27689226
Virtual deadline assignment in distributed real-time database systems
In recent years, the performance of distributed real-time database systems (DRTDBS) have been examined using different simulation models. In these models, the network issues are always being neglected. The networks are usually modeled as simple constant delay servers. However, it is found that the performance of the underlying network has significant impact on the overall performance of the DRTDBS. The results also showed that unpredictable behavior of the underlying network will give non-constant network transit time which in turn deter the overall system performance. To deal with this impact, different virtual deadline assignment schemes are proposed. The schemes assign a virtual deadline based on the current network condition to those transactions which require a comparatively better performance. Our results demonstrate that dynamically tuning the virtual deadlines of transactions depending on the condition of the network, yields a substantial improvement in the performance of that certain type of transactions without severely affecting the overall system performance.
39491407
Mobile Distributed Real-Time Database Systems
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.
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.
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.
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.
18536131
An efficient color compensation scheme for skin color segmentation
Skin color is a useful means for human face detection. In this paper, we propose an efficient color compensation method for skin color segmentation under varying lighting conditions. One of the major problems of using skin color is that a face region may not be detected under poor or uneven lighting conditions. Our approach considers the distribution of the color components of skin pixels and the color response of capturing machines under different illuminations. Experimental results show that this algorithm can improve the performance of face segmentation under poor or strong lighting conditions.
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.
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.
12206604
An adaptive active contour model for highly irregular boundaries
Abstract Snake is an active contour model for representing image contours. In this paper, we propose an efficient active contour model which can represent highly irregular boundaries. The algorithm includes an adaptive force along the contour, and adjusts the number of points for the snake according to the desired boundary. A better stopping criterion based on the area of a closed contour is devised. Furthermore, in this method, a contour can break automatically to represent the contours of multiple objects. Experiments show that this method can extract object's boundaries accurately and efficiently.
39178482
Illumination invariant face recognition
The appearance of a face will vary drastically when the illumination changes. Variations in lighting conditions make face recognition an even more challenging and difficult task. In this paper, we propose a novel approach to handle the illumination problem. Our method can restore a face image captured under arbitrary lighting conditions to one with frontal illumination by using a ratio-image between the face image and a reference face image, both of which are blurred by a Gaussian filter. An iterative algorithm is then used to update the reference image, which is reconstructed from the restored image by means of principal component analysis (PCA), in order to obtain a visually better restored image. Image processing techniques are also used to improve the quality of the restored image. To evaluate the performance of our algorithm, restored images with frontal illumination are used for face recognition by means of PCA. Experimental results demonstrate that face recognition using our method can achieve a higher recognition rate based on the Yale B database and the Yale database. Our algorithm has several advantages over other previous algorithms: (1) it does not need to estimate the face surface normals and the light source directions, (2) it does not need many images captured under different lighting conditions for each person, nor a set of bootstrap images that includes many images with different illuminations, and (3) it does not need to detect accurate positions of some facial feature points or to warp the image for alignment, etc.