Signal Detection for QPSK Based Cognitive Radio Systems using Support Vector Machines

Cognitive radio based network enables opportunistic dynamic spectrum access by sensing, adopting and utilizing the unused portion of licensed spectrum bands. Cognitive radio is intelligent enough to adapt the communication parameters of the unused licensed spectrum. Spectrum sensing is one of the most important tasks of the cognitive radio cycle. In this paper, the auto-correlation function kernel based Support Vector Machine (SVM) classifier along with Welch's Periodogram detector is successfully implemented for the detection of four QPSK (Quadrature Phase Shift Keying) based signals propagating through an AWGN (Additive White Gaussian Noise) channel. It is shown that the combination of statistical signal processing and machine learning concepts improve the spectrum sensing process and spectrum sensing is possible even at low Signal to Noise Ratio (SNR) values up to -50 dB

Phase transition of graph Laplacian of high dimensional noisy random point cloud

We systematically explore the spectral distribution of kernel-based graph Laplacian constructed from high dimensional and noisy random point cloud in the nonnull setup. An interesting phase transition phenomenon is reported, which is characterized by the signal-to-noise ratio (SNR). We quantify how the signal and noise interact over different SNR regimes; for example, how signal information pops out the Marchenko-Pastur bulk. Motivated by the analysis, an adaptive bandwidth selection algorithm is provided and proved, which coincides with the common practice in real data. Simulated data is provided to support the theoretical findings. Our results paves the way towards a foundation for statistical inference of various kernel-based unsupervised learning algorithms, like eigenmap, diffusion map and their variations, for real data analysis

Signal-to-noise ratio in reproducing kernel Hilbert spaces

This paper introduces the kernel signal-to-noise ratio (kSNR) for different machine learning and signal processing applications}. The kSNR seeks to maximize the signal variance while minimizing the estimated noise variance explicitly in a reproducing kernel Hilbert space (rkHs). The kSNR gives rise to considering complex signal-to-noise relations beyond additive noise models, and can be seen as a useful signal-to-noise regularizer for feature extraction and dimensionality reduction. We show that the kSNR generalizes kernel PCA (and other spectral dimensionality reduction methods), least squares SVM, and kernel ridge regression to deal with cases where signal and noise cannot be assumed independent. We give computationally efficient alternatives based on reduced-rank Nyström and projection on random Fourier features approximations, and analyze the bounds of performance and its stability. We illustrate the method through different examples, including nonlinear regression, nonlinear classification in channel equalization, nonlinear feature extraction from high-dimensional spectral satellite images, and bivariate causal inference. Experimental results show that the proposed kSNR yields more accurate solutions and extracts more noise-free features when compared to standard approaches

KLASIFIKASI JENIS BURUNG BERDASARKAN SUARA MENGGUNAKAN ALGORITME SUPPORT VECTOR MACHINE

Birds are one of the animals that need to be protected because of some extinct species. To solve this problem, conservation are needed because not all bird habitats can be reached by humans. Bird sound was one of the unique markers to indicate the existence of bird life. With technology, information, and communication, human could identify bird species by its sound. The classification process based sound is one of the problems that can be solved by Machine Learning. The noise reduction process is needed to remove noise that is not needed like wind and rain. Noise Reduction using Wavelet Transform result is sound signal more cleaner with PSNR value 4,2890. The classification of bird species is based on sound using one of the machine learning algorithms, Support Vector Machine (SVM) with Heavy Tailed RBF kernel with ratio 7:3. The accuracy produced from this study was 77,00% obtained from the use of parameters namely c = 10.000 and epsilon = 1.00E-09

Investigation of Synapto-dendritic Kernel Adapting Neuron models and their use in spiking neuromorphic architectures

The motivation for this thesis is idea that abstract, adaptive, hardware efficient, inter-neuronal transfer functions (or kernels) which carry information in the form of postsynaptic membrane potentials, are the most important (and erstwhile missing) element in neuromorphic implementations of Spiking Neural Networks (SNN). In the absence of such abstract kernels, spiking neuromorphic systems must realize very large numbers of synapses and their associated connectivity. The resultant hardware and bandwidth limitations create difficult tradeoffs which diminish the usefulness of such systems. In this thesis a novel model of spiking neurons is proposed. The proposed Synapto-dendritic Kernel Adapting Neuron (SKAN) uses the adaptation of their synapto-dendritic kernels in conjunction with an adaptive threshold to perform unsupervised learning and inference on spatio-temporal spike patterns. The hardware and connectivity requirements of the neuron model are minimized through the use of simple accumulator-based kernels as well as through the use of timing information to perform a winner take all operation between the neurons. The learning and inference operations of SKAN are characterized and shown to be robust across a range of noise environments. Next, the SKAN model is augmented with a simplified hardware-efficient model of Spike Timing Dependent Plasticity (STDP). In biology STDP is the mechanism which allows neurons to learn spatio-temporal spike patterns. However when the proposed SKAN model is augmented with a simplified STDP rule, where the synaptic kernel is used as a binary flag that enable synaptic potentiation, the result is a synaptic encoding of afferent Signal to Noise Ratio (SNR). In this combined model the neuron not only learns the target spatio-temporal spike patterns but also weighs each channel independently according to its signal to noise ratio. Additionally a novel approach is presented to achieving homeostatic plasticity in digital hardware which reduces hardware cost by eliminating the need for multipliers. Finally the behavior and potential utility of this combined model is investigated in a range of noise conditions and the digital hardware resource utilization of SKAN and SKAN + STDP is detailed using Field Programmable Gate Arrays (FPGA)

Testing and Learning on Distributions with Symmetric Noise Invariance

Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD), the resulting distance between distributions, are useful tools for fully nonparametric two-sample testing and learning on distributions. However, it is rarely that all possible differences between samples are of interest -- discovered differences can be due to different types of measurement noise, data collection artefacts or other irrelevant sources of variability. We propose distances between distributions which encode invariance to additive symmetric noise, aimed at testing whether the assumed true underlying processes differ. Moreover, we construct invariant features of distributions, leading to learning algorithms robust to the impairment of the input distributions with symmetric additive noise.Comment: 22 page

Denoising Deep Neural Networks Based Voice Activity Detection

Recently, the deep-belief-networks (DBN) based voice activity detection (VAD) has been proposed. It is powerful in fusing the advantages of multiple features, and achieves the state-of-the-art performance. However, the deep layers of the DBN-based VAD do not show an apparent superiority to the shallower layers. In this paper, we propose a denoising-deep-neural-network (DDNN) based VAD to address the aforementioned problem. Specifically, we pre-train a deep neural network in a special unsupervised denoising greedy layer-wise mode, and then fine-tune the whole network in a supervised way by the common back-propagation algorithm. In the pre-training phase, we take the noisy speech signals as the visible layer and try to extract a new feature that minimizes the reconstruction cross-entropy loss between the noisy speech signals and its corresponding clean speech signals. Experimental results show that the proposed DDNN-based VAD not only outperforms the DBN-based VAD but also shows an apparent performance improvement of the deep layers over shallower layers.Comment: This paper has been accepted by IEEE ICASSP-2013, and will be published online after May, 201