Search

In this paper, an intelligent reflecting surface (IRS) assisted spectrum sharing underlay cognitive radio (CR) wiretap channel (WTC) is studied, and we aim at enhancing the secrecy rate of secondary user in this channel subject to total power constraint at secondary transmitter (ST), interference power constraint (IPC) at primary receiver (PR) as well as unit modulus constraint at IRS. Due to extra IPC and eavesdropper (Eve) are considered, all the existing solutions for enhancing secrecy rate of IRS-assisted non-CR WTC as well as enhancing transmission rate in IRS-assisted CR channel without eavesdropper fail in this work. Therefore, we propose new numerical solutions to optimize the secrecy rate of this channel under full primary, secondary users’ channel state information (CSI) and three different cases of Eve’s CSI: full CSI, imperfect CSI with bounded estimation error, and no CSI. To solve the difficult non-convex optimization problem, an efficient alternating optimization (AO) algorithm is proposed to jointly optimize the beamformer at ST and phase shift coefficients at IRS. In particular, when optimizing the phase shift coefficients during each iteration of AO, a Dinkelbach based solution in combination with successive approximation and penalty based solution is proposed under full CSI and a penalty convex-concave procedure solution is proposed under imperfect Eve’s CSI. For no Eve’s CSI case, artificial noise (AN) aided approach is adopted to help enhancing the secrecy rate. Simulation results show that our proposed solutions for the IRS-assisted design greatly enhance the secrecy performance compared with the existing numerical solutions with and without IRS under full and imperfect Eve’s CSI. And positive secrecy rate can be achieved by our proposed AN aided approach given most channel realizations under no Eve’s CSI case so that secure communication also can be guaranteed. All of the proposed AO algorithms are guaranteed to monotonic convergence. [ABSTRACT FROM AUTHOR]

In this paper, a novel reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) symbiotic radio (SR) system is proposed, in which an RIS, operating as a secondary transmitter (STx), sends messages to a multi-antenna secondary receiver (SRx) by using cognitive backscattering communication, and simultaneously, it enhances the primary transmission from a multi-antenna primary transmitter (PTx) to a multi-antenna primary receiver (PRx) by intelligently reconfiguring the wireless environment. We are interested in the joint design of active transmit beamformer at the PTx and passive reflecting beamformer at the STx to minimize the total transmit power at the PTx, subject to the signal-to-noise-ratio (SNR) constraint for the secondary transmission and the rate constraint for the primary transmission. Due to the non-convexity of the formulated problem, we decouple the original problem into a series of subproblems using the alternating optimization method and then iteratively solve them. The convergence performance and computational complexity of the proposed algorithm are analyzed. Furthermore, we develop a low-complexity algorithm to design the reflecting beamformer by solving a backscatter link enhancement problem through the semi-definite relaxation (SDR) technique. Then, theoretical analysis is performed to reveal the insights of the proposed system. Finally, simulation results are presented to validate the effectiveness of the proposed algorithms and the superiority of the proposed system. [ABSTRACT FROM AUTHOR]

To enhance spectrum efficiency for the future Internet-of-Things, in this paper, we investigate the joint subcarrier and power allocation problem for a full-duplex network with machine-to-machine nodes and cellular nodes. We aim to maximize the network spectral efficiency while achieving individual quality-of-service (QoS) requirements, considering imperfect and partial channel state information. The spectral efficiency maximization problem is formulated as a mixed-integer nonlinear programming problem and proved to be NP-hard. To make it more tractable, we first solve the coupling between subcarrier assignment and power allocation variables by replacing the product of two variables with one auxiliary variable. We then relax the binary subcarrier assignment variables to integer variables and introduce penalty terms to penalize the objective function. Majorization minimization method is adopted to further handle the coupling between uplink and downlink due to full-duplex transmission. We prove that the proposed joint resource allocation scheme converges to local optimal solution. Simulation results demonstrate the impact of channel estimation error on spectral efficiency and the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In recent years, unmanned aerial vehicles (UAVs) have unlocked numerous sensing applications, which are expected to add billions of dollars to the world economy in the next decade. To further improve the Quality-of-Service in these applications, the 3rd Generation Partnership Project has considered the use of terrestrial cellular networks to support UAV sensing services, also known as the cellular Internet of UAVs. In this paper, we consider a cellular Internet of UAVs, where the sensory data can be transmitted either to the base station via cellular links, or to the mobile devices by underlay UAV-to-Device (U2D) communications. To evaluate the freshness of the sensory data, the concept of age of information (AoI) is adopted, in which a lower AoI implies fresher data. Since UAVs’ AoIs are determined by their trajectories during sensing and transmission, we investigate the AoI minimization problem for UAVs by designing their trajectories. This problem is a Markov decision problem with an infinite state-action space, and thus we utilize multi-agent deep reinforcement learning to approximate the state-action space. Then, we propose a multi-UAV trajectory design algorithm to solve this problem. Simulation results show that our proposed algorithm can achieve a lower AoI than a greedy algorithm, policy gradient algorithm, and overlay U2D scheme. [ABSTRACT FROM AUTHOR]

