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.
41856010
Enhanced utilization bounds for QoS management
In many practical real-time applications, there is a given set of task frequencies (i.e., inverse of task periods) corresponding to predetermined QoS options that the applications can choose. For example, in audio applications, the typical choices of playback frequencies are for CD quality, radio quality, and phone quality. Similar configurations can be found in streaming video and in control applications. In such systems, applications dynamically arrive requesting one of the periods provided by the QoS manager. Thus, the accurate and efficient online schedulability test is essential for any task set whose periods are chosen from the QoS period set. For this purpose, we propose new utilization bounds as a function of given QoS periods. As long as there is a set of given periods that can be chosen by applications, the bounds developed can be used by the QoS manager to quickly determine the schedulability of dynamic applications.
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.
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.
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.
5702505
Contextual recognition of head gestures
Head pose and gesture offer several key conversational grounding cues and are used extensively in face-to-face interaction among people. We investigate how dialog context from an embodied conversational agent (ECA) can improve visual recognition of user gestures. We present a recognition framework which (1) extracts contextual features from an ECA's dialog manager, (2) computes a prediction of head nod and head shakes, and (3) integrates the contextual predictions with the visual observation of a vision-based head gesture recognizer. We found a subset of lexical, punctuation and timing features that are easily available in most ECA architectures and can be used to learn how to predict user feedback. Using a discriminative approach to contextual prediction and multi-modal integration, we were able to improve the performance of head gesture detection even when the topic of the test set was significantly different than the training set.
7718842
Averaging attack resilient video fingerprinting
This paper addresses an effective technique for digital fingerprinting for video. The code modulation technique, that uses an averaging collusion resilient fingerprint code derived from BIBD, is a robust method against averaging attacks. In this paper, we propose a new fingerprint code based on GD-PBIBD theory that is efficient in designing the fingerprint code and describe an effective method to embed and detect the fingerprint code, in which the merits of orthogonal modulation and coded modulation techniques are combined. To analyze the performance, we apply our method to video applications using a spread-spectrum watermarking technique in the spatial domain and the results show the proposed method is effective for fingerprint code embedding and detection.
39638713
Robust H∞ fuzzy static output feedback control of T-S fuzzy systems with parametric uncertainties
This paper addresses robust H"~ fuzzy static output feedback control problem for T-S fuzzy systems with time-varying norm-bounded uncertainties. Sufficient conditions for synthesis of a fuzzy static output feedback controller for T-S fuzzy systems are derived in terms of a set of linear matrix inequalities (LMIs). In comparison with the existing literatures, the proposed approach not only simplifies the design procedure but also achieves a better H"~ performance. Three drawbacks existing in the previous papers such as coordinate transformation, same output matrices and BMI problem have been eliminated. The effectiveness of the proposed design method is demonstrated by an example for the control of a truck-trailer system.
5311612
A genetic algorithm for job sequencing problems with distinct due dates and general early-tardy penalty weights
Abstract A job scheduling problem with distinct due dates in a single machine is considered. General penalty weights which are not necessarily proportional to the processing times are applied to jobs either early or tardy. An optimal timing algorithm is presented which decides the optimal starting time of each job in a given job sequence. Idle times are inserted between blocks of jobs such that the cost function of each newly created job block is minimized. Prominent near optimal sequences are generated via a genetic algorithm N best reproduction without duplicates, uniform order-based crossover and intra-block mutation operators are employed. The proposed genetic algorithm is proved to outperform existing heuristic methods. On average, 12–33% cost reduction effect is obtained by the evolution algorithm compared to the solution by an excellent heuristic procedure. The near optimality of the solutions by the GA is also illustrated by the comparison with an exact algorithm.
15046188
Human Motion Synthesis by Motion Manifold Learning and Motion Primitive Segmentation
We propose motion manifold learning and motion primitive segmentation framework for human motion synthesis from motion-captured data. High dimensional motion capture date are represented using a low dimensional representation by topology preserving network, which maps similar motion instances to the neighborhood points on the low dimensional motion manifold. Nonlinear manifold learning between a low dimensional manifold representation and high dimensional motion data provides a generative model to synthesize new motion sequence by controlling trajectory on the low dimensional motion manifold. We segment motion primitives by analyzing low dimensional representation of body poses through motion from motion captured data. Clustering techniques like k-means algorithms are used to find motion primitives after dimensionality reduction. Motion dynamics in training sequences can be described by transition characteristics of motion primitives. The transition matrix represents the temporal dynamics of the motion with Markovian assumption. We can generate new motion sequences by perturbing the temporal dynamics