In recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open.

An addition is a fundamental arithmetic operation which is used extensively in many very large-scale integration (VLSI) systems such as application-specific digital signal processing (DSP) and microprocessors. An adder determines the overall performance of the circuits in most of those systems. In this paper we propose a novel 1-bit full adder cell which uses only eight transistors. In this design, three multiplexers and one inverter are applied to minimize the transistor count and reduce power consumption. The power dissipation, propagation delay, and power-delay produced using the new design are analyzed and compared with those of other designs using HSPICE simulations. The results show that the proposed adder has both lower power consumption and a lower power-delay product (PDP) value. The low power and low transistor count make the novel 8T full adder cell a candidate for power-efficient applications.

A low temperature drift curvature-compensated complementary metal oxide semiconductor (CMOS) bandgap reference is proposed. A dual-differential-pair amplifier was employed to add compensation with a high-order term of TlnT (T is the thermodynamic temperature) to the traditional 1st-order compensated bandgap. To reduce the offset of the amplifier and noise of the bandgap reference, input differential metal oxide semiconductor field-effect transistors (MOSFETs) of large size were used in the amplifier and to keep a low quiescent current, these MOSFETs all work in weak inversion. The voltage reference’s temperature curvature has been further corrected by trimming a switched resistor network. The circuit delivers an output voltage of 3 V with a low dropout regulator (LDO). The chip was fabricated in Taiwan Semiconductor Manufacturing Company (TSMC)’s 0.35-μm CMOS process, and the temperature coefficient (TC) was measured to be only 2.1×10^?6/°C over the temperature range of ?40–125 °C after trimming. The power supply rejection (PSR) was ?100 dB @ DC and the noise was 42 μV (rms) from 0.1 to 10 Hz.

Congestion in wireless sensor networks (WSNs) not only causes severe information loss but also leads to excessive energy consumption. To address this problem, a novel scheme for congestion avoidance, detection and alleviation (CADA) in WSNs is proposed in this paper. By exploiting data characteristics, a small number of representative nodes are chosen from those in the event area as data sources, so that the source traffic can be suppressed proactively to avoid potential congestion. Once congestion occurs inevitably due to traffic mergence, it will be detected in a timely way by the hotspot node based on a combination of buffer occupancy and channel utilization. Congestion is then alleviated reactively by either dynamic traffic multiplexing or source rate regulation in accordance with the specific hotspot scenarios. Extensive simulation results under typical congestion scenarios are presented to illuminate the distinguished performance of the proposed scheme.

We present an algorithm for image compression based on an image inpainting method. First the image regions that can be accurately recovered are located. Then, to reduce the data, information of such regions is removed. The remaining data besides essential details for recovering the removed regions are encoded to produce output data. At the decoder, an inpainting method is applied to retrieve removed regions using information extracted at the encoder. The image inpainting technique utilizes partial differential equations (PDEs) for recovering information. It is designed to achieve high performance in terms of image compression criteria. This algorithm was examined for various images. A high compression ratio of 1:40 was achieved at an acceptable quality. Experimental results showed attainable visible quality improvement at a high compression ratio compared with JPEG.

It is secure for customers to store and share their sensitive data in the cryptographic cloud storage. However, the revocation operation is a sure performance killer in the cryptographic access control system. To optimize the revocation procedure, we present a new efficient revocation scheme which is efficient, secure, and unassisted. In this scheme, the original data are first divided into a number of slices, and then published to the cloud storage. When a revocation occurs, the data owner needs only to retrieve one slice, and re-encrypt and re-publish it. Thus, the revocation process is accelerated by affecting only one slice instead of the whole data. We have applied the efficient revocation scheme to the ciphertext-policy attribute-based encryption (CP-ABE) based cryptographic cloud storage. The security analysis shows that our scheme is computationally secure. The theoretically evaluated and experimentally measured performance results show that the efficient revocation scheme can reduce the data owner’s workload if the revocation occurs frequently.