We propose a holographic version of a reconfigurable intelligent surface (RIS) and investigate its application to terahertz (THz) massive multiple-input multiple-output systems. Capitalizing on the miniaturization of THz electronic components, RISs can be implemented by densely packing sub-wavelength unit cells, so as to realize continuous or quasi-continuous apertures and to enable holographic communications. In this paper, in particular, we derive the beam pattern of a holographic RIS. Our analysis reveals that the beam pattern of an ideal holographic RIS can be well approximated by that of an ultra-dense RIS, which has a more practical hardware architecture. In addition, we propose a closed-loop channel estimation (CE) scheme to effectively estimate the broadband channels that characterize THz massive MIMO systems aided by holographic RISs. The proposed CE scheme includes a downlink coarse CE stage and an uplink finer-grained CE stage. The uplink pilot signals are judiciously designed for obtaining good CE performance. Moreover, to reduce the pilot overhead, we introduce a compressive sensing-based CE algorithm, which exploits the dual sparsity of THz MIMO channels in both the angular domain and delay domain. Simulation results demonstrate the superiority of holographic RISs over the non-holographic ones, and the effectiveness of the proposed CE scheme. [ABSTRACT FROM AUTHOR]

The orbital angular momentum (OAM)-based communication systems may face severe transmission problems when the transmit and receive uniform circular array pairs are blocked. In this paper, a promising technique named intelligent reflecting surface (IRS) is proposed to help alleviate blockages and provide alternative line-of-sight links. To maximize the achievable rate of the IRS-assisted OAM communication systems, we optimize its transmit power allocation along with the IRS's reflecting phase shifts, and propose an alternative optimization-based algorithm to solve the resulting optimization problem with coupled variables and non-convex structure. Specifically, the proposed algorithm obtains a closed-form solution to the transmit power allocation by applying the majorization-minimization and ℓ1-ball projection approaches, and obtain the locally optimal solution to the IRS's reflecting phase shifts by applying the weighted minimum mean square error-based fixed point iteration approach. Simulation results demonstrate the superiority of our proposed algorithm over existing baseline algorithms and also show its robust stability to the oblique angle errors. [ABSTRACT FROM AUTHOR]

This paper investigates unmanned aerial vehicle (UAV) data collection systems with different multiple access schemes, where a rotary-wing UAV is dispatched to collect data from multiple ground nodes (GNs). Our goal is to maximize the minimum UAV data collection throughput from GNs for both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) transmission, subject to the energy budgets at both the UAV and GNs, namely double energy limitations. 1) For OMA, we propose an efficient algorithm by invoking alternating optimization (AO) method, where each subproblem is alternately solved by applying successive convex approximation (SCA) technique. 2) For NOMA, we first handle subproblems with fixed decoding order using SCA technique. Then, we develop a penalty-based algorithm to solve the decoding order design subproblem. Numerical results show that: i) The proposed algorithms are capable of improving the max-min throughput performance compared with other benchmark schemes; and ii) NOMA yields a higher performance gain than OMA when GNs have sufficient energy. [ABSTRACT FROM AUTHOR]

This paper presents a Gaussian tracking algorithm with direction-of-arrival (DOA) measurements modelled via the Kent distribution. The key aspect of the algorithm is that the Kent distribution directly models the specific characteristics of DOA measurements in the 3-D space, and can account for different uncertainties in azimuth and elevation, which the von Mises-Fisher distribution cannot. At each update step, the algorithm performs iterated statistical linear regressions. We provide two implementations of the algorithms, one based on sigma-points and the other on analytical linearisation. The effectiveness of the approach is evaluated via numerical simulations. [ABSTRACT FROM AUTHOR]

