Quantization Of Systems Research Articles

A Σ-Δ ADC with a DPOQ system and a new multi-Stage digital filter

This paper proposes a high-precision Sigma Delta ADC for automotive electronic sensors and delves into methods for optimizing quantizers and filters, with a focus on DPOQ quantizers and hybrid CIC filters. This ADC features a Dual-path One-bit Quantization (DPOQ) System, which improves the accuracy of Sigma-Delta ADCs while reducing hardware consumption. Meanwhile, this paper presents a new multistage digital filter. The filter uses a cascade of multiple filters, including cosine-like (CL), cascaded integral comb (CIC), interpolated second-order polynomial (ISOP), and half-band (HB) filters, to enhance the ADC’s accuracy. The filter are designed using canonical signed digit (CSD) and common subexpression (CSE) optimized the design algorithm of the filter, to reduce hardware resource consumption and improve computational performance. The ADC is synthesized using 180 nm CMOS technology, achieving an output SNDR of 96.26 dB, ENOB of 15.73 bits, and total power of 518.35 uW.

Water quality constrained adjustment planning for regional breeding management with nonlinear programming model under uncertainty in Wenchang City, China

Basin water pollution caused by livestock, poultry and fish breeding is still a serious problem for remote villages, however, reliable regional breeding management programming have the potentials to improve pollution status. This paper focuses on the optimal model design and water quality analysis of the livestock, poultry and fish breeding system for Wenchang City, China. Methods of multi-objective programming (MOP), interval parameter programming (IPP), fuzzy-stochastic parameter programming (FSPP), and chance constrained programming (CCP) were incorporated into the developed model to tackle multi uncertainties described by interval values, probability distributions, fuzzy membership function. Based on the estimation of local breeding potential and current situation of surface water section, a multi-objective mixed fuzzy-stochastic nonlinear programming optimization model is presented with one-dimensional water quality model. In order to evaluate the environmental carrying capacity of livestock, poultry and fishery manure, predict its development trend and investigate the implementation effect of different emission reduction policies, this paper designs quantization system of the urban water environmental carrying capacity for the model. The results indicated that the water environment pollutant absorption capacity and carrying capacity of Wenchang city have approached the limit especially the towns in the northeast of City which limited the overall development space of the City. The modeling results are valuable for supporting the adjustment of the existing livestock, poultry and fish breeding schemes within a complicated system benefit and surface water quality situation under uncertainty.

Adaptive step size quantized simulation method for gas–electricity integrated energy systems

Gas–electricity integrated energy systems (GE-IES) offers a promising solution for enhancing energy efficiency and accommodating renewable energy sources. Accurate dynamic simulation is essential for optimizing and controlling GE-IES. However, the presence of various local controllers introduces prominent discrete characteristics, posing challenges for the dynamic simulation of the GE-IES. This paper investigates the dynamic simulation method in GE-IES with discrete characteristics. Firstly, we propose an adaptive step size simulation method based on quantized state system theory. This method maintains the event-driven characteristics of the quantized state integration algorithms, while enhancing computational speed through adaptive step size adjustments. Secondly, we establish an event-driven simulation framework that facilitates interactions of different subsystems during the dynamic simulation, improving the compatibility with various models and solving algorithms. Finally, we validate the accuracy, efficiency, and scalability of the proposed method and the framework using two typical GE-IES cases with different scales. Simulation results demonstrate the effectiveness on the dynamic simulation of GE-IES and highlight the feasibility of natural gas networks in consuming and storing surplus renewable energy.

Controlling the interactions in a cold atom quantum impurity system

We implement an experimental architecture in which a single atom of K is trapped in an optical tweezer, and is immersed in a bath of Rb atoms at ultralow temperatures. In this regime, the motion of the single trapped atom is confined to the lowest quantum vibrational levels. This realizes an elementary and fully controllable quantum impurity system. For the trapping of the K atom, we use a species-selective dipole potential, that allows us to independently manipulate the quantum impurity and the bath. We concentrate on the characterization and control of the interactions between the two subsystems. To this end, we perform Feshbach spectroscopy, detecting several inter-dimensional confinement-induced Feshbach resonances for the KRb interspecies scattering length, that parametrizes the strength of the interactions. We compare our data to a theory for inter-dimensional scattering, finding good agreement. Notably, we also detect a series of p-wave resonances stemming from the underlying free-space s-wave interactions. We further determine how the resonances behave as the temperature of the bath and the dimensionality of the interactions change. Additionally, we are able to screen the quantum impurity from the bath by finely tuning the wavelength of the light that produces the optical tweezer, providing us with a new effective tool to control and minimize the interactions. Our results open a range of new possibilities in quantum simulations of quantum impurity models, quantum information, and quantum thermodynamics, where the interactions between a quantized system and the bath is a powerful yet largely underutilized resource.

