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Separable nonlinear models are very common in various research fields, such as machine learning and system identification. The variable projection (VP) approach is efficient for the optimization of such models. In this paper, we study various VP algorithms based on different matrix decompositions. Compared with the previous method, we use the analytical expression of the Jacobian matrix instead of finite differences. This improves the efficiency of the VP algorithms. In particular, based on the modified Gram–Schmidt (MGS) method, a more robust implementation of the VP algorithm is introduced for separable nonlinear least-squares problems. In numerical experiments, we compare the performance of five different implementations of the VP algorithm. Numerical results show the efficiency and robustness of the proposed MGS method-based VP algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new scheme based on neural networks for predicting the packet disordering and sliding mode control (SMC) to stabilize the nonlinear networked control systems (NCSs). It is assumed that the packet disordering is unknown in the NCSs. The stochastic configuration networks (SCNs), which randomly assign the input weights and biases and analytically evaluate the output weights, are designed to solve the problem of unknown packet disordering. A new SMC scheme is developed by integrating the SCNs algorithm to learn and control the system in advance. Specifically, a novel measurement of packet disordering is constructed for the quantization of the packet disordering. In addition, the newest signal principle leads to the existence of stochastic parameters, thereby resulting in a Markovian jumping system. The effectiveness of the proposed approach is verified by some simulation results. [ABSTRACT FROM AUTHOR]

This article proposes a robust method for the synthesis and design of optimal linear group delay filters with prescribed bandwidth and dispersion properties. The proposed filters are based on a cascade of second-order all-pass filter cells, for which all the parameters are optimized in order to reach the desired wideband linear group delay characteristic with positive slope. Compared to the previously published synthesis method, the proposed approach offers appropriate initialization enabling convergence and aiming for the optimal solution. The method’s convergence and robustness are proved through a 21-cells synthesis, which is the highest number of cascaded cells reported to date, due to the usual convergence problem. Then the electronic feasibility is illustrated through the measurement results of a lumped-elements linear group-delay filter prototype dimensioned with this method. The measured group delay presents a positive slope of 8 ns GHz−1 in the frequency range [100–725] MHz. It is, to the best of our knowledge, the highest positive slope reported to date with an electronic implementation (i.e except SAW and waveguide devices). [ABSTRACT FROM AUTHOR]

Feature selection (FS) is an important component of many pattern recognition tasks. In these tasks, one is often confronted with very high-dimensional data. FS algorithms are designed to identify the relevant feature subset from the original features, which can facilitate subsequent analysis, such as clustering and classification. Structured sparsity-inducing feature selection (SSFS) methods have been widely studied in the last few years, and a number of algorithms have been proposed. However, there is no comprehensive study concerning the connections between different SSFS methods, and how they have evolved. In this paper, we attempt to provide a survey on various SSFS methods, including their motivations and mathematical representations. We then explore the relationship among different formulations and propose a taxonomy to elucidate their evolution. We group the existing SSFS methods into two categories, i.e., vector-based feature selection (feature selection based on lasso) and matrix-based feature selection (feature selection based on lr,p -norm). Furthermore, FS has been combined with other machine learning algorithms for specific applications, such as multitask learning, multilabel learning, multiview learning, classification, and clustering. This paper not only compares the differences and commonalities of these methods based on regression and regularization strategies, but also provides useful guidelines to practitioners working in related fields to guide them how to do feature selection. [ABSTRACT FROM AUTHOR]

In this paper, we propose two new algorithms and their hardware implementations for the normal basis multiplication over GF( p^m ), where p \in \2, 3\ . In this case, the proposed multipliers are designed using serial and digit-serial hardware architectures. The normal basis multipliers over GF( 2^m ) and GF( 3^m ) are based on two proposed algorithms to compute the multiplication matrices Tk in order to speed-up the execution time and to reduce the area resources. It can be seen that the new hardware architecture for the NB multiplier over GF( 2^m ) has the best characteristics of area complexity presented by Reyhani [16] and time complexity presented by Azarderakhsh and Reyhani [31]. The proposed hardware architectures for the normal basis multipliers over GF(2163), GF(2233), GF(2283),GF(2409), GF(389) and GF(3233) were described in VHDL, and simulated and synthesized using Modelsim and Quartus Prime v16, respectively. [ABSTRACT FROM AUTHOR]

The paper studies digital redesign of linear time-invariant analog controllers under intermittent sampling. The sampling pattern is only assumed to be uniformly bounded, but otherwise irregular and unknown a priori. The contribution of the paper is twofold. First, it proposes a constructive algorithm to redesign any analog stabilizing controller so that the closed-loop stability is preserved. Second, it is shown that when applied to (sub) optimal \boldsymbolH^2 and \boldsymbolH^\infty controllers, the algorithm produces (sub) optimal sampled-data solutions under any a priori unknown sampling pattern. The proposed solutions are analytic, computationally simple, implementable, and transparent. Transparency pays off in showing the optimality, under a fixed sampling density, of uniform sampling for both performance measures studied. [ABSTRACT FROM AUTHOR]

