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Health issues are rising progressively due to the ignorance of health monitoring on regular basis which can now be looked after by the fast growing communication technologies and smart devices. In this paper, a robust healthcare model is proposed for continuous monitoring of the patient even when the patient is traveling. Sensitive data is collected from the Internet of Things (IoT) sensors connected...
A new radiometry and design framework has been introduced in the latest Digital Imaging and Remote Sensing Image Generation model (DIRSIG5) that allows for faster simulations while streamlining the generation of high-fidelity radiometric data. The same framework that allows for improved computational performance has also modularized simulation components to allow for extensive interchangeability based...
We consider the multivariate linear regression model with shuffled data and additive noise, which arises in various correspondence estimation and matching problems. We focus on the denoising problem and characterize the minimax error rate up to logarithmic factors. We also analyze the performance of two versions of a computationally efficient estimator that are consistent for a large range of input...
This paper introduces a novel paradigm for resource management in distributed systems, called decision-driven execution. The paradigm is appropriate for mission-driven systems, where the goal is to enable faster, leaner, and more effective decision making. All resource consumption, in this paradigm, is tied to the needs of making decisions on alternative courses of action. A point of departure from...
Mobile sensor networks (MSNs) enable extensive applications of data collection, such as accident report in transportation and health prediction in public health. Incentive mechanism (IM) is applied for sensing user (SU) recruitment. However, the IM used in traditional MSN is not efficient due to limited information of SU used for recruitment. With the development of cloud computing technology, cloud-based...
This work presents an approach for the analysis and accommodation of sensor-controller communication failures in spatially-distributed processes controlled over a resource-constrained communication medium. We focus on distributed processes whose dominant dynamics are finite-dimensional, but uncertain, with a finite number of spatially-distributed networked control actuators and measurement sensors...
Fault Detection, Diagnostics and Prognostics (FDD&P) is attracting a lot of attention from building operators and researchers because it can help greatly improve the performance of building operations by reducing energy consumption for heating, ventilation and air-conditioning (HVAC) while improving occupant comfort at the same time. However, FDD&P for building operations remains with many...
Exploitation of complementary information is the principal reason for collecting data from multiple neurological sensors. Since little is known about the latent processes underlying neural function, it is important to minimize the assumptions placed on the data when performing a joint analysis. This motivates the use of data-driven fusion methods, such as independent vector analysis (IVA), for the...
Performance measurements using human sensing and assessment capabilities are limited by an inability to account for the multitude of variables that regulate performance state. Monitoring behavior alone is not adequate for prediction of future performance on a given task and no single physiological measurement can provide a complete assessment that influences performance. Here we investigate how to...
Modern smartphone applications rely on contextual information while providing the users with relevant and timely content and services. One way of generating such contextual information is by employing learning systems to model user behavior. Motion-based sensors, such as the accelerometer or gyroscope, have been previously employed for recognizing predefined high-level physical activities such as...
In order to improve the safety factors of MSV (Manned Submersible Vehicle) and shorten the pilot training cycle, the real hardware and software system combine with the modelling and simulation calculation of its sensors, equipment, actuators submersible ontology and motion status in deep sea environment, realizing the semi physical simulation system. First of all, the system model is built and hardware...
Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the others that are stuck behind the wheels. Early detection of an accident can save lives,...
Mobile crowdsensing emerges as a promising sensing paradigm through leveraging the diverse embedded sensors in massive mobile devices. A key objective in mobile crowdsensing is to efficiently schedule mobile device users to perform multiple sensing tasks. Prior work mainly focused on the interactions between the task layer and the user layer, without considering the similarity of tasks' data requirements...
To meet the changing demands of operational environments, future Department of Defense solutions require the engineering of resilient systems. Scientists, engineers and analysts rely on modeling, simulation, and tradespace analysis to design future resilient systems. During conceptual system design, high performance computing clusters and models from multiple domains are leveraged to conduct large-scale...
Inferring activities on smartphones is a challenging task. Prior works have elaborated on using sensory data from built-in hardware sensors in smartphones or taking advantage of location information to understand human activities. In this paper, we explore two types of data on smartphones to conduct activity inference: 1) Spatial-Temporal: reflecting daily routines from the combination of spatial...
In this paper, we present the "Slow Start Problem" in participatory sensing applications where a service is provided based on data collected by participants. The slow start problem refers to the initial stage in participatory sensing service deployment, during which service adoption remains sparse and, hence, the collected data does not offer adequate coverage. Predictive models, learned...
With the bloom of data generation devices at the network-edge, obtaining data intelligence in real-time posed problems due to network incompetence. In this paper we present a novel computation model based on Fog computing that take advantage of its low latency computing capabilities. A proof of concept is designed on top of the computation model as a layered system architecture that consists of sensors...
The demand for near real-time analysis of streaming data is increasing rapidly in scientific projects. This trend is driven by the fact that it is expensive and time consuming to design and execute complex experiments and simulations. During an experiment, the research team and the team at the experiment facility will want to analyze data as it is generated, interpret it, and collaboratively make...
This paper describes a reasoning approach to decision making with uncertain heterogeneous (soft and hard) information of variable reliability based on belief-based argumentation. Arguments are built to support or refute a set of hypotheses about the situation of interest. Beliefs in the arguments are fused and used for decision making. The fusion process is complicated by different models used for...
This paper proposed a method to build knowledge from one and a half years of UK traffic data sets. The method used is the Fast Incremental Model Trees - Drift Detection (FIMT-DD) with an improvement on the perceptron rule. In order to predict a traditional data set, we first analyze the model. After we have analyzed the model, we then average it from different arrangements of the datasets. In a stream...
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