Search

Reliability is a vital issue in the area of communication. In this paper, we particularly investigate the reliability issue for molecular communication based on drift diffusion (MCD2). Since molecules easily accumulate in the channel to produce strong internal symbol interference (ISI), the receiver nanomachine will generate high bit error rate (BER) for the process of decode information. Based on this problem, on the premise of considering channel diffusion noise and ISI noise, the expression of channel BER is deduced to analyze reliability. Then a ratio detection algorithm (RDA) is proposed to reduce BER to improve the reliability performance that enables the receiver nanomachine to adapt the channel condition. Furthermore, an expression of signal to interference plus noise ratio is defined in numerical simulation to verify our goal with different parameters, as well as with the adoption of RDA. The results indicate that the performance of RDA in reducing BER works well in general case in improving reliability performance for MCD2. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the direction of arrival (DOA) estimation problem of noncircular signals for coprime linear array (CLA). From the perspective of the CLA as extracted from a filled uniform linear array (ULA), a noncircular root-MUSIC algorithm is proposed to estimate the DOA which can avoid the spectral peak search and lower the computational complexity. Due to the noncircular characteristic, the proposed algorithm enables to resolve more sources than sensors. Meanwhile, the proposed algorithm has better angle estimation performance than some conventional DOA estimation algorithms. Numerical simulation results illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper introduces a new chaotic system model that could generate multi-direction multi-scroll (MDMS) chaotic attractors. The fundamental dynamics characteristics of this model are investigated, and the feasibility of the proposed method is confirmed by numerical simulation based on MATLAB. Additionally, the new chaotic system is implemented in FPGA-based platform. Implementation results are the same in MATLAB and FPGA, which indicates that they are suitable for practical applications. To investigate the cryptographic application of the new chaotic system, the chaotic sequences generated by the proposed system are used to encrypt images. The key space of the algorithm as well as key sensitivity, plain image sensitivity, plaintext histogram, correlation and information entropy are simulated and analyzed. In contrast to several encryption schemes, the proposed algorithm is very sensitive to plaintext and the key. [ABSTRACT FROM AUTHOR]

A new meta-algorithm for estimating the conditional average treatment effects is pro-posed in the paper. The basic idea behind the algorithm is to consider a new dataset consisting of feature vectors produced by means of concatenation of examples from control and treatment groups, which are close to each other. Outcomes of new data are defined as the difference between outcomes of the corresponding examples comprising new feature vectors. The second idea is based on the assumption that the number of controls is rather large and the control outcome function is precisely determined. This assumption allows us to augment treatments by generating feature vectors which are closed to available treatments. The outcome regression function constructed on the augmented set of concatenated feature vectors can be viewed as an estimator of the conditional average treatment effects. A simple modification of the Co-learner based on the random subspace method or the feature bagging is also proposed. Various numerical simulation experiments illustrate the proposed algorithm and show its outperformance in comparison with the well-known T-learner and X-learner for several types of the control and treatment outcome functions. [ABSTRACT FROM AUTHOR]

Two-dimensional direction-of-arrival (2D-DOA) estimation for uniform rectangular array (URA) is a canonical problem with numerous applications, e.g., wireless communications, sonar and radar systems. The conventional 2D-DOA estimators usually are derived with the assumption of ideal arrays. However, in practice, the arrays may not be well calibrated and suffer from unknown mutual coupling. Using the conventional estimators may lead to low accuracy estimation and high computational complexity in the condition of large number of array elements. In this paper, a novel real-valued parallel factor (PARAFAC) decomposition algorithm is proposed to tackle this problem. The proposed algorithm has better angle estimation performance than the multiple signal classification (MUSIC) algorithm, estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm and conventional PARAFAC algorithm. But it has lower complexity than MUSIC algorithm. Moreover, the proposed algorithm can obtain automatically paired 2D-DOA estimation, and it is suitable to coherent or closely spaced signals and can eliminate the mutual coupling. Simulation results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

