We propose a six degree-of-freedom multi-body approach for modeling and simulation of biologically-inspired (or Biomimetic) autonomous underwater vehicles (BAUVs), i.e., artificial fish. The proposed approach is based...
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We propose a six degree-of-freedom multi-body approach for modeling and simulation of biologically-inspired (or Biomimetic) autonomous underwater vehicles (BAUVs), i.e., artificial fish. The proposed approach is based on considering the BAUV as comprised of multiple rigid bodies interlinked through joints; the external force and torque on each rigid body in the BAUV is expressed using quasi-steady aerodynamic theory and the joint constraints are imposed through an impulse-based technique. A BAUV simulation platform has been implemented based on the proposed modeling framework and has been applied to analyze a specific BAUV inspired by the electric ray. The hardware implementation of the electric ray inspired BAUV is also presented. Finally, sample simulation results and validation against experimental data collected from the electric ray inspired BAUV are also presented.
The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures or physical manipulation of the devices. We present uWave, an efficient recognition algorithm fo...
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The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures or physical manipulation of the devices. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. Unlike statistical methods, uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures and physical manipulations. We evaluate uWave using a large gesture library with over 4000 samples collected from eight users over an elongated period of time for a gesture vocabulary with eight gesture patterns identified by a Nokia research. It shows that uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. Our evaluation data set is the largest and most extensive in published studies, to the best of our knowledge. We also present applications of uWave in gesture-based user authentication and interaction with three-dimensional mobile user interfaces using user created gestures.
The problem of automatic object categorization is investigated under the proposed bag of feature object categorization framework. The framework consists of feature detection and representation which uses the scale inv...
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The problem of automatic object categorization is investigated under the proposed bag of feature object categorization framework. The framework consists of feature detection and representation which uses the scale invariant feature transform (SIFT) as local feature and bag of feature model to represent the image. Learning process utilizes k-NN (k-nearest neighbour). In this paper, we propose the dimensionality reduction of SIFT using principal component analysis (PCA) on each object category to reduce computational complexity and memory requirement during training process. Experimental results show that our proposed technique can reduce the dimension of SIFT up to around 80% with the same average precision compared to baseline technique without our proposed method.
An important problem in computervision is to determine the orientation of a rigid body in an image. This can be accomplished by matching points or line segments that naturally appear on the object. Several elegant an...
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An important problem in computervision is to determine the orientation of a rigid body in an image. This can be accomplished by matching points or line segments that naturally appear on the object. Several elegant and computationally fast algorithms based on the singular value decomposition and quaternions have been introduced to solve this problem. In this article, the authors first examine the important special case of identifying the attitude of 2D objects and introduce a particularly elegant solution based on the mathematical structure of the complex plane. Motivated by this simple solution to the 2D case, a new derivation of the 3D case based on the polar decomposition is presented. This derivation is in many ways more natural than previous derivations, particularly when the model and data contain no noise.
The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's department of computer Science, was pleased to present the 2009 Spring Symposium Series, held Monday throug...
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The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's department of computer Science, was pleased to present the 2009 Spring Symposium Series, held Monday through Wednesday, March 23-25, 2009, at Stanford University. The titles of the nine symposia were Agents That Learn from Human Teachers, Benchmarking of Qualitative Spatial and Temporal Reasoning Systems, Experimental Design for Real- World Systems, Human Behavior Modeling, Intelligent Event Processing, Intelligent Narrative Technologies II, Learning by Reading and Learning to Read, Social Semantic Web: Where Web 2.0 Meets Web 3.0, and Technosocial Predictive Analytics. The goal of the Agents That Learn from Human Teachers symposium was to investigate how we can enable software and robotics agents to leam from real-time interaction with an everyday human partner. The aim of the Benchmarking of Qualitative Spatial and Temporal Reasoning Systems symposium was to initiate the development of a problem repository in the field of qualitative spatial and temporal reasoning and identify a graded set of challenges for future midterm and long-term research. The Experimental Design symposium discussed the challenges of evaluating AI systems. The Human Behavior Modeling symposium explored reasoning methods for understanding various aspects of human behavior, especially in the context of designing intelligent systems that interact with humans. The Intelligent Event Processing symposium discussed the need for more Al-based approaches in event pro-cessing and defined a kind of research agenda for the field, coined as intelligent complex event processing (iCEP). The Intelligent Narrative Technologies IIAAAI symposium discussed innovations, progress, and novel techniques in the research domain. The Learning by Reading and Learning to Read symposium explored two aspects of making natural language texts semantically accessible to, and processable by, machines. The Social Semantic
Modern power grid is required to become smarter in order to provide an affordable, reliable, and sustainable supply of electricity. Under such circumstances, considerable activities have been carried out in the U.S. a...
