Geospatial susceptibility mapping of earthquake-induced landslides in Nuweiba area, Gulf of Aqaba, Egypt

Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the earthquake-induced landslide susceptibility in Nuweiba area in Egypt with considerations of geological, geomorphological, topographical, and seismological factors. An integrated approach of remote sensing and GIS technologies were applied for that target. Several data sources including Terra SAR-X and SPOT 5 satellite imagery, topographic maps, field data, and other geospatial resources were used to model landslide susceptibility. These data were used specifically to produce important thematic layers contributing to landslide occurrences in the region. A rating scheme was developed to assign ranks for the thematic layers and weights for their classes based on their contribution in landslide susceptibility. The ranks and weights were defined based on the knowledge from field survey and authors experiences related to the study area. The landslide susceptibility map delineates the hazard zones to three relative classes of susceptibility: high, moderate, and low. Therefore, the current approach provides a way to assess landslide hazards and serves for geo-hazard planning and prediction in Nuweiba area.

Tenure-Track Assistant Professor in Integrative Systems Engineering NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere(CASA) Department of Electrical and Computer Engineering

In conjunction with the NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA),the Department of Electrical and Computer Engineering at the University of Massachusetts Amherst invites applications for a tenure-track position in Integrative Systems Engineering(ISE) at the Assistant Professor level to begin September 2009.

Integration designs toward new-generation wearable energy supply-sensor systems for real-time health monitoring:A minireview

Wearable sensing systems,as a spearhead of artificial intelligence,are playing increasingly important roles in many fields especially health monitoring.In order to achieve a better wearable experience,rationally integrating the two key components of sensing systems,that is,power supplies and sensors,has become a desperate requirement.However,limited by device designs and fabrication technologies,the current integrated sensing systems still face many great challenges,such as safety,miniaturization,mechanical stability,energyefficiency,sustainability,and comfortability.In this review,the key challenges and opportunities in the current development of integrated wearable sensing systems are summarized.By summarizing the typical configurations of diverse wearable power supplies,and recent advances concerning the integrated sensing systems driven by such power supplies,the representative integrated designs,and micro/nanofabrication technologies are highlighted.Lastly,some new directions and potential solutions aiming at the device-level integration designs are outlooked.

ARISTOTLE SAID ＂HAPPINESS IS A STATE OF ACTIVITY＂ - PREDICTING MOOD THROUGH BODY SENSING WITH SMARTWATCHES

We measure and predict states of Activation and Happiness using a body sensing applicationconnected to smartwatches. Through the sensors of commercially available smartwatches we collectindividual mood states and correlate them with body sensing data such as acceleration, heart rate, lightlevel data, and location, through the GPS sensor built into the smartphone connected to the smartwatchWe polled users on the smartwatch for seven weeks four times per day asking for their mood state. Wefound that both Happiness and Activation are negatively correlated with heart beats and with the levelsof light. People tend to be happier when they are moving more intensely and are feeling less activatedduring weekends. We also found that people with a lower Conscientiousness and Neuroticism andhigher Agreeableness tend to be happy more frequently. In addition, more Activation can be predictedby lower Openness to experience and higher Agreeableness and Conscientiousness. Lastly, we find thattracking people＇s geographical coordinates might play an important role in predicting Happiness andActivation. The methodology we propose is a first step towards building an automated mood trackingsystem, to be used for better teamwork and in combination with social network analysis studies.

Robust image processing algorithm for computational resource limited smart apple sunburn sensing system

Heat and light stress causes sunburn to the maturing apple fruits and results in crop production and quality losses.Typically,when the fruit surface temperature(FST)rises above critical limits for a prolonged duration,the fruit may suffer several physiological disorders including sunburn.To manage apple sunburn,monitoring FST is critical and our group at Washington State University is developing a noncontact smart sensing system that integrates thermal infrared and visible imaging sensors for real time FST monitoring.Pertinent system needs to perform in-field imagery data analysis onboard a single board computer with processing unit that has limited computational resources.Therefore,key objective of this study was to develop a novel image processing algorithm optimized to use available resources of a single board computer.Algorithm logic flow includes color space transformation,k-means++classification and morphological operators prior to fruit segmentation and FST estimation.The developed algorithm demonstrated the segmentation accuracy of 57.78%(missing error=12.09%and segmentation error=0.13%).This aided successful apple FST estimation that was 10–18C warmer than ambient air temperature.Moreover,algorithm reduced the imagery data processing time cost of the smart sensing systemfrom 87 s to 44 s using image compression approach.