There are different kinds of multiple classifiers in complex recognition systems in pursuit of better recognition capabilities. To exploit the classifiers' potential as individual ones sufficiently and enable them to work cooperatively for the best classification results, they need to be considered as a whole and be dynamically managed according to the changing recognition occasions. In this paper, we present the conception of distributed Multiple Classifiers Management (MCM) and a self-adaptive recursive MCM model based on Mixture-of-Experts (ME). A control subsystem is consisted in the model, which allows the classification progress to be controlled by the systems' priori information when necessary. The model adjusts its parameters dynamically according to the current recognition state and gives the recognition results by combining the current individual classifiers' results with the previous combination result under priori information's control. An algorithm based on one step error correction is presented to acquire the model's parameters dynamically. It takes the previous times' ensemble classification results as true and corrects the current weights of the classifiers. At last, an experiment on the recognition of space objects is simulated. The experiment results show that the MCM model in this paper is effective for complex recognition system containing heterogeneous classifiers on improving the recognition rate and robustness. [ABSTRACT FROM AUTHOR]

Graphical avatars have gained popularity in many application domains such as three-dimensional (3D) animation movies and animated simulations for product design. However, the methods to edit avatars' behaviors in the 3D graphical environment remained to be a challenging research topic. Since the hand-crafted methods are time-consuming and inefficient, the automatic actions of the avatars are required. To achieve the autonomous behaviors of the avatars, artificial intelligence should be used in this research area. In this paper, we present a novel approach to construct a system of automatic avatars in the 3D graphical environments based on the machine learning techniques. Specific framework is created for controlling the behaviors of avatars, such as classifying the difference among the environments and using hierarchical structure to describe these actions. Because of the requirement of simulating the interactions between avatars and environments after the classification of the environment, Reinforcement Learning is used to compute the policy to control the avatar intelligently in the 3D environment for the solution of the problem of different situations. Thus, our approach has solved problems such as where the levels of the missions will be defined and how the learning algorithm will be used to control the avatars. In this paper, our method to achieve these goals will be presented. The main contributions of this paper are presenting a hierarchical structure to control avatars automatically, developing a method for avatars to recognize environment and presenting an approach for making the policy of avatars' actions intelligently. [ABSTRACT FROM AUTHOR]

In this paper, we propose a dual particle swarm optimization (PSO) algorithm for parameter identification of chaotic systems. We also consider altering the search range of individual particles adaptively according to their objective function value. We consider both noiseless and noisy channels between the original system and the estimation system. Finally, we verify the effectiveness of the proposed dual PSO method by estimating the parameters of the Lorenz system using two different data acquisition schemes. Simulation results show that the proposed method always outperforms the traditional PSO algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes to use a state feedback method to control the Hopf bifurcation for a novel congestion control model, i.e. the exponential random early detection (RED) algorithm with a single link and a single source. The gain parameter of the congestion control model is chosen as the bifurcation parameter. The analysis shows that in the absence of the state feedback controller, the model loses stability via the Hopf bifurcation early, and can maintain a stationary sending rate only in a certain domain of the gain parameter. When applying the state feedback controller to the model, the onset of the undesirable Hopf bifurcation is postponed. Thus, the stability domain is extended, and the model possesses a stable sending rate in a larger parameter range. Furthermore, explicit formulae to determine the properties of the Hopf bifurcation are obtained. Numerical simulations are given to justify the validity of the state feedback controller in bifurcation control. [ABSTRACT FROM AUTHOR]

This paper links the polarization-sensitive-array parameter estimation problem to the quadrilinear model. Exploiting this link, it derives a blind joint angle, frequency and polarization estimation algorithm. The simulation results reveal that the proposed algorithm has better angle, frequency and polarization estimation performance than ESPRIT. This algorithm relies on a fundamental result of the uniqueness of low-rank four-way data decomposition. Furthermore, the proposed algorithm does not require pairing among multiple parameters. Simulation results illustrate performance of this algorithm. [ABSTRACT FROM AUTHOR]