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters. [ABSTRACT FROM PUBLISHER]

This paper proposes a fully-digital BIST architecture for the dynamic test of $\Sigma\Delta$ ADCs. The proposed BIST relies on generating a ternary stream that encodes a high-linearity analog sinusoidal and injecting it directly at the input of the $\Sigma\Delta$ modulator. Compared to the well-known bitstream, the use of three logic levels in the ternary stream reduces the quantization noise and, thereby, results in a test with a higher dynamic range that covers the full scale of the ADC. The output response is analyzed on-chip using a simplified version of the sine-wave fitting algorithm to compute the SNDR. A standard SPI bus provides digital external access to the embedded test instruments. The proposed BIST wrapper has been integrated into a 40 nm CMOS 18-bit stereo audio $\Sigma\Delta$ ADC IP core provided by ST Microelectronics. It incurs an overall area overhead of 7.1% and the total test time is 28 ms per channel. Experimental results on fabricated chips demonstrate an excellent correlation between the BIST and the standard functional specification test. [ABSTRACT FROM PUBLISHER]

Embedded DRAM (eDRAM) is an alternative technology that can replace the area and power consumed by SRAM cache memories. eDRAM consumes half the area and an order of magnitude less power than SRAM, but has the drawback of access blockage caused by its periodic data refreshing. This paper presents an opportunistic refreshing algorithm along with the appropriate memory architecture and skim control logic. This architecture takes advantage of the access idleness of the internal partitions of the memory and enables most of the refreshing operations to run concurrently with the ordinary R/W access. This eliminates the refreshing burden almost completely. The algorithm was simulated with industrial DSP access traces, and outperformed in a wide range of eDRAM technologies and internal memory architectures. [ABSTRACT FROM PUBLISHER]

Multiplication is an essential operation in cryptographic computations. One of the important finite fields for such computations is the binary extension field. High-throughput low-complexity multiplication architectures lead to more efficient cryptosystems. In this paper, a high-throughput low-complexity unified multiplier for triangular and dual bases is presented, and is referred to as basic architecture. This multiplier enjoys slightly simpler and more regular structure due to use of the mentioned bases. Additionally, structurally improved architectures have been proposed, which have smaller time complexity than basic ones. This is achieved by the use of parallel processing method. Experimental results show that the proposed basic multipliers implemented using trinomials and pentanomials have, respectively, 37.51% and 68.54% reduction in AT (area $\times$ time). Furthermore, the proposed structurally improved multipliers implemented using trinomials and pentanomials have 94.86% and 34.22% reduction in AT, respectively. [ABSTRACT FROM PUBLISHER]

This note presents further results based on the recent paper [J. Liang, H. Chen, and J. Lam, “An improved criterion for controllability of Boolean control networks,” IEEE Trans. Autom. Control, vol. 62, no. 11, pp. 6012–6018, Nov. 2017]. After some optimizations, the conventional method can be more efficient than the method used in the above paper. We also propose an improved method via combining the well known Tarjan's algorithm and depth-first search technique for the controllability analysis of Boolean control networks (BCNs). As a result, the computational complexity will not exceed $O(N^2)$ with $N=2^n$ , where $n$ is the number of state-variables in a BCN. [ABSTRACT FROM AUTHOR]

We discuss a stochastic algorithm to design tuning controllers for cryptographic True Random Number Generators, compliant to NIST recommendations, as an effective low-complexity solution to counteract entropy variability in integrated architectures implementing tunable entropy sources. Taking as a reference the min-entropy concept, we discussed the proposal from both the theoretical and hardware design points of view, validating claims with proofs and experiments. Depending on the target accuracy, the proposed architecture is scalable, and its profitable use in TRNG design strongly depends on the kind of core entropy sources taken into account. Furthermore, we show that the low-complexity entropy measurement techniques exploited in this proposal can be used to design a legitimate alternative to the Adaptive Proportion Health Test recommended in the NIST 800.90B publication. [ABSTRACT FROM AUTHOR]

Existing results on cooperative control of multiagent systems with unknown control directions require that the underlying topology is either fixed with a strongly connected graph or switching between different strongly connected graphs. Furthermore, in most cases the graph is assumed to be balanced. This paper proposes a new class of nonlinear proportional-integral (PI) based algorithms to relax these requirements and allow for unbalanced and switching topologies having a jointly strongly connected basis. This is made possible for single-integrator (SI) and double-integrator (DI) agents with nonidentical unknown control directions by a suitable selection of the distributed nonlinear PI functions. Moreover, as a special case, the proposed algorithms are applied to strongly connected and fixed graphs. Finally, simulation examples are given to show the validity of our theoretical results. [ABSTRACT FROM AUTHOR]

