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Accurate modeling of magnetic tunnel junction (MTJ) is critical for design of memories such as spin-transfer-torque magnetoresistive random access memory (STT-MRAM) and spin logic circuits such as spin flip flops. This paper reviews several static and dynamic models for the MTJ and compares them for their capabilities and limitations. Furthermore, a Verilog-A model is developed to predict dynamic characteristics of the MTJ. These models are used in simulating a prototype circuit to illustrate their strengths and weaknesses. [ABSTRACT FROM AUTHOR]

This paper introduces a new and unified approach for designing desirable dissipative hyperchaotic systems. Based on the anti-control principle of continuous-time systems, a nominal system of n ~~(n \geq 5) independent first-order linear differential equations are coupled through all state variables, making the controlled system be in a closed-loop cascade-coupling form, where each equation contains only two state variables therefore the system is quite simple. Based on this setting, a simple model for dissipative hyperchaotic systems is constructed, with an adjustable parameter which can ensure the dissipation of the system. In the closed-loop cascade-coupling form, it is shown that all the eigenvalues are symmetrically distributed in a circumferential manner. Consequently, a universal law is derived on the relationship of the number of positive Lyapunov exponents and the number of positive real parts of its Jacobian eigenvalues. For the above-mentioned simple model, the number of positive Lyapunov exponents for any n-dimensional dissipative hyperchaotic system is given by N= round((n-1)/2), n \geq 5. Therefore, in theory, the system can generate any desired number of positive Lyapunov exponents as long as the dimension of the system is sufficiently high. Thus, the proposed method provides a new approach for purposefully constructing desirable dissipative hyperchaotic systems. Finally, two examples are given to demonstrate the feasibility of the proposed design method. [ABSTRACT FROM AUTHOR]

Problems involving discrete Markov Chains are solved mathematically using matrix methods. Recently, several research groups have demonstrated that matrix-vector multiplication can be performed analytically in a single time step with an electronic circuit that incorporates an open-loop memristor crossbar that is effectively a resistive random-access memory. Ielmini and co-workers have taken this a step further by demonstrating that linear algebraic systems can also be solved in a single time step using similar hardware with feedback. These two approaches can both be applied to Markov chains, in the first case using matrix-vector multiplication to compute successive updates to a discrete Markov process and in the second directly calculating the stationary distribution by solving a constrained eigenvector problem. We present circuit models for open-loop and feedback configurations, and perform detailed analyses that include memristor programming errors, thermal noise sources and element nonidealities in realistic circuit simulations to determine both the precision and accuracy of the analog solutions. We provide mathematical tools to formally describe the trade-offs in the circuit model between power consumption and the magnitude of errors. We compare the two approaches by analyzing Markov chains that lead to two different types of matrices, essentially random and ill-conditioned, and observe that ill-conditioned matrices suffer from significantly larger errors. We compare our analog results to those from digital computations and find a significant power efficiency advantage for the crossbar approach for similar precision results. [ABSTRACT FROM AUTHOR]

Memristive technology has experienced explosive growth in the last decade, with multiple device structures being developed for a wide range of applications. However, transitioning the technology from the lab into the marketplace requires the development of an accessible and user-friendly design flow, supported by an industry-grade toolchain. In this work, we demonstrate the behaviour of our in-house fabricated custom memristor model and its integration into the Cadence Electronic Design Automation (EDA) tools for verification. Various input stimuli were given to record the memristive device characteristics both at the device level as well as the schematic level for verification of the memristor model. This design flow from device to industrial level EDA tools is the first step before the model can be used and integrated with Complementary Metal-Oxide Semiconductor (CMOS) in applications for hybrid memristor/CMOS system design. [ABSTRACT FROM AUTHOR]

We present and experimentally validate two minimal compact memristive models for spiking neuronal signal generation using commercially available low-cost components. The first neuron model is called the Memristive Integrate-and-Fire (MIF) model, for neuronal signaling with two voltage levels: the spike-peak, and the rest-potential. The second model MIF2 is also presented, which promotes local adaptation by accounting for a third refractory voltage level during hyperpolarization. We show both compact models are minimal in terms of the number of circuit elements and integration area. Using the MIF and MIF2 models, we postulate the design of a memristive solid-state brain with an estimation of its surface area and power consumption. Analytical projections show that a memristive solid-state brain could be realized within (i) the surface area of the median human brain, 2,400cm2, (ii) the same volume of the median human brain, and (iii) a total power budget of approximately 20 W using a 3.5 nm technology. Distinct from the past decade of memristive neuron literature, our benchmarks are attained using generic commercially available memristors that are reproducible using off-the-shelf components. We expect this work can promote more experimental demonstrations of memristive circuits that do not rely on prohibitively expensive fabrication processes. [ABSTRACT FROM AUTHOR]