Dynamics and Complexity Analysis of Fractional-Order Inventory Management System Model

To accurately depict inventory management over time, this paper introduces a fractional inventory management model that builds upon the existing classical inventory management framework. According to the definition of fractional difference equation, the numerical solution and phase diagram of an inventory management system are obtained by MATLAB simulation. The influence of parameters on the nonlinear behavior of the system is analyzed by a bifurcation diagram and largest Lyapunov exponent (LLE). Combined with the related indexes of time series, the complex characteristics of a quantization system are analyzed using spectral entropy and C0. This study concluded that the changing law of system complexity is consistent with the LLE of the system. By analyzing the influence of order on the system, it is found that the inventory changes will be periodic in some areas when the system is fractional, which is close to the actual conditions of the company’s inventory situation. The research results of this paper provide useful information for inventory managers to implement inventory and facility management strategies.

Discrete-event simulation of continuous-time systems: evolution and state of the art of quantized state system methods

In this work, we attempt to bring together the origins, main results, and recent advances on discrete-event simulation of continuous-time systems. Starting from the early approaches that aimed to represent continuous-time dynamics within the discrete-event system specification (DEVS) formalism framework, the work shows how these ideas gave place to the formalization of the quantized state system (QSS) family of numerical integration algorithms. Then, we describe the QSS algorithms, their properties, their extensions, and the main practical software tools implementing them. We also present a selection of simulation examples illustrating the main features and advantages through comparisons with state-of-the-art continuous-time simulation solvers.

Channel Estimation for Quantized Systems Based on Conditionally Gaussian Latent Models

Channel Estimation for Quantized Systems Based on Conditionally Gaussian Latent Models

Patient Confidential Data Hiding and Transmission System Using Amplitude Quantization in the Frequency Domain of ECG Signals.

The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients' information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients' information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30.

Rate-Splitting Multiple Access for Quantized Multiuser MIMO Communications

This paper investigates the sum spectral efficiency maximization problem in downlink multiuser multiple-input multiple-output systems with low-resolution quantizers at an access point (AP) and users. We consider rate-splitting multiple access (RSMA) to enhance spectral efficiency by offering opportunities to boost achievable degree-of-freedom. Optimizing RSMA precoders, however, is highly challenging due to the minimum rate constraint when determining the common rate. The quantization errors coupled with the precoders make the problem more complicated. In this paper, we develop a novel RSMA precoding algorithm incorporating quantization errors for maximizing the sum spectral efficiency. To this end, we first obtain an approximate spectral efficiency in a smooth function. Subsequently, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We propose a computationally efficient algorithm to find the principal eigenvector of the NEP as a sub-optimal solution. We also extend the weighted minimum mean square error-based RSMA precoding to the considered quantization system. Simulation results validate the proposed methods. The key benefit of using RSMA over spatial division multiple access (SDMA) comes from the ability of the common stream to balance between the channel gain and quantization error in multiuser MIMO systems with different quantization resolutions.

Parameter-Optimal-Gain-Arguable Iterative Learning Control for Linear Time-Invariant Systems with Quantized Error

In this paper, a parameter optimal gain-arguable iterative learning control algorithm is proposed for a class of linear discrete-time systems with quantized error. Based on the lifting model description for ILC systems, the iteration time-variable derivative learning gain in the algorithm is optimized by resolving a minimization problem regarding the tracking error energy and the learning effort amplified by a weighting factor. Further, the tracking error can be monotonically convergent to zero when the condition is guaranteed and the rate of convergence can be adjusted by scaling the weighting factor of an optimization problem. This algorithm is more innovative when compared with the existing iterative learning control algorithm for quantization systems. The innovations of this algorithm are as follows: (i) this optimization-based strategy for selecting learning gains can improve the active learning ability of the control mechanism and avoid the passivity of existing selective learning gains; (ii) the algorithm of POGAILC with data quantization can improve the convergence performance of tracking errors and reduce the negative effects of data quantization on the control performance of the logarithmic quantizer; and (iii) we provide a rigorous algorithm convergence analysis by deriving the existence of the unique solution for the optimal learning-gain vector under a singular and nonsingular tracking-error diagonalized matrix. Finally, numerical simulations are used to demonstrate the effectiveness of the algorithm.