The leader-following consensus problem for linear discrete-time multiagent systems has been studied in the literature using $H_{\infty }$ Riccati inequality design and $H_{2}$ Riccati equation design methods, respectively. These two methods lead to a solvability condition in terms of an inequality that relies on a scaling gain and a fixed weighting vector called nominal weighting vector. In this paper, we further study this problem by proposing a more general class of distributed state feedback control laws, which not only depends on a scaling gain, but also a set of weighting vectors. We first establish a spectral property of a weighted graph matrix, which will be instrumental in solving the problem. Then, we present a solvability condition based on a modified algebraic Riccati equation, which is somehow more general than $H_{\infty }$ and $H_{2}$ Riccati methods, in that the solvability condition can be made satisfied by tuning the weighting vector. We show that our solvability condition is also necessary for single-input follower systems and is always satisfied for leader systems without exponentially growing modes. Moreover, in case the solvability condition is not satisfied under the nominal weighting vector, we show that our solvability condition can always be made satisfied by choosing a weighting vector other than the nominal one for multiagent systems over acyclic digraphs. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of identifiability of dynamical networks in the case where the vector of noises on the nodes does not have full rank. In the full-rank noise case, network identifiability is defined as the capability of uniquely identifying the transfer function matrices describing the network from informative data. This includes the noise model, which can be uniquely defined when the noise vector has full rank. When the noise vector has a singular spectrum, it admits an infinite number of different noise models and the definition of network identifiability must be adapted to demand that the correct noise spectrum be identified from informative data rather than a specific noise model. With this new definition, we show that a network with rank reduced noise is identifiable under the same conditions that apply to a network with full-rank noise. [ABSTRACT FROM AUTHOR]

In this paper, we consider using multiple noisy binary sensors to track a target that moves as a Markov Chain in a finite discrete environment, with symmetric probability of false alarm and missed detection. We study two policies. First, we show that the greedy policy, whereby $m$ sensors are placed at the $m$ most-likely target locations, is one-step optimal in that it maximizes the expected maximum a posteriori (MAP) estimate. Second, we show that a policy in which the $m$ sensors are placed in the second through $(m+1)$ st most likely target locations achieves equal or slightly worse expected MAP performance, but leads to significantly decreased variance on the MAP estimate. The result is proven for $m = 1$ , and Monte Carlo simulations give evidence for $m > 1$. Both policies are closed loop, index-based active sensing strategies that are computationally trivial to implement. Our approach focuses on one-step optimality because of the apparent intractability of computing an optimal policy via dynamic programming in belief space. However, Monte Carlo simulations suggest that both policies perform well over arbitrary horizons. [ABSTRACT FROM AUTHOR]

Models of bipedal walking are hybrid, with continuous-time dynamics representing the swing phases and discrete-time dynamics representing the impact events. The feedback controllers for these systems can be two-level, including both continuous- and discrete-time (event-based) actions. This paper presents a systematic framework to design decentralized event-based controllers for robust stabilization of hybrid periodic orbits against possible disturbances in discrete-time phases. The properties of the Poincaré return map are investigated to study the orbital input-to-state stability for the closed-loop system with respect to disturbance inputs. An optimization problem involving bilinear matrix inequalities is then presented to design $\mathcal {H}_{2}$ - and $\mathcal {H}_{\infty }$ -optimal decentralized event-based controllers. The power of the proposed framework is finally demonstrated through designing a set of decentralized two-level controllers for underactuated walking of a three-dimensional autonomous bipedal robot with nine degrees of freedom and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg. [ABSTRACT FROM AUTHOR]

Frequency-domain analysis of dynamic systems is important across many areas of engineering. However, while there are many analysis methods for linear systems, the problem is much less widely studied for nonlinear systems. Frequency-domain analysis of nonlinear systems using frequency response functions (FRFs) is particularly important to reveal resonances, super/subh-armonics, and energy transfer across frequencies. In this paper, the novel contribution is a time-domain, model-based approach to describing the uncertainty of nonlinear systems in the frequency domain. The method takes a nonlinear input–output time-domain model that has normally distributed parameters and propagates that uncertainty into the frequency domain using analytic expressions based on FRFs. We demonstrate the approach on both synthetic examples of nonlinear systems and a real-world nonlinear system identified from experimental data. We benchmark the proposed approach against a brute-force technique based on Monte Carlo sampling and show that there is good agreement between the methods. [ABSTRACT FROM AUTHOR]

This paper is concerned with the sliding mode control (SMC) problem for a class of uncertain discrete-time systems subject to unmatched external disturbances and communication constraints. In order to reduce the bandwidth usage between the controller and the actuators, the stochastic communication protocol (SCP) is utilized to determine which actuator should be given the access to the network at a certain instant. A key issue of the addressed problem is to design both the sliding surface and the sliding mode controller under the SCP scheduling. An updating rule on actuator input is first introduced and then a token-dependent SMC law is designed. Sufficient conditions are established for the resultant SMC systems such that not only the reachability with a sliding domain around the specified sliding surface is ensured, but also the stochastic stability with a prescribed $H_\infty$ performance level is guaranteed. Based on these conditions, a set of coupled matrix inequalities is given to acquire the token-dependent parameter matrices in the proposed SMC law. Finally, a numerical example is presented to illustrate the effectiveness of the proposed $H_\infty$ SMC scheme under the SCP scheduling. [ABSTRACT FROM AUTHOR]