Recently, an in-memory analog circuit based on crosspoint memristor arrays was reported, which enables solving linear regression problems in one step and can be used to train many other machine learning algorithms. To explore its potential for computing accelerator applications, it is of fundamental importance to improve the computing speed of the circuit, i.e., the circuit response towards correct outputs. In this work, we comprehensively studied the transfer function of this circuit, resulting in a quadratic eigenvalue problem that describes the distribution of poles. The minimal real part of non-zero eigenvalues defines the dominant pole, which in turn dominates the response time. Simulations for multiple linear regression solutions with different datasets evidence that, the computing time does not necessarily increase with problem size. The dominant pole is related to parameters in the circuit, including feedback conductance, and gain bandwidth products of operational amplifiers. By optimizing these parameters synergistically, the dominant pole shifts to higher frequencies and the computing speed is consequently optimized. Our results provide a guideline for design and optimization of in-memory machine learning accelerators with analog memristor arrays. Also, issues including power consumption, impact of noise and variation of sources and memristors are investigated to offer a comprehensive evaluation of the circuit performance. [ABSTRACT FROM AUTHOR]

The increasing demand for data-intensive computing applications, such as artificial intelligence (AI) and more specifically machine learning (ML), raises the need for novel computing hardware architectures capable of massive parallelism in performing core algebraic operations. Among the new paradigms, in-memory computing (IMC) with analogue devices is attracting significant interest for its large-scale integration potential, together with unrivaled speed and energy performance. Here, we present a fully-analogue, universal primitive capable of executing linear algebra operations such as regression, generalized least-square minimization and linear system solution with and without preconditioning. We study the impact of the main circuit parameters on accuracy and bandwidth with analytical closed-form expressions and SPICE simulations. Scaling challenges due to parasitic resistance/capacitance and their impact on key parameters such as bandwidth and accuracy are discussed. Finally, a comparison with existing solvers belonging to the same IMC framework is made to assess advantages and disadvantages of the proposed circuit. [ABSTRACT FROM AUTHOR]

With the development of the Internet of things (IoT), sensors are being mounted on various objects. This trend has prompted demand for low-power, high-performance information processing on the edge side. Here, an Ising model architecture that can efficiently solve optimization problems would be an efficient processing solution for edges. In this study, we implemented a 512- spin fully connected Ising model on an LSI chip fabricated in a 28-nm CMOS process. The fully connected Ising model was implemented in the chip by using pseudo-annealing (PA), which is easier to implement than simulated annealing (SA). In addition, we devised a multi-spin-thread structure, concurrent update structure, and a folded interaction placement for accuracy, speed, and compactness. Because eight spin threads are implemented, the calculation throughput could be increased by a factor of eight in comparison with a single spin-thread implementation. Moreover, as a measure of solution accuracy, the average route length of a 22-city traveling salesman problem was reduced by 19% and the standard deviation (SD) was reduced by 46%. Likewise, the average cut value of a 512- node max-cut problem was increased by 1.6% and SD was decreased by 60%. The concurrent update almost doubled the calculation speed in comparison with the case of no concurrent update. In addition, the circuit area was reduced by about 38% as a result of the folded interaction placement. The time required to obtain a solution was 128 ms. The chip at annealing processing (main processing) had a power consumption of 12 mW at 1 MHz. [ABSTRACT FROM AUTHOR]

The convergence of the waveform relaxation (WR) method is demonstrated for a class of circuits: Chains of identical and symmetrical passive subcircuits. The WR algorithm uses resistive coupling to implement the iteration. Every part is modeled as a symmetric and reciprocal linear two-port network. The iteration matrices of the WR operator are constructed for the Gauss-Jacobi and Gauss-Seidel relaxations in the Fourier domain. An upperbound estimate of the spectral radius of the WR operator is presented. It demonstrates the convergence of the method independently of the number of cascaded parts in the chain and the coupling resistance. [ABSTRACT FROM AUTHOR]

The processing capabilities of biological vision systems are still vastly superior to artificial vision, even though this has been an active area of research for over half a century. Current artificial vision techniques integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power, and performance. A key aspect to modeling the human visual system is the ability to accurately model the behavior and computation within the retina. In particular, we focus on modeling the retinal ganglion cells (RGCs) as they convey the accumulated data of real world images as action potentials onto the visual cortex via the optic nerve. Computational models that approximate the processing that occurs within RGCs can be derived by quantitatively fitting the sets of physiological data using an input–output analysis where the input is a known stimulus and the output is neuronal recordings. Currently, these input–output responses are modeled using computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this paper, we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model retinal ganglion cell behavior, and are a viable alternative to traditional linear–nonlinear approaches. [ABSTRACT FROM PUBLISHER]

In this paper, we present complexity certification results for a distributed augmented Lagrangian (AL) algorithm used to solve convex optimization problems involving globally coupled linear constraints. Our method relies on the accelerated distributed AL (ADAL) algorithm, which can handle the coupled linear constraints in a distributed manner based on local estimates of the AL. We show that the theoretical complexity of ADAL to reach an $\epsilon$-optimal solution both in terms of suboptimality and infeasibility is O(\frac{1}{\epsilon }) iterations. Moreover, we provide a valid upper bound for the optimal dual multiplier, which enables us to explicitly specify these complexity bounds. We also show how to choose the step-size parameter to minimize the bounds on the convergence rates. Finally, we discuss a motivating example, a model predictive control problem, involving a finite number of subsystems, which interact with each other via a general network. [ABSTRACT FROM PUBLISHER]

