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This paper presents an analog-to-digital converter (ADC) dedicated to neural recording systems. By using two continuous-time incremental sigma-delta ADCs in a pipeline configuration, the proposed ADC can achieve high-resolution without sacrificing the conversion rate. This two-step architecture is also power-efficient, as the resolution requirement for the incremental sigma-delta ADC in each step is significantly relaxed. To further enhance the power efficiency, a class-AB output stage and a dynamic summing comparator are used to implement the sigma-delta modulators. A prototype chip, designed and fabricated in a standard 0.18 \mum CMOS process, validates the proposed ADC architecture. Measurement results show that the ADC achieves a peak signal-to-noise-plus-distortion ratio of 75.9 dB over a 4 kHz bandwidth; the power consumption is 34.8 \muW, which corresponds to a figure-of-merit of 0.85 pJ/conv. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results. [ABSTRACT FROM AUTHOR]

Approximate computing is an efficient approach for reducing computational costs. This method involves a trade-off between computational accuracy and the circuit’s power consumption, delay, and area. However, the accuracy requirements may differ for different applications. In some situations, precise results are required. Therefore, this paper proposes an accuracy-configurable radix-4 adder (ACRA), which uses the power gating technique to turn on or turn off the partial logic gates of an adder element dynamically to compute accurate or approximate results. When the ACRA operates in the approximate mode, the partial sum of one adder element is modified to reduce the error distance between the approximate and accurate results. A comparison of the ACRA with two state-of-the-art accuracy-configurable adders indicated that the ACRA achieved the best trade-off between the power–delay product and computational accuracy. In addition, in the image processing experiment conducted in this study, under half-approximate computation, the peak signal-to-noise ratio of the ACRA was less than 1 dB and its structural similarity index measure was maintained above 0.99. These results are superior to those obtained for other accuracy-configurable adders. [ABSTRACT FROM AUTHOR]

This paper proposes a low-temperature coefficient (TC), noise, and offset bandgap reference (BGR) for the high precision applications. In order to reduce the offset voltage and noise introduced by the error amplifier, the offset and noise suppression technique is proposed. A five-piece linear compensation has been applied in the BGR to improve the TC performance. Moreover, an adaptive current loop is added to the bandgap reference to enhance the current driving capability. The proposed BGR is implemented in a $0.35~\mu \text{m}$ standard CMOS process occupying an active area of $0.22 \times 0.28$ mm2. The measured results show that the average TC is 3 ppm/°C −45 °C to 125 °C after trimming. The output reference voltage of 2.47 V can achieve $3.6~\mu \text{V}$ (rms) noise from 0.1 to 10 Hz. In the supply voltage range of 2.6 to 5 V, the proposed BGR can deliver $30~\mu \text{A}$ load current and its PSRR is about −83 dB @ 100Hz. [ABSTRACT FROM AUTHOR]

For the dynamic output-feedback stabilization of continuous-time singular Markovian jump systems, this paper introduces the necessary and sufficient condition, whereas the previous research works suggested the sufficient conditions. A special choice of the block entries of Lyapunov matrices leads to derive the necessary and sufficient condition in terms of linear matrix inequalities. A numerical example shows the validity of the derived results. [ABSTRACT FROM AUTHOR]

In the fields of artificial neural networks and robotics, complicated, often high-dimensional systems can be designed using evolutionary/other algorithms to successfully solve very complex tasks. However, dynamical analysis of the underlying controller can often be near impossible, due to the high dimension and nonlinearities in the system. In this paper, we propose a more restricted form of controller, such that the underlying dynamical systems are forced to contain a dynamical object called a heteroclinic network. Systems containing heteroclinic networks share some properties with finite-state machines (FSMs) but are not discrete: both space and time are still described with continuous variables. Thus, we suggest that the heteroclinic networks can provide a hybrid between continuous and discrete systems. We investigate this innovated architecture in a minimal categorical perception task. The similarity of the controller to an FSM allows us to describe some of the system’s behaviors as transition between states. However, other, essential behavior involves subtle ongoing interaction between the controller and the environment that eludes description at this level. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of constructing a robust stealthy attack that compromises uncertain cyber-physical systems having unstable zeros. We first interpret the (non-robust) conventional zero-dynamics attack based on Byrnes–Isidori normal form, and then present a new robust zero-dynamics attack for uncertain plants. Different from the conventional strategy, our key idea is to isolate the real zero-dynamics from the plant's input–output relation and to replace it with an auxiliary nominal zero-dynamics. As a result, this alternative attack does not require the exact model knowledge anymore. The price to pay for the robustness is to utilize the input and output signals of the system (i.e., disclosure resources). It is shown that a disturbance observer can be employed to realize the new attack philosophy when there is a lack of model knowledge. Simulation results with a hydro-turbine power system are presented to verify the attack performance and robustness. [ABSTRACT FROM AUTHOR]