Data sparseness, the evident characteristic of short text, has always been regarded as the main cause of the low accuracy in the classification of short texts using statistical methods. Intensive research has been conducted in this area during the past decade. However, most researchers failed to notice that ignoring the semantic importance of certain feature terms might also contribute to low classification accuracy. In this paper we present a new method to tackle the problem by building a strong feature thesaurus (SFT) based on latent Dirichlet allocation (LDA) and information gain (IG) models. By giving larger weights to feature terms in SFT, the classification accuracy can be improved. Specifically, our method appeared to be more effective with more detailed classification. Experiments in two short text datasets demonstrate that our approach achieved improvement compared with the state-of-the-art methods including support vector machine (SVM) and Na?ve Bayes Multinomial.

We develop a numerical solution algorithm of the nonlinear potential flow equations with the nonlinear free surface boundary condition. A finite difference method with a predictor-corrector method is applied to solve the nonlinear potential flow equations in a two-dimensional (2D) tank. The irregular tank is mapped onto a fixed square domain with rectangular cells through a proper mapping function. A staggered mesh system is adopted in a 2D tank to capture the wave elevation of the transient fluid. The finite difference method with a predictor-corrector scheme is applied to discretize the nonlinear dynamic boundary condition and nonlinear kinematic boundary condition. We present the numerical results of wave elevations from small to large amplitude waves with free oscillation motion, and the numerical solutions of wave elevation with horizontal excited motion. The beating period and the nonlinear phenomenon are very clear. The numerical solutions agree well with the analytical solutions and previously published results.

Underwater gliders are efficient mobile sensor platforms that can be deployed for months at a time, traveling thousands of kilometers. Here, we describe our development of a coastal 200 m deep underwater glider, which can serve as an ocean observatory platform operating in the East China Sea. Our glider is developed based on dynamic model analysis: steady flight equilibrium analysis gives the varied range of moving mass location for pitch control and the varied vehicle volume for buoyancy control; a stability analysis is made to discuss the relationship between the stability of glider motion and the location of glider wings and rudder by root locus investigation of glider longitudinal- and lateral-directional dynamics, respectively. There is a tradeoff between glider motion stability and control authority according to the specific glider mission requirements. The theoretical analysis provides guidelines for vehicle design, based on which we present the development progress of the Zhejiang University (ZJU) glider. The mechanical, electrical, and software design of the glider is discussed in detail. The performances of glider key functional modules are validated by pressure tests individually; preliminary pool trials of the ZJU glider are also introduced, indicating that our glider functions well in water and can serve as a sensor platform for ocean sampling.

The widespread availability of portable computing power and inexpensive digital cameras are opening up new possibilities for retailers in some markets. One example is in optical shops, where a number of systems exist that facilitate eyeglasses selection. These systems are now more necessary as the market is saturated with an increasingly complex array of lenses, frames, coatings, tints, photochromic and polarizing treatments, etc. Research challenges encompass Computer Vision, Multimedia and Human-Computer Interaction. Cost factors are also of importance for widespread product acceptance. This paper describes a low-cost system that allows the user to visualize different glasses models in live video. The user can also move the glasses to adjust its position on the face. The system, which runs at 9.5 frames/s on general-purpose hardware, has a homeostatic module that keeps image parameters controlled. This is achieved by using a camera with motorized zoom, iris, white balance, etc. This feature can be specially useful in environments with changing illumination and shadows, like in an optical shop. The system also includes a face and eye detection module and a glasses management module.

An optimization method is proposed for designing an LED freeform lens which produces a uniform circular pattern with high energy efficiency. This method is composed of three main aspects: design of the initial guess, parameterization of the freeform surface, and construction of the merit function. The initial guess is created by solving an ordinary differential equation numerically. An approach of selecting optimization points is introduced for parameterization of the freeform surface. The merit function is constructed by use of the irradiance uniformity and the efficiency of the lens. Design examples are given, and the results show that the irradiance distribution is well controlled with a maximum uniformity (the relative standard deviation of irradiance, RSD) of 0.0122 and a maximum efficiency of 93.88%. This optimization method can be generalized to design freeform lenses with different lighting patterns or without rotational symmetry.