Gap metric and stability margin have been proven as important model-based analysis tools for linear time-invariant (LTI) systems. Due to the need for an accurate model, both the gap metric and the optimal stability margin are mostly used for offline analysis. In online analysis, accurate models are rarely available, whereas measurement data could be easily obtained from the systems under consideration. Therefore, in this paper, an approach toward the calculation of the gap metric and the optimal stability margin based on the available measurement data of LTI systems is proposed. A data-driven realization of the so-called stable kernel and stable image representation serves as the foundation of this framework. [ABSTRACT FROM PUBLISHER]

This paper deals with the design, performance, and robustness analysis of nonlinear attitude complementary filters on \textSO(3). We derive explicit time solutions of the attitude estimation error dynamics of the filters (in the disturbance-free case) and analyze their performance and robustness with respect to measurement disturbances. The stability and performance properties of the filters can be easily deduced from the obtained closed-form solutions. A new class of attitude complementary filters on \textSO(3) with state-dependent gains is proposed and shown to exhibit improved performance and robustness properties compared to the fixed-gain traditional complementary filter on \textSO(3). [ABSTRACT FROM PUBLISHER]

This paper revisits the problem of estimating the pose (position and orientation) of a body in 3-D space with respect to (w.r.t.) an inertial frame by using 1) the knowledge of source points positions in the inertial frame, 2) the measurements of the body angular velocity expressed in the body's frame, 3) the measurements of the body translational velocity, either in the body frame or in the inertial frame, and 4) source points bearing measurements performed in the body frame. An important difference with the much studied static Perspective-n-Point problem addressed with iterative algorithms is that body motion is not only allowed but also used as a source of information that improves the estimation possibilities. With respect to the probabilistic framework commonly used in other studies that develop extended Kalman filter solutions, the deterministic approach here adopted is better suited to point out the observability conditions, that involve the number and disposition of the source points in combination with body motion characteristics, under which the proposed observers ensure robust estimation of the body pose. These observers are here named Riccati observers because of the instrumental role played by the continuous Riccati equation in the design of the observers and in the Lyapunov stability and convergence analysis that we develop independently of the well-known complementary (either deterministic or probabilistic) optimality properties associated with Kalman filtering. The set of these observers also encompasses extended Kalman filter solutions. Another contribution of this study is to show the importance of using body motion to improve the observers performance and, when this is possible, of measuring the body translational velocity in the inertial frame rather than in the body frame to allow for the body pose estimation from a single source point taken as the origin of the inertial frame. This latter possibility finds a natural extension in the Simultaneous Localisation And Mapping (SLAM) problem in Robotics. [ABSTRACT FROM PUBLISHER]

This paper generalizes the well-known covariance intersection algorithm for distributed estimation and information fusion of random vectors. Our focus will be on partially correlated random vectors. This is motivated by the restriction of the standard covariance intersection algorithm, which treats all random vectors with arbitrary cross correlations and the restriction of the classical Kalman filter, which requires complete knowledge of the cross correlations. We first give a result to characterize the conservatism of the standard covariance intersection algorithm. We then generalize the covariance intersection algorithm to two random vectors with a given correlation coefficient bound and show in what sense the resulting covariance bound is tight. Finally, we generalize the notion of correlation coefficient bound to multiple random vectors and provide a covariance intersection algorithm for this general case. Our results will make the already popular covariance intersection more applicable and more accurate for distributed estimation and information fusion problems. [ABSTRACT FROM PUBLISHER]

In this paper, the theory of robust min–max control is extended to hierarchical and multiplayer dynamic games for linear quadratic discrete time systems. The proposed game model consists of one leader and many followers, while the performance of all players is affected by disturbance. The Stackelberg–Nash-saddle equilibrium point of the game is derived and a necessary and sufficient condition for the existence and uniqueness of such a solution is obtained. In the infinite time horizon, it is shown that the solution of the Riccati equation is upper bounded under a condition that is called individual controllability. By assuming such a condition and using a time-varying Lyapunov function the input-to-state stability of the hierarchical dynamic game is achieved, considering the optimal feedback strategies of the players and an arbitrary disturbance as the input. [ABSTRACT FROM PUBLISHER]

In this paper, based on \mathbb CR calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence. Some numerical examples and application are presented to substantiate the effectiveness of the proposed neural network. [ABSTRACT FROM PUBLISHER]

In this paper, we study production control problems in a partially flexible production-inventory system. In such a system, the upstream flexible production subsystem can make two different products, with nonnegligible setup time during changeover. The downstream inflexible production subsystem consists of two manufacturing facilities, with each dedicated to one product type only. The two production subsystems are connected by two dedicated buffers, which comprise the inventory subsystem. Using a renewal model, an optimal control policy is developed to switch products by predefined thresholds for inventory levels to minimize starvation (idle) time of downstream productions. Closed formulas are derived, and sensitivity analyses with respect to setup time change, machine reliability variation, and demand fluctuation are carried out. Finally, an application study in a door manufacturing line at an automotive assembly plant making two distinct types of doors is introduced. [ABSTRACT FROM PUBLISHER]

At present, many similar but disparate input–output (I–O) stability criteria exist. Without means for comparison, it is unclear which result is best used in any given application. This paper proposes a means for comparison between I–O stability results involving norms and inner products of inputs and outputs. The extended conic sector theorem provides a framework for determining which results are least conservative and most broadly applicable. In so-doing, numerous existing stability results are unified and revealed as more powerful than previously thought. [ABSTRACT FROM PUBLISHER]