A preprocessing stage in every speech/music applications including audio/speech separation, speech/speaker recognition and audio/genre transcription task is inevitable. The importance of such pre-processing stage is originated from the requisite of determining each frame of the given signal is belonged to which classes, namely: speech only, music only or speech/music mixture. Such classification can significantly decrease the computational burden due to exhaustive search commonly introduced as a problem in model-based speech recognition or separation as well as music transcription scenarios. In this paper, we present a new method to separate mixed type audio frames based on support vector machine (SVM) and neural network. We present a feature type selection algorithm which seeks for the most appropriate features to discriminate possible classes (hypotheses) on the mixed signal. We also propose features based on eigen-decomposition on the mixed frame. Experimental results demonstrate that the proposed features together with the selected audio classifiers achieve acceptable classification results. From the experimental results, it is observed that the proposed system outperforms other classification systems including k-nearest neighbor (k-NN) and multi-layer perceptron (MLP). [ABSTRACT FROM AUTHOR]

Applying gear shifting algorithms to the implementation of Phase Locked Loops (PLLs) can significantly improve their performances. However, the behaviors of gear shifting digital PLLs (GSDPLLs) have not been fully studied due to the existence of newly adaptive control parameters. These parameters play a very important role in the design of GSDPLLs. In this paper, various nonlinear behaviors of GSDPLLs including the steady state periodic behaviors, divergent behaviors and chaotic behaviors, are studied. In particular, the effects of the initial conditions of GSDPLLs on their dynamical behaviors are investigated. The obtained results are useful for the design of GSDPLLs. Numerical simulation results are presented for illustrations. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive fuzzy tracking control is presented for a class of SISO nonlinear strict feedback systems with unknown time delays. The proposed algorithm does not use the backstepping scheme rather it converts the strict-feedback time-delayed system to the normal form. The Mamdani-type fuzzy system is employed to approximate on-line the lumped uncertain system nonlinearity. The developed controller guarantees uniform ultimate boundedness of all signals in the closed-loop system. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector needed to be updated on-line. As a result, the proposed control algorithm is considerably simpler than the previous ones based on backstepping. The Lyapunov stability of the fuzzy system parameters and the filtered tracking error is employed to guarantee semiglobal uniform boundedness for the closed loop system. Simulation results are presented to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we derive an algorithm to solve the linear quadratic (LQ) optimal regulator problems. The new approach is based on efficient Legendre and Chebyshev formulae at the Chebyshev-Gauss-Lobatto points. The technique enjoys advantages of both the Legendre and Chebyshev approximations near the end points. To show the numerical behavior of the proposed method, the simulation results of an example are presented. [ABSTRACT FROM AUTHOR]

In this paper, we describe a new algorithm for radar detection based on the Hough transform which employs the slope-intercept parameter space. Unlike the conventional Hough transform, we shift the parameter space cells to perform the transform. The computation burden is reduced. Another advantage is that those straight lines whose intercept are bigger than the radar maximum range can also be detected. In addition, we also investigate the performance of the algorithm we present and show its efficiency with some simulations. [ABSTRACT FROM AUTHOR]

In this paper, two learning automata based adaptive limited fractional guard channel algorithms for cellular mobile networks are proposed. These algorithms try to minimize the blocking probability of new calls subject to the constraint on the dropping probability of the handoff calls. To evaluate the proposed algorithms, computer simulations are conducted. The simulation results show that the performance of the proposed algorithms are close to the performance of the limited fractional guard channel algorithm for which prior knowledge about traffic parameters are needed. The simulation results also show that the proposed algorithms outperforms the recently introduced dynamic guard channel algorithms. [ABSTRACT FROM AUTHOR]