As a result of noise and intensity non-uniformity, automatic segmentation of brain tissue in magnetic resonance imaging (MRI) is a challenging task. In this study a novel brain MRI segmentation approach is presented which employs Dempster-Shafer theory (DST) to perform information fusion. In the proposed method, fuzzy c-mean (FCM) is applied to separate features and then the outputs of FCM are interpreted as basic belief structures. The salient aspect of this paper is the interpretation of each FCM output as a belief structure with particular focal elements. The results of the proposed method are evaluated using Dice similarity and Accuracy indices. Qualitative and quantitative comparisons show that our method performs better and is more robust than the existing method.

Inverse distance weighting (IDW) interpolation and viewshed are two popular algorithms for geospatial analysis. IDW interpolation assigns geographical values to unknown spatial points using values from a usually scattered set of known points, and viewshed identifies the cells in a spatial raster that can be seen by observers. Although the implementations of both algorithms are available for different scales of input data, the computation for a large-scale domain requires a mass amount of cycles, which limits their usage. Due to the growing popularity of the graphics processing unit (GPU) for general purpose applications, we aim to accelerate geospatial analysis via a GPU based parallel computing approach. In this paper, we propose a generic methodological framework for geospatial analysis based on GPU and its programming model Compute Unified Device Architecture (CUDA), and explore how to map the inherent parallelism degrees of IDW interpolation and viewshed to the framework, which gives rise to a high computational throughput. The CUDA-based implementations of IDW interpolation and viewshed indicate that the architecture of GPU is suitable for parallelizing the algorithms of geospatial analysis. Experimental results show that the CUDA-based implementations running on GPU can lead to dataset dependent speedups in the range of 13–33-fold for IDW interpolation and 28–925-fold for viewshed analysis. Their computation time can be reduced by an order of magnitude compared to classical sequential versions, without losing the accuracy of interpolation and visibility judgment.

Model classification is essential to the management and reuse of 3D CAD models. Manual model classification is laborious and error prone. At the same time, the automatic classification methods are scarce due to the intrinsic complexity of 3D CAD models. In this paper, we propose an automatic 3D CAD model classification approach based on deep neural networks. According to prior knowledge of the CAD domain, features are selected and extracted from 3D CAD models first, and then preprocessed as high dimensional input vectors for category recognition. By analogy with the thinking process of engineers, a deep neural network classifier for 3D CAD models is constructed with the aid of deep learning techniques. To obtain an optimal solution, multiple strategies are appropriately chosen and applied in the training phase, which makes our classifier achieve better performance. We demonstrate the efficiency and effectiveness of our approach through experiments on 3D CAD model datasets.

Baseline wander is a common noise in electrocardiogram (ECG) results. To effectively correct the baseline and to preserve more underlying components of an ECG signal, we propose a simple and novel filtering method based on a statistical weighted moving average filter. Supposed a and b are the minimum and maximum of all sample values within a moving window, respectively. First, the whole region [a, b] is divided into M equal sub-regions without overlap. Second, three sub-regions with the largest sample distribution probabilities are chosen (except M<3) and incorporated into one region, denoted as [a₀, b₀] for simplicity. Third, for every sample point in the moving window, its weight is set to 1 if its value falls in [a₀, b₀]; otherwise, its weight is 0. Last, all sample points with weight 1 are averaged to estimate the baseline. The algorithm was tested by simulated ECG signal and real ECG signal from www.physionet.org. The results showed that the proposed filter could more effectively extract baseline wander from ECG signal and affect the morphological feature of ECG signal considerably less than both the traditional moving average filter and wavelet package translation did.

The suspension system is a key element in motor vehicles. Advancements in electronics and microprocessor technology have led to the realization of mechatronic suspensions. Since its introduction in some production motorcars in the 1980s, it has remained an area which sees active research and development, and this will likely continue for many years to come. With the aim of identifying current trends and future focus areas, this paper presents a review on the state-of-the-art of mechatronic suspensions. First, some commonly used classifications of mechatronic suspensions are presented. This is followed by a discussion on some of the actuating mechanisms used to provide control action. A survey is then reported on the many types of control approaches, including look-ahead preview, predictive, fuzzy logic, proportional–integral–derivative (PID), optimal, robust, adaptive, robust adaptive, and switching control. In conclusion, hydraulic actuators are most commonly used, but they impose high power requirements, limiting practical realizations of active suspensions. Electromagnetic actuators are seen to hold the promise of lower power requirements, and rigorous research and development should be conducted to make them commercially usable. Current focus on control methods that are robust to suspension parameter variations also seems to produce limited performance improvements, and future control approaches should be adaptive to the changeable driving conditions.