Parallel CV-QRNG with Strict Entropy Evaluation

Continuous-variable quantum random number generators (CV-QRNGs) have promising application prospects thanks to their advantages such as high detection bandwidth, robustness of system, and integratability. In major CV-QRNGs, the generation of random numbers is based on homodyne detection and discretization of the quadrature fluctuations of the EM fields. Any defectiveness in physical realization may leak information correlated with the generated numbers and the maximal amount of randomness that can be extracted in presence of such side-information is evaluated by the so-called quantum conditional min-entropy. The parallel CV-QRNG overcomes the rate bottleneck of the previous serial type scheme. As a type of device-trusted QRNG, its security needs to be better guaranteed based on self-testing or monitoring that can be rigorously enforced. In this work, four sideband modes of vacuum state within 1.6 GHz detection bandwidth were extracted parallelly as the entropy source, and 16-bit analog-to-digital conversion in each channel was realized. Without making any ideal assumptions, the transfer function of the homodyne and quantization system was measured based on beat method to calibrate the evaluation of the min-entropy. Based on the rigorous entropy evaluation with a hash security parameter of εhash = 2−110, a real-time generation rate of 7.25 Gbps was finally achieved.

Vector Quantized Semantic Communication System

Although analog semantic communication systems have received considerable attention in the literature, there is less work on digital semantic communication systems. In this paper, we develop a deep learning (DL)-enabled vector quantized (VQ) semantic communication system for image transmission, named VQ-DeepSC. Specifically, we propose a convolutional neural network (CNN)-based transceiver to extract multi-scale semantic features of images and introduce multi-scale semantic embedding spaces to perform semantic feature quantization, rendering the data compatible with digital communication systems. Furthermore, we employ adversarial training to improve the quality of received images by introducing a PatchGAN discriminator. Experimental results demonstrate that the proposed VQ-DeepSC is more robustness than BPG in digital communication systems and has comparable MS-SSIM performance to the DeepJSCC method.

Quantization-Uncertainty-Dependent Analysis and Control of Linear Systems With Multi-Input–Multi-Output Quantization

This paper proposes a novel quantized control strategy for network-based linear systems subject to multi-input-multi-output (MIMO) quantization. A logarithmic quantization scheme is adopted for characterizing the quantization effect on system dynamics. A sufficient and necessary condition on the asymptotic stability is established for quantized MIMO systems. To improve the numerical testability of the obtained results, a polytopic approach approximating the MIMO quantization uncertainties is developed. By constructing a novel Lyapunov function that has dependence on the MIMO quantization uncertainties, asymptotic stability criteria are established for closed-loop quantized MIMO systems. The conditions on the existence of state-feedback controllers that guarantee the closed-loop stability are derived based on the proposed technique that decouples the controller gains and the parameters of MIMO quantization uncertainties. The proposed method and the associated theoretical results are extended to the disturbance attenuation case. Finally, the theoretical results are applied to a benchmark example and a converter circuit to illustrate their efficacy and superiority.

A parallel game model‐based intrusion response system for cross‐layer security in industrial internet of things

SummaryWith the rise of industrialization, the importance of the industrial Internet of Things (IIoT) has increased significantly, and with it comes a variety of security threats. Therefore, the security of these networks is critical. Industrial Response Systems (IRSs), as the last line of security, plays an important role in the security system of the Industrial Internet of Things. In this paper, a new IRS model based on the non‐cooperative game is proposed. First, by combining the Partially Observable Markov Decision Process (POMDP) model with the stochastic game model based on the expanded attack tree, our model could effectively perceive the changes at each node. Second, our model incorporates the alarms of intrusion detection system (IDS) and the physical quantities of sensors in Industrial Cyber‐Physical System (ICPS) into the quantization system so that the model can respond to intruders more accurately and comprehensively. Finally, we develop this model based on multiprocessors to speed up the solution process, and adopt an approximation algorithm to reduce the number of iterations of the POMDP

