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High‐angle cameras are commonly used for trajectory data collection in transportation research. However, without refinement and validation, trajectory data obtained through video processing software may be unreliable, inaccurate, or incomplete. This paper focuses on a critical issue in the field of trajectory data acquisition and analysis—there is still no reliable and fully vetted trajectory dataset...
Stroke is the fifth leading cause of death and a major cause of long-term disability in the United States. Because of its life-threatening consequences, the stroke system of care including acute treatment and post-acute rehabilitation is crucial. In order to support the stroke system of care in the age of big data, predictive analytics can be applied to forecast what will happen in the future. For...
Falls are common and dangerous for the elderly or individuals with decreased independence or functional limitations. Fall recognition is extremely important for fallers, healthcare providers, and society. Immediate fall recognition triggers emergency services and potentially decreases individuals time with injury without care. Acute post-fall intervention works to mitigate life threatening fall consequences,...
This paper will design a general, flexible, and efficient framework for data acquisition, data compression, and data reconstruction in advanced metering infrastructure (AMI). Compressed distributed sensing will be utilized to acquire load data from smart meters and transmit them to the central control unit. Different sparse binary measurement matrices will be exploited for different time instances...
This paper will present a general and efficient methodology for data acquisition in Advanced Metering Infrastructure (AMI). Compressed distributed sensing using random walk (CDS(RW)) will be explored to acquire user load data from smart meters. This paper proposes to perform joint reconstruction of 2D user load profile using both spatial and temporal correlations. In this way, high data compression...
With the growing population of the elderly, the need for mobile health solutions is also increasing. We propose a system which allows patients to perform basic stroke rehabilitation tests from their own homes. This drastically cuts down on patient/therapist visits, freeing up therapists for more pressing work. This paper will focus on the Timed Up and Go Test (TUG) portion of our system. Our system...
For many distributed sensing network applications, the goal is to detect temporal and spatial changes in the network. For this purpose, the sensor readings need to be transmitted to the base station (BS) on a regular basis. Since different sensors partially monitor the same spatial region, their sensor readings are highly correlated. Under normal condition, the readings change very slowly that makes...
In this paper, we develop a distributed compression technique that has low decoding and encoding computational complexity. The proposed scheme exploits both temporal and spatial correlations between nodes in distributed sensor networks. In case of events occurring, the values of both spatial and temporal might change and the compression technique needs to adjust its rate to the changes automatically...
Multiple description coding (MDC) using Compressive sensing (CS) mainly aims at restoring the image from a small subset of samples with reasonable accuracy using an iterative message passing decoding algorithm commonly known as Belief Propagation (BP). CS technique can accurately recover any compressible or sparse signal from a lesser number of non-adaptive, randomized linear projection samples than...
Inspired by our recent work on lossy distributed source coding with side information available at the decoder, we propose a practical scheme for information embedding system with side information available at the encoder. Our proposed scheme is based on sending parity bits using LDPC codes. We provide a design procedure for the LDPC code that guarantees performance close to the Gelfand-Pinsker and...
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