Dynamic battery modeling uncertainties, even if low, may lead to significant performance degradation or even divergence of the state-of-charge estimation algorithm. This paper investigates the integration of the extended-Kalman-filter with the smooth-variable-structure-filter algorithms for state-of-charge estimation of lithium-ion batteries. The robustness of the presented approach to modeling uncertainty is assessed for batteries operating in highly dynamic environments. The presented approach combines the benefit of the smooth- variable-structure-filter in its robustness to model uncertainty with the benefit of the extended-Kalman-filter in its near-optimality for a given dynamics and measurement noise sequences. The algorithm is rigorously tested using various datasets including standardrized and artificial drive cycles with added dynamics. The drive cycle power profile is calculated for an electric Ford F150 truck and scaled for the 18650PF cell used in the tests. Experimental validation is performed by investigating four different scenarios in which knowledge of the initial conditions as well as accuracy of the battery model were varied. The results demonstrate a substantially enhanced estimation accuracy achieved by the adopted approach through its optimality to measurement and model noise as well as its robustness to model uncertainty. The adopted approach results in a reduction in the complexity of the state-of-charge control algorithm and therefore enhances the battery management system. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) with lens antenna array, referred to as beamspace MIMO, is attractive for millimeter wave (mmWave) communications, since it can realize the adequate beamforming gain with a small number of radio frequency (RF) chains. This paper addresses wideband channel estimation for mmWave beamspace MIMO. Specifically, a two-stage channel estimation scheme is proposed. In the first stage, we utilize multi-task sparse Bayesian learning (MT-SBL) to coarsely estimate the beamspace channel vectors, each corresponding to one OFDM subcarrier. In the second stage, we re-formulate the channel estimation problem by artfully exploiting the characteristics of beamspace channels. Then we develop an expectation maximization (EM) based algorithm to estimate the parameters involved in the formulation, thereby refining the channel estimates. Compared with the state-of-the-art counterparts, the proposed scheme can achieve much better performance while requiring a lower training overhead. [ABSTRACT FROM AUTHOR]

The present paper is dedicated to the investigation of a predictive Equivalent Consumption Minimization Strategy. The objective is to determine the torque split between the internal combustion engine and the electric machine of a hybrid vehicle. The energy management is formulated as a receding optimization problem. To avoid a complex prediction of the vehicle speed and acceleration over time, the slow dynamic of their distribution is exploited. A rational tuning of the algorithm parameters is proposed as well as some improved implementations. The number of individual operations (additions, multiplications, interpolations, etc) required per seconds is discussed. Finally, the energy management algorithm energy consumption are assessed over different driving cycles, including one with a \boldsymbol 15406  ${km}$ length obtained using GPS measurements. A comparison with an adaptive Equivalent Consumption Minimization Strategy is provided. The predictive Equivalent Consumption Minimization Strategy allows controlling the state of charge close to a (possibly time varying) set point while providing low fuel consumption. [ABSTRACT FROM AUTHOR]

Radio resource slicing is critical to customize service provisioning in fifth-generation (5G) uplink radio access networks (RANs). Using drone-small-cells (DSCs) as aerial support for terrestrial base stations can enhance the flexibility for resource provisioning in response to traffic distribution variations. In this paper, we study a multi-DSC-assisted radio resource slicing problem for 5G uplink RANs, with the objective of minimizing the total uplink resource consumption under differentiated quality-of-service (QoS) constraints for both human-type and machine-type communication services. We begin with an interference-aware graph model to formulate the joint DSC three-dimension (3D) placement and device-DSC association problem for uplink radio resource slicing and prove that the proposed problem is NP-hard. A complexity-adjustable problem approximation is presented via screening candidate DSC deployment positions, which incorporates flight height adaptation to balance the uplink communication coverage and resource utilization. A lightweight approximation using a fixed DSC flight altitude is also provided with reduced complexity. For mathematical traceability, the DSC placement and device-DSC associations in each approximation are transformed as a special weight clique problem. An upgraded clique algorithm is then developed to determine how to deploy DSCs for a given number of DSCs. Simulation results demonstrate the proposed scheme's effectiveness in terms of resource utilization, network coverage, and drone dispatching cost. [ABSTRACT FROM AUTHOR]

