Current Issue : January - March Volume : 2015 Issue Number : 1 Articles : 4 Articles
In this paper, we propose a high-order lattice adaptive notch filter (LANF) that can robustly track multiple sinusoids.\nUnlike the conventional cascade structure, the proposed high-order LANF has robust tracking characteristics\nregardless of the frequencies of reference sinusoids and initial notch frequencies. The proposed high-order LANF is\napplied to a narrowband adaptive noise cancellation (ANC) to mitigate the effect of the broadband disturbance in the\nreference signal. By utilizing the gradient adaptive lattice (GAL) ANC algorithm and approximately combining it with\nthe proposed high-order LANF, a computationally efficient narrowband ANC system is obtained. Experimental results\ndemonstrate the robustness of the proposed high-order LANF and the effectiveness of the obtained narrowband ANC\nsystem....
The Global Navigation Satellite System (GNSS) signals are always available, globally, and the signal structures are\nwell known, except for those dedicated to military use. They also have some distinctive characteristics, including\nthe use of L-band frequencies, which are particularly suited for remote sensing purposes. The idea of using GNSS\nsignals for remote sensing - the atmosphere, oceans or Earth surface - was first proposed more than two decades\nago. Since then, GNSS remote sensing has been intensively investigated in terms of proof of concept studies, signal\nprocessing methodologies, theory and algorithm development, and various satellite-borne, airborne and ground-based\nexperiments. It has been demonstrated that GNSS remote sensing can be used as an alternative passive remote\nsensing technology. Space agencies such as NASA, NOAA, EUMETSAT and ESA have already funded, or will fund in\nthe future, a number of projects/missions which focus on a variety of GNSS remote sensing applications. It is\nenvisaged that GNSS remote sensing can be either exploited to perform remote sensing tasks on an independent\nbasis or combined with other techniques to address more complex applications. This paper provides an overview\nof the state of the art of this relatively new and, in some respects, underutilised remote sensing technique. Also\naddressed are relevant challenging issues associated with GNSS remote sensing services and the performance\nenhancement of GNSS remote sensing to accurately and reliably retrieve a range of geophysical parameters...
Recordings of neural activity, such as EEG, are an inherent mixture of different ongoing brain processes as well as\nartefacts and are typically characterised by low signal-to-noise ratio. Moreover, EEG datasets are often inherently\nmultidimensional, comprising information in time, along different channels, subjects, trials, etc. Additional information\nmay be conveyed by expanding the signal into even more dimensions, e.g. incorporating spectral features applying\nwavelet transform. The underlying sources might show differences in each of these modes. Therefore, tensor-based\nblind source separation techniques which can extract the sources of interest from such multiway arrays, simultaneously\nexploiting the signal characteristics in all dimensions, have gained increasing interest. Canonical polyadic\ndecomposition (CPD) has been successfully used to extract epileptic seizure activity from wavelet-transformed EEG\ndata (Bioinformatics 23(13):i10ââ?¬â??i18, 2007; NeuroImage 37:844ââ?¬â??854, 2007), where each source is described by a rank-1\ntensor, i.e. by the combination of one particular temporal, spectral and spatial signature. However, in certain scenarios,\nwhere the seizure pattern is nonstationary, such a trilinear signal model is insufficient. Here, we present the application\nof a recently introduced technique, called block term decomposition (BTD) to separate EEG tensors into rank-(Lr, Lr, 1)\nterms, allowing to model more variability in the data than what would be possible with CPD. In a simulation study, we\ninvestigate the robustness of BTD against noise and different choices of model parameters. Furthermore, we show\nvarious real EEG recordings where BTD outperforms CPD in capturing complex seizure characteristics....
In this paper, we investigate the strategy of transmission mode switching for device-to-device (D2D) communication\nin both single-cell scenario and multi-cell scenarios, which selects the transmission mode to guarantee the maximum\nergodic achievable sum-rate among three transmission modes. We first introduce the basic operation principles of\nthree communication transmission modes which are named as traditional cellular communication mode, direct D2D\ncommunication mode and two-way decode-and-forward (DF)-relayed D2D communication mode. Then we derive\nthe corresponding expressions for the ergodic achievable sum-rates of each transmission mode, and get the crossing\npoints of different transmission modes to attain maximum ergodic achievable sum-rate of the system. From the\nanalytical results, we can see that the proper operating region of each transmission mode is related to different\ninterference level and distance of the D2D users. Based on the analytical results, we obtain a reliable communication\ntransmission mode switching strategy which guarantees the system to choose the mode with the maximum ergodic\nachievable sum-rate so as to improve the performance of D2D communication. Numerical results demonstrate that\nby applying mode switching, the ergodic achievable sum-rate of the system achieves a remarkable enhancement...
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