An efficient solution for locating a target was proposed, which by using time difference of arrival (TDOA) measurements in the presence of random sensor position errors to increase the accuracy of estimation. The cause of position estimation errors in two-stage weighted least squares (TSWLS) method is analyzed to develop a simple and effective method for improving the localization performance. Specifically, the reference sensor is selected again and the coordinate system is rotated according to preliminary estimated target position by using TSWLS method, and the final position estimation of the target is obtained by using weighted least squares (WLS). The proposed approach exhibits a closed-form and is as efficient as TSWLS method. Simulation results show that the proposed approach yields low estimation bias and improved robustness with increasing sensor position errors and thus can easily achieve the Cramer-Rao lower bound (CRLB) easily and effectively improve the localization accuracy.

An isotropic electromagnetic (EM) lens based on Huygens’ metasurface is proposed for 28.0 GHz lens antenna design. The lens consists of a series of non-resonant and subwavelength metallic patterns etched on both sides of an ultrathin dielectric substrate. Both electric and magnetic responses are introduced to realize desired abrupt phase change and high-efficiency transmission for the secondary wavelets in the incident wavefront. Then, a substrate-integrated waveguide (SIW) fed patch antenna is combined with the lens as the primary feed to form a low-profile lens antenna system. The simulated and measured results coincide with each other, and demonstrate that the prototype realizes 8.8 dB~12.6 dB gain increment and low side-lobe levels over the bandwidth of 26.7 GHz~30.0 GHz. The novel design leads to a low-profile, light weight, and low-cost antenna solution in a wireless communication system.

As different requirements on mobility support will be introduced by diversified communication scenarios in the fifth generation (5G), on demand mobility management is put forward to simplify signaling process, reduce terminal power consumption, improve network efficiency and so on. In order to enable on demand mobility management in 5G networks, a mobility driven network slicing (MDNS) was proposed, which takes individual mobility support requirements into account while customizing networks for different mobile services. Within the MDNS framework, the actual levels of required mobility support are determined by a mobility description system, and network slice templates with the corresponding mobility management schemes are defined by a network slice description function. By instantiating the network slices, each mobile terminal could be directed to the network slice with the most appropriate mobility management scheme. Based on this, a prototype was implemented to validate the feasibility of MDNS framework, i.e. creating multiple network slices with different mobility management schemes. In addition, the performance evaluation on average cost of processing a mobility event is conducted for the proposed MDNS framework and the long term evolution (LTE) system, and operating benefits are analyzed including efficiency and scalability.

With the boom of wireless devices, the number of wireless users under wireless local area networks (WLANs) has increased dramatically. However, the standard backoff mechanism in IEEE 802.11 adopts fixed initial contention window (CW) size without considering changes of network load, which leads to a high collision probability and low channel utilization in bursty arrivals. In this paper, a novel CW dynamic adjustment scheme is proposed to achieve high throughput performance in dense user environment. In the proposed scheme, the initial CW size is dynamically adjusted to optimum according to the measured packet collision probability. Simulation results show that the proposed scheme can significantly improve the throughput performance.

Information fusion is a key step in multimodal biometric systems. The feature-level fusion is more effective than the score-level and decision-level method owing to the fact that the original feature set contains richer information about the biometric data. In this paper, we present a multiset generalized canonical discriminant projection (MGCDP) method for feature-level multimodal biometric information fusion, which maximizes the correlation of the intra-class features while minimizes the correlation of the between-class. In addition, the serial MGCDP (S-MGCDP) and parallel MGCDP (P-MGCDP) strategy were also proposed, which can fuse more than two kinds of biometric information, so as to achieve better identification effect. Experiments performed on various biometric databases shows that MGCDP method outperforms other state-of-the-art feature-level information fusion approaches.

