Environmental monitoring of aquaculture for the current means of monitoring equipment and a weak infrastructure, relatively backward status quo, using wireless sensor technology, embedded computingtechnology, MEMS te...
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We propose an approach to recognize group activities which involve several persons based on modeling the interactions between human bodies. Benefitted from the recent progress in pose estimation [1], we model the acti...
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We propose an approach to recognize group activities which involve several persons based on modeling the interactions between human bodies. Benefitted from the recent progress in pose estimation [1], we model the activities as the interactions between the parts belong to the same person (intra-person) and those between the parts of different persons (inter-person). Then a unified, discriminative model which integrates both types of interactions is developed. The experiments on the UT-Interaction Dataset [2] show the promising results and demonstrate the power of the interacting models.
We propose an approach for multi-pose face tracking by association of face detection responses in two stages using multiple cues. The low-level stage uses a two-threshold strategy to merge detection responses based on...
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ISBN:
(纸本)9781467322164
We propose an approach for multi-pose face tracking by association of face detection responses in two stages using multiple cues. The low-level stage uses a two-threshold strategy to merge detection responses based on location, size and pose, resulting in short but reliable tracklets. The high-level stage uses different cues for computing a joint similarity measure between tracklets. The facial cue compares facial features of the most frontal face detections in pairs of tracklets. The classifier cue learns a discriminative appearance model for each tracklet, using detection pairs within reliable tracklets and between overlapping tracklets as training data. The constraint cue observes the compatibility of motion of two tracklets. The association of tracklets is globally optimized with the Hungarian algorithm. We validate our approach on two challenging episodes of two TV series and report a Multiple Object Tracking Accuracy (MOTA) of 82% and 68.2%, respectively.
The venue of WETICE 2012 (the 21st WETICE) is Toulouse, France, with Professor Michel Diaz of LAAS-CNRS and Professor Patrick Senac of ISAE, ENSICA, both from Toulouse, France, as the General Co-Chairs. WETICE 2012 co...
The venue of WETICE 2012 (the 21st WETICE) is Toulouse, France, with Professor Michel Diaz of LAAS-CNRS and Professor Patrick Senac of ISAE, ENSICA, both from Toulouse, France, as the General Co-Chairs. WETICE 2012 consists of 12 tracks of which 8 are ongoing tracks from previous years and 4 are new: 1) ACEC: Adaptive computing (and Agents) for Enhanced Collaboration (tenth year). 2) AROSA: Adaptive and Reconfigurable Service-Oriented and Component-Based Applications and Architectures (second year). 3) CDCGM: Convergence of distributed Cloud, Grids, and Their Management (second year). 4) CKDD: Cooperative Knowledge Discovery and Data Mining( third year). 5) CoMetS: Collaborative Modeling and Simulation (third year). 6).. COPECH: Collaboration Tools for Preservation of Environment and Cultural Heritage (third year). 7) CPS: Cyber Physical Society with SOA, BPM, and Sensor Networks (second year). 8) CT2CM: Collaborative technology for Coordinating Crisis Management (second year). 9) CAGing: Collaborative and Autonomic Green computing (new). 10) MADYNE: Management of Dynamic Networked Enterprises (new). 11) CSP: Collaborative Software Processes (new). 12) Web2Touch: Modeling the Collaborative Web Knowledge (new). A total of 132 papers were submitted to the tracks, of which 70 were accepted as full papers (53% acceptance) and 17 as short papers. You will also find the summary reports from each track in the proceedings written by the track chairs/organizers. The authors of the accepted papers are from 26 different countries around the world, thus keeping with the tradition of the international nature of WETICE.
Memory-access speed continues falling behind the growing speeds of network transmission links. High-speed network links provide a means to connect memory placed in hosts, located in different corners of the network. T...
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Memory-access speed continues falling behind the growing speeds of network transmission links. High-speed network links provide a means to connect memory placed in hosts, located in different corners of the network. These hosts are called storage system units (SSUs), where data can be stored. Cloud storage provided with a single server can facilitate large amounts of storage to a user, however, at low access speeds. A distributed approach to cloud storage is an attractive solution. In a distributed cloud, small high-speed memories at SSUs can potentially increase the memory access speed for data processing and transmission. However, the latencies of each SSUs may be different. Therefore, the selection of SSUs impacts the overall memory access speed. This paper proposes a latency-aware scheduling scheme to access data from SSUs. This scheme determines the minimum latency requirement for a given dataset and selects available SSUs with the required latencies. Furthermore, because the latencies of some selected SSUs may be large, the proposed scheme notifies SSUs in advance of the expected time to perform data access. The simulation results show that the proposed scheme achieves faster access speeds than a scheme that randomly selects SSUs and another hat greedily selects SSUs with small latencies.
