Radio signal propagation modeling plays an important role in the design of wireless communication systems. Various models have been developed, over the past few decades, to predict signal propagation and behavior for wireless communication systems in different operating environments. Recently, there has been an interest in the deployment of wireless sensors in soil. To fully exploit the capabilities of sensor networks deployed in soil requires an understanding of the propagation characteristics within this environment. This paper reviews the cutting-edge developments of signal propagation in the subterranean environment. The most important modeling techniques for modeling include electromagnetic waves, propagation loss, magnetic induction, and acoustic wave. These are discussed vis-a-vis modeling complexity and key parameters of the environment including electric and magnetic properties of soil. An equation to model propagation in the soil is derived from the free space model. Results are presented to show propagation losses and at different frequencies and volumetric water content. The channel capacity and the operating frequency are also analyzed against soil moisture at different soil types and antenna sizes.

Summary Fast and accurate detection of a facial data is crucial for both face and facial expression recognition systems. These systems include internet protocol video surveillance systems, crime scene photographs systems, and criminals' databases. The aim for this study is both improvement of accuracy and speed. The salient facial features are extracted through Haar techniques. The sizes of the images are reduced by Bessel down‐sampling algorithm. This method preserved the details and perceptual quality of the original image. Then, image normalization was done by anisotropic smoothing. Multilayer feed‐forward neural network with a back‐propagation algorithm was used as classifier. A detection accuracy of 98.5% with acceptable false positives was registered with test sets from FDDB, CMU‐MIT, and Champions databases. The speed of execution was also promising. An evaluation of the proposed method with other popular detectors on the FDDB set shows great improvement.