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The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in high-frequency continuous firing modes are important indicators to measure the performance of these weapons. The characteristics of multiple projectiles are high randomness and large numbers launched in a short period of time, making it very difficult to obtain the real dispersion parameters of the projectiles due to the occlusion or coincidence of multiple projectiles. Using six intersecting-screen testing system, in this paper, we propose an association recognition and matching algorithm of multiple projectiles using a temporal and spatial information constraint mechanism. We extract the output signal from each detection screen and then use the wavelet transform to process the output signal. We present a method to identify and extract the time values on which the projectiles pass through the detection screens using the wavelet transform modulus maximum theory. We then use the correlation of the output signals of three parallel detection screens to establish a correlation coefficient recognition constraint function for the multiple projectiles. Based on the premise of linear projectile motion, we establish a temporal and spatial constraint matching model using the projectile's position coordinates in each detection screen and the projectile's time constraints within the multiple intersecting-screen geometry. We then determine the time values of the multiple projectiles in each detection screen using an iterative search cycle registration, and finally obtain the flight parameters for the multiple projectiles in the presence of uncertainty. The proposed method and algorithm were verified experimentally and can solve the problem of uncertainty in projectiles flight parameter under different multiple projectile firing states.

In this article, a novel composite hierarchical antidisturbance control (CHADC) algorithm aided by the information-theoretic learning (ITL) technique is developed for non-Gaussian stochastic systems subject to dynamic disturbances. The whole control process consists of some time-domain intervals called batches. Within each batch, a CHADC scheme is applied to the system, where a disturbance observer (DO) is employed to estimate the dynamic disturbance and a composite control strategy integrating feedforward compensation and feedback control is adopted. The information-theoretic measure (entropy or information potential) is employed to quantify the randomness of the controlled system, based on which the gain matrices of DO and feedback controller are updated between two adjacent batches. In this way, the mean-square stability is guaranteed within each batch, and the system performance is improved along with the progress of batches. The proposed algorithm has enhanced disturbance rejection ability and good applicability to non-Gaussian noise environment, which contributes to extending CHADC theory to the general stochastic case. Finally, simulation examples are included to verify the effectiveness of theoretical results.

The natural gas tightly correlates with our everyday life. However, driven by gray incomes, some users are prone to stealing gas by refitting the equipment without permission. Especially for the boiler room users in winter, this phenomenon appears more rampant. Traditional gas-theft detection methods highly rely on the on-site inspection, where exists ineffective and randomness. In this paper, we propose a data-driven approach, named SVOC, to detect gas-theft suspects among boiler room users. Our approach consists of a scenario-based data quality detection algorithm, a deformation-based normality detection algorithm, and an One-Class Support Vector Machine (OCSVM) based anomaly detection algorithm. Specifically, considering the temporal proximity between the gas consumption and the outdoor temperature, the normality detection algorithm adopts a similarity-based deformation correlation to detect normal boiler room users out of abnormal ones. Then, we employ OCSVM as the anomaly detection algorithm to capture various features across multiple data sources, aiming to distinguish gas-theft suspects from the remaining irregular users. Here, the detected normal and abnormal users are fed into the OCSVM for training and prediction, respectively, which can overcome the label scarcity problem. We conduct extensive experiments on a real-world dataset during one heating season. The results demonstrate distinct advantages of our approach over various baselines.

Analyzing user behavior characteristics in a complex power grid environment is essential for user behavior planning and resource coordination optimization. Traditional user behavior analysis methods based on model-driven and causal analysis have the disadvantages of strong subjectivity and physical models that are difficult to deal with the randomness and uncertainty of user behavior in complex grid environments. In this paper, we use unsupervised learning methods to analyze user behavior in complex power grid environments, and propose user behavior analysis methods based on stacked autoencoder and clustering. We first reduce the complexity of user behavior data by proposing adaptive feature selection algorithm of user behavior based on stacked autoencoder and unsupervised learning (AFS-SAEUL). Finally, we build a user behavior analysis model based on adaptive feature selection and improved clustering (UBA-AFSIC). The model improved the performance of unsupervised classification of user behavior by fusing the adaptive generation strategy of the initial cluster centers. The simulation experiment results on two real electricity datasets and one public electric vehicle charging dataset show that compared with the existing feature selection algorithm and clustering algorithm, the algorithms proposed in this paper have higher feature selection rate and better clustering performance.

