16132542
Stochastic analysis of periodic real-time systems
This paper describes a stochastic analysis method for general periodic real-time systems. The proposed method accurately computes the response time distribution of each task in the system, thus making it possible to determine the deadline miss probability of individual tasks, even for systems with maximum utilization factor greater than one. The method uniformly covers both fixed-priority scheduling (such as rate monotonic) as well as dynamic-priority scheduling (such as earliest deadline first) and can handle arbitrary relative deadlines and execution time distributions. The accuracy of the method is proven by comparing the results from the analysis with those obtained from simulations, as well as other methodologies in the literature.
195349457
Analysis of cache-related preemption delay in fixed-priority preemptive scheduling
We propose a technique for analyzing cache-related preemption delays of tasks that cause unpredictable variation in task execution time in the context of fixed-priority preemptive scheduling. The proposed technique consists of two steps. The first step performs a per-task analysis to estimate cache-related preemption cost for each execution point in a given task from the number of useful cache blocks at the execution point. The second step computes the worst case response time of each task using a response time equation and a linear programming technique which takes as its input the preemption cost information of tasks obtained in the first step. Our experimental results show that the proposed technique gives a prediction of the worst case cache-related preemption delay that is up to 60% tighter than that obtained from previous approaches.
17289303
Spare CASH: reclaiming holes to minimize aperiodic response times in a firm real-time environment
Scheduling periodic tasks that allow some instances to be skipped produces spare capacity in the schedule. Only a fraction of this spare capacity is uniformly distributed and can easily be reclaimed for servicing aperiodic requests. The remaining fraction of the spare capacity is non-uniformly distributed, and no existing technique has been able to reclaim it. We present a method for improving the response times of aperiodic tasks by identifying the non-uniform holes in the schedule and adding these holes as extra capacity to the capacity queue of the CASH mechanism. The non-uniform holes can account for a significant portion of spare capacity, and reclaiming this capacity results in considerable improvements to aperiodic response times.
8167367
Online QoS Optimization Using Service Classes in Surveillance Radar Systems
Many application level qualities are functions of available computation resources. Recent studies have handled the computation resource allocation problem to maximize the overall application quality. However, such QoS problems are fundamentally multi-dimensional optimization problems that require extensive computation. Therefore, online usage of optimization procedures may significantly reduce the computation resource available for applications. This raises the question of how to best use the optimization procedures for dynamic real-time task sets. In dynamic real-time systems, it is important to improve the performance by re-allocating the resources adapting to dynamic situations. However, the overhead of changing task parameters (i.e., algorithms and frequencies) for resource re-allocation is non-negligible in many applications. Thus, too frequent change of resource allocation may not be desirable. This paper proposes a method called service classes configuration to address the QoS problem with dynamic arrival and departure of tasks. The method avoids online usage of optimization procedures by offline designing templates (called service classes) of resource allocation, which will be adaptively used depending on online situations. The service classes are designed by best trading-off the accuracy of dynamic adaptation against the overhead of resource re-allocation. A simplified radar application is used as an illustrative example.
7460330
Enhanced analysis of cache-related preemption delay in fixed-priority preemptive scheduling
We propose an enhanced technique for analyzing, and thus bounding cache related preemption delay in fixed priority preemptive scheduling focusing on instruction caching. The proposed technique improves upon previous techniques in two important ways. First, the technique takes into account the relationship between a preempted task and the set of tasks that execute during the preemption when calculating the cache related preemption delay. Second, the technique considers phasing of tasks to eliminate many infeasible task interactions. These two features are expressed as constraints of a linear programming problem whose solution gives a guaranteed upper bound on the cache related preemption delay. The paper also compares the proposed technique with previous techniques. The results show that the proposed technique gives up to 60% tighter prediction of the worst case response time than the previous techniques.
20686264
Finite-Horizon Scheduling of Radar Dwells with Online Template Construction
Timing constraints for radar tasks are usually specified in terms of the minimum and maximum temporal distance between successive radar dwells. We utilize the idea of feasible intervals for dealing with the temporal distance constraints. In order to increase the freedom that the scheduler can offer a high-level resource manager, we introduce a technique for nesting and interleaving dwells online while accounting for the energy constraint that radar systems need to satisfy. Further, in radar systems, the task set changes frequently and we advocate the use of finite horizon scheduling in order to avoid the pessimism that is inherent in schedulers that assume a task executes forever. We also develop the notion of modular schedule update which allows portions of a schedule to be altered without affecting the entire schedule, thereby simplifying the scheduler. Through extensive simulations, we validate our claims of providing greater scheduling flexibility without compromising on performance when compared with earlier work based on templates constructed offline.
