Practical Provably Secure Multi-node Communication

We present a practical and provably-secure multimode communication scheme in the presence of a passive eavesdropper. The scheme is based on a random scheduling approach that hides the identity of the transmitter from the eavesdropper. This random scheduling leads to ambiguity at the eavesdropper with regard to the origin of the transmitted frame. We present the details of the technique and analyze it to quantify the secrecy-fairness-overhead trade-off. Implementation of the scheme over Crossbow Telosb motes, equipped with CC2420 radio chips, shows that the scheme can achieve significant secrecy gain with vanishing outage probability. In addition, it has significant overhead advantage over direct extensions to two-nodes schemes. The technique also has the advantage of allowing inactive nodes to leverage sleep mode to further save energy.Comment: Proceedings of the IEEE International Conference on Computing, Networking and Communications (ICNC 2014

Fast traffic sign recognition using color segmentation and deep convolutional networks

The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelli- gent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Ori- ented Gradients (HOG), and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by a pre- processing step to reduce the search space, while classication is carried out by using a Deep Learning technique. A quantitative evaluation of the proposed approach has been conducted on the well-known German Traf- c Sign data set and on the novel Data set of Italian Trac Signs (DITS), which is publicly available and contains challenging sequences captured in adverse weather conditions and in an urban scenario at night-time. Experimental results demonstrate the eectiveness of the proposed ap- proach in terms of both classication accuracy and computational speed

Clouds Motion Estimation from Ground-Based Sky Camera and Satellite Images

Estimation of cloud motion is a challenging task due to the non-linear phenomena of cloud formation and deformation. Satellite images processing is a popular tool used to study the characteristics of clouds which constitute major factors in forecasting the meteorological parameters. Due to the low resolution of satellite images, researchers have turned towards analyzing the high-resolution images captured by ground-based sky cameras. The first objective of this chapter is to demonstrate the different techniques used to estimate clouds motion and to compare them with respect to the accuracy and the computational time. The second aim is to propose a fast and efficient block matching technique based on combining the two types of images. The first idea of our approach is to analyze the low-resolution satellite images to detect the direction of motion. Then, the direction is used to orient the search process to estimate the optimal motion vectors from the high-resolution ground-based sky images. The second idea of our method is to use the entropy technique to find the optimal block sizes. The third idea is to imply an adaptive cost function to perform the matching process. The comparative study demonstrates the high performance of the proposed method with regards to the robustness, the accuracy and the computation time

DTW-Global Constraint Learning Using Tabu Search Algorithm

AbstractMany methods have been proposed to measure the similarity between time series data sets, each with advantages and weaknesses. It is to choose the most appropriate similarity measure depending on the intended application domain and data considered. The performance of machine learning algorithms depends on the metric used to compare two objects. For time series, Dynamic Time Warping (DTW) is the most appropriate distance measure used. Many variants of DTW intended to accelerate the calculation of this distance are proposed. The distance learning is a subject already well studied. Indeed Data Mining tools, such as the algorithm of k-Means clustering, and K-Nearest Neighbor classification, require the use of a similarity/distance measure. This measure must be adapted to the application domain. For this reason, it is important to have and develop effective methods of computation and algorithms that can be applied to a large data set integrating the constraints of the specific field of study. In this paper a new hybrid approach to learn a global constraint of DTW distance is proposed. This approach is based on Large Margin Nearest Neighbors classification and Tabu Search algorithm. Experiments show the effectiveness of this approach to improve time series classification results

Critical Care Nurses' Knowledge and Practice Regarding Administration of Selected Positive Inotropics at Cairo University Hospitals

Critical care  nurses are responsible for administering Inotropics  drugs that affects the patients cardiovascular functions. Nurses must  know proper diluents of each drug and  should be expert in calculating the dose of medication to prevent errors. Each nurse should be aware of indication,  action, contraindications, adverse reactions  interactions of drugs.  Moreover, nurses monitor  patients for  any negative signs of a change in condition, administer  medication,  and  develop a plan of action for patients care. Aim of the study: to assess critical care nurses 'nurses knowledge and practices regarding selected positive Inotropics. Research Design: A descriptive exploratory  design was utilized in this study. Research questions: To achieve the aim of the present study, the following two research questions were formulated ;a)what the nurses know about the  administration of selected positive Inotropics ?,b) what are the practices the nurses perform while administering the selected positive Inotropics?. Setting: The  study  was  carried  out  at  different  Critical  Care  units at  Cairo University Hospitals, in Egypt. Sample: A sample of convenience of 70 nurses from different critical care units with a minimum one year of experience were included in the present study. Tools of data collection: Two tools were used to collect data; the first tool has two parts ;part one is background data sheet that included  gender, age, years of  experience, educational level  and  area of work.  part two was positive inotropics knowledge questionnaire that was designed by researcher  to assess knowledge regarding indication, contraindication,   and nursing measures taken with selected inotropics.  The second tool was positive inotropic observational checklist  that was designed to assess nurses practices while administering positive inotropics  Results: The  current  study  findings revealed that critical care nurses have  got low knowledge and practice scores and  no significant  correlations  were existed  between years of experience , area of work and their level of knowledge and practice regarding selected positive inotropic medications.. Conclusion: it can be concluded  that critical care nurses have inadequate knowledge and practice regarding selected positive Inotropics. Recommendations : Carrying out educational programs about  nursing  management of  Inotropics and training on calculation of drug doses. Keywords: Nurses ' knowledge  , Nurses practice  , Positive  Inotropics  administratio

The Efficacy of Using Augmented Reality Technology to Develop Multiple Intelligences for Children in Early Childhood

The current study aims to measures the effectiveness of using augmented reality technology to develop multiple intelligences in children in early childhood. The semi-experimental method was used with one group (pre and post). The research was applied to (30 children) from kindergarten children. Their ages ranged between (5-6) years. The study used the following materials and tools: a program based on the use of augmented reality technology to develop multiple intelligences in children in early childhood, a measure of multiple intelligences (linguistic - social - logical-mathematical - personal - natural intelligence) among children in early childhood (prepared by the researchers), and the study reached the following results: the effectiveness of using augmented reality in the development of multiple intelligences in children in early childhood, where the experimental group in the pre-application obtained an average of (13.97), while in the post-application it got an average of (25.80). The pre-application had a general average of (2.87), while it got an average of (5.13) in the post-application. The post-test has an average of (5.27), the effectiveness of using augmented reality technology in developing social intelligence Where the experimental group in the pre-application obtained a general average of (2.73), while in the post-application it got an average of (5.20). The post application has an average of (5.07) the effectiveness of using augmented reality technology in developing natural intelligence, where the experimental group in the pre application got an average of (2.73), while in the post application it got an average of (5.13), in the light of the results of the study, the researchers presented several Recommendations for the development of multiple intelligences in children in early childhood, which are: directing those in charge of preparing kindergarten curricula to include augmented reality technology in kindergarten curricula, directing the interest of kindergarten teachers, using augmented reality technology in developing multiple intelligences in children in early childhood, directing kindergarten teachers the diversity of methods and strategies used to develop multiple intelligences in children in early childhood