17018329
An efficient method for generating location updates for processing of location-dependent continuous queries
Recent advances in mobile computing and mobile communication technology have led to the emergence of many innovative mobile computing applications. Some of them require providing support to location-dependent continuous queries (LDCQs) on moving objects. The result of a location-dependent query depends on the current locations of the moving objects. When the query is specified as continuous, the requesting client can get continuously changing results. In order to provide correct and timely results to requesting clients, the locations of moving objects have to be closely monitored. In this paper, we propose an adaptive monitoring method (AMM) for managing the locations of moving objects to maintain the correctness of the results of query evaluation without significantly increasing the wireless bandwidth requirements. Extensive simulation experiments have been conducted to investigate the performance of the proposed method as compared to plain dead-reckoning (PDR).
16685984
A multi-version data model for executing real-time transactions in a mobile environment
With the significant advances in mobile computing technology, there is an increasing demand for various mobile applications to process transactions in a real-time fashion. When remote data access is considered in a mobile environment, data access delay becomes one of the most serious problems in meeting transaction deadlines. In this paper, we propose a multi-version data model and adopt the relative consistency as the correctness criterion for processing of real-time transactions in a mobile environment. The purpose is to reduce the impacts of unpredictable and unreliable mobile network on processing of the real-time transactions. Under the proposed model, the overheads for concurrency control can be significantly reduced, and the data availability is much enhanced even under network failures. A real-time transaction may access stale data, provided that they are relatively consistent with the data accessed by the transaction, and the staleness of the data is within the requirements. An image transaction model, which pre-fetches multiple data versions at fixed hosts, is proposed to reduce the data access delay and to simplify the management of the real-time transactions in a mobile environment. The image transaction model also helps in reducing the transaction-restart overheads and minimizing the impacts of the unpredictable performance of mobile network on transaction executions.
62523912
Concurrency control strategies for ordered data broadcast in mobile computing systems
Although data broadcast has been shown to be an efficient method for disseminating data items in mobile computing systems, the issue on how to ensure consistency and currency of data items provided to mobile transactions (MT), which are generated by mobile clients, has not been examined adequately. While data items are being broadcast, update transactions may install new values for them. If the executions of update transactions and the broadcast of data items are interleaved without any control, mobile transactions may observe inconsistent data values. The problem will be more complex if the mobile clients maintain some cached data items for their mobile transactions. In this paper, we propose a concurrency control method, called ordered update first with order (OUFO), for the mobile computing systems where a mobile transaction consists of a sequence of read operations and each MT is associated with a time constraint on its completion time. Besides ensuring data consistency and maximizing currency of data to mobile transactions, OUFO also aims at reducing data access delay of mobile transactions using client caches. A hybrid re-broadcast/invalidation report (IR) mechanism is designed in OUFO for checking the validity of cached data items so as to improve cache consistency and minimize the overhead of transaction restarts due to data conflicts. This is highly important to the performance of the mobile computing systems where the mobile transactions are associated with a deadline constraint on their completion times. Extensive simulation experiments have been performed to compare the performance of OUFO with two other efficient schemes, the multi-version broadcast method and the periodic IR method. The performance results show that OUFO offers better performance in most aspects, even when network disconnection is common.
39491407
Mobile Distributed Real-Time Database Systems
8292420
Priority Scheduling of Transactions in Distributed Real-Time Databases
One of the most important issues in the design of distributed real-time database system (DRTDBS) is transaction scheduling which consists of two parts: priority scheduling and real-time concurrency control. In the past studies, mostly, these issues are studied separately although they have a very close interaction with each other. In this paper, we propose new priority assignment policies for DRTDBS and study their impact on two typical real-time concurrency control protocols (RT-CCPs), High Priority Two Phase Locking (HP-2PL) and Optimistic Concurrency Control with Broadcast Commit (OCC-BC). Our performance results show that many factors, such as data conflict resolution, degree of data contention and transaction restarts, that are unique to database systems, have significant impact on the performance of the policies which in turn affect the performance of the real-time concurrency control protocols. OCC-BC is more affected by the priority assignment policies than HP-2PL owing to the late detection of conflict. In the design of priority assignment policies, we have found that neither the purely deadline driven policies nor data contention driven policies are suitable for DRTDBS. Our proposed policy, the Mixed Method (MM), which considers both transaction timeliness and data contention, outperforms other policies over a wide range of system parameter settings.
16283914
Approaches for broadcasting temporal data in mobile computing systems
Abstract Rapid advances in mobile communication technology have spawned many new mobile applications. A key element in many of these systems is the need to distribute real-time information from a database server to mobile clients. While data broadcast has been shown to be an efficient data dissemination technique, many issues such as selection of broadcast data and caching strategies at mobile clients are still active research areas. In this paper, we consider an important characteristic of many mobile computing systems which has often been ignored in the design of broadcast algorithms: the fact that many data items are associated with temporal constraints on their validity. We introduce the notion of absolute validity interval (AVI) to capture the temporal constraints of the data items, formulate a temporal data model and examine both static and dynamic approaches to select data items based on their access frequencies as well as their AVI. The reason for considering the AVI of the data items in broadcast selection is to increase the client cache hit probability so that the access delay for a data item will be much reduced. Based on the results from extensive simulation experiments, it is concluded the AVI-based approaches, by improving cache hit probability, can significantly improve the mean response time and reduce the number of deadline missing requests.