The circle formation problem for a group of mobile agents with limited interaction range is addressed in this paper. Distributed switching control laws are developed for multiagent systems to form prescribed formations from any initial configurations on a circle. It is shown that the desired circle formation can be achieved if a parameter depending on the agents’ interaction range is larger than the prescribed angular distance from each agent to its immediate counterclockwise agent. When the agents’ interaction range is not sufficiently large, the formation error of each agent converges to a limit, and an upper bound and a lower bound on the limit value are provided. It is also shown that under the proposed control laws, the mobile agents’ spatial order on the circle is always preserved and thus collision between mobile agents is avoided. [ABSTRACT FROM AUTHOR]

Conjugate gradient (CG) methods are a class of important methods for solving linear equations and nonlinear optimization problems. In this paper, we propose a new stochastic CG algorithm with variance reduction and we prove its linear convergence with the Fletcher and Reeves method for strongly convex and smooth functions. We experimentally demonstrate that the CG with variance reduction algorithm converges faster than its counterparts for four learning models, which may be convex, nonconvex or nonsmooth. In addition, its area under the curve performance on six large-scale data sets is comparable to that of the LIBLINEAR solver for the $L2$ -regularized $L2$ -loss but with a significant improvement in computational efficiency. 1 CGVR algorithm is available on github: https://github.com/xbjin/cgvr [ABSTRACT FROM AUTHOR]

The distributed convex optimization problem is studied in this paper for any fixed and connected network with general constraints. To solve such an optimization problem, a new type of continuous-time distributed subgradient optimization algorithm is proposed based on the Karuch–Kuhn–Tucker condition. By using tools from nonsmooth analysis and set-valued function theory, it is proved that the distributed convex optimization problem is solved on a network of agents equipped with the designed algorithm. For the case that the objective function is convex but not strictly convex, it is proved that the states of the agents associated with optimal variables could converge to an optimal solution of the optimization problem. For the case that the objective function is strictly convex, it is further shown that the states of agents associated with optimal variables could converge to the unique optimal solution. Finally, some simulations are performed to illustrate the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper is concerned with the $H_2$ control of linear systems with multiple quantization channels. The quantization parameters of each channel are not required to be identical. The resultant mismatches are represented by polytopic uncertainties. A composite controller composed of linear and nonlinear parts is designed to meet the required $H_2$ performance and offset the quantization error. Resorting to a vertex separation technique and Finsler lemma instead of matrix inverse operations, new synthesis conditions for the desired linear part are derived in terms of linear matrix inequalities, which are further extended to treat systems with norm-bounded uncertainties. A comparison of conservativeness between the proposed methods and the existing ones is demonstrated by two numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach to state estimation of multiple state-space models. Unlike the traditional methods (including the interacting multiple-model algorithm) that approximate a Gaussian mixture distribution with a single Gaussian distribution, the proposed method approximates the joint probability density functions of the state and model identity through Bayesian inference. It is shown that the proposed method reduces the approximation error considerably, and improves estimation accuracy without increasing computational cost. Analysis of its specific features as well as a potential extension is also presented. Numerical examples with a practically oriented simulation are employed to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper considers a class of stochastic source-seeking problems to drive a mobile robot to the minimizer of a source signal. Our approach is first analyzed in an obstacle-free scenario, where measurements of the signal at the robot location and information of a contact sensor are required. We extend our results to environments with obstacles under mild assumptions on the step size. Our approach builds on the simultaneous perturbation stochastic approximation idea to obtain information of the signal field. We prove the practical convergence of the algorithms to a ball whose size depends on the step size that contains the location of the source. The novelty relies in that we consider nondifferentiable convex functions, a fixed step size, and the environment may contain obstacles. Our proof methods employ nonsmooth Lyapunov function theory, tools from convex analysis, and stochastic difference inclusions. Finally, we illustrate the applicability of the proposed algorithms in a two-dimensional scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we consider the composition optimization with two expected-value functions in the form of $({1}/{n})\sum _{i = 1}^{n} F_{i}\left({({1}/{m})\sum _{j = 1}^{m} G_{j}(x)}\right)+R(x)$ , which formulates many important problems in statistical learning and machine learning such as solving Bellman equations in reinforcement learning and nonlinear embedding. Full gradient- or classical stochastic gradient descent-based optimization algorithms are unsuitable or computationally expensive to solve this problem due to the inner expectation $({1}/{m})\sum _{j = 1}^{m} G_{j}(x)$. We propose a dualityfree-based stochastic composition method that combines the variance reduction methods to address the stochastic composition problem. We apply the stochastic variance reduction gradient- and stochastic average gradient algorithm-based methods to estimate the inner function and the dualityfree method to estimate the outer function. We prove the linear convergence rate not only for the convex composition problem but also for the case that the individual outer functions are nonconvex, while the objective function is strongly convex. We also provide the results of experiments that show the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

