The Infrared Data Association has developed the Advanced Infrared (AIr) protocol standard for infrared wireless LANs. AIr employs a go-back-N automatic repeat request (ARQ) scheme at the link control layer to deal wit...
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The Infrared Data Association has developed the Advanced Infrared (AIr) protocol standard for infrared wireless LANs. AIr employs a go-back-N automatic repeat request (ARQ) scheme at the link control layer to deal with transmission errors. Alternatively, an optional stop-and-wait ARQ scheme at the medium access control (MAC) layer may be employed. The effectiveness of employing the optional stop-and-wait ARQ scheme at the MAC layer is examined. Results presented in the literature for this issue have led to misleading conclusions due to poor selection of parameter values and inconsistencies in the analysis. Analytical models for protocol performance are presented and protocol utilization for various parameter values is studied.
This paper presents a system-level approach for modelling and implementing hardware-software systems, which contain Run-Time Reconfigurable (RTR) hardware. The developed technique provides management and scheduling of...
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This paper presents a system-level approach for modelling and implementing hardware-software systems, which contain Run-Time Reconfigurable (RTR) hardware. The developed technique provides management and scheduling of RTR tasks from system-level simulations to synthesizable VHDL descriptions. The developed technique was implemented using OCAPI-xl - a system-level modelling and implementation tool based on C + + libraries. The proposed approach allows designers to explore the tradeoffs between implementation of system partitions in software, static hardware, and RTR hardware. After the system has been partitioned, an OCAPI-xl-based design flow can be utilized for implementation of all the system components.
This paper presents design and analysis of a rigid link finger, which may be suitable for a number of adaptive end effectors. The design has evolved from an industrial need for a tele-operated system to be used in nuc...
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This paper presents design and analysis of a rigid link finger, which may be suitable for a number of adaptive end effectors. The design has evolved from an industrial need for a tele-operated system to be used in nuc...
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Speed of product to the market place was always important. Hiis has become increasingly so with the onslaught of globalisation. What is released on the High Street in the USA today will be sold on the shelves and coun...
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The need for accurate software prediction systems increases as software becomes larger and more complex. A variety of techniques have been proposed, but none has proved consistently accurate. The underlying characteri...
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ISBN:
(纸本)0769510434
The need for accurate software prediction systems increases as software becomes larger and more complex. A variety of techniques have been proposed, but none has proved consistently accurate. The underlying characteristics of the data set influence the choice of the prediction system to be used. It has proved difficult to obtain significant results over small data sets; consequently, we required large validation data sets. Moreover, we wished to control the characteristics of such data sets in order to systematically explore the relationship between accuracy, choice of prediction system and data set characteristics. Our solution has been to simulate data, allowing both control and the possibility of large validation cases. We compared regression, rule induction and nearest neighbours (a form of case-based reasoning). The results suggest that there are significant differences depending upon the characteristics of the data set. Consequently, researchers should consider the prediction context when evaluating competing prediction systems. We also observed that the more "messy" the data and the more complex the relationship with the dependent variable, the more variability in the results. This became apparent since we sampled two different training sets from each simulated population of data. In the more complex cases, we observed significantly different results depending upon the training set. This suggests that researchers will need to exercise caution when comparing different approaches and utilise procedures such as bootstrapping in order to generate multiple samples for training purposes.
It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant ...
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ISBN:
(纸本)0769510434
It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. The authors describe their sparse data method (SDM) based upon a pairwise comparison technique and T.L. Saaty's (1980) Analytic Hierarchy Process. Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach, based upon expert judgement, adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practising project manager. From this empirical work we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction.
Building and evaluating prediction systems is an important activity for software engineering researchers. Increasing numbers of techniques and datasets are now being made available. Unfortunately systematic comparison...
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Building and evaluating prediction systems is an important activity for software engineering researchers. Increasing numbers of techniques and datasets are now being made available. Unfortunately systematic comparison is hindered by the use of different accuracy indicators and evaluation processes. We argue that these indicators are statistics that describes properties of the estimation errors or residuals and that the sensible choice of indicator is largely governed by the goals of the estimator. For this reason it may be helpful for researchers to provide a range of indicators. We also argue that it is useful to formally test for significant differences between competing prediction systems and note that where only a few cases are available this can be problematic, in other words the research instrument may have insufficient power. We demonstrate that this is the case for a well known empirical study of cost models. Simulation, however, could be one means of overcoming this difficulty.
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