As minimally invasive surgical techniques become widely known to patients, medical training systems based on virtual simulation are highly desired. These systems help surgeon trainees to acquire, practice and evaluate their surgical skills. A key component in a virtual training system is to simulate the dynamics that occur in surgical procedures. Tissue cutting, as a common phenomenon during surgery, has attracted many research efforts in computer simulation. In this paper, we propose an approach to endoscopic image cutting simulation which is based on both mass-spring model and Computational Geometry. In the cutting simulation model, the springs to be cut off are imagined into line segments. In the calculation of the elastic force on mass points, we have found that whether some adjacent springs of a mass point to be eliminated or not during a cutting is critical. If a spring intersects the cutting plane, we set the elastic force of this spring to zero. We adopted properties of cross product and related algorithms (the rapid exclusion test, the crossover test) in Computational Geometry to determine the springs that are intersected with the cutting plane. And then, we utilized the bilinear interpolation and OpenGL techniques to render the cutting procedure of the soft tissue. The experimental results show that our cutting simulation is effective and practical. [ABSTRACT FROM AUTHOR]

In neuroscience, phase is assumed to contain more important information about the neural activity than amplitude. However, the most exploited feature in electroencephalogram (EEG) based brain computer interface (BCI) is the amplitude change, phase has been largely ignored, and only phase locking values (PLV) has been introduced in EEG classification recently. In this paper, we define phase interval value (PIV) to explore the phase information of EEG from a new perspective and propose a computational model based on the ordered Parallel Factors (PARAFAC) algorithm to extract feature from multi-way PIV data for single trial EEG classification. Application to the motor imagery task demonstrates that PIV is quite effective for EEG classification, providing significant and discriminative features in spatial and spectral dimension. PIV might become an important new tool in the analysis of EEG phase characteristic, and has the great potential use in BCI. [ABSTRACT FROM AUTHOR]

Time delay introduced by sampling and calculation exists between the perturbation and the duty ratio updating in the digital control loop. This time delay degrades the transient performance of digital control switching power supplies. By overcoming the sampling and calculation time effect and to calibrate the ripple that occurs during switching cycles, new control algorithm is proposed in this paper to improve the transient performance of switching power supplies by eliminating the time delay in the control loop. Simulation results show the improvement of load transient performance by the proposed algorithms. Experimental system is also set up to verify the analysis and computer simulation results. [ABSTRACT FROM AUTHOR]

Random number generators are widely used in different applications. However, it is difficult to obtain a good random number generator in low precision and resource constrained system, such as an eight-bit micro-controller system which is still commonly used in industrial and consumer markets. This paper provides a practical solution for this problem based on chaotic systems. By the use of a modified Chua's circuit, it is demonstrated that the sampled state, after post-processing by a high-dimensional chaotic map, can be used as a random source even in an eight-bit environment. The randomness of the generated sequence is testified and confirmed by different statistical tests and the up-to-date statistical suite. [ABSTRACT FROM AUTHOR]

Reconstruction of freeform surfaces from scattered coordinate data is a difficult problem encountered in many surface fitting and geometric modeling applications. Conventional tessellation and parametric surface fitting techniques are limited because they require prior knowledge about the connectivity between the sampled points. The method of surface reconstruction described in this paper exploits the learning capability of a self-organizing feature map (SOFM) to adaptively fit a deformable sphere to the unorganized 3D coordinate data. The learning algorithm automatically establishes the connectivity between the measured points by iteratively changing the topological relationships within the map. By incorporating additional constraints during the learning process it is possible to have the deformable map follow the shape of objects with surface holes and cavities. Several examples of freeform surfaces with varying levels of complexity are discussed in order to demonstrate the performance of the algorithm. [ABSTRACT FROM AUTHOR]

Microsurgery is a highly complex surgical procedure on small body parts performed by a dedicated surgical team. An operating microscope is typically used to obtain a precise view of the soft tissues. The complexity of the microsurgical procedure makes it a suitable application of virtual/augmented reality technology for training purpose. In this paper, we present an overview of our simulator and then describe in details the visualization work that reconstructs the magnified view of the operating area from medical images. The visualization component is based entirely on our newly proposed dynamic linear level octree (DLLO)-based volume rendering methods. In comparison with the conventional ray casting algorithms, our computational method achieves fast rendering speed, reduces hardware requirement and combines surface-volume rendering for realistic and interactive PC-based medical simulations. The new algorithms are suitable for rendering dynamic medical data which are increasingly popular clinically. [ABSTRACT FROM AUTHOR]