Color image enhancement is an active research field in image processing. Currently, many image enhancement methods are capable of enhancing the details of the color image. However, these methods only process the red, green and blue (RGB) color channels separately, which leads to color distortion easily. In order to overcome this problem, the paper presents an approach to integrate the quaternion theory into the traditional guided filter to obtain a quaternion guided filter (QGF). This method makes full use of the color information of an image to realize the holistic processing of RGB color channels. So as to preserve color information while enhancing details, this paper proposes a color image detail enhancement algorithm based on the QGF. Experimental results show that the proposed algorithm is effective in the applications of the color image detail enhancement, and enables image’s edges to be more prominent and texture clearer while avoiding color distortion. Compared with the existing image enhancement methods, the proposed method achieves better enhancement performance in terms of the visual quality and the objective evaluating indicators.

Along with the increasing number of vehicles, parking space becomes narrow gradually, safety parking puts forward higher requirements on the driver’s driving technology. How to safely, quickly and accurately park the vehiclo to parking space right? This paper presents an automatic parking scheme based on trajectory planning, which analyzing the mechanical model of the vehicle, establishing vehicle steering model and parking model, coming to the conclusion that it is the turning radius is independent of the vehicle speed at low speed. The Matlab simulation environment verifies the correctness and effectiveness of the proposed algorithm for parking. A class of the automatic parking problem of intelligent vehicles is solved.

To extract and express the knowledge hidden in information systems, discernibility matrix and its extensions were introduced and applied successfully in many real life applications. Binary discernibility matrix, as a representative approach, has many interesting superior properties and has been rapidly developed to find intuitive and easy to understand knowledge. However, at present, the binary discernibility matrix is mainly adopted in the complete information system. It is a challenging topic how to achieve the attribute reduction by using binary discernibility matrix in incomplete information system. A form of generalized binary discernibility matrix is further developed for a number of representative extended rough set models that deal with incomplete information systems. Some useful properties and criteria are introduced for judging the attribute core and attribute relative reduction. Thereafter, a new algorithm is formulated which supports attribute core and attribute relative reduction based on the generalized binary discernibility matrix. This algorithm is not only suitable for consistent information systems but also inconsistent information systems. The feasibility of the proposed methods was demonstrated by worked examples and experimental analysis.

Area-efficient design methodology is proposed for the analog decoding implementations of the rate-1/2 accumulate repeat-4 jagged-accumulate (AR4JA) low density parity check (LDPC) code. The proposed approach is designed using optimized decoding architecture and regularized routing network, in such a way that the overall wiring overhead is minimized and the silicon area utilization is significantly improved. The prototyping chip used to verify the approach is fully integrated in a four-metal double-poly 0.35 μm complementary metal oxide semiconductor (CMOS) technology, and includes an input-output interface that maximizes the decoder throughput. The decoding core area is 2.02 mm2 with a post-layout area utilization of 80%. The decoder was successfully tested at the maximum data rate of 10 Mbit/s, with a core power consumption of 6.78 mW at 3.3 V, which corresponds to an energy per decoded bit of 0.677 nJ. The proposed analog LDPC decoder with low processing power and high-reliability is suitable for space- and power-constrained spacecraft system.

In modern data centers, power consumed by network is an observable portion of the total energy budget and thus improving the energy efficiency of data center networks (DCNs) truly matters. One effective way for this energy efficiency is to make the size of DCNs elastic along with traffic demands by flow consolidation and bandwidth scheduling, i.e., turning off unnecessary network components to reduce the power consumption. Meanwhile, having the instinct support for data center management, software defined networking (SDN) provides a paradigm to elastically control the resources of DCNs. To achieve such power savings, most of the prior efforts just adopt simple greedy heuristic to reduce computational complexity. However, due to the inherent problem of greedy algorithm, a good-enough optimization cannot be always guaranteed. To address this problem, a modified hybrid genetic algorithm (MHGA) is employed to improve the solution’s accuracy, and the fine-grained routing function of SDN is fully leveraged. The simulation results show that more efficient power management can be achieved than the previous studies, by increasing about 5% of network energy savings.