In this paper, source localization by a network of primitive binary sensors under various imperfections are studied. Detailed analysis and mathematical modeling of imperfect binary sensors are presented. Imperfections include sensor failures of two types, drifting, uncertainty, and heterogeneity in binary sensor trigger thresholds, presence of noise, and nonradial symmetry of sensing ranges. Theoretical results, including asymptotical convergence, are established, in particular in the presence of substantial outliers due to sensor failure and large noise. Efficient numerical algorithms are proposed and simulated supporting the theoretical analysis. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of estimating a parameter matrix from observations which are affected by two types of noise components: (i) a sparse noise sequence which, whenever nonzero can have arbitrarily large amplitude (ii) and a dense and bounded noise sequence of “moderate” amount. This is termed a robust regression problem. To tackle it, a quite general optimization-based framework is proposed and analyzed. When only the sparse noise is present, a sufficient bound is derived on the number of nonzero elements in the sparse noise sequence that can be accommodated by the estimator while still returning the true parameter matrix. While almost all the restricted isometry-based bounds from the literature are not verifiable, our bound can be easily computed through solving a convex optimization problem. Moreover, empirical evidence tends to suggest that it is generally tight. If in addition to the sparse noise sequence, the training data are affected by a bounded dense noise, we derive an upper bound on the estimation error. [ABSTRACT FROM PUBLISHER]

In this paper, we present an adaptive optimal control approach applicable to a wide class of large-scale nonlinear systems. The proposed approach avoids the so-called loss-of-stabilizability problem and the problem of poor transient performance that are typically associated with adaptive control designs. Moreover, it does not require the system model to be in a certain parameterized form, and most importantly, it is able to efficiently handle systems of large dimensions. Theoretical analysis establishes that the proposed methodology guarantees stability and exponential convergence to state trajectories that can be made as close as desired to the optimal ones. A numerical example demonstrates the capability of the proposed approach to overcome loss-of-stabilizability problems. Moreover, simulation experiments for energy-efficient climate control performed on a ten-office building demonstrate the effectiveness of the proposed approach in large-scale nonlinear applications. [ABSTRACT FROM PUBLISHER]

This paper puts forward a simple mean-field game that captures some of the key dynamic features of crowd and pedestrian flows in multilevel building evacuations. It considers both microscopic and macroscopic route choice by strategic agents. To achieve this, we use mean-field differential game with local congestion measure based on the location of the agent in the building. Including the local mean-field term and its evolution along the path causes a sort of dispersion of the flow: the agents will try to avoid high density areas in order to reduce their overall walking costs and queuing costs at the stairs and exits. Each agent state is represented by a center of a box that follows a simple first-order dynamical system in an Euclidean space. Each agent will move to one of the closest exits that is safer and with less congested path. First, we formulate the problem and derive optimality equations using maximum principle and dynamic programming with boundary conditions. Second, well posedness and existence results are provided. Numerics and simulations are carried out to illustrate mean-field equilibria of a safer evacuation process. Finally, the methodology is shown to be flexible enough to include movement noises and stochastic structural component of the building. [ABSTRACT FROM AUTHOR]

A rough neuron is defined as a pair of conventional neurons that are called the upper and lower bound neurons. In this paper, the sinusoidal rough-neural networks (SR-NNs) are used to identify the discrete dynamic nonlinear systems (DDNSs) with or without noise in series–parallel configuration. In the identification of periodic nonlinear systems, sinusoidal activation functions provide more efficient neural networks than the sigmoidal activation functions. Based on the Lyapunov stability theory, an online learning algorithm is developed to train the SR-NNs. The asymptotically convergence of the identification error to zero and the boundedness of parameters as well as predictions are proved. SR-NNs are used to identify some DDNSs and the cement rotary kiln (CRK). CRK is a complex nonlinear system in the cement factory, which produces the cement clinker. The experiments show that the SR-NNs in the identification of nonlinear systems have better performances than multilayer perceptrons (MLPs), sinusoidal neural networks, and rough MLPs, particularly in the presence of noise. [ABSTRACT FROM AUTHOR]

In this paper, we review different memristive threshold logic (MTL) circuits that are inspired from the synaptic action of the flow of neurotransmitters in the biological brain. The brainlike generalization ability and the area minimization of these threshold logic circuits aim toward crossing Moore’s law boundaries at device, circuits, and systems levels. Fast switching memory, signal processing, control systems, programmable logic, image processing, reconfigurable computing, and pattern recognition are identified as some of the potential applications of MTL systems. The physical realization of nanoscale devices with memristive behavior from materials, such as TiO2, ferroelectrics, silicon, and polymers, has accelerated research effort in these application areas, inspiring the scientific community to pursue the design of high-speed, low-cost, low-power, and high-density neuromorphic architectures. [ABSTRACT FROM AUTHOR]

