Device-to-device (D2D) communication underlaying cellular systems is proposed to support short-range data-intensive services. In a D2D-enabled system, a closely located user pair is allowed to communicate over a direct data link, instead of being relayed through the network. In this study, based on the power control framework in traditional cellular networks, a combined power control and link selection algorithm with temporary removal for D2D-enabled systems is proposed. It is proved that the proposed algorithm converges to the optimal power and link selection vector in all feasible systems. In an infeasible system, convergence of the temporary removal algorithm cannot be guaranteed. Therefore two adaptive gradual removal algorithms are proposed, which are suitable for lightly and heavily loaded systems, respectively. Numerical results show that both of the proposed algorithms outperform the existing ones in terms of outage probability and convergence rate.

In describing the mean square convergence of the LMS algorithm, the update formula based on independence assumption will bring explicit errors, especially when step-size is large. A modifier formula that describes the convergence well, is proposed. Simulations support the proposed formula in different conditions

Nanoscale pore characteristics are crucial in assessing the resource potential of gas shales. Although the Niutitang formation was widely deposited in the upper Yantze Platform, South China and has been recognized as a promising shale gas reservoir, there lacks substantial breakthrough in the exploitation of shale gas from the Niutitang formation. Aiming at better understanding the reservoir properties and corresponding influential factors, 14 core samples from the Lower Cambrian Niutitang formation locating in the central Guizhou province were investigated in the current study to characterize the nanoscale pore system in the shale. Organic geochemical analyses (i.e., total organic carbon content and thermal maturity), X-ray diffraction, low pressure nitrogen adsorption, and field emission scanning electron microscopy were employed to obtain complementary information of the pore system. Measured TOC in this study is generally > 1.50% and averages 3.35%. All of the samples are in the over-maturity stage with R-o ranging from 2.39% to 3.29%. X-ray diffraction shows that quartz, clay minerals and plagioclase are the dominant minerals. Nitrogen adsorption results indicate that all of samples show type IIb nitrogen adsorption isotherms with type H3 hysteresis loops, which imply the coexistence of micropores, mesopores and macropores in the shale. The mesopores account for 60-70% of total pore volume, and are likely contributed by clay minerals and quartz. Organic matter appears to be the major contributor of the micropores and specific surface area, and is closely linked to the rapid decrease of average pore size with increasing burial depth. The field emission scanning electron microscopy reveals abundant organic matter pores in the middle-upper Niutitang formation, but lesser or smaller in the bottom of Niutitang formation. The lower Niutitang formation seems to develop substantial amounts of organic-clay aggregates, which preferentially lie parallel to the shale bedding and contain lots of nanoscale pores. The perpendicular variation of pore structure features is explained with multiple mechanisms, including thermal maturation of organic matter, compaction by strata pressure, dissipation of shale gas, etc. The results of our study have emphasized the interesting and complex features of the nanoscale pore structures in the gas shales, which may facilitate future assessment and exploitation of shale gas resources.

This letter considers the problem of channel estimation for millimeter wave (mmWave) multiple-input multiple-output systems under a transmitter impairments model. Specifically, taking the transmitter hardware impairments into account, the performance of conventional pilots-based channel estimation scheme will be degraded due to the destroyed training pilots. By exploiting the sparsity of mmWave channel in the angular domain, a new channel estimation algorithm based on the Bayesian compressive sensing (BCS) and least square estimation (LSE) is proposed. First, the expectation maximization algorithm is presented to solve the BCS problem, and the refined measurement matrix and the support of the channel vector are obtained. Next, the channel gain coefficients are estimated by using the LSE. Simulation results show that the proposed algorithm can achieve better performance compared with the conventional BCS and orthogonal matching pursuit algorithm.

