6706494
Real-Time Sensor Fusion Framework for Distributed Intelligent Sensors
Multi-sensor data fusion has found widespread applications in industrial and research sectors. The purpose of real time multi-sensor data fusion is to dynamically estimate an improved system model from a set of different data sources, i.e., sensors. This paper presented a systematic and unified real time sensor fusion framework (RTSFF) based on distributed intelligent sensor network. The RTSFF is an open architecture which consists of four layers – the transaction layer, the process fusion layer, the control layer, and the planning layer. This paradigm facilitates distribution of intelligence to the sensor level and sharing of information among sensors, controllers, and other devices in the system. The transducer layer is populated with intelligent sensors. The learning ability of the intelligent sensor model enables it to extract characteristics of monitored signal. The representation issue is managed at this level. After describing the RTSFF, the paper then focuses on the fundamental units of the framework, the highly autonomous transducers.
1221771
Multi-scale feature identification using evolution strategies
An image usually contains a number of different features and regions. Many image-related applications, such as content-based image retrieval and MRI-based diagnosis, often require the ability to identify and mark features within the image. For images containing a specific sort of feature (e.g. convective storm) or region (e.g. earthquake debris), that feature or region is always located adjacent to other features and regions on the image. A generic framework for automatically identifying features in images based on evolutionary computation is proposed here. The significant characteristic of the method is that it does not require segmentation. We use evolution strategies as the optimization algorithm to identify features. The system is based on a conjecture that certain filters will give prominent responses to certain features. The identified features are represented as regions enclosed within the chosen search structure-the ellipse. By defining filter response criteria as the fitness function, evolution strategies succeeds in finding the feature in a much more efficient way than, say, segmentation.
14295071
Sample Complexity of Real-Coded Evolutionary Algorithms
Researchers studying Evolutionary Algorithms and their applications have always been confronted with the sample complexity problem. The relationship between population size and global convergence is not clearly understood. Population size is usually chosen depending on researcher’s experience. In this paper, we study the population size using Probably Approximately Correct (PAC) learning theory. A ruggedness measure for fitness functions is defined. A sampling theorem that theoretically determines an appropriate population size towards effective convergence is proposed. Preliminary experiments show that the initial population of the proposed size provides good starting point(s) for searching the solution space and thus leads to finding global optima.
38619191
Function Granularity Estimation for Multimodal Optimization
In this article, we study the subspace function granularity and present a method to estimate the sharing distance and the optimal population size. To achieve multimodal function optimization, niching techniques diversify the population of Evolutionary Algorithms (EA) and encourage heterogeneous convergence to multiple optima. The key to a successful diversification is effective resource sharing. Without knowing the fitness landscape, resource sharing is usually determined by uninformative assumptions on the number of peaks. Using the Probably Approximately Correct (PAC) learning theory and the ∊-cover concept, a PAC neighborhood for a set of samples is derived. Within this neighborhood, we sample the fitness landscape and compute the subspace Fitness Distance Correlation (FDC) coefficients. Using the estimated granularity feature of the fitness landscape, the sharing distance and the population size are determined. Experiments demonstrate that by using the estimated population size and sharing distance an Evolutionary Algorithm successfully identifies multiple optima.
223678
Automatic classification of digestive organs in wireless capsule endoscopy videos
Wireless Capsule Endoscopy (WCE) allows a physician to examine the entire small intestine without any surgical operation. With the miniaturization of wireless and camera technologies the ability comes to view the entire gestational track with little effort. Although WCE is a technical break-through that allows us to access the entire intestine without surgery, it is reported that a medical clinician spends one or two hours to assess a WCE video, It limits the number of examinations possible, and incur considerable amount of costs. To reduce the assessment time, it is critical to develop a technique to automatically discriminate digestive organs such as esophagus, stomach, small intestinal (i.e., duodenum, jejunum, and ileum) and colon. In this paper, we propose a novel technique to segment a WCE video into these anatomic parts based on color change pattern analysis. The basic idea is that the each digestive organ has different patterns of intestinal contractions that are quantified as the features. We present the experimental results that demonstrate the effectiveness of the proposed method.
2765235
Volumetric tongue reconstruction by fusing bidirectional MR images
In this paper we describe a 3D reconstruction method that fuses sagittal and coronal images. In our volumetric MRI study of the human tongue, multiple contiguous slices were acquired in the sagittal and the coronal planes. A bidirectional linear interpolation method was developed that estimated target voxel value using weighted neighbor pixels in the bounding slices. A wavelet fusion method was then employed to integrate prominent subband coefficients. Experiments have shown that fusion-based image reconstruction reduced artifacts, greatly improved reconstruction accuracy, and enhanced image details. Our method casts new light on MR imaging and image processing that demand high resolution in a short acquisition time
7299656
Communication Characteristics in the NAS Parallel Benchmarks
In this paper, we investigate the communication characteristics of the Message Passing Interface (MPI) implementation of the NAS parallel benchmarks and study the effectiveness of compiled communication for MPI programs. Compiled communication is a technique that utilizes the compiler knowledge of both the application communication requirement and the underlying network architecture to significantly optimize the performance of communications whose information can be determined at compile time (static communications). The results indicate that compiled communication can be applied to a large portion of the communications in the benchmarks. In particular, the majority of collective communications are static.