Learning algorithms are being increasingly adopted in various applications. However, further expansion will require methods that work more automatically. To enable this level of automation, a more powerful solution representation is needed. However, by increasing the representation complexity, a second problem arises. The search space becomes huge, and therefore, an associated scalable and efficient searching algorithm is also required. To solve both the problems, first a powerful representation is proposed that unifies most of the neural networks features from the literature into one representation. Second, a new diversity preserving method called spectrum diversity is created based on the new concept of chromosome spectrum that creates a spectrum out of the characteristics and frequency of alleles in a chromosome. The combination of spectrum diversity with a unified neuron representation enables the algorithm to either surpass or equal NeuroEvolution of Augmenting Topologies on all of the five classes of problems tested. Ablation tests justify the good results, showing the importance of added new features in the unified neuron representation. Part of the success is attributed to the novelty-focused evolution and good scalability with a chromosome size provided by spectrum diversity. Thus, this paper sheds light on a new representation and diversity preserving mechanism that should impact algorithms and applications to come. [ABSTRACT FROM AUTHOR]

Discharge lamps can be classified as high-pressure and low-pressure lamps, which operate under different scientific principles. They have exhibited the well-known fingerprints of memristors. This paper describes the mathematical models of both of high- and low-pressure discharge lamps based on their respective physical nature and behaviors, and then explains how these models can be unified into a generalized mathematical framework that confirms their memristor characteristics. Practical and theoretical results from high-pressure and low-pressure lamps are included to illustrate their 3 fingerprints of the memristor characteristics. The results indicate that gas discharge lamps are not ideal but volatile memristors. [ABSTRACT FROM AUTHOR]

This paper deals with the observer problem for dynamical systems in a behavioral context. We are given a dynamical system together with a partition of the system variables into a set of known or measured variables and a set of unknown, to be estimated variables. The observer problem is to find a system that produces an estimate of the unknown variables on the basis of the known or measured variables. For a given plant and partition, we establish a characterization of all error behaviors that can be achieved by interconnecting the plant with some observer. The main result of this paper is a very general, behavioral formulation of an internal model principle for observers. We will show that a nonintrusive observer achieves a stable error behavior if and only if, in addition to a detectability condition on the observer, the observer behavior contains the anti-stabilizable part of the plant behavior. [ABSTRACT FROM AUTHOR]

Strain engineering for performance enhancement is an integral part of a state-of-the-art CMOS process flow. However, use of stressors makes the performance of CMOS devices layout dependent. Performance variability arising due to the use of stressor materials is often referred to as Layout Dependent Effect (LDE) variability. The existing delay models do not take LDE into consideration and, therefore, results into unaccounted change in performance and degraded design robustness. In this paper we propose an analytical delay model for Inverter, 2-input NAND and NOR gates while considering LDE variability due to the use of strain engineered devices. We compare our derived model with TCAD calibrated HSPICE simulation results and observe that our model estimates delay well for varying transistor sizes, load capacitances and input signal transition times. [ABSTRACT FROM AUTHOR]

Dynamic average-value models (AVMs) for AC-DC rectifier circuits are generally formulated in state-space form and hence are straightforward to implement in state-variable-based simulation languages. In the nodal analysis-based approach used in electromagnetic transient (EMTP-type) simulation packages, the development of AVMs requires additional effort to reformulate and interface the models with the external ac and dc networks. This paper proposes a new averaged-circuit model for three-phase line-commutated rectifiers which is directly interfaced with the ac and dc networks, thereby achieving a simultaneous solution of the respective variables in EMTP-type solution. The proposed model is verified against conventionally interfaced model, as well as detailed switching model of the original rectifier circuit. A significant improvement of numerical accuracy and stability of the solution is demonstrated even at fairly large time steps. [ABSTRACT FROM AUTHOR]

Oscillator phase noise (PN) is one of the major problems that affect the performance of communication systems. In this paper, a direct connection between oscillator measurements, in terms of measured single-side band PN spectrum, and the optimal communication system performance, in terms of the resulting error vector magnitude (EVM) due to PN, is mathematically derived and analyzed. First, a statistical model of the PN, considering the effect of white and colored noise sources, is derived. Then, we utilize this model to derive the modified Bayesian Cramér-Rao bound on PN estimation, and use it to find an EVM bound for the system performance. Based on our analysis, it is found that the influence from different noise regions strongly depends on the communication bandwidth, i.e., the symbol rate. For high symbol rate communication systems, cumulative PN that appears near carrier is of relatively low importance compared to the white PN far from carrier. Our results also show that 1/f ³ noise is more predictable compared to 1/f ² noise and in a fair comparison it affects the performance less. [ABSTRACT FROM AUTHOR]

This paper describes the development of an energy-efficient amplify-and-forward distributed estimation scheme using realistic amplifier models. Specifically, a novel algorithm is presented that enables distributed estimation in the presence of amplifier compression resulting from the energy-efficient but non-linear class AB operation. In this system, a digital predistortion scheme is utilized to fit the amplifier at each sensor to a mathematically tractable, soft compression function that roughly mimics the compression region of the amplifier. It is shown both analytically and via simulation that using this scheme has two benefits over linear amplifier operation: improved transmitter efficiency by operating the amplifier in compression, and reduced sensitivity to heavy-tailed distributions due to the soft saturation. [ABSTRACT FROM AUTHOR]

