Extraction of Dynamic Trajectory on Multi-Stroke Static Handwriting Images Using Loop Analysis and Skeletal Graph Model

The recovery of handwriting’s dynamic stroke is an effective method to help improve the accuracy of any handwriting’s authentication or verification system. The recovered trajectory can be considered as a dynamic feature of any static handwritten images. Capitalising on this temporal information can significantly increase the accuracy of the verification phase. Extraction of dynamic features from static handwritings remains a challenge due to the lack of temporal information as compared to the online methods. Previously, there are two typical approaches to recover the handwriting’s stroke. The first approach is based on the script’s skeleton. The skeletonisation method has highly computational efficiency whereas it often produces noisy artifacts and mismatches on the resulted skeleton. The second approach deals with the handwriting’s contour, crossing areas and overlaps using parametric representations of lines and thickness of strokes. This method can avoid the artifacts, but it requires complicated mathematical models and may lead to computational explosion. Our paper is based on the script’s extracted skeleton and provides an approach to processing static handwriting’s objects, including edges, vertices and loops, as the important aspects of any handwritten image. Our paper is also to provide analysing and classifying loops types and human’s natural writing behavior to improve the global construction of stroke order. Then, a detailed tracing algorithm on global stroke reconstruction is presented. The experimental results reveal the superiority of our method as compared with the existing ones

Multi-Objective Optimization for IRS-Aidded Multi-user MIMO SWIPT Systems

In this paper, we investigate an intelligent reflecting surface (IRS) assisted simultaneous wireless information and power transfer (SWIPT) system in which users equipped with multiple antennas exploit power-splitting (PS) strategies for simultaneously information decoding (ID) and energy harvesting (EH). Different from the majority of previous studies which focused on single objective optimization problems (SOOPs) and assumed the linearity of EH models, in this paper, we aim at studying the multi-objective optimization problem (MOOP) of the sum rate (SR) and the totalharvested energy (HE) subject to the maximum transmit power (TP) constraint, the user quality of service (QoS), and HE requirements at each user with taking a practical non-linear EH (NLEH) model into consideration. To investigate insightful tradeoffs between the achievable SR and total HE, we adopt the modified weighted Tchebycheff method to transform the MOOP into a SOOP. However, solving the SOOPs and modified SOOP is mathematically difficult due to the non-convexity of the object functions and the constraints of the coupled variables of the  base station (BS) transmit precoding matrices (TPMs), the user PS ratios (PSRs), and the IRS phase shift matrix (PSM). To address these challenges, an alternating optimization (AO) framework is used to decompose the formulated design problem into sub-problems. In addition, we apply the majorization-minimization (MM) approach to transform the sub-problems into convex optimization ones. Finally,  numerical simulation results are conducted to verify the tradeoffs between the SR and the total amount of HE. The numerical results also indicate that the considered system using the IRS with optimal phase shifts provides considerable performance improvement in terms of the achievable SR and HE as compared to the counterparts without using the IRS or with the IRS of fixed phase shifts

On the Interference Alignment Designs for Secure Multiuser MIMO Systems

In this paper, we propose two secure multiuser multiple-input multiple-output transmission approaches based on interference alignment (IA) in the presence of an eavesdropper. To deal with the information leakage to the eavesdropper as well as the interference signals from undesired transmitters (Txs) at desired receivers (Rxs), our approaches aim to design the transmit precoding and receive subspace matrices to minimize both the total inter-main-link interference and the wiretapped signals (WSs). The first proposed IA scheme focuses on aligning the WSs into proper subspaces while the second one imposes a new structure on the precoding matrices to force the WSs to zero. When the channel state information is perfectly known at all Txs, in each proposed IA scheme, the precoding matrices at Txs and the receive subspaces at Rxs or the eavesdropper are alternatively selected to minimize the cost function of an convex optimization problem for every iteration. We provide the feasible conditions and the proofs of convergence for both IA approaches. The simulation results indicate that our two IA approaches outperform the conventional IA algorithm in terms of average secrecy sum rate.Comment: Updated version, updated author list, accepted to be appear in IEICE Transaction

Precoding Designs for Full-Duplex Multi-User MIMO Cognitive Networks with Imperfect CSI

This paper studies a cognitive radio (CR) network which consists of a full-duplex (FD) multi-user (MU) multipleinput multiple-output (MIMO) secondary user (SU) networks operating within the coverage of multiple primary users (PUs). It is assumed that the channel state information (CSI) matrices associated with SU systems are perfectly known whereas the CSI ones from SUs to PUs are imperfectly estimated. The problem of interest is to design robust precoding matrices at the SUs to maximize the CR sum rate subject to the SU transmit power constraints and harmful interference restrictions at PUs. Due to non-concavity of the objective function and intractability of robust PU interference constraints, the design problem is non-convex and challenging to directly solve. We exploit the difference of two concave functions to recast the sum rate objective function as a lower bounded concave one. In addition, a linear matrix inequality (LMI) transformation is used to handle the semi-infinite robust interference constraints. Then, the sequential convex programming method is carried out to iteratively solve a convex optimization problem in each iteration. The simulation results are provided to investigate the CR sum-rate (spectral efficiency) performance and the robustness against the CSI uncertainty

