Yuli Fu

Energy-Efficient Hybrid Spectrum Access Scheme in Cognitive Vehicular Ad hoc Networks

This letter focuses on an energy-efficient hybrid spectrum access scheme in a vehicle-to-infrastructure uplink communication scenario in cognitive vehicular ad hoc networks (cognitive VANETs). Considering path loss exponent of the channel between a vehicle and an access point, a constrained optimization problem is formulated to minimize the overall energy consumption. Meanwhile, the quality of service of the vehicular communications is maintained. The vehicle detects optimal spectrum bands for transmission, and selects different access schemes adaptively based on the energy efficiency and the location information. Numerical results show that the vehicle using the proposed scheme can achieve lower energy consumption, comparing with both direct transmission scheme and equal switching time scheme.

Structured occlusion coding for robust face recognition

Occlusion in face recognition is a common yet challenging problem. While sparse representation based classification (SRC) has been shown promising performance in laboratory conditions (i.e. noiseless or random pixel corrupted), it performs much worse in practical scenarios. In this paper, we consider the practical face recognition problem, where the occlusions are predictable and available for sampling. We propose the structured occlusion coding (SOC) to address occlusion problems. The structured coding here lies in two folds. On one hand, we employ a structured dictionary for recognition. On the other hand, we propose to use the structured sparsity in this formulation. Specifically, SOC simultaneously separates the occlusion and classifies the image. In this way, the problem of recognizing an occluded image is turned into seeking a structured sparse solution on occlusion-appended dictionary. In order to construct a well-performing occlusion dictionary, we propose an occlusion mask estimating technique via locality constrained dictionary (LCD), showing striking improvement in occlusion sample. On a category-specific occlusion dictionary, we replace l1 norm sparsity with the structured sparsity which is shown more robust, further enhancing the robustness of our approach. Moreover, SOC achieves significant improvement in handling large occlusion in real world. Extensive experiments are conducted on public data sets to validate the superiority of the proposed algorithm.

An efficient hybrid spectrum access algorithm in OFDM-based wideband cognitive radio networks

In cognitive radio networks, wideband spectrum sensing is a promising technology which allows a secondary user (SU) to detect the signals of primary users (PUs) over multiple channels, the sensing overhead is reduced effectively. Together with spectrum sensing, spectrum access strategy affects the system performance. In this paper, we propose an efficient hybrid access algorithm in OFDM-based wideband uplink model. An SU senses multiple channels via wideband spectrum sensing, and accesses these channels via a hybrid access strategy. The sensing time and transmission power of each channel are jointly optimized, in order to maximize the ergodic throughput of SUs, while the interferences to PUs are under the predefined thresholds. It is shown that the optimization problem can be formulated as a convex problem. Moreover, in order to reduce the computational complexity, two low complexity spectrum sensing and access schemes are proposed, in which the SU selects several specific channels to sense, and accesses all channels via a modified hybrid access strategy. For sensing channels selection, we present an effective selection criterion and an optimal selection order. Simulation results show that the proposed algorithm can effectively improve the system performance.

On Throughput Maximization in Multichannel Cognitive Radio Networks Via Generalized Access Strategy

Spectrum access strategy plays a critical role in multichannel cognitive radio networks (CRNs). However, the CRNs cannot obtain the maximal throughput, when the existing access strategies, including overlay, underlay, and hybrid access strategies, are applied to multichannel CRNs. In this paper, we present a generalized access strategy in a multichannel CRN smart home environment, in which a secondary user (SU) system selects part of channels for sequential spectrum sensing, and accesses these channels based on the sensing results. Moreover, it accesses the remaining channels directly. We then formulate a two-phase optimization framework, which takes the sensing channel selection, sensing time allocation, and the power allocation into consideration, to maximize the gross average throughput of the multichannel CRN. In the sensing phase, a generalized access strategy algorithm (GAS) is first proposed, where we prove that only part of channels needs to be selected for spectrum sensing to achieve the maximum throughput. An optimal stopping rule is proposed to determine the optimal number of selected sensing channels. In addition, a completed hybrid access strategy algorithm is further investigated where the SU system senses all channels. An approximation algorithm is also presented to achieve suboptimal results with low computational complexity. In the transmission phase, the transmission powers of all channels are optimized via convex algorithms. Numerical experiments show that, compared with the existing schemes, the proposed schemes are able to achieve considerable throughput improvement.