Regulation of gene expression stages within a cellular creature deals with all the complexities and functionalities of the organism. These genetic information processing activities inside the cell can imitate the specific operations carried out by different combinations of semiconductor devices. Appropriate gene regulation is the basis of correct system biological functionality within all living organisms. Any biochemical aberrations (mutations) in a cell cycle which are not diminished genetically can result in progressive cellular dysfunction. Controlling mutations can be approached by realizing a “silicon mimetic” electronic circuit emulating the gene expression stages. This paper presents an integrated circuit model mimicking the post-transcriptional stages in gene expression regulated by microRNAs and Proteasome. The mRNA degradation by microRNA is modeled using emitter degeneration, while the protein degradation is modeled by a mixed-signal CMOS circuit. The effect of enzymes in the degradation reaction is also explored using a “chemo-inductor.” Probabilistic analysis using Monte Carlo simulations indicates that the proposed staged gene circuit model is also robust in an environment of stochastic biochemical reactions. The model is found to be in close agreement with experimentally reported data. [ABSTRACT FROM PUBLISHER]

In applications where signal frequencies are high, but information bandwidths are low, analog-to-information converters (AICs) have been proposed as a potential solution to overcome the resolution and performance limitations of high-speed analog-to-digital converters (ADCs). However, the hardware implementation of such systems has yet to be evaluated. This paper aims to fill this gap, by evaluating the impact of circuit impairments on performance limitations and energy cost of AICs. We point out that although the AIC architecture facilitates slower ADCs, the signal encoding, typically realized with a mixer-like circuit, still occurs at the Nyquist frequency of the input to avoid aliasing. We illustrate that the jitter and aperture of this mixing stage limit the achievable AIC resolution. In order to do so, we designed an end-to-end system evaluation framework for examining these limitations, as well as the relative energy-efficiency of AICs versus high-speed ADCs across the resolution, receiver gain and signal sparsity. The evaluation shows that the currently proposed AICs have no performance benefits over high-speed ADCs. However, AICs enable 2–10X in energy savings in low to moderate resolution (ENOB), low gain applications. [ABSTRACT FROM PUBLISHER]

In this paper, we propose classifiers derived from quadratically constrained maximum a posteriori (QCMAP) estimation. The QCMAP consists of the maximization of the expectation of a cost function, which is derived from the maximum a posteriori probability and a quadratic constraint. This criterion is highly general since its forms include least squares regressions and a support vector machine. Furthermore, the criterion provides a novel classifier, the "Gaussian QCMAP." The QCMAP procedure still has large theoretical interest and its full extensibility has yet to be explored. In this paper, we propose using the mixture of Gaussian distributions as the QCMAP weight function. The mixture of Gaussian distributions has wideranging applicability, and encompasses forms, such as a normal distribution model and a kernel density model. We propose four types of mixture of Gaussian functions for QCMAP classifiers, and conduct experiments to demonstrate their advantages. [ABSTRACT FROM AUTHOR]

Consider a weighted undirected graph and its corresponding Laplacian matrix, possibly augmented with additional diagonal elements corresponding to self-loops. The Kron reduction of this graph is again a graph whose Laplacian matrix is obtained by the Schur complement of the original Laplacian matrix with respect to a specified subset of nodes. The Kron reduction process is ubiquitous in classic circuit theory and in related disciplines such as electrical impedance tomography, smart grid monitoring, transient stability assessment, and analysis of power electronics. Kron reduction is also relevant in other physical domains, in computational applications, and in the reduction of Markov chains. Related concepts have also been studied as purely theoretic problems in the literature on linear algebra. In this paper we analyze the Kron reduction process from the viewpoint of algebraic graph theory. Specifically, we provide a comprehensive and detailed graph-theoretic analysis of Kron reduction encompassing topological, algebraic, spectral, resistive, and sensitivity analyses. Throughout our theoretic elaborations we especially emphasize the practical applicability of our results to various problem setups arising in engineering, computation, and linear algebra. Our analysis of Kron reduction leads to novel insights both on the mathematical and the physical side. [ABSTRACT FROM PUBLISHER]

Dissipativity is an important property of individual systems that guarantees a stable interconnected system. However, due to errors in the modeling process weakly non-dissipative models may be constructed. This paper introduces an enhanced method to perturb a non-dissipative LTI system in order to enforce dissipativity using spectral perturbation results for para-Hermitian pencils. Compared to earlier algorithms the new method is applicable to a wider class of problems, it utilizes a simpler framework, and employs a larger class of allowable perturbations resulting in smaller perturbations. Moreover, system stability can be enforced as well. Numerical examples are provided to show the effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

Unlike many complex networks studied in the literature, social networks rarely exhibit unanimous behavior, or consensus. This requires a development of mathematical models that are sufficiently simple to be examined and capture, at the same time, the complex behavior of real social groups, where opinions and actions related to them may form clusters of different size. One such model, proposed by Friedkin and Johnsen, extends the idea of conventional consensus algorithm (also referred to as the iterative opinion pooling) to take into account the actors’ prejudices, caused by some exogenous factors and leading to disagreement in the final opinions. In this paper, we offer a novel multidimensional extension, describing the evolution of the agents’ opinions on several topics. Unlike the existing models, these topics are interdependent, and hence the opinions being formed on these topics are also mutually dependent. We rigorously examine stability properties of the proposed model, in particular, convergence of the agents’ opinions. Although our model assumes synchronous communication among the agents, we show that the same final opinions may be reached “on average” via asynchronous gossip-based protocols. [ABSTRACT FROM PUBLISHER]