In this paper, under investigation is an integrable reduced spin Hirota-Maxwell-Bloch (rsHMB) system, which describes the transmission of the femtosecond pulses in an erbium doped fiber. Based on the existing N -fold Darboux transformation, we establish a generalized (n , N − n) -fold Darboux transformation for the rsHMB system, where n and N are the positive integers (n ≤ N). The N th-order degenerate soliton and N th-order rogue-wave solutions for the rsHMB system are obtained via the generalized (1 , N − 1) -fold Darboux transformation. By means of the generalized (2 , N − 2) -fold Darboux transformation, we derive the N th-order hybrid wave solutions describing the interaction between the (N − 1) th-order rogue wave and the first-order breather. The above solutions are discussed graphically, which exhibit the diverse wave structures including the triangle and pentagon, among others. • We construct a generalized (n , N − n) -fold Darboux transformation for the rsHMB system. • We obtain the N th-order degenerate soliton and N th-order rogue-wave solutions for the rsHMB system. • The N th-order hybrid wave solutions describing the interaction between the (N − 1) th-order rogue wave and the first-order breather are derived. • We discuss graphically the wave structures of the above solutions. [ABSTRACT FROM AUTHOR]

Examining the effectiveness of control in networked systems is a thriving research area. Autonomous systems that can be intermittently influenced (controlled) by external agents find applications ranging from machine calibration to satellite control. We refer to this class of networks as semi-autonomous. If the semi-autonomous agents' interaction dynamics are consensus-based, we dub this subclass as semi-autonomous consensus, which is the focus of the paper. Within such a subclass, we consider the dynamics of networked agents in the context of performance (friendly influence) and security (unfriendly influence). Our approach to appraise a semi-autonomous consensus network is to expose the network to fundamental test signals, namely white noise and an impulse, and use the resultant system response to quantify network performance and security. Traditionally, input-output properties are varied by altering the dynamics of the network agents. We instead adopt topological methods for this task, designing five protocols for tree graphs that rewire the network topology, leaving the network agents' dynamics untouched. In pursuit of this objective, four adaptive protocols are introduced to either increase or decrease the mean tracking and variance damping measures, respectively. Finally, a proposed fifth hybrid protocol is shown to have a guaranteed performance for both measures using a game-theoretic formalism. [ABSTRACT FROM AUTHOR]

An investigation has been made into the sliding mode control (SMC) problem for singular stochastic Markov systems (SSMSs) in this paper. By using replacement of matrix variables, a new mean square admissibility criterion of SSMSs is proposed first. Based on this admissibility criterion, the desired state-feedback controller is designed to guarantee the closed-loop system to be mean-square admissible. Then, the obtained results given in the form of strict linear matrix inequalities are applied to solve SMC problem of SSMSs. In order to illustrate the workability and applicability of the theoretic results developed, numerical examples are provided. [ABSTRACT FROM AUTHOR]

The data transfer bottleneck in Von Neumann architecture owing to the separation between processor and memory hinders the development of high-performance computing. The computing in memory (CIM) concept is widely considered as a promising solution for overcoming this issue. In this article, we present a time-domain CIM (TD-CIM) scheme using spintronics, which can be applied to construct the energy-efficient convolutional neural network (CNN). Basic Boolean logic operations are implemented through recording the bit-line output at different moments. A multi-addend addition mechanism is then introduced based on the TD-CIM circuit, which can eliminate the cascaded full adders. To further optimize the compatibility of TD-CIM circuit for CNN, we also propose a quantization method that transforms floating-point parameters of pre-trained CNN models into fixed-point parameters. Finally, we build a TD-CIM architecture integrating with a highly reconfigurable array of field-free spin-orbit torque magnetic random access memory (SOT-MRAM) and evaluate its benefits for the quantized CNN. By performing digit recognition with the MNIST dataset, we find that the delay and energy are respectively reduced by 1.2-2.7 times and $2.4\times 10 ^{3} - 1.1\times 10 ^{4}$ times compared with STT-CIM and CRAM based on spintronic memory. Finally, the recognition accuracy can reach 98.65% and 91.11% on MNIST and CIFAR-10, respectively. [ABSTRACT FROM AUTHOR]

This article describes the cross-correlation function as an alternative method to adapt loop gain in digital CDRs. Cross-correlation function inherent properties and their link with the cross-power spectral density help consolidating the adaptive loop gain technique, XCALG, for clock and data recovery systems that implement bang-bang phase detector. Considering the modulation of the loop gain due to jitter noise, previous works exploit the autocorrelation function at the output of the bang-bang phase detector as a proper indicator to track the system dynamics and perform loop gain adaptation. In contrast, we propose the XCALG as a new alternative to perform loop gain adaptation featuring better observability, less impact from jitter sources while keeping a safe phase margin. Theory behind the idea is enhanced, and the XCALG is demonstrated through behavioral system simulations. In addition, preliminary implementation costs of the cross-correlation function are discussed using a 65nm CMOS technology node. [ABSTRACT FROM AUTHOR]

