2303584
Constructing high assurance asynchronous real-time distributed systems: a proof-based system engineering approach
Asynchronous real-time distributed systems raise fundamental issues of how to build timely systems that operate in the presence of uncertain timeliness, and furthermore, how to certify that such systems will deliver properties including timeliness and safety. In this paper, we discuss an approach for constructing certifiable asynchronous real-time distributed systems.
6241696
A formally verified application-level framework for real-time scheduling on POSIX real-time operating systems
We present a framework, called meta scheduler, for implementing real-time scheduling algorithms. The meta scheduler is a portable middleware layer component designed for implementations over POSIX-compliant operating systems. It accommodates pluggable real-time scheduling algorithms while offering the flexibility of platform independence - the singular underlying OS requirement is the now nearly ubiquitous POSIX compliance. The versatility of pluggable schedulers positions the meta scheduler for deployment in an interoperable heterogeneous real-time environment. We present the design of the meta scheduler and outline its implementation. Furthermore, we present a mechanized correctness verification using the UPPAAL model checker. Prototype implementation of the meta scheduler over QNX Neutrino real-time operating system demonstrates high performance and a small footprint.
13992284
CPU scheduling for statistically-assured real-time performance and improved energy efficiency
We present a CPU scheduling algorithm, called Energy-efficient Utility Accrual Algorithm (or EUA), for battery-powered, embedded real-time systems. We consider an embedded software application model where repeatedly occurring application activities are subject to deadline constraints specified using step time/utility functions. For battery-powered embedded systems, system-level energy consumption is also a primary concern. We consider CPU scheduling that (1) provides assurances on individual and collective application timeliness behaviors and (2) maximizes system-level timeliness and energy efficiency. Since the scheduling problem is intractable, EUA heuristically computes CPU schedules with a polynomial-time cost. Several properties of EUA are analytically established, including timeliness optimality during under-load situations and statistical assurances on timeliness behavior. Further, our simulation results confirm EUA's superior performance.
51883995
Energy-efficient, utility accrual scheduling under resource constraints for mobile embedded systems
We present an energy-efficient real-time scheduling algorithm called the Resource-constrained Energy-Efficient Utility Accrual Algorithm (or ReUA). ReUA considers an application model where activities are subject to time/utility function-time constraints, resource dependencies including mutual exclusion constraints, and statistical performance requirements including probabilistically satisfied, activity (timeliness) utility bounds. Further, ReUA targets mobile embedded systems where system-level energy consumption is a major concern. For such a model, we consider the scheduling objectives of (1) satisfying statistical performance requirements, and (2) maximizing system-level energy efficiency, while respecting resource dependencies. Since the problem is NP-hard, ReUA allocates resources using statistical properties of application cycle demands and heuristically computes schedules with a polynomial-time cost. We analytically establish several timeliness and non-timeliness properties of the algorithm. Further, our simulation experiments illustrate ReUA's effectiveness.
14201193
Adaptive time-critical resource management using time/utility functions: past, present, and future
Time/utility function time constraints (or TUFs) and utility accrual (UA) scheduling optimality criteria, constitute, arguably, the most effective and broadest approach for adaptive, dynamic time-critical resource management. A TUF, which is a generalization of the classical deadline constraint, specifies the utility of completing an application activity as an application-or situation-specific function of that activity's completion time. With TUF time constraints, timeliness optimality criteria can be specified in terms of accrued (e.g., summed) activity utilities. This work overviews past and recent advances on adaptive resource management for dynamic time-critical systems using UA algorithms. Emerging challenges and new research directions are also identified.
14712997
Proactive QoS negotiation in asynchronous real-time distributed systems
We present a fast, proactive, quality of service (QoS) negotiation algorithm called Best Effort Negotiation (or BEN), for asynchronous real-time distributed systems. BEN considers an application model where trans-node application timeliness and fault-tolerance requirements are expressed using benefit functions, and anticipated workload and system failure rates during future time intervals are expressed using adaptation functions and reliability functions, respectively. Furthermore, BEN considers an adaptation model where subtasks of application tasks are replicated at run-time for tolerating failures as well as for sharing workload increases. Given such models, the objective of the algorithm is to maximize the sum of aggregate real-time and fault-tolerance benefits during the time window of adaptation functions. Since determining the optimal solution is computationally intractable, BEN heuristically computer sub-optimal resource allocations in polynomial-time. To determine how well BEN performs, we describe another algorithm called HLC, that is inspired by the well-known Hill Climbing heuristic. We show that HLC is significantly slower than BEN. However, our experimental studies reveal that the performance of BEN, in general, is as good as that of HLC.