2000177
A distributed visual surveillance system
We present a distributed vision-based surveillance system. The system acquires and processes grey level images through one or multiple camera units monitoring certain area(s) via a local area network (LAN) and is capable of combining information from multiple camera units to obtain a consensus decision. It can be trained to detect certain type of intrusions, for example pedestrians, a group of pedestrians, vehicles, pets, etc., and minimizes false alerts due to other non-interested intrusions. As a case study, we aim to detect pedestrian/vehicle in an observation area. Our vision-based intrusion detection approach consists of two main steps: background subtraction based hypothesis generation (HG) and appearance-based hypothesis verification (HV). HG hypothesizes possible threats (intrusions), and HV verifies those hypotheses using a Gabor filter for feature extraction and support vector machines (SVMs) for classification. The system has been tested in an unconstrained outdoor environment, illustrating good performance.
1794602
HDR VolVis: high dynamic range volume visualization
In this paper, we present an interactive high dynamic range volume visualization framework (HDR VolVis) for visualizing volumetric data with both high spatial and intensity resolutions. Volumes with high dynamic range values require high precision computing during the rendering process to preserve data precision. Furthermore, it is desirable to render high resolution volumes with low opacity values to reveal detailed internal structures, which also requires high precision compositing. High precision rendering will result in a high precision intermediate image (also known as high dynamic range image). Simply rounding up pixel values to regular display scales will result in loss of computed details. Our method performs high precision compositing followed by dynamic tone mapping to preserve details on regular display devices. Rendering high precision volume data requires corresponding resolution in the transfer function. To assist the users in designing a high resolution transfer function on a limited resolution display device, we propose a novel transfer function specification interface with nonlinear magnification of the density range and logarithmic scaling of the color/opacity range. By leveraging modern commodity graphics hardware, multiresolution rendering techniques and out-of-core acceleration, our system can effectively produce an interactive visualization of large volume data, such as 2.048/sup 3/.
460263
Heuristic algorithms for multi-constrained quality of service routing
Multi-constrained quality of service (QoS) routing finds a route in the network that satisfies multiple independent quality of service constraints. This problem is NP-hard and a number of heuristic algorithms have been proposed to solve the problem. This paper studies two heuristics, the limited granularity heuristic and the limited path heuristic, for solving general k-constrained problems. Analytical and simulation studies are conducted to compare the time/space requirements of the heuristics and the effectiveness of the heuristics in finding the paths that satisfy the QoS constraints. We prove analytically that for an N nodes and E edges network with k (a small constant) independent QoS constraints, the limited granularity heuristic must maintain a table of size O(|N|/sup k-1/) in each node to be effective, which results in a time complexity of O(|N|/sup k/|E|). We also prove that the limited path heuristic can achieve very high performance by maintaining O(|N|/sup 2/lg(|N|)) entries in each node, which indicates that the performance of the limited path heuristic is not sensitive to the number of constraints. We conclude that although both the limited granularity heuristic and the limited path heuristic can efficiently solve 2-constrained QoS routing problems, the limited path heuristic is superior to the limited granularity heuristic in solving k-constrained QoS routing problems when k>3. Our simulation study further confirms this conclusion.
2841955
Constructing Inter-Domain Packet Filters to Control IP Spoofing Based on BGP Updates
The Distributed Denial-of-Service (DDoS) attack is a serious threat to the legitimate use of the Internet. Prevention mechanisms are thwarted by the ability of attackers to forge or spoof the source addresses in IP packets. By employing IP spoofing, attackers can evade detection and put a substantial burden on the destination network for policing attack packets. In this paper, we propose an inter-domain packet filter (IDPF) architecture that can mitigate the level of IP spoofing on the Internet. A key feature of our scheme is that it does not require global routing information. IDPFs are constructed from the information implicit in Border Gateway Protocol (BGP) route updates and are deployed in network border routers. We establish the conditions under which the IDPF framework correctly works in that it does not discard packets with valid source addresses. Based on extensive simulation studies, we show that, even with partial deployment on the Internet, IDPFs can proactively limit the spoofing capability of attackers. In addition, they can help localize the origin of an attack packet to a small number of candidate networks.