Electronic-voting(e-voting), compared with paper voting, has advantages in several aspects. Among those benefits, the ability to audit the electoral process at every stage is one of the most desired features. In Eurocrypt2015, Kiayias et al. proposed an E2E verifiable e-voting system which first provides E2E verifiability without relying on external sources of randomness or the random oracle model. The main advantage of the system is that election auditors need only the election transcript and the feedback from the voters to pronounce the election process unequivocally valid. Unfortunately, their system comes with a huge performance and storage penalty for the election authority (EA) compared to other e-voting systems such as Helios. The reason is the EA forms the proof of tally results. It is required to precompute several ciphertexts. The performance penalty on the EA appears to be intrinsic: voters cannot compute an enciphered ballot themselves because it seems unprovable. In this work, we construct a new e-voting system that retains strong E2E characteristics while eliminating the performance and storage penalty of the EA. Our construction is practical and has a similar performance to Helios. The privacy of our construction relies on the SXDH assumption over bilinear groups via complexity leveraging.

Accurately estimating the capacity of lithium-ion batteries in electric vehicles (EVs) is critical for making correct management decisions. However, the randomness of the charging voltage range of EVs can lead to missing observations or reduced accuracy of capacity estimation methods. This paper proposes an adaptive battery capacity estimation method suitable for arbitrary charging voltage range based on incremental capacity (IC) analysis and data-driven techniques. All charging conditions of EVs are divided into three categories according to the charging voltage range. Three data-driven estimation sub-methods with sequential application priority are designed for the three charging conditions separately, including back-propagation neural network with IC peak coordinates as input, ensemble learning with local high IC curve as input, and linear regression with ampere-hour coordinate transformation. The method is based on a priori knowledge to select a suitable estimation sub-method under different charging conditions, so as to improve the adaptability. Experimental data is collected from eight commercial lithium-ion battery modules for model establishment and verification. Over 250,000 experimental samples at different states of health and random charging ranges show that the method can accurately estimate battery capacity under arbitrary charging conditions, with a maximum error of 2%.

Cyber-physical systems (CPSs) are complex and exhibit both continuous and discrete dynamics, hence it is difficult to guarantee that they satisfy given specifications, i.e., the properties that must be fulfilled by the system. Falsification of temporal logic properties is a testing approach that searches for counterexamples of a given specification that can be used to increase the confidence that a CPS does fulfill its specifications. Falsification can be done using random search methods or optimization methods, both of which have their own benefits and drawbacks. This paper introduces two methods that exploit randomness to different degrees: the optimization-free Hybrid-Corner-Random (), and the direct-search method Line-search Falsification (). combines randomly chosen parameter values with extreme parameter values, which performs surprisingly well on benchmark evaluations. The gradient-free optimization-based LSF optimizes over line segments through a vector of inputs in the n-dimensional parameter space. The two methods are compared to the Nelder-Mead and SNOBFIT methods, using a well-known set of benchmark problems and shows better performance than any of the evaluated methods. Falsification can be done using random search methods or optimization methods. This paper proposes a method based on combining randomly chosen parameter values with parameter extreme values. Evaluation results on benchmark problems show that this method performs well on many of the problems. Optimization-based methods are needed when optimization-free methods do not perform well in falsification. The efficiency of the falsification is affected by the optimization methods used to search for inputs that might falsify the specifications. This paper presents a new optimization method for falsification, Line-search Falsification, where optimization is done over line segments through a vector of inputs in the n-dimensional parameter space. The evaluation results on the benchmark problems show that using this method improves the falsification performance by reducing the number of simulations necessary to falsify a specification. .

This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and successive packet losses. The sampling intervals are subject to noisy perturbations, and categorical distribution is used to characterize the sampling errors of noisy sampling intervals. By means of the input delay approach, the CDN under consideration is first converted into a delay system with delayed input subject to dual randomness and probability distribution characteristic. To verify the probability distribution characteristic of the delayed input, a novel characterization method is proposed, which is not the same as that of some existing literature. Based on this, a unified framework is then established. By recurring to the techniques of stochastic analysis, a probability-distribution-dependent controller is designed to guarantee the mean-square exponential synchronization of the error dynamical network. Subsequently, a special model is considered where only the lower and upper bounds of delayed input are utilized. Finally, to verify the analysis results and testify the effectiveness and superiority of the designed synchronization algorithm, a numerical example and an example using Chua's circuit are given.