Leaf Disease Detection in Android

Abstract: In agriculture, early disease identification is crucial for a productive crop product. Ails similar as bacterial spot, late scar, Septoria splint spot, and unheroic twisted splint the quality of the tomato crop. Automatic bracket ways of factory conditions also help in taking action once they're discovered diseased splint symptoms Presented below is a Convolutional Learning Vector Quantization and Neural Network( CNN) model system for detecting tomato splint dis- ease grounded on the( LVQ) algorithm and categorization. There are 500 tomato prints in the dataset. leaves that display four complaint symptoms. We created a model of CNN for point birth and categorization automatically. Color Research on factory splint conditions laboriously uses information. In our model, three channels grounded on RGB are subordinated to pollutants

Traffic models of periodic event-triggered quantized control systems

Periodic Event-Triggered Quantized Control (PETQC) is a type of event-triggered control (ETC) that takes into account the quantization effect introduced by network transmission and only requires to measure the plant output periodically instead of continuously. In this work, we present an abstract model for the traffic generated by the dynamics of PETQC implementations. Construction of the abstract models is done in two steps. Our first step is to divide the state space into finite regions. Each region is then analyzed with LMIs to examine the event-triggering behavior. The state transitions among different regions are resulted from reachability analysis.

Data-driven control for dynamic quantized nonlinear systems with state constraints based on barrier functions

In this study, the data-driven control problem is investigated for discrete-time nonlinear systems with state constraints and dynamic quantization. A dynamic quantizer is utilized to encode the control signal to relieve the impact of inherent quantization errors in comparison with traditional uniform quantizers. By employing three sets of neural networks (NNs), a data-based goal representation heuristic dynamic programming algorithm with the concurrent learning technique is offered to achieve the near-optimal control objective without model requirement. Specifically, by introducing the relaxed barrier function into the cost function, a barrier-based control strategy is proposed to generate the repulsive control force when the state moves to the constraint boundary. Subsequently, the recorded historical data is adequately leveraged to build a new weight updating rule, and persistent excitation of weight parameter estimation is hence removed. Furthermore, the Lyapunov method is utilized to demonstrate that the weights estimation errors of NNs are bounded. Finally, numerical simulation and power system examples are employed to demonstrate the validity of the control strategy.

Towards quantum turbulence theory: A simple model with interaction of vortex loops

This work investigates a quantized system of thin vortex rings with a flow in the core. Examples show that the set of permissible circulation values has a fractal structure. Circulation values in the domain of a round tube with a radius R${}_{0}$ in the shape of a torus of radius R${}_{1}\ensuremath{\gg}{R}_{0}$ are given by the accompanying expression where quantities ${\ensuremath{\zeta}}_{k}^{(l)}$ stand for zeros of the Bessel function J${}_{l}$(\ensuremath{\rho}). A general expression for the partition function of a turbulent flow is also suggested.

Two-component density functional theory study of quantized muons in solids

The quantum effect of light nuclei in materials is usually considered as lattice vibration and zero-point motion from an ab initio perspective. Here we start from full-quantized particles and take the muon as an example, considering the two-body quantized system of the muon and the electrons within two-component density functional theory, which can calculate the related two-body wave functions directly and automatically taking into account the quantum effect of the muon. We first simulate the two-body correlation energy and the pair-correlation function by quantum Monte Carlo, then construct a two-body self-consistent loop to solve the two-body density and then the hyperfine couplings are estimated. This is an attempt to reveal the quantum effect of light nuclei in materials from a different perspective. We finally find this fundamental method agrees better with experiments and shows good potential for further such calculations.

Quantized control for networked switched systems under denial-of-service attacks via a barrier event-triggered mechanism

This paper studies the quantized control problem for networked switched systems (NSSs) under denial-of-service (DoS) attacks. The quantized state information, together with the switching signal, is transmitted to the controller through a network. In order to reduce communication consumption and controller update frequency, a barrier event-triggered mechanism is utilized to monitor the state at discrete time. Because of the event-triggered mechanism and the DoS attacks on the network, the mismatch between the system mode and the controller mode is thus frequently encountered, which may lead to quantization saturation and system instability. To solve the problem, an update rule is presented for the dynamic quantizer by switching between zooming in and zooming out of the zooming variable, and a feedback controller is proposed with a jointly designed event-triggered mechanism and a dynamic quantizer. Sufficient conditions on the constraints of DoS frequency and duration are obtained to ensure the exponential stability of the switched system. The effectiveness of the obtained results is illustrated by simulation examples and comparative studies.