AROMA: Automatic Generation of Radio Maps for Localization Systems

WLAN localization has become an active research field recently. Due to the wide WLAN deployment, WLAN localization provides ubiquitous coverage and adds to the value of the wireless network by providing the location of its users without using any additional hardware. However, WLAN localization systems usually require constructing a radio map, which is a major barrier of WLAN localization systems' deployment. The radio map stores information about the signal strength from different signal strength streams at selected locations in the site of interest. Typical construction of a radio map involves measurements and calibrations making it a tedious and time-consuming operation. In this paper, we present the AROMA system that automatically constructs accurate active and passive radio maps for both device-based and device-free WLAN localization systems. AROMA has three main goals: high accuracy, low computational requirements, and minimum user overhead. To achieve high accuracy, AROMA uses 3D ray tracing enhanced with the uniform theory of diffraction (UTD) to model the electric field behavior and the human shadowing effect. AROMA also automates a number of routine tasks, such as importing building models and automatic sampling of the area of interest, to reduce the user's overhead. Finally, AROMA uses a number of optimization techniques to reduce the computational requirements. We present our system architecture and describe the details of its different components that allow AROMA to achieve its goals. We evaluate AROMA in two different testbeds. Our experiments show that the predicted signal strength differs from the measurements by a maximum average absolute error of 3.18 dBm achieving a maximum localization error of 2.44m for both the device-based and device-free cases.Comment: 14 pages, 17 figure

Unconventional TV Detection using Mobile Devices

Recent studies show that the TV viewing experience is changing giving the rise of trends like "multi-screen viewing" and "connected viewers". These trends describe TV viewers that use mobile devices (e.g. tablets and smart phones) while watching TV. In this paper, we exploit the context information available from the ubiquitous mobile devices to detect the presence of TVs and track the media being viewed. Our approach leverages the array of sensors available in modern mobile devices, e.g. cameras and microphones, to detect the location of TV sets, their state (ON or OFF), and the channels they are currently tuned to. We present the feasibility of the proposed sensing technique using our implementation on Android phones with different realistic scenarios. Our results show that in a controlled environment a detection accuracy of 0.978 F-measure could be achieved.Comment: 4 pages, 14 figure

Estimating the Number of Patents in the World Using Count Panel Data Models

In this paper, we review some estimators of count regression (Poisson and negative binomial) models in panel data modeling. These estimators based on the type of the panel data model (the model with fixed or random effects). Moreover, we study and compare the performance of these estimators based on a real dataset application. In our application, we study the effect of some economic variables on the number of patents for seventeen high-income countries in the world over the period from 2005 to 2016. The results indicate that the negative binomial model with fixed effects is the better and suitable for data, and the important (statistically significant) variables that effect on the number of patents in high-income countries are research and development (R&D) expenditures and gross domestic product (GDP) per capita

Ductile corrosion-free self-centering concrete elements

Corrosion is a major factor in the deterioration of reinforced concrete (RC) structures. To mitigate this problem, steel bars can be replaced with glass-fiber-reinforced-polymer (GFRP) bars. However, the lack of ductility of GFRP-RC elements has prevented their use in many structural applications, especially in seismic areas. Superelastic shape memory alloy (SMA) bars have been proposed to be used in seismic areas because of their self-centering characteristics. Also, they have the added advantage of being corrosion resistant. This paper examines the combined use of SMA and GFRP bars to achieve ductile self-centering and corrosion-free elements. The first challenge for such a proposal relates to designing concrete frames, reinforced with SMA and GFRP bars, that have adequate lateral performance in terms of initial stiffness, ductility, and strength. A comprehensive parametric study is conducted to better understand the structural behavior of concrete elements reinforced with SMA and/or GFRP bars. Results from the study are utilized to develop design equations that allow designing an SMA/GFRP RC section to replace a steel RC section, while maintaining lateral strength, stiffness, and ductility. To examine the adequacy of the developed equations, a six-storey concrete frame is designed, and its lateral performance is examined using pushover analysis

Seismic performance of ductile corrosion-free reinforced concrete frames

Corrosion of steel bars is the main cause of the deterioration of reinforced concrete (RC) structures. To avoid this problem, steel rebars can be replaced with glass-fiber-reinforced-polymer (GFRP). However, the brittle behaviour of GFRP RC elements has limited their use in many applications. The use of shape memory alloy (SMA) and/or stainless steel (SS) rebars can solve this problem, because of their ductile behaviour and corrosion resistance. However, their high price is a major obstacle. To address issues of ductility, corrosion, and cost, this paper examines the hybrid use of GFRP, SS, and SMA in RC frames. The use of SMA provides an additional advantage as it reduces seismic residual deformations. Three frames were designed. A steel RC frame, SS-GFRP RC frame, and SMA-SS-GFRP RC frame. The design criteria for the two GFRP RC frames followed previous research by the authors, which aimed at having approximately equal lateral resistance, stiffness, and ductility for GFRP and steel RC frames. The three frames were then analyzed using twenty seismic records. Their seismic performance confirmed the success of the adopted design methodology in achieving corrosion-free frames that provide adequate seismic performance

