The size and depth of layered Boolean circuits

We consider the relationship between size and depth for layered Boolean circuits and synchronous circuits. We show that every layered Boolean circuit of size s can be simulated by a layered Boolean circuit of depth . For synchronous circuits of size s, we obtain simulations of depth . The best known result so far was by Paterson and Valiant (1976) [17], and Dymond and Tompa (1985) [6], which holds for general Boolean circuits and states that , where C(f) and D(f) are the minimum size and depth, respectively, of Boolean circuits computing f. The proof of our main result uses an adaptive strategy based on the two-person pebble game introduced by Dymond and Tompa (1985) [6]. Improving any of our results by polylog factors would immediately improve the bounds for general circuits.     

Generalized nonlinear discriminant analysis and its small sample size problems

This paper develops a generalized nonlinear discriminant analysis (GNDA) method and deals with its small sample size (SSS) problems. GNDA is a nonlinear extension of linear discriminant analysis (LDA), while kernel Fisher discriminant analysis (KFDA) can be regarded as a special case of GNDA. In LDA, an under sample problem or a small sample size problem occurs when the sample size is less than the sample dimensionality, which will result in the singularity of the within-class scatter matrix. Due to a high-dimensional nonlinear mapping in GNDA, small sample size problems arise rather frequently. To tackle this issue, this research presents five different schemes for GNDA to solve the SSS problems. Experimental results on real-world data sets show that these schemes for GNDA are very effective in tackling small sample size problems.     

A nonlinear lower bound for constant depth arithmetical circuits via the discrete uncertainty principle

We prove a superlinear lower bound on the size of a bounded depth bilinear arithmetical circuit computing cyclic convolution. Our proof uses the strengthening of the Donoho–Stark uncertainty principle [D.L. Donoho, P.B. Stark, Uncertainty principles and signal recovery, SIAM Journal of Applied Mathematics 49 (1989) 906–931] given by Tao [T. Tao, An uncertainty principle for cyclic groups of prime order, Mathematical Research Letters 12 (2005) 121–127], and a combinatorial lemma by Raz and Shpilka [R. Raz, A. Shpilka, Lower bounds for matrix product, in arbitrary circuits with bounded gates, SIAM Journal of Computing 32 (2003) 488–513]. This combination and an observation on ranks of circulant matrices, which we use to give a much shorter proof of the Donoho–Stark principle, may have other applications.     

Examples when nonlinear model predictive control is nonrobust

We consider nominal robustness of model predictive control for discrete-time nonlinear systems. We show, by examples, that when the optimization problem involves state constraints, or terminal constraints coupled with short optimization horizons, the asymptotic stability of the closed loop may have absolutely no robustness. That is to say, it is possible for arbitrarily small disturbances to keep the closed loop strictly inside the interior of the feasibility region of the optimization problem and, at the same time, far from the desired set point. This phenomenon does not occur when using model predictive control for linear systems with convex constraint sets. We emphasize that a necessary condition for the absence of nominal robustness in nonlinear model predictive control is that the value function and feedback law are discontinuous at some point(s) in the interior of the feasibility region.     

Weighted average extended FIR filter bank to manage the horizon size in nonlinear FIR filtering

In this paper, we propose a novel approach to manage the horizon size in nonlinear finite impulse response (FIR) filtering. The proposed approach is to perform state estimation through a bank of FIR filters called a weighted average extended FIR filter bank (WAEFFB). In the WAEFFB, the state estimate is obtained by weighting the average of multiple estimates from a bank of extended FIR filters that uses different horizon sizes. The horizon sizes used for the WAEFFB are adjusted constantly by maximizing the likelihood function. We show through simulations that the WAEFFB yields better results than the conventional approach that uses a constant (i.e., fixed) horizon size.     