42009299
Probabilistic Object Tracking Based on Machine Learning and Importance Sampling
The paper presents a novel particle filtering framework for visual object tracking. One of the contributions is the development of a likelihood function based on one of machine learning algorithm–AdaBoost algorithm. The likelihood function can capture the structure characteristics of one class of objects, and is thus robust to clutters and noise in the complex background. The other contribution is the adoption of mean shift iteration as a proposal distribution, which can steer discrete samples towards regions which most likely contain the targets, and is therefore leading to computational efficiency in the algorithm. The effectiveness of such a framework is demonstrated with a particular class of objects–human faces.
9618917
Towards A Robust and Real-time Face Detection and Tracking Framework
Traditional research and development in face detection and tracking are mostly focused on either face detection in still images, or face tracking in image streams, without remarkable combination of both. As a result, few impressive face detection and tracking systems can be seen nowadays. The paper contributes to effectively incorporation of two modules, detection module and tracking module, into one integrated and coupled framework. In the detection module we construct AdaBoost detectors for face detection in video stream, which are able to detect multi-view faces and are robust to illumination changes in video stream. The tracking module is based on the improved mean shift tracking, which has advantage of target model adaption. The detection module, triggered every ten frames, is not only responsible for initiating a tracking thread, but also, together with tracking module, contributes to target model update. The system is able to benefit from the strengths of different techniques and overcome their respective limitations. Experiments on real image sequences show that the system is robust and real-time in realistic environment.
13006853
Novel likelihood estimation technique based on boosting detector
This paper presents novel likelihood estimation to be used for particle filter based object tracking. The likelihood estimation is built upon cascade object detector trained with Gentle AdaBoost (GAB), in order to capture the probability of existence of object. Two strategies are adopted to construct the likelihood functions: probability-intra-stage (PIS) corresponding to real output of each weak classifier in the same stage, and probability-outer-stage (POS) corresponding to the depth reached in the cascade detector. Five kinds of likelihood functions are thus proposed based on the trained GAB detector. Our experiment shows the likelihood functions are able to characterize probabilistically the existence of object accurately, having much higher confidence value in object regions than that in background, and that the integral strategy of PIS and POS is the best choice.
35689052
Visual contour tracking based on sequential importance sampling/resampling algorithm
The condensation algorithm can deal with non-Gaussian, nonlinear visual contour tracking in a unified way. Despite its simple implementation and generality, it has two main limitations. The first limitation is that in sampling stage the algorithm does not take advantage of the new measurements. As a result of the inefficient sampling strategy, the algorithm needs a large number of samples to represent the posterior distribution of state. The next is in the selection step, resampling may introduce the problem of sample impoverishment. To address these two problems, we present an improved visual tracker based on an importance sampling/resampling algorithm. Gaussian density of each sample is adopted as the sub-optimal importance proposal distribution, which can steer the samples towards the high likelihood by considering the latest observations. We also adopt a criterion of effective sample size to determine whether the resampling is necessary or not. Experiments with real image sequences show that the performance of new algorithm improves considerably for tracking in visual clutter.
2872365
A frequency relaxation approach for analog/RF system-level simulation
The increasing complexity of today's mixed-signal integrated circuits necessitates both top-down and bottom-up system-level verification. Time-domain state-space modeling and simulation approaches have been successfully applied for such purposes (e.g. Simulink); however, analog circuits are often best analyzed in the frequency domain. Circuit-level analyses, such as harmonic balance, have been successfully extended to the frequency domain [2], but these algorithms are impractical for simulating large systems with wide-band input and noise signals. In this paper we proposed a frequency-domain approach for analog/RF system-level simulation that is capable of capturing various second order effects (e.g. nonlinearity, noise, etc.) for both time-invariant and time-varying systems with wide-band inputs. The simulator directly evaluates the frequency domain response at each node via a relaxation scheme that is proven to be convergent under typical circuit conditions. Our experimental results demonstrate the accuracy and efficiency of the proposed simulator under various wide-band input and noise excitations.