This paper considers the problem of a leader that seeks to optimally influence the opinions of agents in a directed network through connecting with a limited number of the agents (“direct followers”), possibly in the presence of a fixed competing leader. The settings involving a single leader and two competing leaders are unified into a general combinatoric optimization problem, for which two heuristic approaches are developed. The first approach is based on a convex relaxation scheme, possibly in combination with the $\ell _1$ -norm regularization technique, and the second is based on a greedy selection strategy. The main technical novelties of this work are in the establishment of supermodularity of the objective function and convexity of its continuous relaxation. The greedy approach is guaranteed to have a lower bound on the approximation ratio sharper than $(1-1/e)$ , while the convex approach can benefit from efficient (customized) numerical solvers to have practically comparable solutions possibly with faster computation times. The two approaches can be combined to provide improved results. In numerical examples, the approximation ratio can be made to reach ${\text{90}\%}$ or higher depending on the number of direct followers. [ABSTRACT FROM AUTHOR]

This paper is concerned with a general class of distributed constrained optimization problems over a multiagent network, where the global objective function is represented by the sum of all local objective functions. Each agent in the network only knows its own local objective function, and is restricted to a global nonempty closed convex set. We discuss the scenario where the communication of the whole multiagent network is expressed as a sequence of time-varying general unbalanced directed graphs. The directed graphs are required to be uniformly jointly strongly connected and the weight matrices are only row-stochastic. To collaboratively deal with the optimization problems, existing distributed methods mostly require the communication graph to be fixed or balanced, which is impractical and hardly inevitable. In contrast, we propose a new distributed projection subgradient algorithm which is applicable to the time-varying general unbalanced directed graphs and does not need each agent to know its in-neighbors’ out-degree. When the objective functions are convex and Lipschitz continuous, it is proved that the proposed algorithm exactly converges to the optimal solution. Simulation results on a numerical experiment are shown to substantiate feasibility of the proposed algorithm and correctness of the theoretical findings. [ABSTRACT FROM AUTHOR]

This paper presents a discrete-time distributed and noncooperative routing algorithm, which is proved, via Lyapunov arguments, to asymptotically converge to a specific equilibrium condition among the traffic flows over the network paths, known as Wardrop equilibrium. This convergence result improves the discrete-time algorithms in the literature, which achieve approximate convergence to the Wardrop equilibrium. Numerical simulations show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a bounded confidence gossip algorithm for describing the process of opinion formation over a communication network. Each agent in the network keeps a time-varying opinion vector (or state), which represents its opinion about a set of matters. A common confidence threshold is set for all of the agents. The states of agents in the network will be updated time by time according to an iterative procedure: At each time, 1) one agent is chosen randomly, then it chooses one of its neighbors on the communication graph to contact with; 2) they exchange their states; and 3) if they have different states and the distance between their states is strictly smaller than the confidence threshold, they update their states as the average of the two. This algorithm converges almost surely to some equilibrium point such that any two adjacent agents either have the same state or have distinct states whose distance is no less than the confidence threshold. This is called the constant confidence threshold algorithm. An increasing confidence threshold algorithm, which repeats the constant confidence threshold algorithm several times with increasing confidence threshold, is also proposed. The algorithm is also convergent almost surely to some equilibrium point. Applicability of the method to clustering problems is shown through numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, we study the interacting multiple model (IMM) estimator for networked control systems with packet loss but without packet acknowledgment (ACK). The ACK is a signal sent by the actuator to inform the estimator of whether control packets are lost or not. A system with ACK is usually called a transmission control protocol (TCP) like system; otherwise, it is called a user datagram protocol (UDP) like system. We show that the stability of the IMM estimator for UDP-like systems is determined by the observation packet arrival rate (p.a.r.) and is independent of the control p.a.r. and control inputs. The IMM estimator is stable if the observation p.a.r. is greater than a critical value. We show that this critical value is the same as the critical value for the stability of the optimal estimator for its corresponding TCP-like system. If control inputs eventually tend to zero, the error covariance of the IMM estimator converges to that of the optimal estimator for TCP-like systems. We characterize the impact of the control/observation p.a.r. and the control input on estimation performance. Finally, we prove that the average estimation performance of the IMM estimator approximates that of the optimal estimator within a finite bound, and is superior to that of the linear minimum mean square error estimator. [ABSTRACT FROM AUTHOR]

A reduction of a source distribution is a collection of smaller sized distributions that are collectively equivalent to the source distribution with respect to the property of decomposability. That is, an arbitrary language is decomposable with respect to the source distribution if and only if it is decomposable with respect to each smaller sized distribution (in the reduction). The notion of reduction of distributions has previously been proposed to improve the complexity of decomposability verification. In this paper, we address the problem of generating (optimal) reductions of distributions automatically. A (partial) solution to this problem is provided, which consists of an incremental algorithm for the production of candidate reductions and a reduction validation procedure. In the incremental production stage, backtracking is applied whenever a candidate reduction that cannot be validated is produced. A strengthened substitution-based proof technique is used for reduction validation, while a fixed template of candidate counter examples is used for reduction refutation; put together, they constitute our (partial) solution to the reduction verification problem. In addition, we show that a recursive approach for the generation of (small) reductions is easily supported. [ABSTRACT FROM AUTHOR]