It is a well-established fact that wireless sensor networks (WSNs) are very power constraint networks, but besides this, they are inherently more fault-prone than any other type of wireless network and their protocol design is very application specific. Major reasons for the faults are the unpredictable wireless communication channel, battery depletion, as well as fragility and mobility of the nodes. Furthermore, as traditional protocol design methods have proved inadequate, the cross-layer design (CLD) approach, which allows for interactions between different layers, providing more flexible and energy-efficient functionality, has emerged as a viable solution for WSNs. In this study we define a fault tolerance management module suitable to the requirements, limitations, and specifics of WSNs, encompassing methods for fault detection, fault prevention, fault management, and recovery. The suggested solution is in line with the CLD approach, which is an important factor in increasing the network performance. Through simulations the functionality of the network is evaluated, based on packet loss, delay, and energy consumption, and is compared with a similar solution not including fault management. The results achieved support the idea that the introduction of a unified approach to fault management improves the network performance as a whole.

We propose a mobility assisted spectrum aware routing (MASAR) protocol for cognitive radio ad hoc networks (CRAHNs), providing robustness to primary user activity and node mobility. This protocol allows nodes to collect spectrum information during a spectrum management interval followed by a transmission period. Cognitive users discover next hops based on the collected spectrum and mobility information. Using a beaconless mechanism, nodes obtain the mobility information and spectrum status of their neighbors. A geographical routing scheme is adopted to avoid performance degradation specially due to the mobility of the nodes and the activity of the primary users. Our scheme uses two approaches to find either short or stable routes. Since mobility metrics have a significant role in the selection of the next hop, both approaches use a reactive mobility update process assisted by mobility prediction to avoid location errors. MASAR protocol performance is investigated through simulations of different scenarios and compared with that of the most similar protocol, CAODV. The results indicate that MASAR can achieve significant reduction in control overhead as well as improved packet delivery in highly mobile networks.

Proportional integrator (PI) is always adopted in the resonant frequency servo loop in a resonator micro optic gyro (RMOG). The oscillation phenomenon is observed when adjusting the loop gain surpassing a threshold. This phenomenon limits system performance on step response speed and residual error. Based on the experiment system, a simulation model was set up. Further analysis shows that the threshold gain is related to the system loop filter setting and the loop delay. The traditional PI frequency servo loop technique in the RMOG system cannot keep up with the environment’s disturbance quickly enough, which leads to a large residual error. A compensating method is proposed to optimize the tracking performance, solve the oscillation problem, and speed up the system response. Simulation and experiment results show that the compensated system is superior in performance. It has less residual error in the stable state and is 10 times quicker than the uncompensated system on the step response.

Wireless sensor networks (WSNs) are vulnerable to security attacks due to their deployment and resource constraints. Considering that most large-scale WSNs follow a two-tiered architecture, we propose an efficient and denial-of-service (DoS)-resistant user authentication scheme for two-tiered WSNs. The proposed approach reduces the computational load, since it performs only simple operations, such as exclusive-OR and a one-way hash function. This feature is more suitable for the resource-limited sensor nodes and mobile devices. And it is unnecessary for master nodes to forward login request messages to the base station, or maintain a long user list. In addition, pseudonym identity is introduced to preserve user anonymity. Through clever design, our proposed scheme can prevent smart card breaches. Finally, security and performance analysis demonstrates the effectiveness and robustness of the proposed scheme.

Emotion recognition via facial expressions (ERFE) has attracted a great deal of interest with recent advances in artificial intelligence and pattern recognition. Most studies are based on 2D images, and their performance is usually computationally expensive. In this paper, we propose a real-time emotion recognition approach based on both 2D and 3D facial expression features captured by Kinect sensors. To capture the deformation of the 3D mesh during facial expression, we combine the features of animation units (AUs) and feature point positions (FPPs) tracked by Kinect. A fusion algorithm based on improved emotional profiles (IEPs) and maximum confidence is proposed to recognize emotions with these real-time facial expression features. Experiments on both an emotion dataset and a real-time video show the superior performance of our method.