This paper studies a novel design of user pairing and resource allocation in a simultaneous wireless information and power transfer (SWIPT)-enabled cooperative non-orthogonal multiple access (NOMA) system with coexisting power-splitting (PS) and time-switching (TS) users. In this system, near NOMA users (NUs) that are close to a source can assist the communications between the source and far NOMA users (FUs) using the energy harvested by either PS or TS strategy. The design aims to maximize the sum-rate of the system by optimizing user pairing and power allocation between NUs and FUs as well as performing PS and TS control at NUs, while guaranteeing the target data rates and the minimum harvested energy requirements of users. Since the joint design is formulated as a non-convex mixed-integer non-linear programming (MINLP) problem that is challenging to tackle, we propose a two-step user pairing and resource allocation algorithm. Specifically, we decouple the original problem into an inner resource allocation problem and an outer user pairing problem. For the inner problem, we propose successive convex approximation (SCA) and block coordinate descent (BCD)-based algorithms to perform power allocation as well as PS and TS control iteratively, with the algorithms’ convergence being theoretically proved. By exploiting the characteristics of the outer problem of user pairing, this problem is well tackled by the Hungarian algorithm. Simulation results demonstrate the proposed joint design yields better performance gains than the existing schemes. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) or drone, is an evolving technology in today's market with an enormous number of applications. Mini UAVs are developed in order to compensate the performance constraints imposed by larger UAVs during emergency situations. Multiple mini autonomous UAVs require communication and coordination for ubiquitous coverage and relaying during deployment. Multi-UAV coordination or swarm optimization is required for reliable connectivity among UAVs, due to its high mobility and dynamic topology. In this paper, a Secured UAV (S-UAV) model is proposed which takes the location of the UAVs as inputs to form a Wireless Mesh Network (WMN) among multiple drones with the help of a centralized controller. After WMN formation, efficient communication takes place using A* search, an intelligent algorithm that finds the shortest communication path among UAVs. Further, the S-UAV model utilizes cryptographic techniques such as Advanced Encryption Standard (AES) and Blowfish to overcome the security attacks efficiently. Simulation results show that the S-UAV model offers higher throughput, reduced power consumption and guaranteed message transmission with reduced encryption and decryption time. [ABSTRACT FROM AUTHOR]

This paper proposes a novel secure channel estimation technique to provide security against leakage of the channel estimates to any malicious user by utilizing artificial noise (AN) along with full-duplex (FD) transmissions. AN overcomes the drawback of FD transmission, where any strategically located eavesdropper can minimize the interference signal received from the FD receiver. The proposed secure channel estimation technique comprises three stages, where the first stage is responsible for the estimation of the residual self-interference (SI) channel. The second stage acquires rough channel estimates to design AN orthogonal to the channel between legitimate transmitter-receiver for the next training stage. In the third stage, both legitimate nodes transmit orthogonal AN signals along with the known training signals using FD transmissions. For power allocation, we have presented a novel local adaptive power allocation algorithm at each legitimate node to allocate the powers to the training signals, and AN signals while ensuring equivocation at the eavesdropper. We provide the mean square error (MSE) to indicate the performance achieved by the respective nodes. We have also provided the bit error rate (BER) simulation analysis to indicate the secure communication achieved by securing the channel estimation process. The presented simulation analysis indicates that the eavesdropper is unable to decode the transmitted information while the legitimate receiver has robustly decoded the transmitted information. [ABSTRACT FROM AUTHOR]

In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded extra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates with limited number of radio-frequency (RF) transceivers due to high cost, power consumption and interconnection bandwidth associated to the fully digital implementation. The BS, which is implemented with a subarray switching architecture, selects groups of active antennas inside each subarray to transmit the DL signal. This work proposes efficient resource allocation (RA) procedures to perform joint antenna selection (AS) and power allocation (PA) to maximize the DL spectral efficiency (SE) of an XL-MIMO system operating under different loading settings. Two metaheuristic RA procedures based on the genetic algorithm (GA) are assessed and compared in terms of performance, coordination data size and computational complexity. One algorithm is based on a quasi-distributed methodology while the other is based on the conventional centralized processing. Numerical results demonstrate that the quasi-distributed GA-based procedure results in a suitable trade-off between performance, complexity and exchanged coordination data. At the same time, it outperforms the centralized procedures with appropriate system operation settings. [ABSTRACT FROM AUTHOR]

