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Reliable spectrum sensing ability is a key factor in cognitive radios. However, there are many aspects that impact the sensing reliability. One important aspect is impairments in the cognitive radio receiver hardware. Received signals tend to have high dynamic range which drives the receiver to the nonlinear zone. This may cause nonlinear distortion falling to the sensing band and therefore either...
This work addresses the problem of estimating the interference temperature in cognitive radio systems. The proposed technique exploits higher order moments of the received signal to achieve accurate estimation of the noise and signal power. Then, the noise power is used for the evaluation of the interference temperature. Performance analysis has been carried out in comparison with the conventional...
Emerging high throughput wireless communication standards, such as LTE/LTE-A and IEEE 802.11ac, impose exciting challenges on SDR baseband implementations. Our work explores the feasibility of SDR baseband for the most demanding-modes in those emerging high throughput standards. On a customized C programmable SDR baseband processor (with compiler support), we have accomplished realtime inner receiver...
Orthogonal frequency division multiplexing (OFDM) has been proven to be very sensitive to carrier drifts due to fact that both carrier frequency offset (CFO) and sampling frequency offset (SFO) destroy the orthogonality cross multiple subcarriers. The sampling clock and the local oscillator, from which the carrier frequency is derived, sharing a common reference clock is becoming a configuration extensively...
Relationships between entities in datasets are often of multiple types, which can naturally be modeled by a multi-layer graph; a common vertex set represents the entities and the edges on different layers capture different types of relationships between the entities. In this paper, we address the problem of analyzing multi-layer graphs and propose methods for clustering the vertices by efficiently...
Non-negative Matrix Factorisation (NMF) is a popular tool in which a ‘parts-based’ representation of a non-negative matrix is sought. NMF tends to produce sparse decompositions. This sparsity is a desirable property in many applications, and Sparse NMF (S-NMF) methods have been proposed to enhance this feature. Typically these enforce sparsity through use of a penalty term, and a ℓ1 norm penalty term...
Online education affords the opportunity to revolutionize learning by providing access to high-quality educational resources at low costs. The recent popularity of so-called MOOCs (massive open online courses) further accelerates this trend. However, these exciting advancements result in several challenges for the course instructors. Among these challenges is the detection of collaboration between...
Modern wireless communication systems use orthogonal frequency division multiplexing (OFDM) and multiple input multiple output (MIMO) schemes, which call for fast Fourier transforms (FFT). Traditionally power-of-two FFT lengths have been exploited but recently also non-power-of-two transform lengths have been defined. For example, 3GPP LTE specification defines 1536- point FFT. In this paper, we propose...
Signal and image reconstruction from Fourier Transform magnitude is a difficult inverse problem. Fourier transform magnitude can be measured in many practical applications, but the phase may not be measured. Since the autocorrelation of an image or a signal can be expressed as convolution of x(n) with x(−n), it is possible to formulate the inverse problem as a non-negative matrix factorization problem...
Many adaptive sensing and sensor management strategies seek to determine a sequence of sensor actions that successively optimizes an objective function. Frequently the goal is to adjust a sensor to best estimate a partially observed state variable, for example, the objective function may be the final mean-squared state estimation error. Information-driven sensor planning strategies adopt an objective...
Many complicated network problems can be easily understood on small networks. Difficulties arise when small networks are combined into larger ones. Fortunately, the mathematical theory of sheaves was constructed to address just this kind of situation; it extends locally-defined structures to globally valid inferences by way of consistency relations. This paper exhibits examples in network monitoring...
In this paper, we provide a novel frequency-domain approach to locate an arbitrary number of sources in a large number of zones. In typical source localization methods, the sources are assumed to be acoustic or RF; sensors are placed in different zones to listen to these sources where each zone-to-sensor has a unique path loss and delay. Since each zone has a path loss and delay to each sensor, the...
This paper studies distributed subspace tracking in wireless networks based on consensus averaging. Most prior approaches to this require the exchange of many inter-node messages between the arrival of new measurements, forcing communications to happen at a faster timescale than measurements. By contrast, this paper proposes a technique, termed hierarchical subspace tracking, which leverages recent...
This paper considers the problems of distributed online prediction and optimization. Each node in a network of processors processes a stream of data in an online manner. Before the next data point arrives, the processor must make a prediction. Then, after receiving the next point, the processor accrues some loss or regret. The goal of the processors is to minimize the total aggregate regret. We propose...
In this work we propose using the coefficient of variation as a cost function to improve seismic data representation in the curvelet domain. Performance improvement is demonstrated in denoising and compressed sensing data recovery. The demonstrated approach can be extended to other seismic applications and alternate transforms.
While manifolds have attracted significant attention from the image processing and computer vision communities, we are not aware of openly available tools for visualization of manifold-modeled data that allows for interactive navigation over the embedded dataset. We introduce the Manifold Analysis GUI (MAGI), a freely available toolbox for Matlab that provides a navigation interface for manifold-modeled...
This paper presents a novel, power efficient and low-latency method for detecting touch locations on capacitive touch screens. In capacitive touch screens, only a few touch sensors experience detectable change corresponding to the touch locations. We show that power consumption can be reduced by monitoring fewer number of samples than the standard method to sense changes in capacitances and their...
Reconstructing compressed sensing signals involves solving an optimization problem. An example is Basis Pursuit (BP) [1], which is applicable only in noise-free scenarios. In noisy scenarios, either the Basis Pursuit Denoising (BPDN) [1] or the Noise-Aware BP (NABP) [2] can be used. Consider a distributed scenario where the dictionary matrix and the vector of observations are spread over the nodes...
This work presents a geometrical analysis of the Large Step Gradient Descent (LGD) dictionary learning algorithm. LGD updates the atoms of the dictionary using a gradient step with a step size equal to twice the optimal step size. We show that the large step gradient descent can be understood as a maximal exploration step where one goes as far away as possible without increasing the error. We also...
Characterizations of environmental energy availability and properties provide important insights for designing energy harvesting nodes and developing energy harvesting adaptive systems and algorithms. Previous characterizations of light energy availability provided baseline estimates of the total available energy that could be harvested by a crystalline silicon solar cell. However, these measurements...
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