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Modern power grid is required to become smarter in order to provide an affordable, reliable, and sustainable supply of electricity. Under such circumstances, considerable activities have been carried out in the U.S. and Europe to formulate and promote a vision for the development of the future smart power grids. However, the majority of these activities only placed emphasis on the distribution grid and demand side; while the big picture of the transmission grid in the context of smart grids is still unclear. This paper presents a unique vision for the future smart transmission grids in which the major features that these grids must have are clearly identified. In this vision, each smart transmission grid is regarded as an integrated system that functionally consists of three interactive, smart components, i.e., smart control centers, smart transmission networks, and smart substations. The features and functions of each of the three functional components as well as the enabling technologies to achieve these features and functions are discussed in detail in the paper.
We consider in this paper the problem of large scale natural image classification. As the explosion and popularity of images in the Internet, there are increasing attentions to utilize millions of or even billions of ...
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We consider in this paper the problem of large scale natural image classification. As the explosion and popularity of images in the Internet, there are increasing attentions to utilize millions of or even billions of these images for helping image related research. Beyond the opportunities brought by unlimited data, a great challenge is how to design more effective classification methods under these large scale scenarios. Most of existing attempts are based on k-nearest-neighbor method. However, in spite of the optimistic performance in some tasks, this strategy still suffers from that, one single fixed global parameter k is not robust for different object classes from different semantic levels. In this paper, we propose an alternative method, called lscr 1 -nearest-neighbor, based on a sparse representation computed by lscr 1 -minimization. We first treat a testing sample as a sparse linear combination of all training samples, and then consider the related samples as the nearest neighbors of the testing sample. Finally, we classify the testing sample based on the majority of these neighbors' classes. We conduct extensive experiments on a 1.6 million natural image database on different semantic levels defined based on WordNet, which demonstrate that the proposed lscr 1 -nearest-neighbor algorithm outperforms k-nearest-neighbor in two aspects: 1) the robustness of parameter selection for different semantic levels, and 2) the discriminative capability for large scale image classification task.
This research presents an optimum approach for designing Rotary Inverted Penduhnn (RIP) controller using PSO algorithm. The primary design goal is to balance the pendulum in an inverted position and the control criter...
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In this study, we present the local reconstruction of differential-drive mobile robots position and orientation with an accurate odometry calibration. Starting from the encoders readings and assuming an absolute measu...
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In this paper, we present a multi-label sparse coding framework for feature extraction and classification within the context of automatic image annotation. First, each image is encoded into a so-called supervector, de...
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In this paper, we present a multi-label sparse coding framework for feature extraction and classification within the context of automatic image annotation. First, each image is encoded into a so-called supervector, derived from the universal Gaussian Mixture Models on orderless image patches. Then, a label sparse coding based subspace learning algorithm is derived to effectively harness multi-label information for dimensionality reduction. Finally, the sparse coding method for multi-label data is proposed to propagate the multi-labels of the training images to the query image with the sparse ℓ 1 reconstruction coefficients. Extensive image annotation experiments on the Corel5k and Corel30k databases both show the superior performance of the proposed multi-label sparse coding framework over the state-of-the-art algorithms.
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