This paper introduce the thought of USB interface application, an automatic measurement system is as an example. The PC control system of automatic measurement by the Labview software programming, then control relay, ...
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The photovoltaic energy can become competitive by the conjugate effort of the researchers and manufacturers. Increasing the amount of electricity supplied by photovoltaic panels is a challenge. The paper briefly prese...
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The photovoltaic energy can become competitive by the conjugate effort of the researchers and manufacturers. Increasing the amount of electricity supplied by photovoltaic panels is a challenge. The paper briefly presents some methods which can lead to achieving this goal. A simple method to increase the quantity of the electrical energy delivered by the photovoltaic panels is also presented in this paper alongside the experimental setup and the software created in LabVIEW for monitoring the output of the panels.
MC/DC (modified condition/decision coverage) is a structural code coverage metric, originally defined in the standard DO-178B [1], intended to be an efficient coverage metric for the evaluation of the testing process ...
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ISBN:
(纸本)9781479921133
MC/DC (modified condition/decision coverage) is a structural code coverage metric, originally defined in the standard DO-178B [1], intended to be an efficient coverage metric for the evaluation of the testing process of software incorporating decisions with complex Boolean expressions. The upcoming standard ISO 26262 [2] for safety-relevant automotive systems prescribes MC/DC for ASIL D as a highly recommended coverage metric. One assumed benefit of MC/DC is that it requires a much smaller number of test cases in comparison to MCC (multiple condition coverage), while sustaining a quite high error detection probability [3]. programminglanguages like C, commonly used for implementing software for the automotive domain, are using short-circuit evaluation. For short-circuit evaluation the number of test cases for MCC is much smaller than in a non-shortcircuit environment because many redundant test cases occur. We evaluated the trade-off between the number of test cases for MCC and MC/DC for a case study from the automotive domain and observed an overhead of only approximately 5% for the number of test cases necessary for MCC compared to MC/DC. This motivated an analysis of programs containing decisions where the number and structure of the referring Boolean expressions vary. Our results show that the overhead for a test suite for MCC is on the average only about 35% compared MC/DC (for decisions with up to 5 conditions). We conclude with the strong recommendation to use MCC as a coverage metric for testing safety-relevant software implemented in programminglanguages with short-circuit evaluation.
Agent programminglanguages based on the Belief, Desire and Intentions (BDI) framework present some interesting challenges for logicians. While BDI logics have been studied extensively, problems related to belief upda...
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We propose a framework for processing source code support- ing multiple programminglanguages, named UNICOEN. UNICOEN reduces development costs and differences be- tween tool implementations. We evaluated UNICOEN by d...
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The recent boom in use of speech recognition technology has made the access to potentially large amounts of training data easier. This, however, also constitutes a challenge in processing such large, continuously grow...
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ISBN:
(纸本)9781479928941
The recent boom in use of speech recognition technology has made the access to potentially large amounts of training data easier. This, however, also constitutes a challenge in processing such large, continuously growing amount of information. Here we present a stochastic modification of traditional iterative training approach which leads to the same or even better accuracy of acoustic models and reduces the cost of processing large data sets. The algorithm relies on model updates from statistics collected on randomly selected subsets of training data. The approach is demonstrated on maximum likelihood (ML) training and on discriminative training (DT) with minimum phone error (MPE) objective function both in the feature and the model space. Based on our experiments on 30 thousand hours of mobile data, the number of data passes can be reduced to 1/5 of the original for ML training and to 1/10 for model space DT training.
We are motivated by many applications such as problems that arise in online marketing applications, where the observations are governed by non-homogeneous Poisson models. We analyze the performance of a Maximum Likeli...
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ISBN:
(纸本)9781479928941
We are motivated by many applications such as problems that arise in online marketing applications, where the observations are governed by non-homogeneous Poisson models. We analyze the performance of a Maximum Likelihood (ML) decoder. We prove consistency and show an exponential rate of converge for sparse recovery in the high-dimensional Poisson setting. After verifying the efficiency of ML estimator empirically, we apply the ML decoder to study the dynamics of online marketing methods over time.
This paper considers cost savings that can be achieved by a multi-layer (ML) dimensioning and interworking within a practical packet-optical backbone network. They are based on several multi-layer resilience (MLR) sch...
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ISBN:
(纸本)9783800736041
This paper considers cost savings that can be achieved by a multi-layer (ML) dimensioning and interworking within a practical packet-optical backbone network. They are based on several multi-layer resilience (MLR) schemes. This includes optical restoration against optical failures (MLR-O) and spare backup interfaces, provisioned against port failures (MLR-P). The design steps comprise an ML router bypass optimization (MLBO) of 100Gbit/s wavelengths through an agile photonic layer equipped with colorless-directionless ROADM. In order to minimize costs, dedicated L1/L2 (ODU or Ethernet) switching devices will not be deployed. We apply this methodology to DT‚s IP backbone, showing about 25-30% fewer interfaces are required. The total cost saving potential is estimated by using today‚s average market prices, proving the superiority of the approach. Finally, we extend MLR to the aggregation network with its typically ring-like topologies.
In this paper, we propose a new signal-to-noise-ratio (SNR) maximum likelihood (ML) estimator over time-varying single-input multiple-output (SIMO) channels, for both data-aided (DA) and non-data-aided (NDA) cases. Un...
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ISBN:
(纸本)9781479928941
In this paper, we propose a new signal-to-noise-ratio (SNR) maximum likelihood (ML) estimator over time-varying single-input multiple-output (SIMO) channels, for both data-aided (DA) and non-data-aided (NDA) cases. Unlike the classical techniques which assume the channel to be slowly time-varying and, therefore, considered as constant during the observation period, we address the more challenging problem of instantaneous SNR estimation over fast time-varying channels. The channel variations are locally tracked using a polynomial-in-time expansion. In the DA scenario, the ML estimator is developed in closed-form expression. In the NDA scenario, however, the ML estimates of the per-antenna SNRs are obtained iteratively, with very few iterations, using the expectation-maximization (EM) procedure. Our estimator is able to accurately estimate the instantaneous SNRs over a wide range of average SNR. We show through extensive Monte-Carlo simulations that the new estimator outperforms previously developed solutions.
In this paper, we address the issue of the joint detection schemes for uplink cellular system when base station cooperation is possible for multi-cell users in multi-cell scenario. The ML, MMSE and MMSE based SIC dete...
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ISBN:
(纸本)9781479952564
In this paper, we address the issue of the joint detection schemes for uplink cellular system when base station cooperation is possible for multi-cell users in multi-cell scenario. The ML, MMSE and MMSE based SIC detection are analyzed and evaluated. ML attains the optimal performance but the complexity increases exponentially. MMSE is simple but has poor detection performance and SIC has better performance but it has large complexity and the potential of the error propagation. We propose a new ML and MMSE-SIC detection scheme, which combines the partial ML decoding and MMSE based SIC combined detection in order to improve the uncoded BER performance and also decrease the decoding complexity. Simulation results show that the proposed scheme attains the maximum 2 dB reduction of required SNR value to achieve the same BER performance and a similar or a little bit reduced level of decoder complexity over the MMSE based SIC scheme. The proposed scheme can be applied to the MIMO detection for single user and can be extended to other types of multi-user and multi-antenna based BS Cooperation.
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