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In our daily life, some sensitive cargos (e.g., refrigerators) are required to keep one right side up. If these items are turned upside down due to incorrect transportation or wrong storage, some unpredictable exception will happen, leading to potential economic loss. In this paper, we propose a lightweight system TagUP that uses RFID to detect the upside-down exception. By dynamically changing the...
Both traditional wireless sensor networks and novel crowdsensing techniques generate tremendous real- time data, which provides great opportunities for real- time and long-term analytics. However, how to integrate the heterogeneous data sources and create data analytics toolboxes that can be connected together to solve various problems in urban environment remains open problems. As a PhD student,...
Cloud service adoption has increased in recent years. With the adoption of cloud service, many of the companies are using these cloud to store and process Big Data. Security measures provided by the service providers might not be enough to secure the data in the cloud. In this paper, we discuss the practical solution on which we are working at the moment to protect the data in a cloud environment...
In this paper we propose five Neural Network models for forecasting public transit. These models are evaluated in terms of accuracy and robustness. The research has two major objectives: to identify the best performing machine learning model in predicting bus travel time and to establish a set of methods in order to obtain a detailed dataset (a variety of practical input values) which will further...
This work stems from the project with National Institutes of Health on elderly human posture recognition in their real-world wearable sensor data streams. The problem presented several challenges: near real-time posture recognition, skewed class distribution, time-changing data streams and sensor management. To tackle these challenges, in this project, we present our design integrates with resampling,...
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