The energy detection technology is recommended in the licensed assisted access (LAA) scheme by 3GPP because of its simplicity and low cost. However, due to its inherent limitation, there may exist imperfect channel detection, which can lead to the decrease of the channel utilization efficiency and the deterioration of fairness. The imperfect detection can generally be represented by the detection probability and false alarm probability, which depend on detection time, signal to noise ration (SNR), sampling rate and energy threshold. However, among the parameters, only the energy threshold can be dominated by LAA small base stations (SBSs) in the LAA scheme. Therefore, the energy threshold should be dynamically adjusted in the changeable channel environment such that the detection accuracy can improved as high as possible. Consider the fact that the optimization theory cannot be used to optimize the energy threshold since the expressions of performance indexes about the energy threshold are extremely complex, a Q-learning based energy threshold optimization algorithm (QLET) is thus proposed in the paper, where LAA SBSs act as the agent, the energy threshold is defined as the agent action, the different combinations of fairness and throughput are defined as the agent states, and the fairness and the reward function are also redefined. In order to ensure the smooth implementation of the proposed QLET algorithm, the information exchange mechanism, where the sending-confirmation mechanism and 1- persistent CSMA are used, is also proposed. Based on the proposed QL framework, the agent can learn the optimal energy threshold by repeatedly interacting with the environment, which enables the coexistence system to obtain the best coexistence performance. A large number of simulation results show that the proposed QLET is superior to the traditional fixed energy threshold scheme (FET) in terms of the fairness, WiFi collision probability and transmission delay, and that QLET is almost the same as FET in term of throughput. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have been widely applied for classification tasks in wireless communications such as link scheduling, user association and base station (BS) muting. When training a DNN using datasets with highly-skewed class distribution where most data belong to a few majority classes, learning performance for minority classes will degrade since the imbalanced data forces the training process to be biased towards the majority classes. In many wireless problems, multiple decisions for classification are made jointly, say deciding which BSs in a network should be muted to avoid interference. In this paper, we employ DNN to learn an optimal predictive interference coordination policy, and strive to avoid biased learning in such a multi-label multi-class classification problem. The major contribution is proposing a training method to encourage fairness among classes by minimizing the maximal cost of decisions among classes, which is converted into a problem to optimize the weighting factors on the training cost of each class. We provide a gradient descent algorithm to optimize the weighting factors and the model parameters of the DNN by alternative updates. Simulation results show that the training method can improve the learning performance for the minority class and can achieve higher network utility than existing training methods. [ABSTRACT FROM AUTHOR]

Face Super-Resolution (FSR) aims to infer High-Resolution (HR) face images from the captured Low-Resolution (LR) face image with the assistance of external information. Existing FSR methods are less effective for the LR face images captured with serious low-quality since the huge imaging/degradation gap caused by the different imaging scenarios (i.e., the complex practical imaging scenario that generates test LR images, the simple manual imaging degradation that generates the training LR images) is not considered in these algorithms. In this paper, we propose an image homogenization strategy via re-expression to solve this problem. In contrast to existing methods, we propose a homogenization projection in LR space and HR space as compensation for the classical LR/HR projection to formulate the FSR in a multi-stage framework. We then develop a re-expression process to bridge the gap between the complex degradation and the simple degradation, which can remove the heterogeneous factors such as serious noise and blur. To further improve the accuracy of the homogenization, we extract the image patch set that is invariant to degradation changes as Robust Neighbor Resources (RNR), with which these two homogenization projections re-express the input LR images and the initial inferred HR images successively. Both quantitative and qualitative results on the public datasets demonstrate the effectiveness of the proposed algorithm against the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Defocus blur detection (DBD), which has been widely applied to various fields, aims to detect the out-of-focus or in-focus pixels from a single image. Despite the fact that the deep learning based methods applied to DBD have outperformed the hand-crafted feature based methods, the performance cannot still meet our requirement. In this paper, a novel network is established for DBD. Unlike existing methods which only learn the projection from the in-focus part to the ground-truth, both in-focus and out-of-focus pixels, which are completely and symmetrically complementary, are taken into account. Specifically, two symmetric branches are designed to jointly estimate the probability of focus and defocus pixels, respectively. Due to their complementary constraint, each layer in a branch is affected by an attention obtained from another branch, effectively learning the detailed information which may be ignored in one branch. The feature maps from these two branches are then passed through a unique fusion block to simultaneously get the two-channel output measured by a complementary loss. Additionally, instead of estimating only one binary map from a specific layer, each layer is encouraged to estimate the ground truth to guide the binary map estimation in its linked shallower layer followed by a top-to-bottom combination strategy, gradually exploiting the global and local information. Experimental results on released datasets demonstrate that our proposed method remarkably outperforms state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Line spectral estimation is a classical signal processing problem that aims to estimate the line spectra from their signal which is contaminated by deterministic or random noise. Despite a large body of research on this subject, the theoretical understanding of this problem is still elusive. In this paper, we introduce and quantitatively characterize the two resolution limits for the line spectral estimation problem under deterministic noise: one is the minimum separation distance between the line spectra that is required for exact detection of their number, and the other is the minimum separation distance between the line spectra that is required for a stable recovery of their supports. The quantitative results imply a phase transition phenomenon in each of the two recovery problems, and also the subtle difference between the two. We further propose a sweeping singular-value-thresholding algorithm for the number detection problem and conduct numerical experiments. The numerical results confirm the phase transition phenomenon in the number detection problem. [ABSTRACT FROM AUTHOR]