With the development of high resolution images technique, large scale data intensive computing and computing-intensity is highly demanded. However, isolated storage and dispersive management cannot meet exponentially ...
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With the development of high resolution images technique, large scale data intensive computing and computing-intensity is highly demanded. However, isolated storage and dispersive management cannot meet exponentially increasing demand for storage and management of crop images. On the basis of the crop images grasped by our independent developed agricultural intelligent search engine, a kind of extraction technique will be studied to extract the special feature behind scattered image data and to build up a cloud platform for the crop images information. Fast and accurate searching for crop images and its highly efficient management under cloud computation environment are achieved through map-reduce parallel mechanism.
Wireless Sensors have seen a lot of applications in our daily lives in the recent years. The market has been flooded with high end consumer electronics using wireless sensor technology. However, most of the current te...
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Wireless Sensors have seen a lot of applications in our daily lives in the recent years. The market has been flooded with high end consumer electronics using wireless sensor technology. However, most of the current technologies require the sensor to be in the vicinity of the end-user application. There has been some study in the techniques for sensor provisioning and sharing for the large number of existing Wireless Sensor Networks. Virtualization of Wireless Sensor Networks (WSNs) is a step forward in exposing these WSNs to large user base from remote locations. However, there is still a huge gap in bringing together information available from heterogeneous, distributed resources of Wireless Sensor Networks to a non-localized user. In this work, we utilize IaaS paradigm of Cloud computing in virtualization of sensor networks which gives the flexibility of handling heterogeneous systems. The system also enables a smart device user to access information generated by Wireless Sensors through the cloud via SaaS based design. This allows the system to take common computational tasks to be hosted as a service through the cloud. It frees the smart device user from running heavy applications for data processing and storing. Thus, system provides the smart device user a Cloud Enabled Wireless Sensor Network infrastructure. The system architecture provides the necessary features for it to be scalable and flexible, ensuring reliable sensor data transfer and processing through cloud infrastructure. We also present a small test bed implementation of the system.
Nowadays, evacuation is mostly based on individual decisions, which is unreliable. This paper introduces a practical solution towards emergency evacuation by guiding people out of building efficiently. Built on wirele...
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Nowadays, evacuation is mostly based on individual decisions, which is unreliable. This paper introduces a practical solution towards emergency evacuation by guiding people out of building efficiently. Built on wireless sensor network (WSN), our system is capable of learning and dynamically generating evacuation strategy. distributed mechanism and backup mechanism feature the scalability and invulnerability. Simulations prove the efficiency and reliability of the system.
The current state of information technologies requires high-performance processing and storing of large data volumes. To satisfy these constantly growing requirements, leading IT companies provide cloud computing serv...
Popular Internet services are hosted by multiple geographically distributed datacenters. The location of the datacenters has a direct impact on the services' response times, capital and operational costs, and (ind...
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ISBN:
(纸本)9780769543642
Popular Internet services are hosted by multiple geographically distributed datacenters. The location of the datacenters has a direct impact on the services' response times, capital and operational costs, and (indirect) carbon dioxide emissions. Selecting a location involves many important considerations, including its proximity to population centers, power plants, and network backbones;the source of the electricity in the region;the electricity, land, and water prices at the location;and the average temperatures at the location. As there can be many potential locations and many issues to consider for each of them, the selection process can be extremely involved and time-consuming. In this paper, we focus on the selection process and its automation. Specifically, we propose a framework that formalizes the process as a non-linear cost optimization problem, and approaches for solving the problem. Based on the framework, we characterize areas across the United states as potential locations for datacenters, and delve deeper into seven interesting locations. Using the framework and our solution approaches, we illustrate the selection tradeoffs by quantifying the minimum cost of (1) achieving different response times, availability levels, and consistency times, and (2) restricting services to green energy and chiller-less datacenters. Among other interesting results, we demonstrate that the intelligent placement of datacenters can save millions of dollars under a variety of conditions. We also demonstrate that the selection process is most efficient and accurate when it uses a novel combination of linear programming and simulated annealing.
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