Abstract
This paper presents a method that allows mobile devices to be globally self-localised in indoor localisation systems by transmitting to them data from position reference anchors. The objective is to establish a reliable one-way down-link communication through signals used in the localisation process in a typically strong fading and multipath channel environment. This is accomplished by using signal processing techniques, including coding and forward error correction, to transmit data using a specific transmission control protocol. Experimental results, using audio as the signal between anchors and the mobile device, demonstrate successful data transmission in realistic scenarios like a common noisy and reverberant room. Spread spectrum noise-like masked signals 49 dB below background noise were sufficient to attain correct data reception at four metres distance between a loudspeaker anchor and a mobile device's microphone.

Abstract
Over the last three decades, several despeckling filters have been developed by researchers to reduce the speckle noise inherently present in ultrasound B-scan images without losing the diagnostic information. This paper compiles and compares well-known techniques mostly used in the smoothing or suppression of speckle noise in ultrasound images. A comparison of the methods studied is done based on an experiment, using quality metrics, texture analysis and interpretation of profiles to evaluate their performance and show the benefits each one can contribute to denoising and feature preservation. To test the methods, a noise-free image of a kidney is used and then the Field II program simulates a B-mode ultrasound image. By this way, the smoothing techniques can be compared using numeric metrics, taking the noise-free image as a reference. In this study, a total of 17 different speckle reduction algorithms have been documented based on spatial filtering, diffusion filtering and wavelet filtering, with 15 qualitative metrics estimation. We use the tendencies observed in our study in real images. A new evaluation metric is proposed to evaluate the despeckling results.

Abstract

Abstract
Warri is one of the mega cities in Delta State in the coastal region of Nigeria hosting many industries. One of the three of Nigeria oil refineries is located in Warn. This study reveals the occurence of land deformation in the Warri area which is not only because it was formed in a tectonic setting but because of the high demand of the underground water for indutrial and domestics uses, high population density and oil and gas exploration in the area. Land deformation in this area is currently being accelerated by this indiscriminate withdrawal of underground water from the aquifers and exploration of the oil and gas leading to the compacting sediments. In this study Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique was applied to monitor land deformation in Warri metropolis in the period between 09/02/2006 and 12/08/2010 with Envisat data obtained from European Space Agency (ESA). The processing was done using Stanford Method for Persistent Scatterers/Multi-Temporal InSAR (StaMPS). From the analysis of the results, land deformation is occurring in Warri metropolis at a moderate rates (+/-5 mm/yr). During the preliminary investigations, structures, particularly buildings, were physically observed subsiding while some others with cracks of various types and degrees. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract

Abstract
With new trends like 3D and deep learning alternatives for face recognition becoming more popular, it becomes essential to establish a complete benchmark for the evaluation of such algorithms, in a wide variety of data sources and non-ideal scenarios. We propose a new RGB-depth-infrared (RGB-D-IR) dataset, RealFace, acquired with the novel Intel® RealSense TM collection of sensors, and characterized by multiple variations in pose, lighting and disguise. As baseline for future works, we assess the performance of multiple deep and “shallow” feature descriptors. We conclude that our dataset presents some relevant challenges and that deep feature descriptors present both higher robustness in RGB images, as well as an interesting margin for improvement in alternative sources, such as depth and IR.