Optimal input design for parameter estimation has obtained extensive coverage in the past. A key problem here is that the optimal input depends on some unknown system parameters that are to be identified. Adaptive design is one of the fundamental routes to handle this problem. Although there exist a rich collection of results on this problem, there are few results that address dynamical systems. This paper presents sufficient conditions for convergence/consistency and asymptotic optimality for a class of adaptive systems consisting of a recursive prediction error estimator and an input generator depending on the time-varying parameter estimates. The results apply to a general family of single input single output linear time-invariant systems. An important application is adaptive input design for which the results imply that, asymptotically in the sample size, the adaptive scheme recovers the same accuracy as the off-line prediction error method that uses data from an experiment where perfect knowledge of the system has been used to design an optimal input spectrum. [ABSTRACT FROM PUBLISHER]

The paper addresses the 2D observer-based control of a magnetic microrobot navigating in a cylindrical blood vessel along a reference trajectory. In particular, this robot faces the nonlinear drag force induced by the pulsatile blood flow, which can hardly be measured. We consequently propose a mean value theorem (MVT) based observer to estimate the blood velocity from the sole measurement of the robot position. We also prove the stability of the observer-based backstepping controller. The resulting estimation and tracking are then illustrated through simulations, as well as robustness to parametric uncertainty, measurement noise, and dynamical errors when the pulsatile blood flow is incorrectly modeled. [ABSTRACT FROM PUBLISHER]

For linear discrete state-space models, under certain conditions, the linear least-mean-squares filter estimate has a convenient recursive predictor/corrector format, aka the Kalman filter. The purpose of this paper is to show that the linear minimum variance distortionless response (MVDR) filter shares exactly the same recursion, except for the initialization which is based on a weighted least-squares estimator. If the MVDR filter is suboptimal in mean-squared error sense, it is an infinite impulse response distortionless filter (a deconvolver) which does not depend on the prior knowledge (first- and second-order statistics) on the initial state. In other words, the MVDR filter can be pre-computed and its behaviour can be assessed in advance independently of the prior knowledge on the initial state. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel winner-take-all (WTA) architecture employing neurons with nonlinear dendrites and an online unsupervised structural plasticity rule for training it. Furthermore, to aid hardware implementations, our network employs only binary synapses. The proposed learning rule is inspired by spike-timing-dependent plasticity but differs for each dendrite based on its activation level. It trains the WTA network through formation and elimination of connections between inputs and synapses. To demonstrate the performance of the proposed network and learning rule, we employ it to solve two-class, four-class, and six-class classification of random Poisson spike time inputs. The results indicate that by proper tuning of the inhibitory time constant of the WTA, a tradeoff between specificity and sensitivity of the network can be achieved. We use the inhibitory time constant to set the number of subpatterns per pattern we want to detect. We show that while the percentages of successful trials are 92%, 88%, and 82% for two-class, four-class, and six-class classification when no pattern subdivisions are made, it increases to 100% when each pattern is subdivided into 5 or 10 subpatterns. However, the former scenario of no pattern subdivision is more jitter resilient than the later ones. [ABSTRACT FROM PUBLISHER]

This brief paper provides an approximate online adaptive solution to the infinite-horizon optimal tracking problem for control-affine continuous-time nonlinear systems with unknown drift dynamics. To relax the persistence of excitation condition, model-based reinforcement learning is implemented using a concurrent-learning-based system identifier to simulate experience by evaluating the Bellman error over unexplored areas of the state space. Tracking of the desired trajectory and convergence of the developed policy to a neighborhood of the optimal policy are established via Lyapunov-based stability analysis. Simulation results demonstrate the effectiveness of the developed technique. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the following problem: what passivity properties can be inferred for a system by studying only an approximate mathematical model for it. Our results show that an excess of passivity (whether in the form of input strictly passive, output strictly passive or very strictly passive) in the approximate model guarantees a certain passivity index for the system, provided that the norm of the error between the approximate and the true models is sufficiently small in a suitably defined sense. Further, we consider $(Q,S,R)$-dissipative systems and show that $(Q,S,R)$- dissipativity has a similar robustness property, even though the supply rates for the system and its approximation may be different. These results may be particularly useful if either the approximate model is much easier to analyze, or if the precise system model is unknown. We illustrate the results by considering particular approximation methods, e.g., model reduction, discretization, quantization, and linearization around an equilibrium point. [ABSTRACT FROM PUBLISHER]

Manufacturing systems with perishable products are widely observed in practice (e.g., food industry, biochemical productions, battery and semiconductor manufacturing). In such systems, the quality of the product is highly affected by its exposure time while waiting for the next operation, i.e., the residence time of intermediate parts within the system. Such a time should be strictly limited in order to ensure the product usability. The parts that reach the maximum allowable residence time need to be scrapped, thus impeding the production. To achieve an efficient production, the time-dependent or transient analysis is important to uncover the underlying principles governing production operations. In this paper, a serial production line model with two Bernoulli reliability machines, a finite buffer and perishable products is presented to analyze the transient behavior of such systems. The analytical formulas are derived to evaluate transient performance, and structural properties are investigated to study the effect of system parameters. In addition, using the model, we address problems of settling time estimation and production control to demonstrate the importance of the proposed method for transient analysis. [ABSTRACT FROM PUBLISHER]