We propose a new approach to adaptive system identification when the system model is sparse. The approach applies ℓ1 relaxation, common in compressive sensing, to improve the performance of LMS-type adaptive methods. This results in two new algorithms, the zero-attracting LMS (ZA-LMS) and the reweighted zero-attracting LMS (RZA-LMS). The ZA-LMS is derived via combining a ℓ1 norm penalty on the coefficients into the quadratic LMS cost function, which generates a zero attractor in the LMS iteration. The zero attractor promotes sparsity in taps during the filtering process, and therefore accelerates convergence when identifying sparse systems. We prove that the ZA-LMS can achieve lower mean square error than the standard LMS. To further improve the filtering performance, the RZA-LMS is developed using a reweighted zero attractor. The performance of the RZA-LMS is superior to that of the ZA-LMS numerically. Experiments demonstrate the advantages of the proposed filters in both convergence rate and steady-state behavior under sparsity assumptions on the true coefficient vector. The RZA-LMS is also shown to be robust when the number of non-zero taps increases.

Square-root least absolute shrinkage and selection operator (Lasso), a variant of Lasso, has recently been proposed with a key advantage that the optimal regularization parameter is independent of the noise level in the measurements. In this letter, we introduce a class of nonconvex sparsity-inducing penalties to the square-root Lasso to achieve better sparse recovery performance over the convex counterpart. The resultant formulation is converted to a nonconvex but multiconvex optimization problem, i.e., it is convex in each block of variables. Alternating direction method of multipliers is applied as the solver, according to which two efficient algorithms are devised for row-orthonormal sensing matrix and general sensing matrix, respectively. Numerical experiments are conducted to evaluate the performance of the proposed methods.

Face tracking has many visual applications such as human-computer interfaces, video communications and surveillance. Color-based particle trackers have been proved robust and versatile for a modest computational cost. In this paper, a probabilistic method for integrating multi-camera information is introduced to track human face 3D-pose variations. The proposed method fuses information coming from several calibrated cameras via one color-based particle filter. The algorithm relies on the following novelties. First, the human head other than face is defined as the target of our algorithm. To distinguish the face region and hair region, a dual-color-ball is utilized to model the human head in 3D space. Second, to enhance the robustness to illumination variety, the Fisher criterion is applied to measure the separability of the face region and the hair region on the color histogram. Consequently, the color distribution template can be adapted at the proper time. Finally, the algorithm is performed based on the distributed framework, therefore the computation is implemented equally by all client processors. To demonstrate the performance of the proposed algorithm, several scenarios of visual tracking are tested in an office environment with three to four calibrated cameras. Experiments show that accurate tracking results are achieved, even in some difficult scenarios, such as the complete occlusion and the temptation of anything with skin color. Furthermore, the additional information of our track results, including the head posture and the face orientation schemes, can be used for further work such as face recognition and eye gaze estimation, which is also explained by elaborated designed experiments.

A novel method is presented to detect roads in Synthetic Aperture Radar (SAR) images. A multi-segmented poly-line model is introduced to provide a more accurate description of the road as well as to ensure the road curve's smoothness in the model level. We then solve the road detection problem using the Bayesian tracking theory, where the Particle Filtering algorithm is adopted to provide a simple and consistent framework. The effectiveness and robustness of the proposed method is demonstrated by experimental results.

This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom ( columns in the dictionary matrix) related to some label, we propose cross-label suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar. Upon the cross-label suppression, we donot resort to frequently-used l(0)-norm or l(1)-norm for coding, and obtain computational efficiency without losing the discriminative power for categorization. Moreover, two simple classification schemes are also developed to take full advantage of the learnt dictionary. Extensive experiments on six data sets, including face recognition, object categorization, scene classification, texture recognition, and sport action categorization are conducted, and the results show that the proposed approach can outperform lots of recently presented dictionary algorithms on both recognition accuracy and computational efficiency.

A novel scheme of optical analog-to-digital conversion system is proposed based on compressive sampling and the recent electrical modulated wideband converter. The proposed optical solution is more stable and implementable in practice. Because of the ultranarrow optical pulseswidth, the proposed solution can provide a uniform signal-to-noise attenuate of all frequency bins, compared to its electrical counterpart.