基于NDO的ROV变深自适应终端滑模控制器设计

针对ROV的深度控制问题, 提出基于非线性干扰观测器的自适应终端滑模控制方法. 详细叙述了控制器的设计过程, 并利用Lyapunov 稳定性判据, 验证了存在模型参数不确定性和外干扰时, 系统的全局渐近稳定性和跟踪误差的收敛性. 仿真实验表明, 所提出的控制器不仅能够很好地估计并克服外干扰和模型不确定性等因素, 具有很好的鲁棒性能, 而且还可以实现在任意规定时刻变深运动的快速收敛.

     

Conditions under which suboptimal nonlinear MPC is inherently robust

We address the inherent robustness properties of nonlinear systems controlled by suboptimal model predictive control (MPC), i.e., when a suboptimal solution of the (generally nonconvex) optimization problem, rather than an element of the optimal solution set, is used for the control. The suboptimal control law is then a set-valued map, and consequently, the closed-loop system is described by a difference inclusion. Under mild assumptions on the system and cost functions, we establish nominal exponential stability of the equilibrium, and with a continuity assumption on the feasible input set, we prove robust exponential stability with respect to small, but otherwise arbitrary, additive process disturbances and state measurement/estimation errors. These results are obtained by showing that the suboptimal cost is a continuous exponential Lyapunov function for an appropriately augmented closed-loop system, written as a difference inclusion, and that recursive feasibility is implied by such (nominal) exponential cost decay. These novel robustness properties for suboptimal MPC are inherited also by optimal nonlinear MPC. We conclude the paper by showing that, in the absence of state constraints, we can replace the terminal constraint with an appropriate terminal cost, and the robustness properties are established on a set that approaches the nominal feasibility set for small disturbances. The somewhat surprising and satisfying conclusion of this study is that suboptimal MPC has the same inherent robustness properties as optimal MPC.     

Stochastic gradient-adaptive complex-valued nonlinear neural adaptive filters with a gradient-adaptive step size.

A class of variable step-size learning algorithms for complex-valued nonlinear adaptive finite impulse response (FIR) filters is proposed. To achieve this, first a general complex-valued nonlinear gradient-descent (CNGD) algorithm with a fully complex nonlinear activation function is derived. To improve the convergence and robustness of CNGD, we further introduce a gradient-adaptive step size to give a class of variable step-size CNGD (VSCNGD) algorithms. The analysis and simulations show the proposed class of algorithms exhibiting fast convergence and being able to track nonlinear and nonstationary complex-valued signals. To support the derivation, an analysis of stability and computational complexity of the proposed algorithms is provided. Simulations on colored, nonlinear, and real-world complex-valued signals support the analysis.     

On the optimum load step size selection scheme for nonlinear finite element stress analysis

A user-fault-proof algorithm has been proposed for the selection of load increments in a nonlinear finite element stress analysis, which will result in minimum deviations from the given material's stress vs strain curves. The user needs only to specify the starting stress level for the automatic selection and the allowable deviation for the first increment. The selection procedure is fully automatic and hence is particularly suitable for less experienced users.     

Neural networks and nonlinear regression modelling and control of depth of anaesthesia for spontaneously breathing and ventilated patients

Various attempts have been made to control the depth of anaesthesia by observing different variables. In some studies, depth of anaesthesia has been correlated with inferential parameters and the control has been made through these inferred parameters. No single system has been reported which provides a fully developed architecture to control the depth of anaesthesia. This study is concerned with the development of controllers and patient models via Artificial Neural Networks and regression analysis. Two types of data sets were used for the training and development of models and controllers. The first set was for spontaneously breathing and the second set for ventilated patients. All of the controllers and patient models gave satisfactory results when tested individually. Later these two sets of controllers and patient models were studied in closed-loop modes. The robustness to the sensitivity of the regression patient model was also investigated. Various tests were performed with these closed-loop situations. Results and performance of these tests are discussed in the paper.     