This technical note proposes local exponential observers for systems on linear Lie groups. We study two classes of systems. In the first class, the full state of the system evolves on a linear Lie group and is available for measurement. In the second class, only part of the system's state evolves on a linear Lie group and this portion of the state is available for measurement. In each case, we propose two different observer designs. We show that, depending on the observer chosen, local exponential stability of one of the two observation error dynamics, left- or right-invariant error dynamics, is obtained. For the first class of systems these results are developed by showing that the estimation error dynamics are differentially equivalent to a stable linear differential equation on a vector space. For the second class of system, the estimation error dynamics are almost linear. We illustrate these observer designs on an attitude estimation problem. [ABSTRACT FROM PUBLISHER]

A study conducted by researchers at Beijing Information Science and Technology University in China has proposed a wavelength switching system for a linear cavity erbium-doped random fiber laser (RFL) based on cascaded fiber Bragg gratings (FBGs). The researchers used femtosecond laser processing technology to fabricate high reflection cascaded FBGs. The designed laser system achieved a low threshold power and demonstrated excellent wavelength and power stability. The findings of this study have implications for laser sensing, laser communication, and diffuse spot imaging. [Extracted from the article]

We are motivated by the real challenges presented in a human–robot system to develop new designs that are efficient at data level and with performance guarantees, such as stability and optimality at system level. Existing approximate/adaptive dynamic programming (ADP) results that consider system performance theoretically are not readily providing practically useful learning control algorithms for this problem, and reinforcement learning (RL) algorithms that address the issue of data efficiency usually do not have performance guarantees for the controlled system. This study fills these important voids by introducing innovative features to the policy iteration algorithm. We introduce flexible policy iteration (FPI), which can flexibly and organically integrate experience replay and supplemental values from prior experience into the RL controller. We show system-level performances, including convergence of the approximate value function, (sub)optimality of the solution, and stability of the system. We demonstrate the effectiveness of the FPI via realistic simulations of the human–robot system. It is noted that the problem we face in this study may be difficult to address by design methods based on classical control theory as it is nearly impossible to obtain a customized mathematical model of a human–robot system either online or offline. The results we have obtained also indicate the great potential of RL control to solving realistic and challenging problems with high-dimensional control inputs. [ABSTRACT FROM AUTHOR]

In this paper, a generalized variable-coefficient Hirota–Maxwell–Bloch system is investigated, which can describe the propagation of optical solitons in an erbium-doped optical fiber. Higher-step generalized Darboux transformation and rogue-wave solutions are obtained. Rogue-wave interaction is analyzed as follows: (1) Variable coefficients in the system affect the shape, background and number of the wave crests and troughs of the first-step rogue waves for the modulus of the normalized slowly varying amplitude of the complex pulse envelope, modulus of the measure of the polarization of the resonant medium and extant population inversion; (2) Variable coefficients in the system affect the shape, background and number of the wave crests and troughs of the second-step rogue-wave interaction. Those phenomena can not be attained through the existing Hirota–Maxwell–Bloch system. [ABSTRACT FROM AUTHOR]

This work describes new types of optical sensing systems based on Depressed Cladding Erbium Doped Fiber (DC-EDF). Due to the amplification characteristics developed for S-Band uses, this sensing system has high sensitivity, high dynamic range and wide bandwidth. It can be used to monitor static parameters such as force, pressure, displacement and dynamic parameters used in acoustics and vibrations. Two types of sensing systems with different approaches have been studied: the amplifier and the laser sensor. We describe the performance of these sensing systems in S band (1490 and 1510 nm) in static and dynamic conditions. We also appointed for some sensing applications for many areas of interest, such as, electric energy and civil engineering. [ABSTRACT FROM AUTHOR]

This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation. [ABSTRACT FROM PUBLISHER]

We provide bounds on the smallest eigenvalue of grounded Laplacian matrices (which are obtained by removing certain rows and columns of the Laplacian matrix of a given graph). The gap between our upper and lower bounds depends on the ratio of the smallest and largest components of the eigenvector corresponding to the smallest eigenvalue of the grounded Laplacian. We provide a graph-theoretic bound on this ratio, and subsequently obtain a tight characterization of the smallest eigenvalue for certain classes of graphs. Specifically, for weighted Erdos-Renyi random graphs, we show that when a (sufficiently small) set S of rows and columns is removed from the Laplacian, and the probability p of adding an edge is sufficiently large, the smallest eigenvalue of the grounded Laplacian asymptotically almost surely approaches \muw\vert S\vert p, where \muw is the mean edge weight. We also show that for weighted random $d$-regular graphs with a single row and column removed, the smallest eigenvalue is $\Theta(1/n)$, where $n$ is the number of nodes in the network. Our bounds have applications to the study of the convergence rate in consensus dynamics with stubborn or leader nodes. [ABSTRACT FROM PUBLISHER]

The synchronization of pulse-coupled oscillators (PCOs) interacting on general (strongly) connected graphs is addressed. It is shown that the network synchronizes if the phases are contained in an arc of length less than $\pi$, for every (strongly) connected interaction topology. Moreover, global synchronization is feasible in the all-to-all, strongly rooted, and connected bidirectional cases. Examples, counter-examples, and numerical simulations are given to illustrate the analytical findings. [ABSTRACT FROM AUTHOR]