16122349
Feature-Based Image Watermarking Method Using Scale-Invariant Keypoints
This paper addresses feature-based image watermarking methods for digital contents. Most previous watermarking algorithms suffer from geometric distortion attacks that desynchronize the location of the inserted watermark. In order to synchronize the watermark location, watermark synchronization should be performed. One solution for watermark synchronization is to use image features. This paper describes a feature-based watermarking method based on scale-invariant keypoints. We extract feature points from the scale-invariant keypoint extractor and then decompose feature points into a set of disjoint triangles. These triangles are watermarked by an additive spread-spectrum method on the spatial domain. We perform an intensive simulation in comparison with other feature-based watermarking methods using 100 test images. Results show that the proposed method is considerably robust against both geometric distortion attacks and signal processing attacks listed in Stirmark 3.1.
33610569
Compression-based broadcast strategies in wireless information systems
Data delivery is through broadcast in wireless information systems due to the asymmetric communication property. However, broadcasting data easily wastes large access time because the large amount of data pages needs to be broadcast. In this paper we propose three compression-based strategies considering access order to reduce access time. Then, the expected access time of three proposed strategies is also derived by using the probability model. Our analytic and experimental results show that the compression-based broadcast strategies can dramatically reduce the access time for mobile users (MU).
32239506
An intelligent fuzzy agent for meeting scheduling decision support system
Abstract An Intelligent Fuzzy Agent for Meeting Scheduling Decision Support System is proposed in this paper. The proposed Intelligent Fuzzy Agent including Meeting Negotiation Agent, Fuzzy Inference Agent and Genetic Learning Agent can search and decide the suitable meeting time for the specified meeting in an organization. When a meeting host requests a meeting, the Meeting Negotiation Agent immediately sends the invitees’ names to Meeting Scheduling Decision Support System for retrieving their schedules from Group Calendar Data Base, then Meeting Scheduling Decision Support System will compute the possible meeting time and respond the results to Meeting Negotiation Agent. Moreover, the Fuzzy Inference Agent infers the adequate meeting time based on the information provided by Meeting Negotiation Agent and Personalized Knowledge Base, and sends the computing result back to Meeting Scheduling Decision Support System. The meeting host will decide the final meeting time based on Meeting Scheduling Decision Support System and announce the meeting information by various devices including PDA, WAP, FAX, or E-mail. Furthermore, the invitees’ decisions for attending the meeting or not will be stored into Meeting Information Knowledge Base, then the Genetic Learning Agent will adjust the Personalized Knowledge Base for the next meeting. By the experimental results, the proposed Intelligent Fuzzy Agent can work efficiently and effectively for Meeting Scheduling Decision Support System.
13302890
An Efficient and Scalable Management of Ontology
OWL is a recommended language for publishing and sharing ontologies on the Semantic Web. To manage the ontologies, several OWL data management systems have been proposed. However, the existing systems have limitations of the scalability and the reasoning. In this paper, we propose an OWL data management system, ONTOMS, which stores OWL data into class based relations, performs complete inverseOf, symmetric, and transitive reasoning for instances, and efficiently evaluates OWL-QL queries against ontologies in a relational database.
8220070
Facial Expression Analysis Using Nonlinear Decomposable Generative Models
We present a new framework to represent and analyze dynamic facial motions using a decomposable generative model. In this paper, we consider facial expressions which lie on a one dimensional closed manifold, i.e., start from some configuration and coming back to the same configuration, while there are other sources of variability such as different classes of expression, and different people, etc., all of which are needed to be parameterized. The learned model supports tasks such as facial expression recognition, person identification, and synthesis. We aim to learn a generative model that can generate different dynamic facial appearances for different people and for different expressions. Given a single image or a sequence of images, we can use the model to solve for the temporal embedding, expression type and person identification parameters. As a result we can directly infer intensity of facial expression, expression type, and person identity from the visual input. The model can successfully be used to recognize expressions performed by different people never seen during training. We show experiment results for applying the framework for simultaneous face and facial expression recognition. 
 
Sub-categories: 1.1 Novel algorithms, 1.6 Others: modeling facial expression.