This paper studies a large random matrix system (LRMS) model involving an arbitrary signal distribution and forward error control (FEC) coding. We establish an area property based on the approximate message passing (AMP) algorithm. Under the assumption that the state evolution for AMP is correct for the coded system, the achievable rate of AMP is analyzed. We prove that AMP achieves the constrained capacity of the LRMS with an arbitrary signal distribution provided that a matching condition is satisfied. As a byproduct, we provide an alternative derivation for the constraint capacity of an LRMS using a proved property of AMP. We discuss realization techniques for the matching principle of binary signaling using irregular low-density parity-check (LDPC) codes and provide related numerical results. We show that the optimized codes demonstrate significantly better performance over un-matched ones under AMP. For quadrature phase shift keying (QPSK) modulation, bit error rate (BER) performance within 1 dB from the constrained capacity limit is observed. [ABSTRACT FROM AUTHOR]

This paper revisits the ordered statistics decoding (OSD). It provides a comprehensive analysis of the OSD algorithm by characterizing the statistical properties, evolution and the distribution of the Hamming distance and weighted Hamming distance from codeword estimates to the received sequence in the reprocessing stages of the OSD algorithm. We prove that the Hamming distance and weighted Hamming distance distributions can be characterized as mixture models capturing the decoding error probability and code weight enumerator. Simulation and numerical results show that our proposed statistical approaches can accurately describe the distance distributions. Based on these distributions and with the aim to reduce the decoding complexity, several techniques, including stopping rules and discarding rules, are proposed, and their decoding error performance and complexity are accordingly analyzed. Simulation results for decoding various eBCH codes demonstrate that the proposed techniques can significantly reduce the decoding complexity with a negligible loss in the decoding error performance. [ABSTRACT FROM AUTHOR]

Communication for omniscience (CO) refers to the problem where the users in a finite set V observe a discrete multiple random source and want to exchange data over broadcast channels to reach omniscience, the state where everyone recovers the entire source. This paper studies how to improve the computational complexity for the problem of minimizing the sum-rate for attaining omniscience in V. While the existing algorithms rely on the submodular function minimization (SFM) techniques and complete in O(|V|2⋅SFM (|V|) time, we prove the strict strong map property of the nesting SFM problem. We propose a parametric (PAR) algorithm that utilizes the parametric SFM techniques and reduces the complexity to O(|V| ⋅SFM (|V|). We propose efficient solutions to the successive omniscience (SO): attaining omniscience successively in user subsets. We first focus on how to determine a complimentary subset X* ⊄ V in the existing two-stage SO such that if the local omniscience in X* is reached first, the global omniscience whereafter can still be attained with the minimum sum-rate. It is shown that such a subset can be extracted at one of the iterations of the PAR algorithm. We then propose a novel multi-stage SO strategy: a nesting sequence of complimentary user subsets X*(1) ⊄ . . . ⊆ X*(K) = V, the omniscience in which is attained progressively by the monotonic rate vectors rV(1) ≤ . . . rV(K). We propose algorithms to obtain this K-stage SO from the returned results by the PAR algorithm. The run time of these algorithms is the same as the PAR algorithm. [ABSTRACT FROM AUTHOR]

