense breasts. This evaluation is conducted based on quantitative metrics including Cohen’s Kappa, correlation, and false positive and false negative rates. The evolutionary algorithms are then ranked based on two multi-criteria decision analysis methods, the Preference Ranking Organization Method for the Enrichment of Evaluations (PROMETHEE) and the Graphical Analysis for Interactive Aid (GAIA).

     

Detection of breast abnormalities in digital mammograms using the electromagnetism-like algorithm

Multimedia Tools and Applications - The detection of abnormalities in the breast at an early stage can be so helpful for breast cancer treatment. Currently, mammography is the cheapest and the most...     

A performance comparison of measurement matrices in compressive sensing

Compressive sensing involves 3 main processes: signal sparse representation, linear encoding or measurement collection, and nonlinear decoding or sparse recovery. In the measurement process, a measurement matrix is used to sample only the components that best represent the signal. The choice of the measurement matrix has an important impact on the accuracy and the processing time of the sparse recovery process. Hence, the design of accurate measurement matrices is of vital importance in compressive sensing. Over the last decade, a number of measurement matrices have been proposed. Therefore, a detailed review of these measurement matrices and a comparison of their performances are strongly needed. This paper explains the foundation of compressive sensing and highlights the process of measurement by reviewing the existing measurement matrices. It provides a 3‐level classification and compares the performance of 8 measurement matrices belonging to 4 different types using 5 evaluation metrics: the recovery error, processing time, recovery time, covariance, and phase transition diagram. The theoretical performance comparison is validated with experimental results, and the results show that the Circulant, Toeplitz, and Hadamard matrices outperform the other measurement matrices.