14093058
A virtual mouse system for mobile device
This paper presents a novel virtual mouse system for mobile phones. The system is based on computer vision techniques. It first captures an image using the camera of the mobile phone, then extracts feature points of the scene and tracks them. By using this algorithm we can estimate the camera motion and give the translation vector to our virtual mouse interface. We use NOKIA 6630 camera phone as the developing platform which has a Mega Pixel camera on the back of it. The experiments show the promising results.
9126668
Hand Gesture Tracking for Wearable Computing Systems
A spin stabilized, earth orbitting satellite having a phased array antenna capable of radiating a plurality of steered electromagnetic beams to predetermined earth locations. The antenna array extends around the satellite and is made up of individual elements which are energized through semiconductor diode devices by electron beams. The electron beams are controlled by fields having the same frequency as the spin frequency of the satellite.
16868039
Automatic text discovering through stroke-based segmentation and text string combination
In this paper we present a novel framework of automatic text discovering for content-based multimedia application. For single image, the stroke-based binarization and the coarse-to-fine text extraction will collaborate to generate a clean text image for recognition. For image sequence, multi-frame text enhancement is adopted to increase the text/background contrast, and the recognition results are finally refined by the text string combination algorithm to get more precise semantic information. Two prototype demos have been successfully developed on mobile phones. The experimental results on different platforms show the superior performance of the proposed method.
206800753
Automatic character location and segmentation in color scene images
This paper describes a connected component (CC)-based approach to automatic text location and segmentation in natural scene images. A multi-group decomposition scheme is used to deal with the complexity of the color background. Connected component extraction is implemented using the block adjacency graph (BAG) algorithm after noise filtering and runlength smearing (RLS) operation. Some heuristic features and priority adaptive segmentation (PAS) of characters are proposed in block candidate verification and grayscale-based recognition. A prototype system is completed and the experimental results prove the effectiveness of the proposed method.
12667544
Camera Readable 2D Bar Codes Design and Decoding for Mobile Phones
2D bar codes were designed to carry significantly more data than its 1D counterpart. Thanks to the popular combination of cameras and mobile phones, it will naturally bring great commercial value to use the camera phone for 2D bar code reading. This paper addresses the problem of specific 2D bar code design for mobile phones and introduces a low-level solution of matrix codes. At the same time, we propose an efficient scheme for 2D bar codes decoding, of which the effort is put on solutions of the difficulties introduced by low image quality that is very common in bar code images taken by a phone camera.
17705372
1D Bar Code Reading on Camera Phones
The availability of camera phones provides people with a mobile platform for decoding bar codes, whereas conventional scanners lack mobility. However, using a normal camera phone in such applications is challenging due to the out-of-focus problem. In this paper, we present the research effort on the bar code reading algorithms using a VGA camera phone, NOKIA 7650. EAN-13, a widely used 1D bar code standard, is taken as an example to show the efficiency of the method. A wavelet-based bar code region location and knowledge-based bar code segmentation scheme is applied to extract bar code characters from poor-quality images. All the segmented bar code characters are input to the recognition engine, and based on the recognition distance, the bar code character string with the smallest total distance is output as the final recognition result of the bar code. In order to train an efficient recognition engine, the modified Generalized Learning Vector Quantization (GLVQ) method is designed for optimizing a feature extraction matrix and the class reference vectors. 19 584 samples segmented from more than 1000 bar code images captured by NOKIA 7650 are involved in the training process. Testing on 292 bar code images taken by the same phone, the correct recognition rate of the entire bar code set reaches 85.62%. We are confident that auto focus or macro modes on camera phones will bring the presented method into real world mobile use.
940637
Apply ant colony optimization to Tetris
Tetris is a falling block game where the player's objective is to arrange a sequence of different shaped tetrominoes smoothly in order to survive. In the intelligence games, agent imitates the real player and chooses the best move based on a linear value function. In this paper, we apply Ant Colony Optimization (ACO) method to learn the weights of the function, trying to search an optimal weight-path in the weight graph. We use dynamic heuristic to prevent premature convergence to local optima. Our experimental result is better than most of traditional reinforcement learning methods.
7290502
Towards automatic generation of vulnerability-based signatures
In this paper we explore the problem of creating vulnerability signatures. A vulnerability signature matches all exploits of a given vulnerability, even polymorphic or metamorphic variants. Our work departs from previous approaches by focusing on the semantics of the program and vulnerability exercised by a sample exploit instead of the semantics or syntax of the exploit itself. We show the semantics of a vulnerability define a language which contains all and only those inputs that exploit the vulnerability. A vulnerability signature is a representation (e.g., a regular expression) of the vulnerability language. Unlike exploit-based signatures whose error rate can only be empirically measured for known test cases, the quality of a vulnerability signature can be formally quantified for all possible inputs. We provide a formal definition of a vulnerability signature and investigate the computational complexity of creating and matching vulnerability signatures. We also systematically explore the design space of vulnerability signatures. We identify three central issues in vulnerability-signature creation: how a vulnerability signature represents the set of inputs that may exercise a vulnerability, the vulnerability coverage (i.e., number of vulnerable program paths) that is subject to our analysis during signature creation, and how a vulnerability signature is then created for a given representation and coverage. We propose new data-flow analysis and novel adoption of existing techniques such as constraint solving for automatically generating vulnerability signatures. We have built a prototype system to test our techniques. Our experiments show that we can automatically generate a vulnerability signature using a single exploit which is of much higher quality than previous exploit-based signatures. In addition, our techniques have several other security applications, and thus may be of independent interest
10659233
Wiener indices of balanced binary trees
We study a new family of trees for computation of the Wiener indices. We introduce general tree transformations and derive formulas for computing the Wiener indices when a tree is modified. We present several algorithms to explore the Wiener indices of our family of trees. The experiments support new conjectures about the Wiener indices.
24647482
En-route Security Monitoring Based on an Incident Detection Algorithm for Commercial Vehicles
This paper presents the development of a real-time system for monitoring the security of commercial vehicles in road networks. Embedded in the security monitoring system is a commercial vehicle tracking and incident detection algorithm which relies on a combination of vehicle telemetry data obtained from Global Positioning Systems and on-board sensors to continuously monitor the route choice and car-following behavior of the driver. The performance of this algorithm has been tested in a microscopic simulation model, on a set of hypothetical scenarios, which included deviations from the approved routes, forced to travel at unreasonably low speeds, or even stopped at unexpected places in the network. The initial results indicate that the proposed system has good potential in detecting abnormal driving behaviors, with 100% detection rate, 6.0 seconds of mean detection time, and less than 0.5% false alarm rate.
6929789
WSN02-2: Adaptive Network Resource Management in IEEE 802.11 Wireless Random Access MAC
Effective and efficient management of wireless network resources is attracting more and more research attention, due to the rapid growing deployment of wireless mesh and ad hoc networks and to the increasing demand for Quality of Service (QoS) support in these networks. This paper proposes an adaptive network resource management scheme in the popular IEEE 802.11 random access MAC by adaptively adjusting the minimum contention window sizes of traffic flows. First, a novel generalized processor sharing (GPS) model is presented for the IEEE 802.11 random access MAC revealing the relationship between the minimum contention window size of a traffic flow and the amount of network resource this flow can receive. Using this GPS MAC model, a feedback control system model for the proposed adaptive network resource management system is developed, by directly extending our previous work in wireline GPS networks. Based on the feedback control system model, adaptive P and adaptive PI controllers are designed, and their performances are studied in simulations. Simulation results show that by using the designed controllers, the proposed adaptive network resource management approach is able to provide guaranteed distinct QoS support to traffic flows.