In this article, we attempt to document a technical overview on modern miniature unmanned rotorcraft systems. We first give a brief review on the historical development of the rotorcraft unmanned aerial vehicles (UAVs), and then move on to present a fairly detailed and general overview on the hardware configuration, software integration, aerodynamic modeling and automatic flight control system involved in constructing the unmanned system. The applications of the emerging technology in the military and civilian domains are also highlighted.

Providing different service priority levels for different kinds of traffic is an important research area in the Internet. As compared with the existing best-effort (BE) service, recent researchers consider devising the below-best-effort algorithm to realize a low priority service. The aim of below-best-effort service is to use the available bandwidth as much as possible in the premise of not interfering standard best-effort flows. With the best-effort transmission control protocol (TCP) transporting foreground traffic, below-best-effort service can transport background traffic which users do not urgently need to use. In this paper, we review the evolution of below-best-effort service and classify and compare these below-best-effort schemes. We also present some open problems in the research of below-best-effort service and point out the future work.

Access network selection (ANS) strategy is one of the most important issues in future heterogeneous networks. The current solutions for this issue are not very efficient because they do not consider the motion scenarios and cannot predict the next location for mobile node. In this paper, an effective ANS strategy based on global positioning system (GPS) is proposed. Making use of information such as position coordinates and moving velocity acquired by GPS, the ANS proposed can predict the next point of attachment for mobile node (MN), assist existing ANS strategy to make more reasonable decision, and achieve better performance.

With the continuous improvement of transmission rate, high speed network links require good performance of packet capture. The multiprocessor platform has strong computational capabilities, and brings new chance for high rate packet capture. In this paper, we analyze the performance of common packet capture approaches that are based on general-purpose multiprocessor platform. The analysis contains two aspects: one is the maximum packet capture rate and throughput on multiprocessor platform, the other is central processing unit (CPU) load under the maximum capture rate. By comparing and analyzing the experimental result, we give the maximum packet capture rate and throughput of different capture approaches. Furthermore, we analyze the CPU load which is produced by two capture processes run on the multiprocessor platform simultaneously and make a comparison with the single capture process.

In this paper, a novel dynamic addressing scheme for wireless sensor networks (WSNs) is proposed by using variable length coding. A WSN is typically composed of numerous tiny energy-constrained sensor nodes with limited information processing and data storage capabilities; thus, the energy-efficient strategy is the key issue in designing protocols for WSN. Traditional addressing strategies adopt flat addressing (static and uniform addresses) for sensor nodes. However, the proposed variable length dynamic addressing (VLDA) for sensor nodes is based on the fact that different nodes in the network have uneven traffic loads. Therefore, nodes with more data to receive or send are allocated with shorter addresses. Whether a node is busy or not is determined by the network traffic distribution (NTD), which is defined as the number of data packets each node has received or sent in a period of time. Sensor nodes’ energy is saved by VLDA scheme; hence, the wireless sensor network’s lifetime is extended. In the simulation, a 20% improvement has been achieved through the addressing scheme compared to traditional flat addressing.

As a solution for data storage and information sharing for peer-to-peer (P2P) networks, a novel distributed hash table (DHT) structure called PChord is presented in this paper. PChord adopts a bi-directional searching mechanism superior to Chord and enhances the structure of the finger table. Based on Hilbert space filling curve, PChord realizes the mapping mechanism for multi-keyword approximate searching. Compared with the Chord and Kademlia protocols, PChord evidently increases speed on resource searching and message spreading via theoretic proof and simulation results, while maintaining satisfactory load balance.

A multi-input multi-output (MIMO) detection scheme that requires considerable low complexity but still achieves the near optimal performance is proposed. The fundamental idea of the proposed MIMO detection scheme consists of two points: 1) the computational complexity is restrained by a complexity limit in low signal-to-noise ratio (SNR) region; 2) while in high SNR region, the complexity is significantly reduced by the proposed search space method. Comparing with existing fixed-complexity techniques of MIMO detection (e.g., K-best sphere detector and reduced-search maximum-likelihood (RS ML) detection), the significant benefit of proposed detection scheme is that less computational power will be spent for the given data rate, or the throughput of detector can be increased for high SNR cases. According to the simulation results, the near optimal performance can be obtained while the detection complexity is kept considerable small.

