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Recent advances in Neural Networks (NN) are enabling more and more innovative applications. As an energy-efficient hardware solution, machine learning accelerators for CNNs or traditional ANNs are also gaining popularity in the area of embedded vision, robotics and cyberphysics. However, the design parameters of NN models vary significantly from application to application. Hence, it's hard to provide...
This paper introduces an incremental closed frequent item sets mining algorithm, which is based on a shadow prefix tree to get closed frequent item sets. By the use of shadow technology this algorithm can avoid the cost of generating and testing of candidate subsets. Shadow prefix tree can find nodes by virtual node without generating all nodes. The experiment results show that this algorithm can...
Service-Oriented Computing (SOC) provides a flexible framework in which applications may be built up from services, often distributed across a network. One of the promises of SOC is that of Dynamic Binding where abstract consumer requests are bound to concrete service instances at runtime, thereby offering a high level of flexibility and adaptability. Existing research has so far focused mostly on...
Service-Oriented Computing (SOC) provides a flexible framework in which applications are built up from services, often distributed across a network. One of the promises of SOC is that of Dynamic Binding where abstract consumer requests are bound to concrete service instances at runtime. What is clear from existing research is that there exist several components that help to provide the necessary behavior...
In recent years, Service Oriented Architectures (SOA) have risen in use as an architectural style for distributed systems. They have many desirable features such as flexibility, software reuse and cost benefits. In addition to this, SOA enables and indeed encourages the binding of services at runtime in the form of dynamic binding. Here, services are bound to service requests at runtime and the choice...
In this paper, we propose a novel approach to automatically generating, instead of manually designing, discriminative visual features for face detection. The features are composed by multiple local features (e.g., Haar features), and such features can capture not only the local texture information but also their spatial configurations. Therefore, the proposed feature contains rich semantic information...
Human action recognition is a significant task in automatic understanding systems for video surveillance. Probabilistic Latent Semantic Analysis (PLSA) model has been used to learn and recognize human actions in videos. Specifically, PLSA employs the expectation maximization (EM) algorithm for parameter estimation during the training. The EM algorithm is an iterative estimation scheme that is guaranteed...
The angular correlation of received fields in multipath environments is studied. The focus is put on two-dimensional (2-D) cases, and the frequently used uncorrelated scattering assumption is tested through full-wave Monte Carlo simulations. The results show that this assumption is valid for the discrete finite spectra of the received waves when the scattering objects in the environments are distributed...
Detecting and recognizing pedestrians in video footages are two essential and significant tasks in many automatic video understanding systems. In this paper, we propose an efficient approach to moving pedestrian detection and recognition in video. The testing process of this approach involves two main steps: moving edge detection and hypotheses generation. Moving edges are firstly extracted by comparing...
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