This paper proposes distributed algorithms to solve robust convex optimization (RCO) when the constraints are affected by nonlinear uncertainty. We adopt a scenario approach by randomly sampling the uncertainty set. To facilitate the computational task, instead of using a single centralized processor to obtain a “global solution” of the scenario problem (SP), we resort to multiple interconnected processors that are distributed among different nodes of a network to simultaneously solve the SP. Then, we propose a primal-dual subgradient algorithm and a random projection algorithm to distributedly solve the SP over undirected and directed graphs, respectively. Both algorithms are given in an explicit recursive form with simple iterations, which are especially suited for processors with limited computational capability. We show that, if the underlying graph is strongly connected, each node asymptotically computes a common optimal solution to the SP with a convergence rate $O(1/(\sum _{t=1}^k\zeta ^t))$ , where $\lbrace \zeta ^t\rbrace$ is a sequence of appropriately decreasing stepsizes. That is, the RCO is effectively solved in a distributed way. The relations with the existing literature on robust convex programs are thoroughly discussed and an example of robust system identification is included to validate the effectiveness of our distributed algorithms. [ABSTRACT FROM AUTHOR]

Sensors are activated/deactivated according to a sensor activation policy (SAP), which is a decision function whose domain is the language of the system. An SAP is minimal for diagnosability if it is at the point where the system is diagnosable, but any additional deactivation prevents correct system diagnosis. Given that the system is diagnosable when all sensors are always activated, we find in this paper that minimal SAP for diagnosability does not always exist. We therefore develop algorithms to compute a minimal SAP under which the system is pseudodiagnosable. Adhering to such a policy extends the window of opportunity for proper system diagnosis indefinitely, but with the compromise that later policy amendments are allowed. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed model predictive control (MPC) approach for a family of discrete-time linear systems with local (uncoupled) and global (coupled) constraints. The proposed approach is based on the dual problem of an overall MPC optimization problem involving all systems, which is then solved distributively using a modified distributed Nesterov-accelerated-gradient algorithm. To further reduce the computational requirement, this approach allows for early termination of the distributed gradient algorithm. This is made possible via a consensus algorithm that determines the satisfaction of the termination condition and by appropriate tightening of the coupled constraints. Under reasonable assumptions, the approach is able to produce a suboptimal solution as long as the network of the systems is connected while ensuring recursive feasibility and exponential stability of the closed-loop system. The performance of the proposed approach is demonstrated by a numerical example. [ABSTRACT FROM AUTHOR]

Finite mixtures of skew distributions provide a flexible tool for modeling heterogeneous data with asymmetric distributional features. However, parameter estimation via the Expectation–Maximization (EM) algorithm can become very time consuming due to the complicated expressions involved in the E-step that are numerically expensive to evaluate. While parallelizing the EM algorithm can offer considerable speedup in time performance, current implementations focus almost exclusively on distributed platforms. In this paper, we consider instead the most typical operating environment for users of mixture models—a standalone multicore machine and the R programming environment. We develop a block implementation of the EM algorithm that facilitates the calculations on the E- and M-steps to be spread across a number of threads. We focus on the fitting of finite mixtures of multivariate skew normal and skew $t$ distributions, and show that both the E- and M-steps in the EM algorithm can be modified to allow the data to be split into blocks. Our approach is easy to implement and provides immediate benefits to users of multicore machines. Experiments were conducted on two real data sets to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Robotic control in a continuous action space has long been a challenging topic. This is especially true when controlling robots to solve compound tasks, as both basic skills and compound skills need to be learned. In this paper, we propose a hierarchical deep reinforcement learning algorithm to learn basic skills and compound skills simultaneously. In the proposed algorithm, compound skills and basic skills are learned by two levels of hierarchy. In the first level of hierarchy, each basic skill is handled by its own actor, overseen by a shared basic critic. Then, in the second level of hierarchy, compound skills are learned by a meta critic by reusing basic skills. The proposed algorithm was evaluated on a Pioneer 3AT robot in three different navigation scenarios with fully observable tasks. The simulations were built in Gazebo 2 in a robot operating system Indigo environment. The results show that the proposed algorithm can learn both high performance basic skills and compound skills through the same learning process. The compound skills learned outperform those learned by a discrete action space deep reinforcement learning algorithm. [ABSTRACT FROM AUTHOR]

The coverage control problem of a circle by a mobile sensor network affected by bounded measurement errors is addressed in this paper. The mobile sensors are assumed to have different maximum velocities. Our main objective is to study the effect of measurement errors on the coverage cost function, which is defined as the largest time for the sensor network to arrive at any point on the circle. Coverage control laws with input constraints are developed based on the noisy position measurements among sensors, under which the mobile sensor network converges to a neighborhood of the optimal configuration. Furthermore, the conditions for the sensors to preserve their spatial order on the circle are derived in order to avoid collision among sensors. An upper bound on the limit of the coverage cost function as time goes to infinity is also given. [ABSTRACT FROM AUTHOR]