Robust Sparse Signal Recovery in the Presence of the S αS Noise.

In this letter, robust sparse signal recovery is considered in the presence of the symmetric α-stable distributed noise. An M-estimate type model is constructed by approximating the location score function of the noise. A reweighed iterative hard thresholding algorithm is proposed to recover the sparse signal. The basis functions for the approximation and the recovery performance of the proposed algorithm are discussed. Simulations are given to demonstrate the validity of our results.

A novel low-rank model for MRI using the redundant wavelet tight frame

Abstract The low-rank matrix reconstruction has been attracted significant interest in compressed sensing magnetic resonance imaging (CS-MRI). To the end of computability, rank is often modeled by nuclear norm. The singular value thresholding (SVT) algorithm is taken as a solver of this model, usually. However, this model with the solver may be insufficient to obtain a high quality magnetic resonance (MR) image at high speed. Still inspired by the low-rank matrix reconstruction idea, we proposes a novel low-rank model with a new scheme of the weight selection to reconstruct the MR image under the redundant wavelet tight frame. A fast and accurate solver is given for the proposed model. Further, a new scheme is presented to accelerate the proposed solver. Numerical experiments demonstrate that the proposed solver and its accelerated version can converge stably. The proposed method is faster than some existing methods with the comparable quality.

Optimal wideband mixed access strategy algorithm in cognitive radio networks

In cognitive radio networks, spectrum sensing and access scheme affects the system performance. In this paper, a new wideband mixed access scheme is proposed, in which the Secondary Users (SUs) sense the channels via wideband spectrum sensing, and access them with a mixed access strategy. In order to maximize the ergodic throughput of SUs, we find optimal sensing time and transmission power of each channel, while protecting the Primary Users (PUs) from interference. It is shown that the optimization problem can be formulated as a convex problem. Moreover, we present a QoS-aware low complexity scheme, in which the SUs select several specific channels to sense. An effective sensing channels selection criterion is proposed. Numerical results show that the proposed schemes can effectively improve the system performance.

A Novel Model for Compressed Sensing MRI via Smoothed e1-Norm Regularization

Compressed sensing magnetic resonance imaging (CS-MRI) using ℓ1-norm minimization has been widely and successfully applied. However, ℓ1-norm minimization often leads to bias estimation and the solution is not as accurate as desired. In this paper, we propose a novel model for MR image reconstruction, which takes as a smoothed ℓ1-norm regularization model that is convex, has a unique solution. More specifically, we employ the logarithm function with the parameter in our optimization, and an iteration technique is developed to solve the proposed minimization problem for MR image reconstruction efficiently. The model is simple and effective in the solution procedure. Simulation results on normal brain image demonstrated that the performance of the proposed method was better than some traditional methods.

A Lorentzian IHT for Complex-Valued Sparse Signal Recovery

In this paper, robust complex-valued sparse signal recovery is considered in the presence of impulse noise. A generalized Lorentzian norm is defined for complex-valued signals. A complex Lorentzian iterative hard thresholding algorithm is proposed to realize the signal recovery. Simulations are given to demonstrate the validity of our results.

Candidate of Initial Value in Lloyd Algorithm for Constructing Low-Coherence Matrix

The problem of constructing matrix with lowcoherence is arised in many applications, such as CDMA, compressive sensing (CS), beamforming, etc. Usually the design of low-coherence codebook can be modeled as vector quantization (VQ) problem, and generalized Lloyd algorithm is designed to solve it. Since Lloyd algorithm is a method of local optimization, its performance is influenced by initial value. In this paper, candidate of initial value of Lloyd algorithm is studied. A construction based on finite abelian group which can be used as candidate of initial value will be analyzed. Experimental results prove that using this construction as initial value in Lloyd algorithm can improve the performance significantly.