Optimization of depth modeling modes in 3D-HEVC depth intra coding

In the development of 3D video extension of high efficiency video coding (HEVC) standard, known as 3D-HEVC, depth modeling modes (DMMs) are introduced in depth intra coding to represent object edges in depth maps. With the DMMs, a depth block is approximated by partitioning the block into two non-rectangular regions using Wedgelet or Contour partition, where each region is represented by a constant value referred to as a constant partition value (CPV). To predict the CPV more accurately and efficiently, we develop three approaches in this study. First, a better CPV predictor may be obtained by simply extending the actual depth map boundary, which can also simplify the CPV prediction by removing comparisons and average operations. Second, we propose to choose an optimal combination of delta CPVs in terms of view synthesis optimization at the encoder by checking more candidates. Finally, zero residual coding is suggested for DMMs coding units in the rate-distortion optimization loop. Experimental results demonstrate that about 0.2 and 0.1 % Bjøntegaard delta rate saving can be achieved on average for synthesized views with less complexity, under the all-intra and random access configurations, respectively.     

On depth in EDTOL languages

Languages of the form L₁={aⁿ:n?1}, L₂={(abⁿ)^m: n,m?1}, L₃={(a(bcⁿ) ^m)^k: n,m,k?1}, etc. differ in what may be called ‘depth’. It turns out that this depth is closely related to the structure of finite state automata associated with EDTOL systems generating these languages. For a given system the corresponding automaton is defined, and the relevant characteristics of its structure are examined.     

Minimizing the size of an identifying or locating-dominating code in a graph is NP-hard

Let G=(V,E) be an undirected graph and C a subset of vertices. If the sets B_r(v)∩C, vV (respectively, vVC), are all nonempty and different, where B_r(v) denotes the set of all points within distance r from v, we call C an r-identifying code (respectively, an r-locating-dominating code). We prove that, given a graph G and an integer k, the decision problem of the existence of an r-identifying code, or of an r-locating-dominating code, of size at most k in G, is NP-complete for any r.     

Robust sampled-data fuzzy control of nonlinear systems with parametric uncertainties: Its application to depth control of autonomous underwater vehicles

This paper presents a new direct discrete-time design methodology of a robust sampled-data fuzzy controller for a class of nonlinear system with parametric uncertainties that is exactly represented by Takagi-Sugeno (T-S) fuzzy model. Based on an exact discrete-time fuzzy model in an integral form, sufficient conditions for a robust asymptotic stabilization of the nonlinear system are investigated in the discrete-time Lyapunov sense. It is shown that the resulting sampled-data controller indeed robustly asymptotically stabilizes the nonlinear plant. To illustrate the effectiveness of the proposed methodology, an example, a sampled-data depth control of autonomous underwater vehicles (AUVs) is provided.     

Robust state feedback control of LTV systems: nonlinear is betterthan linear

Examples of linear control systems with fast time-varying uncertain coefficients are given, which can be stabilized by a nonlinear memoryless state feedback, but cannot be stabilized by a linear time-invariant dynamic state feedback. By means of one of these examples the authors show that the closed loop quadratic stability margin may be infinitely smaller than the actual stability margin     

Consideration of illumination effects and optimization of window size for accurate calculation of depth map for 3D shape recovery

Obtaining an accurate and precise depth map is the ultimate goal for 3D shape recovery. For depth map estimation, one of the most vital parts is the initial selection of the focus measure and processing the images with the selected focus measure. Although, many focus measures have been proposed in the literature but not much attention has been paid to the factors affecting those focus measures as well as the manner the images are processed with those focus measures. In this paper, for accurate calculation of depth map, we consider the effects of illumination on the depth map as well as the selection of the window size for application of the focus measures. The resulting depth map can further be used in techniques and algorithms leading to recovery of three-dimensional structure of the object which is required in many high-level vision applications. It is shown that the illumination effects can directly result in incorrect estimation of depth map if proper window size is not selected during focus measure computation. Further, it is shown that the images need some kind of pre-processing to enhance the dark regions and shadows in the image. For this purpose, an adaptive enhancement algorithm is proposed for pre-processing. In this paper, we prove that without such pre-processing for image enhancement and without the use of proper window size for the estimation of depth maps, it is not possible to obtain the accurate depth map.