Energy-Spectral Efficiency Trade-Offs in Full-Duplex MU-MIMO Cloud-RANs with SWIPT

The present paper investigates the trade-offs between the energy efficiency (EE) and spectral efficiency (SE) in the full-duplex (FD) multiuser multi-input multioutput (MU-MIMO) cloud radio access networks (CRANs) with simultaneous wireless information and power transfer (SWIPT). In the considered network, the central unit (CU) intends to concurrently not only transfer both energy and information toward downlink (DL) users using power splitting structures but also receive signals from uplink (UL) users. This communication is executed via FD radio units (RUs) which are distributed nearby users and connected to the CU through limited capacity fronthaul (FH) links. In order to unveil interesting trade-offs between the EE and SE metrics, we first introduce three conventional single-objective optimization problems (SOOPs) including (i) system sum rate maximization, (ii) total power minimization, and (iii) fractional energy efficiency maximization. Then, by making use of the multiobjective optimization (MOO) framework, the MOO problem (MOOP) with the objective vector of the achievable rate and power consumption is addressed. All considered problems are nonconvex with respect to designing variables comprising precoding matrices, compression matrices, and DL power splitting factors; thus, it is extremely intractable to solve these problems directly. To overcome these issues, we develop iterative algorithms by utilizing the sequential convex approximation (SCA) approach for the first two SOO problems and the SCA-based Dinkelbach method for the fractional EE problem. Regarding the MOOP, we first rewrite it as an SOOP by applying the modified weighted Tchebycheff method and, then, propose the iterative algorithm-based SCA to find its optimal Pareto set. Various numerical simulations are conducted to study the system performance and appealing EE-SE trade-offs in the considered system

On the Optimal Precoder Design for Energy-Efficient and Secure MIMO Systems

In this research work, we investigate precoder designs to maximize the energy efficiency (EE) of secure multiple-input multiple-output (MIMO) systems in the presence of an eavesdropper. In general, the secure energy efficiency maximization (SEEM) problem is highly nonlinear and nonconvex and hard to be solved directly. To overcome this difficulty, we employ a branch-and-reduce-and-bound (BRB) approach to obtain the globally optimal solution. Since it is observed that the BRB algorithm suffers from highly computational cost, its globally optimal solution is importantly served as a benchmark for the performance evaluation of the suboptimal algorithms. Additionally, we also develop a low-complexity approach using the well-known zero-forcing (ZF) technique to cancel the wiretapped signal, making the design problem more amenable. Using the ZF based method, we transform the SEEM problem to a concave-convex fractional one which can be solved by applying the combination of the Dinkelbach and bisection search algorithm. Simulation results show that the ZF-based method can converge fast and obtain a sub-optimal EE performance which is closed to the optimal EE performance of the BRB method. The ZF based scheme also shows its advantages in terms of the energy efficiency in comparison with the conventional secrecy rate maximization precoder design

An Efficient Digital Watermarking Technique for Color Images Using Directional Transforms

This paper is concerned with a digital watermarking technique for color images based on directional transforms. Different from the traditional watermarking schemes which embed the watermarks into the spatial domain or frequency domain of the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), this paper investigates the performance of the watermarking schemes using the Fast Discrete Curvelet Transforms (FDCT) and Contourlet Transform (CT). We evaluate the performance of the watermarking schemes using the directional transforms on a standard database of color images in terms of invisibility and robustness. The performance metrics are measured by Peak Signal-to-Noise Ratio (PSNR), Normalized Correlation (NC), Structural SIMilarity (SSIM) and required time for extracting and embedding process. The experimental results reveal that watermarking schemes in the directional transform domains outperform the other schemes in DWT domains

Transceiver Designs to Improve Spectrum Utilization in MIMO Interference Channels

This paper is concerned with a multiple-input multiple-output (MIMO) multi-user wireless networks in which multiple secondary users (SUs) can share the same radio spectrum with a single primary user (PU). The design problems of the transceivers in such MIMO interference channels are to find the precoding matrices at the transmitters and the receiving matrices at the receivers to minimize the mean square error (MSE) or to maximize the sum-rate of the SUs while guaranteeing the interference power at the PU receiver below an acceptable threshold. In this paper, we consider to design the transceivers using the interference alignment techniques. The objective is to align the interference at the SUs and maintain an acceptable leakage interference level from the SUs into the signal subspace of the PU receiver. Due to the nonlinearity and nonconvexity of the underlaying problems, we develop an alternating algorithm which efficiently solves a convex optimization in each iteration. The numerical results are provided to validate the performance of our algorithm