The Demirci-Selçuk’s meet-in-the-middle attack is one of the most important methods among all the cryptanalytic vectors, which gives the best result against the round-reduced AES with respect to the rounds, and tradeoffs between data, time and memory. While we have already built provable security models against the differential cryptanalysis, linear cryptanalysis cryptanalysis, impossible differential and zero-correlation linear cryptanalysis, the provable security against the meet-in-the-middle attack is missing. In this paper, we propose the subset representation of function based on which we could give an algorithm to compute the exact number of parameters of the Demirci-Selçuk’s distinguisher given the input and output, respectively. Experiments show that this algorithm can be more efficient than the automatical tool presented by Shi et al. at Asiacrypt 2018. We further extract a formula based on this algorithm and show an upper bound for the length of the Demirci-Selçuk’s distinguisher of an iterative SPN cipher. We prove that for an SPN block cipher whose block size equals the key size, an effective Demirci-Selçuk-type meet-in-the-middle distinguisher covers at most twice the maximum of the primitive indexes of the linear layer and its inverse. As a result, we show that the known length of the Demirci-Selçuk’s distinguisher of the AES-128 cannot be improved unless the details of the S-boxes are exploited, which demonstrates that the AES has a provable security against the Demirci-Selçuk’s meet-in-the-middle attack. [ABSTRACT FROM AUTHOR]

A fast wind turbine abnormal data cleaning algorithm via image processing for wind turbine power generation performance measurement and evaluation is proposed in this paper. The proposed method includes two stages, data cleaning and data classification. At the data cleaning stage, pixels of normal data are extracted via image processing based on pixel spatial distribution characteristics of abnormal and normal data in wind power curve (WPC) images. At the data classification stage, wind power data points are classified as normal and abnormal based on the existence of corresponding pixels in the processed WPC image. To accelerate the proposed method, the cleaning operation is executed parallelly using graphics processing units (GPUs) via compute unified device architecture (CUDA). The effectiveness of the proposed method is validated based on real data sets collected from 37 wind turbines of two commercial farms and three types of GPUs are employed to implement the proposed algorithm. The computational results prove the proposed approach has achieved better performance in cleaning abnormal wind power data while the execution time is tremendously reduced. Therefore, the proposed method is available and practical for real wind turbine power generation performance evaluation and monitoring tasks. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of quasi-synchronization (QS) for the periodic neural networks (NNs). In order to address more general NNs, the parameter and the period mismatches are both considered, that is, excluding parameters, periods of the target dynamic and the followers are also different. In addition, the constrainted target information is studied, where the logarithmic quantizer is used to overcome the limited communication capacity and Bernoulli processes are employed to model cases of the information loss. A new period is established based on the lowest common multiple period of the target dynamic and the followers to obtain an augmented synchronization error system (ASES). Further, a suboptimal iterative algorithm is proposed to cut down the QS range of the ASES and the corresponding controllers are designed. At last, the controller design method is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

This paper investigates the positive edge consensus problems for undirected networks, where each edge is modeled by a continuous-time positive system with sector input nonlinearities. Based on a given nodal network, the corresponding edge network is presented, and some relationships between the given nodal network and its edge network are given. The observer-based protocol for non-negative consensus is proposed. Rigorous theoretical analysis which considers both the input nonlinearities and the positivity constraint is introduced to achieve a global consensus result. Simultaneous stabilization problems are constructed for edge-based non-negative consensus using the maximum and the minimum nonzero eigenvalues of the edge Laplacian matrix. Furthermore, the robustness of the positive edge consensus to parameter perturbation is illustrated. Sufficient non-negative consensus conditions related to the bounds of system matrices are given, and the non-negative edge consensus conditions for star nodal networks are also given only using the vertex number. Iterative linear matrix inequalities (LMIs)/LMI algorithm is presented to obtain the feedback matrix and the observer matrix. The given theoretical results are finally illustrated by some simulations. [ABSTRACT FROM AUTHOR]

Linear Regression (LR) is a popular and effective technique in pattern recognition area, which aims to find a transform matrix between source data and target data (usually label matrix). However, a binary zero-one label matrix may be too strict and inappropriate for regression. Besides, directly projecting source data to target data by one transform matrix may lose some intrinsic data information. To address these issues, this paper proposes a novel Pairwise Relations oriented Discriminative Regression (PRDR) method. In PRDR, the source data is regressed into a latent space instead of label space. To supervise the discriminative projection learning, the pairwise relations in source data space and label space are exploited in the latent space simultaneously. The pairwise label relations are transferred into the latent subspace by solving a distance-distance difference minimization problem, and the intraclass instance relations are also preserved in latent space. These two constraints ensure the pairwise similarity of data points after transformation which is beneficial for classification. By further enlarging the margins between true and false classes, PRDR is extended to a robust version, i.e., R-PRDR. An efficient algorithm is presented to solve the PRDR model. Extensive experiments on several popular image datasets demonstrate the effectiveness and efficiency of the proposed method compared with some state-of-the-art regression approaches. [ABSTRACT FROM AUTHOR]