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas

Ductile Corrosion-Free GFRP-Stainless Steel Reinforced Concrete Elements

Corrosion of steel rebars is known to cause deterioration of concrete structures that can lead to catastrophic failures. To mitigate this problem, steel rebars can be replaced with Glass Fiber-Reinforced Polymer (GFRP) rebars. However, the lack of ductility of GFRP-reinforced elements has prevented their use in many structural applications, especially in seismic areas. Stainless Steel (SS) rebars are corrosion resistant and have adequate energy absorption and ductility. However, they are much more expensive than steel rebars. This paper proposes the combined use of SS and GFRP rebars to achieve ductile and corrosion-free elements. The first challenge for such a proposal relates to designing SS-GFRP reinforced concrete frame with adequate lateral performance in terms of initial stiffness, ductility, and strength. Design equations, which are based on a comprehensive parametric study, are developed to allow designing such a frame. A six-storey concrete frame is then designed using the proposed equations and its lateral performance is examined using pushover analysis

Non-dominated sorting gravitational search algorithm for multi-objective optimization of power transformer design

Transformers are crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower mass and losses. Multi-objective Optimization Problems (MOPs) consist of several competing and incommensurable objective functions. Recently, as a search optimization technique inspired by nature, evolutionary algorithms have been broadly applied to solve MOPs. In this paper, a power Transformer Design (TD) methodology using Non-dominated Sorting Gravitational Search Algorithm (NSGSA) is proposed. Results are obtained and presented for NSGSA approach. The obtained results for the study case are compared with those results obtained when using other multi objective optimization algorithms which are Novel Gamma Differential Evolution (NGDE) Algorithm, Chaotic Multi-Objective Algorithm (CMOA), and Multi- Objective Harmony Search (MOHS) algorithm. From the analysis of the obtained results, it has been concluded that NSGSA algorithm provides the most optimum solution and the best results in terms of normalized arithmetic mean value of two objective functions using NSGSA to the TD optimization

Rise and Demise of the New Lakes of Sahara

Multispectral remote sensing data and digital elevation models were used to examine the spatial and temporal evolution of the New Lakes of Sahara in southern Egypt. These lakes appeared in September 1998, when water spilled northwestward toward the Tushka depression due to an unusual water rise in Lake Nasser induced by high precipitation in the Ethiopian Highlands. Five lakes were formed in local depressions underlain by an impermeable Paleocene shale and chalk formation. The lakes developed through three stages. (1) A rise stage occurred from September 1998 to August 2001; the area covered by the lakes reached ~1586 km2. In this stage the rate of water supply far exceeded the rate of water loss through evaporation. This stage was characterized by an early phase (August 1998-August 1999) when the area covered by the lakes increased by ~75 km2/month. This was followed by a late phase (August 1999-August 2001), in which area increase averaged ~28 km2/month. (2) A steady-state stage occurred from August 2001 to August 2003, during which the area covered by the lakes remained relatively unchanged and water lost through evaporation was continuously replaced by water supply from Lake Nasser. (3) A demise stage occurred from August 2003 to April 2007, during which water supply from Lake Nasser stopped completely and water was continuously evaporating. The area covered by the lakes decreased to ~800 km2 with an average loss of ~17 km2/month. If this trend continues, the New Lakes of Sahara will disappear completely by March 2011. The spatial distribution of the New Lakes of Sahara is strongly controlled by morphologically defined east-, north-, northeast-, and northwest-trending faults. The water recharge of the Nubian aquifer by the New Lakes of Sahara is insignificant; much of the lakes\u27 area is above an impermeable formation

Structured Health Literacy Intervention for Mothers Regarding Stem Cells Therapy

Background: Stem cells have tremendous promise uses for the future to treat a variety of diseases, injuries, and other health-related conditions. Their potential is evident to promote the repair response of diseased, dysfunctional or injured tissue. Aim: This study aimed to assess the effectiveness of applying a structured health literacy intervention regarding stem cells therapy (SCT) on the mothers' level of knowledge and attitude. Methodology: A quasi-experimental one-group pre-post test design was utilized. The study involved 302 mothers recruited from the pediatric outpatient clinics of Mansoura University Children Hospital (MUCH), Egypt. Data were collected using two structured interview sheets (knowledge and attitude). Results: There were highly statistically significant differences in mothers’ knowledge and attitude one-month post-application of the structured health literacy intervention regarding stem cells therapy compared to pre-application (P=<0.001). Conclusion: Application of a structured health literacy intervention showed a significant improvement in mothers' level of knowledge and attitude regarding stem cells therapy. Keywords: Attitude, Health Literacy, Knowledge, Stem Cells Therapy. DOI: 10.7176/JHMN/61-06 Publication date: April 30th 201