This paper presents a hybrid brain-computer interface (BCI) control strategy, the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment. The hybrid control strategy utilizes P300 potential to control virtual devices and motor imagery related sensorimotor rhythms to navigate in the virtual world. The two electroencephalography (EEG) patterns serve as source signals for different control functions in their corresponding system states, and state switch is achieved in a sequential manner. In the current system, imagination of left/right hand movement was translated into turning left/right in the virtual apartment continuously, while P300 potentials were mapped to discrete virtual device control commands using a five-oddball paradigm. The combination of motor imagery and P300 patterns in one BCI system for virtual environment control was tested and the results were compared with those of a single motor imagery or P300-based BCI. Subjects obtained similar performances in the hybrid and single control tasks, which indicates the hybrid control strategy works well in the virtual environment.

A novel frequency-selective metamaterial with negative permittivity and permeability for improving directivity and gain of a helix antenna is presented in this paper. The proposed metamaterial is composed of two Z-shape resonators printed on opposite sides of a dielectric substrate. Two forms of multilayered cells are found to be suitable for antennas and waveguides applications. In addition, a new method of designing a metamaterial-based helix antenna is presented with high directivity and gain. A comparison on radiation properties is given between the conventional and the new metamaterial-based helix antennas. Two comparisons on radiation properties are performed: (1) the effect of proposed Z-structure on monopole, dipole, and helix antennas; (2) the effect of OE3, split-ring resonator (SRR), and proposed Z-structure unit cells on the performance of helix antennas. The results show improvement of parameters such as directivity, gain, and radiation power of the new metamaterial-based helix antenna. Therefore, the combination of Z-structure with the helix antenna shows the best performance.

Many important developments in video compression technologies have occurred during the past two decades. The block-based discrete cosine transform with motion compensation hybrid coding scheme has been widely employed by most available video coding standards, notably the ITU-T H.26xand ISO/IEC MPEG-xfamilies and video part of China audio video coding standard (AVS). The objective of this paper is to provide a review of the developments of the four basic building blocks of hybrid coding scheme, namely predictive coding, transform coding, quantization and entropy coding, and give theoretical analyses and summaries of the technological advancements. We further analyze the development trends and perspectives of video compression, highlighting problems and research directions.

In this paper, novel dual-band (DB) branch-line couplers (BLCs) employing a composite right/left handed transmission line (CRLH TL) and fractal geometry are presented for the first time. The CRLH TL, with specified characteristic impedance and phase shift, consists of lumped elements for the left handed (LH) part and fractal-shaped microstrip lines (MLs) for the right handed (RH) part, which can be designed separately. Two designed BLCs are involved in size reduction, one using a 3/2 fractal curve of first iteration, the other constructed based on a hybrid shape of fractal and meandered lines. A miniaturized principle for CRLH TL realization is derived and an exact design method for fractal implementation is developed. For verification, an example coupler was fabricated and measured. Consistent numerical and experimental results confirmed the design concept, showing that the BLCs obtain DB behavior centered at 0.9 GHz and 1.8 GHz respectively with good in-band performance, except for slightly larger coupled insertion loss for the hybrid-shaped BLC case. In addition, the proposed fractal- and hybrid-shaped BLCs obtained a 49.7% and 64.1% size reduction respectively relative to their conventional counterparts working in the lower band. The most important contributions of this article are the demonstration of compatibility between the fractal and CRLH TL techniques and the provision of an alternative approach and a new concept for designing devices.