Several optimization-based control design techniques can be cast in the form of parametric optimization problems. The multiparametric quadratic programming (mpQP) represents a popular class often related to the control of constrained linear systems. The complete solution to mpQP takes the form of explicit feedback functions with a piecewise affine structure, valid in polyhedral partitions of the feasible parameter space known as critical regions. The recently proposed combinatorial approach for solving mpQP has shown better efficiency than geometric approaches in finding the complete solution to problems with high dimensions of the parameter vectors. The drawback of this method, on the other hand, is that it tends to become very slow as the number of constraints increases in the problem. This paper presents an alternative method for enumerating all optimal active sets in an mpQP based on theoretical properties of adjacent critical regions and their corresponding optimal active sets. Consequently, it results in excluding a noticeable number of feasible but not optimal candidate active sets from investigation. Therefore, the number of linear programs that should be solved decreases noticeably and the algorithm becomes faster. Simulation results confirm the reliability of the suggested method in finding the complete solution to the mpQPs while decreasing the computational time compared favorably with the best alternative approaches. [ABSTRACT FROM AUTHOR]

The mixture of Gaussian processes (GPs) is capable of learning any general stochastic process based on a given set of (sample) curves for the regression and prediction problems. However, it is ineffective for curve clustering and prediction, when the sample curves are derived from different stochastic processes as independent sources linearly mixed together. In this paper, we propose a two-layer mixture model of GP functional regressions (GPFRs) to describe such a mixture of general stochastic processes or independent sources, especially for curve clustering and prediction. Specifically, in the lower layer, the mixture of GPFRs (MGPFRs) is developed for a cluster (or class) of curves within the input space. In the higher layer, the mixture of MGPFRs is further established to divide the curves into clusters according to its components in the output space. For the parameter estimation of the two-layer mixture of GPFRs, we develop a Monte Carlo EM algorithm based on a Monte Carlo Markov chain (MCMC) method, in short, the MCMC EM algorithm. We validate the hierarchical mixture of GPFRs and MCMC EM algorithm using synthetic and real-world data sets. Our results show that our new model outperforms the conventional mixture models in curve clustering and prediction. [ABSTRACT FROM AUTHOR]

When dealing with kernel methods, one has to decide which kernel and which values for the hyperparameters to use. Resampling techniques can address this issue but these procedures are time-consuming. This problem is particularly challenging when dealing with structured data, in particular with graphs, since several kernels for graph data have been proposed in literature, but no clear relationship among them in terms of learning properties is defined. In these cases, exhaustive search seems to be the only reasonable approach. Recently, the global Rademacher complexity (RC) and local Rademacher complexity (LRC), two powerful measures of the complexity of a hypothesis space, have shown to be suited for studying kernels properties. In particular, the LRC is able to bound the generalization error of an hypothesis chosen in a space by disregarding those ones which will not be taken into account by any learning procedure because of their high error. In this paper, we show a new approach to efficiently bound the RC of the space induced by a kernel, since its exact computation is an NP-Hard problem. Then we show for the first time that RC can be used to estimate the accuracy and expressivity of different graph kernels under different parameter configurations. The authors’ claims are supported by experimental results on several real-world graph data sets. [ABSTRACT FROM AUTHOR]

For a system matrix $M$ , this paper explores the smallest (Frobenius) norm additive structured perturbation $\delta M$ for which a system property $\mathcal {P}$ (e.g., controllability, observability, stability, etc.) fails to hold, i.e., $\delta M$ is the structured perturbation with smallest Frobenius norm such that there exists a property matrix $R\in \mathcal {P}$ for which $M{-}\delta M{-}R$ drops rank. The Frobenius norm is used because of its direct dependence on the magnitude of each entry in the perturbation matrix. Necessary conditions on a locally minimum norm structured rank-reducing perturbation $\delta M$ and associated property matrix $R$ are set forth and proven. An iterative algorithm is also set forth that computes a locally minimum norm structured perturbation and associated property matrix satisfying the necessary conditions. Algorithm convergence is proven using a discrete Lyapunov function. [ABSTRACT FROM AUTHOR]

In this paper, we propose a distance-based formation control strategy for a group of mobile agents to achieve almost global convergence to a target formation shape provided that the formation is represented by a complete graph, and each agent is governed by a single-integrator model. The fundamental idea of achieving almost global convergence is to use a virtual formation of which the dimension is augmented with some virtual coordinates. We define a cost function associated with the virtual formation and apply the gradient-descent algorithm to the cost function so that the function has a global minimum at the target formation shape. We show that all agents finally achieve the target formation shape for almost all initial conditions under the proposed control law. [ABSTRACT FROM AUTHOR]