207326749
Optimal sampling of Gabor features for face recognition
The Gabor feature is effective for facial image representation, while linear discriminant analysis (LDA) can extract the most discriminant information from the Gabor feature for face recognition. In practice, the dimension of a Gabor feature vector is so high that the computation and memory requirements are prohibitively large. To reduce the dimension, one simple scheme is to extract the Gabor feature at sub-sampled positions, usually in a regular grid, in a face region. However, this scheme is not effective enough and degrades the recognition performance. In this paper, we propose a method to determine the optimal position for extracting the Gabor feature such that the number of feature points is as small as possible while the representation capability of the points is as high as possible. The subsampled positions of the feature points are determined by a mask generated from a set of training images by means of principal component analysis (PCA). With the feature vector of reduced dimension, a subspace LDA is applied for face recognition, i.e., PCA is first used to reduce the dimension of the Gabor feature vectors generated from the subsampled positions, and then a common LDA is applied. Experimental results show that the new sampling method is simple, and effective for both dimension reduction and image representation. The recognition rate based on our proposed scheme is also higher than that achieved using a regular sampling method in a face region.
2162231
A Robust Line-Feature-Based Hausdorff Distance for Shape Matching
The Hausdorff distance can be used to measure the similarity of two point sets. In matching the two point sets. one of the point sets is translated. rotated and scaled in order to obtain an optimal matching. which is a computationally intensive process. In this paper, a robust line-feature-based approach for model-based recognition is proposed. which can achieve a good performance level in matching, even in a noisy environment or with the existence of occlusion. The method is insensitive to noise and can find the rotation and scale of the image point set accurately and reliably. For this reason, instead of 4D matching, a 2D-2D matching algorithm can be used. This can greatly reduce the required memory and computation. Having rotated and scaled the image point set, the difference between the query point set and the model point set can be computed by considering translation only. The performance and the sensitivity to noise of our algorithm are evaluated using simulated data. Experiments show that our 2D-2D algorithm can give a high performance level when determining the relative scale and orientation of two point sets.
31880885
Channel-adaptive error protection for streaming stored MPEG-4 FGS over error-prone environments
In this paper, we investigate an adaptive channel error-protection scheme for streaming stored MPEG-4 fine granular scalability (FGS) bitstreams over error-prone environments. The human eye is sensitive to the variations in reconstructed image quality. Therefore, instead of using only the minimal average frame distortion as the optimization criterion, we propose a rate-distortion (R-D)-based bit-allocation method to determine the source coding rate and the channel coding rate under a given channel condition so as to minimize both the average image distortion and quality variation. Based on our proposed piecewise model of the MPEG-4 FGS enhancement layer, a balance between the average frame quality and quality variation will be obtained. Experimental results show that, compared with other existing methods, our proposed method can achieve less quality variation and comparable average frame distortion under various channel conditions.
10239266
Scene cut detection using the colored pattern appearance model
In this paper, we propose to use the colored pattern appearance model (CPAM) as a content representation for video scene break detection. This model represents a scene by means of global statistics of the local visual appearance, and was originally motivated by studies in human color vision. The performance of this method is compared to several histogram-based approaches. An adaptive thresholding technique, namely entropic thresholding, is applied to determine the respective optimal threshold values for each of the approaches. In the experiments, the two video sequences in the MPEG-7 content set are used to evaluate the performances of the CPAM and the histogram-based methods. Experimental results show that our proposed model outperforms other histogram-based approaches in scene break detection.
12996781
Spatially eigen-weighted Hausdorff distances for human face recognition
Abstract Hausdorff distance is an efficient measure of the similarity of two point sets. In this paper, we propose a new spatially weighted Hausdorff distance measure for human face recognition. The weighting function used in the Hausdorff distance measure is based on an eigenface, which has a large value at locations of importance facial features and can reflect the face structure more effectively. Two modified Hausdorff distances, namely, “spatially eigen-weighted Hausdorff distance” (SEWHD) and “spatially eigen-weighted ‘doubly’ Hausdorff distance” (SEW2HD) are proposed, which incorporate the information about the location of important facial features such as eyes, mouth, and face contour so that distances at those regions will be emphasized. Experimental results based on a combination of the ORL, MIT, and Yale face databases show that SEW2HD can achieve recognition rates of 83%, 90% and 92% for the first one, the first three and the first five likely matched faces, respectively, while the corresponding recognition rates of SEWHD are 80%, 83% and 88%, respectively.