1690905
Broadcast of consistent data to read-only transactions from mobile clients
We study the inconsistency problem in data broadcast. While data items in a mobile computing system are being broadcast, update transactions may install new values for the data items. If the executions of update transactions and the broadcast of data items are interleaved without any control, it is possible that the mobile transactions, which are generated by mobile clients, may observe inconsistent data values. We propose a new algorithm, called Update-First with Order (UFO), for concurrency control among the mobile transactions and update transactions. The mobile transactions are assumed to be read-only. In the UFO algorithm, all the schedules among them are serializable. Two important properties of the UFO algorithm are that: (1) the mobile transactions do not need to set any lock before they read the data items from the "air"; and (2) its impact on the adopted broadcast algorithm, which has been shown to be an efficient method for data dissemination in mobile computing systems, is minimal.
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.
39491407
Mobile Distributed Real-Time Database Systems
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.
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.
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.
33676247
Locating and extracting the eye in human face images
Facial feature extraction is an important step in automated visual interpretation and human face recognition. Among the facial features, the eye plays the most important part in the recognition process. The deformable template can be used in extracting the eye boundaries. However, the weaknesses of the deformable template are that the processing time is lengthy and that its success relies on the initial position of the template. In this paper, the head boundary is first located in a head-and-shoulders image. The approximate positions of the eyes are estimated by means of average anthropometric measures. Corners, the salient features of the eyes, are detected and used to set the initial parameters of the eye templates. The corner detection scheme introduced in this paper can provide accurate information about the corners. Based on the corner positions, we can accurately locate the templates in relation to the eye images and greatly reduce the processing time for the templates. The performance of the deformable template is assessed with and without using the information on corner positions. Experiments show that a saving in execution time of about 40% on average and a better eye boundary representation can be achieved by using the corner information.
18244295
An efficient illumination normalization method for face recognition
In this paper, an efficient representation method insensitive to varying illumination is proposed for human face recognition. Theoretical analysis based on the human face model and the illumination model shows that the effects of varying lighting on a human face image can be modeled by a sequence of multiplicative and additive noises. Instead of computing these noises, which is very difficult for real applications, we aim to reduce or even remove their effect. In our method, a local normalization technique is applied to an image, which can effectively and efficiently eliminate the effect of uneven illuminations while keeping the local statistical properties of the processed image the same as in the corresponding image under normal lighting condition. After processing, the image under varying illumination will have similar pixel values to the corresponding image that is under normal lighting condition. Then, the processed images are used for face recognition. The proposed algorithm has been evaluated based on the Yale database, the AR database, the PIE database, the YaleB database and the combined database by using different face recognition methods such as PCA, ICA and Gabor wavelets. Consistent and promising results were obtained, which show that our method can effectively eliminate the effect of uneven illumination and greatly improve the recognition results.
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.
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.