Current Issue : April - June Volume : 2018 Issue Number : 2 Articles : 5 Articles
This paper presents a new time of arrival (TOA) estimation technique using an improved energy detection (ED)\nreceiver based on the empirical mode decomposition (EMD) in an impulse radio (IR) 60 GHz millimeter wave\n(MMW) system. A threshold is employed via analyzing the characteristics of the received energy values with an\nextreme learning machine (ELM). The effect of the channel and integration period on the TOA estimation is\nevaluated. Several well-known ED-based TOA algorithms are used to compare with the proposed technique. It is\nshown that this ELM-based technique has lower TOA estimation error compared to other approaches and provides\nrobust performance with the IEEE 802.15.3c channel models....
A reverberation-time-aware deep-neural-network (DNN)-based multi-channel speech dereverberation framework is\nproposed to handle a wide range of reverberation times (RT60s). There are three key steps in designing a robust\nsystem. First, to accomplish simultaneous speech dereverberation and beamforming, we propose a framework,\nnamely DNNSpatial, by selectively concatenating log-power spectral (LPS) input features of reverberant speech from\nmultiple microphones in an array and map them into the expected output LPS features of anechoic reference speech\nbased on a single deep neural network (DNN). Next, the temporal auto-correlation function of received signals at\ndifferent RT60s is investigated to show that RT60-dependent temporal-spatial contexts in feature selection are needed\nin the DNNSpatial training stage in order to optimize the system performance in diverse reverberant environments.\nFinally, the RT60 is estimated to select the proper temporal and spatial contexts before feeding the log-power\nspectrum features to the trained DNNs for speech dereverberation. The experimental evidence gathered in this study\nindicates that the proposed framework outperforms the state-of-the-art signal processing dereverberation algorithm\nweighted prediction error (WPE) and conventional DNNSpatial systems without taking the reverberation time into\naccount, even for extremely weak and severe reverberant conditions. The proposed technique generalizes well to\nunseen room size, array geometry and loudspeaker position, and is robust to reverberation time estimation error....
Fault detection and isolation is crucial for the efficient operation and safety of any industrial process. There is a variety\nof methods from all areas of data analysis employed to solve this kind of task, such as Bayesian reasoning and Kalman\nfilter. In this paper, the authors use a discrete Field Kalman Filter (FKF) to detect and recognize faulty conditions in a\nsystem. The proposed approach, devised for stochastic linear systems, allows for analysis of faults that can be\nexpressed both as parameter and disturbance variations. This approach is formulated for the situations when the fault\ncatalog is known, resulting in the algorithm allowing estimation of probability values. Additionally, a variant of\nalgorithm with greater numerical robustness is presented, based on computation of logarithmic odds. Proposed\nalgorithm operation is illustrated with numerical examples, and both its merits and limitations are critically discussed\nand compared with traditional EKF....
We present a computationally efficient blind sequential detection method for data transmitted over a sparse\nintersymbol interference channel. Unlike blind sequential detection methods designed for general channels, the\nproposed method exploits the channel sparsity by using estimated channel sparsity to assist in the detection of the\ntransmitted sequence. A Gaussian mixture model is used to describe sparse channels, and two tree-search strategies\nare applied to estimate the channel sparsity and the transmitted sequence, respectively. To demonstrate the\nperformance improvement achieved by the proposed blind detector, we compare it to conventional joint channel\nand sequence detection methods that use sparse channel estimation techniques. Simulation results show that the\nproposed detector not only reduces computational complexity compared to existing methods but also provides\nsuperior performance, particularly when the signal to noise ratio is low....
To date, attribute discretization is typically performed by replacing the original set of continuous features with a\ntransposed set of discrete ones. This paper provides support for a new idea that discretized features should often be\nused in addition to existing features and as such, datasets should be extended, and not replaced, by discretization. We\nalso claim that discretization algorithms should be developed with the explicit purpose of enriching a non-discretized\ndataset with discretized values. We present such an algorithm, D-MIAT, a supervised algorithm that discretizes data\nbased on minority interesting attribute thresholds. D-MIAT only generates new features when strong indications exist\nfor one of the target values needing to be learned and thus is intended to be used in addition to the original data. We\npresent extensive empirical results demonstrating the success of using D-MIAT on 28 benchmark datasets. We also\ndemonstrate that 10 other discretization algorithms can also be used to generate features that yield improved\nperformance when used in combination with the original non-discretized data. Our results show that the best\npredictive performance is attained using a combination of the original dataset with added features from a ââ?¬Å?standardââ?¬Â\nsupervised discretization algorithm and D-MIAT....
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