Theory of fuzzy integrals and its applications

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

The computational brain: Patricia S. Churchland and Terrence J. Sejnowski (MIT Press, Cambridge, MA, 1992); xi, 544 pages, $39.95

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Chapter 1. Introduction and Overview of Wireless Sensor Networks. Chapter 2. Commercial and Scientific Applications of Wireless Sensor Networks. Chapter 3. Basic Wireless Sensor Technology. Chapter 4. Wireless Sensors Networks Protocols: Physical Layer. Chapter 5. Medium Access Control Protocols for Wireless Sensor Networks. Chapter 6. Sensors Network Protocols: Routing Protocols. Chapter 7. Transport Control Protocols for Wireless Sensors Networks. Chapter 8. Middleware for Sensor Networks. Chapter 9. Network Management for Wireless Sensor Networks. Chapter 10. Operating Systems for Sensor Networks. Chapter 11. Performance and Traffic Management Issues. Appendix A: Analysis. Appendix B: Discussions. Index.

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Wireless Sensor Networks: Technology, Protocols, and Applications

Active learning is an effective learning approach. In this article we present an intelligent agent-assisted environment for active learning to better support the student-centered, selfpaced, and highly interactive learning approach. The environment uses the students learningrelated profile such as learning style and background knowledge in selecting, organizing, and presenting learning material, and it adopts a new approach to course content organization and delivery based on smart instructional components that can be integrated into a wide range of courses. The environment is being implemented using the prevalent Internet, Web, digital library, and multiagent technologies.

/pdf/an-intelligent-distributed-environment-for-active-learning-6ckhax1k4x.pdf

An intelligent distributed environment for active learning

In this paper, we propose a novel decentralized traffic light control using wireless sensor network. The system architecture is classified into three layers; the wireless sensor network, the localized traffic flow model policy, and the higher level coordination of the traffic lights agents. The wireless sensors are deployed on the lanes going in and out the intersection. These sensors detect vehicles' number, speed, etc. and send their data to the nearest Intersection Control Agent (ICA) which, determines the flow model of the inter- section depending on sensors' data (e.g., number of vehicles approaching a specific intersection). Coping with dynamic changes in the traffic volume is one of the biggest challenges in intelligent transportation system (ITS). Our main contribution is the real-time adaptive control of the traffic lights. Our aim is to maximize the flow of vehicles and reduce the waiting time while maintaining fairness among the other traffic lights. Each traffic light controlled intersection has an intersection control agent that collects information from the sensor nodes. An intersection control agent manages its intersection by controlling its traffic lights. Multiple intersection agents can exchange information among themselves to control a wider area.

Adaptive Traffic Light Control with Wireless Sensor Networks

Recent advances in MEMS (micro-electromechanical systems), processor, radio, and memory technologies have dramatically enabled development of wireless sensor networks A sensor network is a large network of small sensor nodes, capable of sensing, communication, and computation It can be deployed to sense some physical phenomenon for a wide variety of applications During recent years, research in wireless sensor networks has become more and more active Network protocols developed for sensor networks are of great importance to meet specific design goals of sensor networks We present a survey of recent work addressing network protocols, including routing and information dissemination algorithms, for wireless sensor networks We evaluate them in terms of design goals, assumptions, operation models, energy models, and performance metrics

A survey on network protocols for wireless sensor networks

Active learning is an e ective learning approach. In this paper, we present an intelligent agent assisted environment for active learning. The system is to better support studentcentered, self-paced, and highly interactive learning approach. Students' learning-related pro les, such as learning styles and background knowledge, are used in selecting, organizing, and presenting learning materials. A new approach to course content organization and delivery is being developed based on smart instructional components, which can be integrated into a wide range of courses. The system is being implemented using the prevalent Internet, Web, digital library, and multi-agent technologies.

/pdf/an-intelligent-distributed-environment-for-active-learning-3jiudcou4x.pdf

For wireless sensor networks, localization is crucial due to the dynamic nature of deployment. In relative localization, nodes use the distance measurements to estimate their positions relative to some coordinate system. In absolute localization, a few nodes (called anchors) need to know their absolute positions, and all the other nodes are absolutely localized in the coordinate system of the anchors. Relative and absolute localization methods differ in both the performance and the cost. We present a new approach to relative localization that we refer to as: simple hybrid absolute-relative positioning (SHARP). In SHARP, a relative localization method (M1) is used to relatively localize N/sub r/ reference nodes. Then, an absolute localization method (M2) uses these N/sub r/ nodes as anchors to localize the rest of the nodes. Choosing N/sub r/, M1, and M2 gives a wide range of performance-cost tuning. We have done extensive simulation using the multidimensional scaling (MDS) method as M1 and the ad-hoc positioning system (APS) method as M2. While previous research shows that MDS gives better localization results than APS, our simulation shows that SHARP outperforms MDS if both the localization error and the cost are considered.

Hongchi Shi

Papers

An intelligent distributed environment for active learning

Adaptive Traffic Light Control with Wireless Sensor Networks

A survey on network protocols for wireless sensor networks

An intelligent distributed environment for active learning

SHARP: a new approach to relative localization in wireless sensor networks