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Approximate Signal Processing
, 1997
"... It is increasingly important to structure signal processing algorithms and systems to allow for trading off between the accuracy of results and the utilization of resources in their implementation. In any particular context, there are typically a variety of heuristic approaches to managing these tra ..."
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Cited by 516 (2 self)
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It is increasingly important to structure signal processing algorithms and systems to allow for trading off between the accuracy of results and the utilization of resources in their implementation. In any particular context, there are typically a variety of heuristic approaches to managing these tradeoffs. One of the objectives of this paper is to suggest that there is the potential for developing a more formal approach, including utilizing current research in Computer Science on Approximate Processing and one of its central concepts, Incremental Refinement. Toward this end, we first summarize a number of ideas and approaches to approximate processing as currently being formulated in the computer science community. We then present four examples of signal processing algorithms/systems that are structured with these goals in mind. These examples may be viewed as partial inroads toward the ultimate objective of developing, within the context of signal processing design and implementation,...
Algorithms for Scheduling RealTime Tasks with Input Error and EndtoEnd Deadlines
 IEEE Transactions on Software Engineering
, 1997
"... Abstract—This paper describes algorithms for scheduling preemptive, imprecise, composite tasks in realtime. Each composite task consists of a chain of component tasks, and each component task is made up of a mandatory part and an optional part. Whenever a component task uses imprecise input, the pr ..."
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Cited by 26 (3 self)
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Abstract—This paper describes algorithms for scheduling preemptive, imprecise, composite tasks in realtime. Each composite task consists of a chain of component tasks, and each component task is made up of a mandatory part and an optional part. Whenever a component task uses imprecise input, the processing times of its mandatory and optional parts may become larger. The composite tasks are scheduled by a twolevel scheduler. At the high level, the composite tasks are scheduled preemptively on one processor, according to an existing algorithm for scheduling simple imprecise tasks. The lowlevel scheduler then distributes the time budgeted for each composite task across its component tasks so as to minimize the output error of the composite task Index Terms—Realtime systems and applications, scheduling, imprecise computation, error, endtoend timing constraints. 1
Algorithms for Scheduling Tasks with Input Error and EndtoEnd Deadlines
, 1994
"... This paper describes heuristic algorithms for scheduling preemptive, imprecise, composite tasks with input error and endtoend timing constraints. Each composite task consists of a chain of component tasks, where each component task is made up of a mandatory part and an optional part. Whenever a co ..."
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Cited by 6 (3 self)
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This paper describes heuristic algorithms for scheduling preemptive, imprecise, composite tasks with input error and endtoend timing constraints. Each composite task consists of a chain of component tasks, where each component task is made up of a mandatory part and an optional part. Whenever a component task uses imprecise input, the mandatory and optional parts of the component task may be extended in order to compensate for input error. We use a twolevel scheduler. At the high level, the scheduler schedules the composite tasks preemptively on one processor, according to an existing algorithm for scheduling simple imprecise tasks. The result is the total amount of time budgeted to each composite task in order for all composite tasks to meet their endtoend deadlines. The lowlevel scheduler then distributes the time budgeted for each composite task across its component tasks so as to minimize the output error of each composite task. 1 Introduction A hard realtime system contai...
Scheduling jobs with multiple feasible intervals
 In RTCSA ’03: Proceedings of the 9th IEEE International Conference on Embedded and RealTime Computing Systems and Applications. 53–71
, 2003
"... This paper addresses the problem of scheduling realtime jobs that have multiple feasible intervals. The problem is NPhard. We present an optimal branchandbound algorithm. When there is time to compute the schedule, this algorithm can be used. Otherwise, the simple heuristics presented here can b ..."
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Cited by 5 (1 self)
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This paper addresses the problem of scheduling realtime jobs that have multiple feasible intervals. The problem is NPhard. We present an optimal branchandbound algorithm. When there is time to compute the schedule, this algorithm can be used. Otherwise, the simple heuristics presented here can be used. In addition, a priorityboosting EDF algorithm is designed to enhance the timeliness of jobs. Simulation results show that the combined use of the heuristics and the priority boosting EDF algorithm performs nearly as well as the optimal algorithm. 1
Incremental Refinement Structures for Approximate Signal Processing
, 1997
"... This work investigates approximate signal processing as a design philosophy supporting the realization of efficient, robust, and flexible digital signal processing systems through the use of incremental refinement structures that allow tradeoffs to be easily made between the accuracy or optimality o ..."
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Cited by 4 (1 self)
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This work investigates approximate signal processing as a design philosophy supporting the realization of efficient, robust, and flexible digital signal processing systems through the use of incremental refinement structures that allow tradeoffs to be easily made between the accuracy or optimality of results and the utilization of computing resources such as time, power, and chip area. The value of this approach is demonstrated through the theoretical development of incremental refinement structures for signal detection using the fast Fourier transform (FFT), image decoding using the twodimensional inverse discrete cosine transform (2D IDCT), and spectral analysis using the discrete Fourier transform (DFT). Using both deterministic and probabilistic techniques, the theoretical performance of these structures under various resource constraints is quantified in terms of welldefined measures such as probability of detection, SNR, and frequency resolution. These analyses are verified for...
Overlay networks monitoring
, 2008
"... This Dissertation is brought to you for free and open access by the Graduate College at Digital Repository @ Iowa State University. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Digital Repository @ Iowa State University. For more informati ..."
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This Dissertation is brought to you for free and open access by the Graduate College at Digital Repository @ Iowa State University. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Digital Repository @ Iowa State University. For more information, please contact