This paper presents a robust lossless data hiding scheme. The original cover image can be recovered without any distortion after data extraction if the stego-image remains intact, and conversely, the hidden data can still be extracted correctly if the stego-image goes through JPEG compression to some extent. A cover image is divided into a number of non-overlapping blocks, and the arithmetic difference of each block is calculated. By shifting the arithmetic difference value, we can embed bits into the blocks. The shift quantity and shifting rule are fixed for all blocks, and reversibility is achieved. Furthermore, because the bit-0- and bit-1-zones are separated and the particularity of the arithmetic differences, minor changes applied to the stego-image generated by non-malicious attacks such as JPEG compression will not cause the bit-0- and bit-1-zones to overlap, and robustness is achieved. The new embedding mechanism can enhance embedding capacity and the addition of a threshold can make the algorithm more robust. Experimental results showed that, compared with previous schemes, the performance of the proposed scheme is significantly improved.

The Open Mobile Terminal Platform (OMTP) is a global forum made by telecommunications providers to promote user-oriented mobile services and data businesses. Devised by OMTP, BONDI is a browser-based application or a mobile Web run-time platform to help widgets make good use of the functions of mobile devices in a secure way. BONDI enables applications programmed with Web standard technologies such as HTML, JavaScript, CSS, and AJAX to reach the internal functions of mobile devices. Since BONDI, which is not just a simple network application, can reach the internal resources of devices in standard ways, it enables the application and widgets to be developed regardless of the operating system (OS) or platform. Web browser-based widgets are vulnerable to the network environment, and their execution speed can be slowed as the operations of the widgets or applications become heavy. Compared with the native widgets inside the device, however, those Web widgets will be continuously used thanks to the user-friendly simple interface and the faster speed in using Web resources. This study deals with a method to effectively operate and manage the resources of OMTP BONDI Web widget and provides improved results based on running performance evaluation experiments. The experiments were carried out to improve the entire operating time by enhancing the module-loading speed. In this regard, only indispensable modules were allowed to be loaded while the BONDI widget was underway. For this purpose, the widget resource list, which can make the operating speed of the BONDI widget faster, was redefined while a widget cache was employed. In addition, the widget box, a management tool for removed widgets, was devised to store temporarily idle widgets.

A low power high performance Delta-Sigma modulator for portable measurement applications is presented. To reduce power consumption while maintaining high performance, a fully feedforward architecture with a comprehensive system-level design is implemented. As a key building block, a novel power efficient current mirror operational transconductance amplifier (OTA) with a fast-settling less-error switched-capacitor common-mode feedback (SC CMFB) circuit is introduced, and the effects of both gain nonlinearity and 1/f noise of OTA are discussed. A new method to determine the voltage gain of an OTA is also proposed. The bottom terminal parasitic effect of poly-insulator-poly (PIP) capacitors is considered. About an extra 20% of capacitance is added to the total capacitance load. A power and area efficient resonator is adopted to realize a coefficient of 1/90 for 50% power and 75% area reduction compared with conventional designs. The chip is implemented in a low cost 0.35 μm complementary metal oxide semiconductor (CMOS) process. The total power consumption is 20 μW with a 1.5 V supply, and the measured dynamic range (DR) is 95 dB over a 1 kHz bandwidth. Experimental results show that a high figure-of-merit (FOM) is achieved for the designed modulator in comparison with those from the literature.

In video applications, real-time image scaling techniques are often required. In this paper, an efficient implementation of a scaling engine based on 4×4 cubic convolution is proposed. The cubic convolution has a better performance than other traditional interpolation kernels and can also be realized on hardware. The engine is designed to perform arbitrary scaling ratios with an image resolution smaller than 2560×1920 pixels and can scale up or down, in horizontal or vertical direction. It is composed of four functional units and five line buffers, which makes it more competitive than conventional architectures. A strict fixed-point strategy is applied to minimize the quantization errors of hardware realization. Experimental results show that the engine provides a better image quality and a comparatively lower hardware cost than reference implementations.

We present a novel control approach for trajectory tracking of an autonomous airship. First, the dynamics model and the trajectory control problem of an airship are formulated. Second, the sliding mode control law is designed to track a time-varying reference trajectory. To achieve better control performance, fuzzy adaptive sliding mode control is proposed in which the control gains are tuned according to fuzzy rules, and an adaptation law is used to guarantee that the control gains can compensate for model uncertainties of the airship. The stability of the closed-loop control system is proven via the Lyapunov theorem. Finally, simulation results illustrate the effectiveness and robustness of the proposed control scheme.