Multiple-size units-based acoustic modeling has been proposed for large vocabulary speech recognition system to improve the recognition accuracy with limited training data. By introducing a limited number of long-size units into unit set, this modeling scheme can make better acoustic model precision than complete short-size unit modeling without losing model trainability. However, such a multiple-size unit acoustic modeling paradigm does not always bring reliable improvement on recognition perform-ance, since when a large number of long-size units are added in, the amount of training data for short-size units will decrease and result in insufficiently trained models. In this paper, a modified Baum-Welch training method is proposed, which uses product hidden Markov models (PHMMs) to couple units with different sizes and enables them to share same portions of training data. The validity of proposed method is proved by experiment results.

Traditional multi-class classification methods based on Fisher kernel combine generative models such as Gaussian mixture models (GMMs) of all the classes together. However, the combination generates high dimensional feature vectors and leads to large computation. In this paper, a new classification method is proposed. This method adopts an intelligent feature space selection strategy by clustering similar Gaussian mixtures in order to reduce the feature dimensions. Audio classification experiments show that the proposed method is more accurate and effective with less computation compared with traditional methods.

Besides their decorative purposes, vehicle manufacturer logos can provide rich information for vehicle verification and classification in many applications such as security and information retrieval. However, unlike the license plate, which is designed for identification purposes, vehicle manufacturer logos are mainly designed for decorative purposes such that they might lack discriminative features themselves. Moreover, in practical applications, the vehicle manufacturer logos captured by a fixed camera vary in size. For these reasons, detection and recognition of vehicle manufacturer logos are very challenging but crucial problems to tackle. In this paper, based on preparatory works on logo localization and image segmentation, we propose a size-self-adaptive method to recognize vehicle manufacturer logos based on feature extraction and support vector machine (SVM) classifier. The experimental results demonstrate that the proposed method is more effective and robust in dealing with the recognition problem of vehicle logos in different sizes. Moreover, it has a good performance both in preciseness and speed.

The traditional intrusion detection system has the problem of high false positive rate and false negative rate. This paper deeply analyzes the differences of statistical features between single-flow and multi-flow on the database network, and presents a group of features that are easy to acquire and can be used to detect the anomaly in database network efficiently. By applying this group of features in Fisher algorithm for anomaly detection, the false positive rate and false negative rate are dramatically reduced. Simultaneously, the model made by using the group of features has the advantages of low algorithm complexity, good detection result and strong generalization ability. Experimental results show that there is higher accuracy when using the features of single-flow and multi-flow to construct the anomaly detection model than only using single-flow features.

This paper is concerned with two popular and powerful methods in electrical drive applications: field-oriented control (FOC) and space vector modulation (SVM). The proposed FOC-SVM method is incorporated with a predictive current control (PCC)-based technique. The suggested method estimates the desirable electrical torque to track mechanical torque at a fixed speed operation of permanent magnet synchronous motor (PMSM). The estimated torque is used to calculate the reference current based on FOC. In order to improve the performance of the traditional SVM, a PCC method is established as a switching pattern modifier. Therefore, PCC-based SVM is employed to further minimize the torque ripples and transient response. The performance of the controller is evaluated in terms of torque and current ripple and transient response to step variations of the torque command. The proposed method has been verified with MATLAB-Simulink model. Simulation results confirm the ability of this technique in minimizing the torque and speed ripples and fixing switching frequency, simultaneously. However, it is sensitive to parameter changes.

The electromagnetic interference (EMI) from the valve hall as an important consideration in the design of high voltage direct current (HVDC) converter stations is analyzed by electromagnetic field numerical method and measurements. By using moment method, the EMI filed strength level as well as radio interference level (RIL) of the valve hall during the normal operation are computed after an antenna model is built for the valve tower. According to the character of EMI obtained, the practical shielding measures for valve hall are discussed to satisfy the relative standards. The test results for the±500 kV converter station show that both the numerical method and shielding technique used in this paper are practical.