Mehrnoosh Shafiee

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We present a modified Temporal Conditional Random Fields framework for modeling and predicting object motion. To facilitate such a powerful graphical model with prediction and come up with a CRF-based predictor, we propose a set of new temporal relations for object tracking, with feature functions such as optical flow (calculated among consequent frames).(More)
We study the problem of load balancing in datacenter networks, namely, assigning the end-to-end data flows among the available paths in order to efficiently balance the load in the network. The solutions used today rely typically on ECMP (Equal Cost Multi Path) mechanism which essentially attempts to balance the load in the network by hashing the flows to(More)
Increase in competition level and unclear demand in the market have made organizations to utilize scientific models and techniques for optimizing their management process. Among the main processes effective on organizations performance is to select appropriate providers. The following research tries to present an optimized solution for providers, assessment(More)
Automatic vehicle Make and Model Recognition (MMR) system offers a competent way to vehicle classification and recognition systems. This paper proposes a real time while robust vehicle make and model recognition system extracting the vehicle sub-image from the background and studies some sparse feature coding methods such as Orthogonal Matching Pursuit(More)
Singular systems have been the subject of interest over the last two decades due to their many practical applications. But it has to be said that system identification of such system is still a challenging area because of the difficulty of identification of such systems for their complex structures. In addition, it seems that by developing a useful method(More)
<i>Coflow</i> is a recently proposed networking abstraction to capture communication patterns in data-parallel computing frameworks. We consider the problem of efficiently scheduling coflows with release dates in a shared datacenter network so as to minimize the total weighted completion time of coflows. Specifically, we propose a randomized algorithm with(More)
In data-parallel computing frameworks, intermediate parallel data is often produced at various stages which needs to be transferred among servers in the datacenter network (e.g. the shuffle phase in MapReduce). A stage often cannot start or be completed unless all the required data pieces from the preceding stage are received. \emph{Coflow} is a recently(More)
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