Summary Image dehazing technology has attracted much interest in the field of image processing. Most existing dehazing methods based on neural networks are inflexible and do not consider the loss in haze‐related feature space. They sacrificed texture details and perceptual characteristics in images. To overcome these weaknesses, we propose an image‐to‐image dehazing model based on generative adversarial networks (DHGAN) with dark channel prior. The DHGAN takes a hazy image as input and directly outputs a haze‐free image by applying a U‐net‐based generator. In addition to pixelwise loss and perceptual loss, we introduce dark‐channel‐minimizing loss to constrain the generated images to the manifold of natural images, thus leading to better texture details and perceptual properties. Comparative experiments on benchmark images with several state‐of‐the‐art dehazing methods demonstrate the effectiveness of the proposed DHGAN.

For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

Mobile robot localization has attracted substantial consideration from the scientists during the last two decades. Mobile robot localization is the basics of successful navigation in a mobile network. Localization plays a key role to attain a high accuracy in mobile robot localization and robustness in vehicular localization. For this purpose, a mobile robot localization technique is evaluated to accomplish a high accuracy. This paper provides the performance evaluation of three localization techniques named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In this work, three localization techniques are proposed. The performance of these three localization techniques is evaluated and analyzed while considering various aspects of localization. These aspects include localization coverage, time consumption, and velocity. The abovementioned localization techniques present a good accuracy and sound performance compared to other techniques.

This study focuses on the spectral efficiency of massive multiple‐input–multiple‐output (MIMO) systems over Ricean fading channels. Assuming the channel reciprocity of uplink and downlink transmissions, the channels are estimated through uplink pilot sequence with least minimum mean square error (MMSE) estimator, which facilitates the investigation of equivalent channel model. With the equivalent channel model, adopting joint MMSE receiver, the uplilnk achievable sum‐rate is studied, and finally, the asymptotic expression of spectral efficiency is obtained. Based on the proposed asymptotic spectral efficiency, numerical results give some guidelines about system parameters design: the authors should choose a proper length of the block fading channels to reduce the effect of pilot overhead, and an optimal length of pilot sequence always exists to guarantee a satisfying system performance in terms of spectral efficiency. Furthermore, the simulation results show that when fixing the number of users, shrinking the cell serving area and increasing the number of antennas can improve the system performance.

With the advent of software-defined networking, network configuration through programmable interfaces becomes practical, leading to various on-demand opportunities for network routing update in multi-tenant datacenters, where tenants have diverse requirements on network routings such as short latency, low path inflation, large bandwidth, high reliability, etc. Conventional solutions that rely on topology search coupled with an objective function to find desired routings have at least two shortcomings: ${\sf (i)}$(i) they run into scalability issues when handling consistent and frequent routing updates and ${\sf (ii)}$(ii) they restrict the flexibility and capability to satisfy various routing requirements. To address these issues, this paper proposes a novel search and optimization decoupled design, which not only saves considerable topology search costs via search result reuse, but also avoids possible sub-optimality in greedy routing search algorithms by making decisions based on the global view of all possible routings. We implement a prototype of our proposed system, OpReduce, and perform extensive evaluations to validate its design goals.

Summary Secure mechanisms have been adapted to satisfy the needs of mobile subscribers; however, the mobile environment is quite different from a desktop PC or laptop‐based environment. The existing attack patterns in mobile environments are also quite different, and the countermeasures applied should be enhanced. In regards to usability, the mobile environment is based on mobility, and thus, mobile devices are designed and developed to enhance the owner's efficiency. To avoid forgetting passwords, people are willing to adopt simple alphanumeric‐character combinations, which are easy to remember and convenient to enter. As a result, the passwords have a high probability of being cracked or exposed. In this paper, we study the potential security problems caused by simple and weak passwords, discuss drawbacks of some conventional works, and propose 3 creative schemes to increase the complexity and strength of passwords by applying the envisioned features. Note that our proposals are based on the assumption that the textual passwords are not difficult for users to remember or enter and do not cause inconvenience to users. In other words, the proposed methods can increase the complexity of simple passwords without the awareness of users.

To improve contrast and restore color for underwater images without suffering from insufficient details and color cast, this paper proposes a fusion algorithm for different color spaces based on contrast limited adaptive histogram equalization (CLAHE). The original color image is first converted from RGB space to two different spaces: YIQ and HSI. Then, the algorithm separately applies CLAHE in YIQ and HSI color spaces to obtain two different enhanced images. After that, the YIQ and HSI enhanced images are respectively converted back to RGB space. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, using a 4-direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse the YIQ-RGB and HSI-RGB images together to achieve the final image. The experimental results showed that the proposed algorithm provided more detail enhancement and higher values of color restoration than other image enhancement algorithms. The proposed algorithm can effectively reduce noise interference and observably improve the image quality for underwater images.

In this paper, we propose a new power allocation method on non-orthogonal multiple access (NOMA) schemes in cellular uplink network. It is known that NOMA could be a promising candidate as a wireless access scheme for future 5G radio access technology. To enhance the spectrum efficiency, NOMA adopts a successive interference cancellation (SIC) receiver as the baseline receiver scheme for robust multiple access. With the goal of maximum total throughput for all users and make full use of bandwidth, an effective power allocation method should be utilized, in the conventional algorithm, it is a good way to get a satisfied performance such as average power allocation, water-filling method. We propose a method based on particle swarm optimization with genetic algorithm, which makes the power allocation by taking the advantage of channel state information (CSI) for each sub-channel; and can obtain a better performance comparing with the conventional method.

Without priori information, anomaly detector has more important utility compared with supervised target detection. Many classical anomaly detectors have obtained perfect performance in many situations. However, there still have two problems which are correlated with accuracy of anomaly detector. Firstly, clutter background induced more and more difficult pixel which have moderate statistical difference. Then, ideal uncontaminated subset of clutter background is hard to be obtain which is used to estimate background model. Secondly, difference of spectral content of different background objects will effect salience of anomaly targets. And it is arbitrary that uncertain pixels is nominated as non-anomalies by one threshold. For above two problems, a multi-aperture anomaly detector is proposed in this paper. Without selection of anomaly-free pixels and accurate statistical model, the proposed anomaly detector is expected to decrease false alarm rate with clutter background. A multi-aperture division for hyperspectral cube is conducted by iterative process. Statistical data of ever subaperture will be named as basis, which represent spectral characteristic of a certain range of spectral cube. Then, anomaly salience is proposed to measure the difference between pixels and sub-aperture basis. On the other hand, continuity of membership value based on fuzzy logical theory is more suitable to nominate difficulty pixels which has moderate anomaly salience. At last defuzzification ruler can be used to fuse different detection results from multi-aperture.

The multi-sensor image fusion technology can obtain a more accurate and reliable image to understand the scene or recognize the target more easily. However, most existing algorithms are mainly based on optical images, which are highly susceptible by media interference, and cannot save the textural feature and color information at the same time. In view of these problems, this paper presents a multi-sensor image fusion algorithm based on multi-scale feature analysis. After preprocessing, the SAR image is divided into irregular area and regular area based on an adaptive segmentation method. Then, these two areas are separately decomposed using SIDWT algorithm to extract image texture. A multi-scale feature analysis rule is proposed to be used on wavelet coefficients of SAR and PAN image. The fused image is finally got after inverse wavelet transform with new wavelet coefficients. Experimental results showed the effectiveness of the proposed algorithm by subjective and objective evaluation.