Consider a connected network of agents endowed with local cost functions representing private objectives. Agents seek to find an agreement on some minimizer of the aggregate cost, by means of repeated communications between neighbors. Consensus on the average over the network, usually addressed by gossip algorithms, is a special instance of this problem, corresponding to quadratic private objectives. Consensus on the median, or more generally, consensus on a given quantile, is also a special instance of this problem. In this paper we show that optimizing the aggregate cost function regularized by a total variation (TV) term has appealing properties. First, it can be done very naturally in a distributed way, yielding algorithms that are efficient on numerical simulations. Secondly, the optimum for the regularized cost is shown to be also the optimum for the initial aggregate cost function under assumptions that are simple to state. Finally, these algorithms are robust to unreliable agents that keep injecting some false value in the network. This is remarkable enough, and is not the case, for instance, of gossip algorithms that are entirely ruled by unreliable agents as detailed in the paper. [ABSTRACT FROM PUBLISHER]

A disturbance observer-based control (DOBC) approach is proposed in this paper for control design and qualitative robustness analysis of PWM-based DC-DC buck power converters. A disturbance compensation gain is constructed for the composite control algorithm to cancel the unmatched uncertainties in the output voltage channel. Rigorous criterion is provided to show the quantitative robustness between controller parameters and circuit uncertainties. The DOBC approach delivers not only a promising disturbance rejection performance but also a superior property of tracking performance recovery. The feasibility and efficiency of the proposed approach is validated by experimental results based on LabVIEW real-time control test setup. [ABSTRACT FROM AUTHOR]

This paper presents a 14.9-bit 3.57-MS/s algorithmic ADC that uses iterative gain enhancement, a technique that uses multiple clock phases to increase the effective op-amp gain in a switched-capacitor circuit. Using an op-amp that gives only 30-dB loop gain in a feedback circuit without gain enhancement, application of the iterative gain enhancement technique boosts the loop gain to 81 dB. The algorithmic ADC uses a capacitor sharing and scaling technique, which saves power and reduces errors. The ADC has an active area of 0.75 \textmm^2 in 0.25- \mu\textm CMOS and dissipates 16.2 mW. Iterative gain enhancement increases the SNDR from 44.6 dB to 78.5 dB and the SFDR from 45.9 dB to 96.2 dB. Reducing the number of gain-enhancement iterations for the LSBs increases the conversion rate from 3.57 MS/s to 4.65 MS/s with only minor performance degradation. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via \ell 1 constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme. [ABSTRACT FROM AUTHOR]

This paper addresses the robust gradient learning (RGL) problem. Gradient learning models aim at learning the gradient vector of some target functions in supervised learning problems, which can be further used to applications, such as variable selection, coordinate covariance estimation, and supervised dimension reduction. However, existing GL models are not robust to outliers or heavy-tailed noise. This paper provides an RGL framework to address this problem in both regression and classification. This is achieved by introducing a robust regression loss function and proposing a robust classification loss. Moreover, our RGL algorithm works in an instance-based kernelized dictionary instead of some fixed reproducing kernel Hilbert space, which may provide more flexibility. To solve the proposed nonconvex model, a simple computational algorithm based on gradient descent is provided and the convergence of the proposed method is also analyzed. We then apply the proposed RGL model to applications, such as nonlinear variable selection and coordinate covariance estimation. The efficiency of our proposed model is verified on both synthetic and real data sets. [ABSTRACT FROM AUTHOR]

Graph-based heuristic for Multiple Constant Multiplication (MCM) has been an area of intensive research over the last two decades. To overcome the pitfalls of hidden partial sums and local minima inherent in trivial digraph formulation, this paper introduces an elementary digraph-to-hypergraph transformation to enable a new minimum arborescence (MA) formulation of MCM problem. Our proposed algorithm iteratively solves the MA problem from a strongly connected graph formed by adding new vertex from the least cost surviving hypergraph in the preceding iteration until no new vertex is found. As the visible and hidden edges of an elementary hypergraph (EHG) are interchangeable by swapping their values encoded in its hyperedge, previously removed vertex can be revived if it is a predecessor of the least cost surviving hyperedge as the hypergraph evolves. A by-product of this iterative algorithm is that its underlying EHG minimization can also be used for quality improvement of existing MCM solutions generated by other design algorithms or for engineering changes. Experimental results on benchmark filters show that our proposed algorithm can reduce their multiplier block areas by 2.9% to 14.9% and power consumptions by 2.8% to 20.1% over other advanced algorithms. A further $\sim$20% of area can be saved from the less area-efficient solutions of other design algorithms by the proposed EHG reoptimization. [ABSTRACT FROM AUTHOR]

A multi-purpose mechanical platform to track moving objects in three-dimensional space has been developed. It is composed of one main microcontroller board that processes all system data, two cameras, three motors, and one secondary microcontroller board to position a platform with three degrees of freedom. The system computes the optical flow and moves the cameras accordingly, tracking motion within the visual scene. The platform operates autonomously. To the best of our knowledge, there are no similar systems reported with low-resolution image sensors and low-cost microcontrollers. Existing solutions rely on personal computers and advanced FPGAs to process image data. This article concludes that the optical flow operation is efficient even using an image sensor with very low resolution. Thus, the system complexity and image data processing are alleviated significantly. The platform can be easily adapted to different application scenarios by adding new peripherals, sensors, or image processing algorithms. A detailed description of the system design and experimental results are provided. [ABSTRACT FROM AUTHOR]