Disparity estimation is an essential task taking part in many light-field applications. Due to the complexity of algorithms and high dimensional property of light-field data, performing this task involves a significant computational effort and results in very long processing time on CPU. Graphics processing units (GPUs), which is capable of massively parallel processing, is a promising solution to cover the computation requirement and speed up the task. In this paper, we develop a GPU-accelerated approach for light-field disparity estimation using a variational computation framework (GVLD). Our algorithm combines the intrinsic sub-pixel precision of variational formulation and the effectiveness of weighted median filtering to produce a highly accurate solution. The proposed algorithm is fully parallelized and optimized for the implementation using the OpenCL framework. An intensive evaluation including a quantitative comparison to related works and a detailed analysis of the proposed approach’s performance is presented. Experimental results demonstrate our superior performance compared to state-of-the-art approaches. The proposed approach is 10+ times faster than other approaches running on a similar GPU platform and provides the most accurate solution among optimization-based approaches. Compared to the implementation running on CPU, our GPU-accelerated method achieves up to $365\times $ speed up. [ABSTRACT FROM AUTHOR]

Social networks are everywhere and currently transmitting very large messages. As a result, transmitting secret messages in such an environment is worth researching. However, the images used in transmitting messages are usually compressed with a JPEG compression channel, which is lossy and damages the transmitted data. Therefore, to prevent secret messages from being damaged, a robust JPEG steganography is urgently needed. In this paper, a secure robust JPEG steganographic scheme based on an autoencoder with an adaptive BCH encoding (Bose-Chaudhuri-Hocquenghem encoding) is proposed. In particular, the autoencoder is first pretrained to fit the transformation relationship between the JPEG image before and after compression by the compression channel. In addition, the BCH encoding is adaptively utilized according to the content of cover image to decrease the error rate of secret message extraction. The DCT (Discrete Cosine Transformation) coefficient adjustment based on practical JPEG channel characteristics further improves the robustness and statistical security. Comparisons with prior state-of-the-art schemes demonstrate that the proposed robust JPEG steganographic algorithm can provide a more robust performance and statistical security. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) clustering is a challenging task due to the complex characteristics in HSI data, such as spatial-spectral structure, high-dimension, and large spectral variability. In this paper, we propose a novel deep spatial-spectral subspace clustering network (DS3C-Net), which explores spatial-spectral information via the multi-scale auto-encoder and collaborative constraint. Considering the structure correlations of HSI, the multi-scale auto-encoder is first designed to extract spatial-spectral features with different-scale pixel blocks which are selected as the inputs. Then, the collaborative constrained self-expressive layers are introduced between the encoder and decoder, to capture the self-expressive subspace structures. By designing a self-expressiveness similarity constraint, the proposed network is trained collaboratively, and the affinity matrices of the feature representation are learned in an end-to-end manner. Based on the affinity matrices, the spectral clustering algorithm is utilized to obtain the final HSI clustering result. Experimental results on three widely used hyperspectral image datasets demonstrate that the proposed method outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

As the training dataset size and the model size of machine learning increase rapidly, more computing resources are consumed to speedup the training process. However, the scalability and performance reproducibility of parallel machine learning training, which mainly uses stochastic optimization algorithms, are limited. In this paper, we demonstrate that the sample difference in the dataset plays a prominent role in the scalability of parallel machine learning algorithms. We propose to use statistical properties of dataset to measure sample differences. These properties include the variance of sample features, sample sparsity, sample diversity, and similarity in sampling sequences. We choose four types of parallel training algorithms as our research objects: (1) the asynchronous parallel SGD algorithm (Hogwild! algorithm), (2) the parallel model average SGD algorithm (minibatch SGD algorithm), (3) the decentralization optimization algorithm, and (4) the dual coordinate optimization (DADM algorithm). Our results show that the statistical properties of training datasets determine the scalability upper bound of these parallel training algorithms. [ABSTRACT FROM AUTHOR]

We correct errors in step 6 of Algorithm 1 given in the paper ‘'Sparse-Group Lasso for Graph Learning from Multi-Attribute Data.’' Other parts of the paper including numerical results are unchanged as the algorithm was implemented correctly. [ABSTRACT FROM AUTHOR]