This paper is motivated by the desire to develop optimal defensive control strategies that discourage an attacker from engaging in attack while simultaneously encouraging retreat. We develop a general, two-player, differential game in which one player represents an attacker and the opposing player represents the defender. The attacker possesses superior dynamics such that it is capable of terminating the game either in engagement or retreat as it so chooses. The defender is incapable of directly preventing engagement. Instead, the defender uses the manipulation of the attacker’s utility function as a form of indirect control in an attempt to make retreat a more attractive option over engagement. The solution to the overall engage or retreat differential game is found by solving two related optimization problems: the differential game of engagement and the optimal constrained retreat. The equilibrium open-loop control strategies and resulting game values of the attack or retreat game are expressed in terms of the solutions to these subproblems. Within the optimal constrained retreat problem, a value function constraint is imposed in order to prevent the attacker from moving into regions where engagement becomes optimal. This leads to regions of constrained retreat which we refer to as escort regions. [ABSTRACT FROM PUBLISHER]

Based on the alternating direction method of multipliers, in this paper, we propose, analyze, and test a coupled linearized method, which aims to minimize an unconstrained problem consisting of a loss term and a regularization term in an online setting. To solve this problem, we first transform it into an equivalent constrained minimization problem with a separable structure. Then, we split the corresponding augmented Lagrangian function and minimize the resulting subproblems distributedly with one variable by fixing another one. This method is easy to execute without calculating matrix inversion by implementing three linearized operations per iteration, and at each iteration, we can obtain a closed-form solution. In particular, our update rule contains the well-known soft-thresholding operator as a special case. Moreover, upper bound on the regret of the proposed method is analyzed. Under some mild conditions, it can achieve O(1/\sqrt T) convergence rate for convex learning problems and O((log T)/ T) for strongly convex learning. Numerical experiments and comparisons with several state-of-the-art methods are reported, which demonstrate the efficiency and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) are widely used in applications with complex decision boundaries. A large number of activation functions have been proposed in the literature to achieve better representations of the observed data. However, only a few works employ Tsallis statistics, which has successfully been applied to various other fields. This paper presents a random neural network (RNN) with $q$ -Gaussian activation functions [ $q$ -generalized RNN (QRNN)] based on Tsallis statistics. The proposed method employs an additional parameter $q$ (called the entropic index) which reflects the degree of nonextensivity. This approach has the flexibility to model complex decision boundaries of different shapes by varying the entropic index. We conduct numerical experiments to analyze the efficiency of QRNN compared with RNNs and several other classical methods. Statistical tests (Wilcoxon and Friedman) are used to validate our results and show that the QRNN performs significantly better than RNNs with different activation functions. In addition, we find that QRNN outperforms many of the compared classical methods, with the exception of support vector machines, in which case it still exhibits a substantial advantage in terms of implementation simplicity and speed. [ABSTRACT FROM AUTHOR]

Pursuit-evasion is fascinating in both nature and artificial world. Typically, a pursuer runs faster than its targeted evader while with less agile maneuverability. Naturally, there is a wonder that how an evader escapes from a faster pursuer or how faster a pursuer is able to capture an agile evader? This is not yet answered from the dynamics (i.e., Lagrangian or Newtonian) perspective. In this paper, we first provide a concise dynamics formulation from a bio-inspired perspective, in which the evader's escape strategy consists of two simplest possible yet efficient ingredients integrated as an organic whole, i.e., the suddenly turning-left or turning-right propelling maneuver, together with the early alert condition for starting and maintaining this maneuver. Then, we characterize the dynamic properties of the system at two different levels: 1) the maneuvers and non-trivial escape of the evader, at the level of individual runs of the system; and further 2) the non-trivial escape zones, the sharp phase-transitions and the phase-transition lines of the gaming outcome, at the level of the running results with respect to different ranges of the system parameters. The results are consistent with natural observations and may disclose some clues of natural laws, as well as imply applications in competition of autonomous mobile robots. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the optimal distributed control (ODC) problem for linear discrete-time deterministic and stochastic systems. The objective is to design a static distributed controller with a prespecified structure that is globally optimal with respect to a quadratic cost functional. It is shown that this NP-hard problem has a quadratic formulation, which can be relaxed to a semidefinite program (SDP). If the SDP relaxation has a rank-1 solution, a globally optimal distributed controller can be recovered from this solution. By utilizing the notion of treewidth, it is proved that the nonlinearity of the ODC problem appears in such a sparse way that an SDP relaxation of this problem has a matrix solution with rank at most 3. Since the proposed SDP relaxation is computationally expensive for a large-scale system, a computationally cheap SDP relaxation is also developed with the property that its objective function indirectly penalizes the rank of the SDP solution. Various techniques are proposed to approximate a low-rank SDP solution with a rank-1 matrix, leading to near globally optimal controllers together with a bound on the optimality degree of each controller. The above results are developed for both finite-horizon and infinite-horizon ODC problems. The SDP relaxations developed in this work are exact for the design of a centralized controller, hence serving as an alternative for solving Riccati equations. The efficacy of the proposed SDP relaxations is elucidated through a case study on the distributed frequency control of power systems. [ABSTRACT FROM PUBLISHER]