In the fully mechanized mining face, the proper chain tension of chain transmission system is the key to reduce the failure rate of scraper conveyor and improve the service life of expensive chain. However, the interference of random load and variable stiffness in the chain transmission system of the scraper conveyor make it very difficult for tensionable tail drive (TTD) to control the chain tension. In this research, the mathematical model of the TTD based on catenary theory is established, and the dynamic characteristics are analyzed to investigate the influence of load and stiffness on the tension control. An adaptive neural command filtering backstepping algorithm with parameter identification is proposed to control the chain tension of the chain transmission system. The combination of stiffness identification with the linearized tension model weakens the influence of variable stiffness and load on the tension control. Based on state observation and stiffness estimation, radial basis function neural network is introduced into the tension controller to compensate the load randomness caused by tail sprocket drive. Comparative experimental results are completed to verify the high-accuracy tension tracking performance and a strong robustness of the proposed controller in the random load mode.

Pedestrian trajectory prediction in dynamic, multi-agent traffic junction scene is an important problem in the context of self-driving cars. Accurately predicting the trajectory of the agent, especially the pedestrian with high randomness, is of great significance to autonomous driving technology. In this paper, we propose Multi-PPTP, a novel trajectory prediction model that first utilizes a composite rasterized map to model the complex representations and interactions of road components, including dynamic obstacles (e.g., pedestrians, vehicles, and cyclists) and static road information (e.g., lanes, traffic lights, and sidewalks). Then it uses MapNet and AgentNet to extract spatio-temporal features by deep convolutional networks and LSTM to automatically derive relevant features, and next exploits Interaction-AttNet aggregate features to learn the high interactions among all components by affine transformation and multi-head attention mechanism. Additionally, we also propose a series of unique loss functions to predict multiple possible trajectories of each pedestrian while estimating their probabilities. Following extensive offline evaluation and comparison to the state-of-the-art models, our approach outperforms significantly the state-of-the-art models on our large scale in-house and public nuScenes dataset.

Since the high randomness feature of traffic flows, it is challenging to be aware of the spatial-temporal characteristics of dynamic vehicle loads on a long-span bridge. Compared with conventional measuring approaches, computer vision-based techniques have competitive advantages in the measurement of factual vehicle loads parameters, including the passing time, velocity, vehicle type, and corresponding locations on the bridge at each timestamp. In this paper, a vision-based framework for the identifications of dynamic vehicle loads on long-span bridges is proposed. To be specific, the framework consists of two major parts. The first part is the extraction of spatial-temporal factors of vehicles, including the detection of vehicles and the estimation of tracklets. Here, Single Shot Multi-Box Detection (SSD) architecture with MobileNets is utilized for detecting vehicles, while the data association technique with Kalman filter is applied on detection results for generating tracklets. Then, in the second part, based on the detection and tracking results, a Multi-layer Monte Carlo (MLMC) model is developed for axle-load intensities inference and estimation. The learned model is utilized to infer the dynamic load of vehicles from the detection results and generated tracklets. The proposed methodology is validated via an experiment of a case study on the Jiangyin bridge, Jiangsu Province, China, and several spatial-temporal distributions of vehicle loads are specifically analyzed and discussed. Experimental results indicate our proposed framework is a feasible and reliable solution to predict and identify dynamic vehicle loads on long-span bridges, without the assistance of the deck-embedded Weigh-in-Motion (WIM) system.

With the nonlinearity and plasticity, memristors are widely used as nonlinear devices for chaotic oscillations or as biological synapses for neuromorphic computations. But discrete memristors and their coupling maps have not received much attention, yet. Using a discrete memristor model, this paper presents a general three-dimensional discrete memristor-based (3D-DM) map model. By coupling the discrete memristor with four two-dimensional discrete maps, four examples of 3D-DM maps with no or infinitely many fixed points are generated. We simulate the coupling coefficient-depended and memristor initial-boosted bifurcation behaviors of these 3D-DM maps using numerical measures. The results demonstrate that the memristor can enhance the chaos complexity of existing discrete maps and its coupling maps can display hyperchaos. Furthermore, a hardware platform is developed to implement the 3D-DM maps and the acquired hyperchaotic sequences have high randomness. Particularly, these hyperchaotic sequences can be applied to the auxiliary classifier generative adversarial nets (AC-GANs) for greatly improving the discriminator accuracy.

Subway security check systems (SSCS), which play an important role for smooth operation of subways of metropolises, exhibit a high complexity and randomness due to their multiple queues, mixed services, and heterogeneous passengers. Traditional approaches for evaluating configurations of SSCS either assume a single-queueing model or ignore various attributes of passengers for the sake of simplicity of models, leading to unsatisfactory results. In this paper, we propose a robust framework for evaluating the performance of configurations of SSCS. By considering passenger attributes (e.g., with or without bags) and special services (e.g., special check, additional search), we regard SSCS as two subsystems each of which is modeled as an iterative probabilistic formula, and introduce a hybrid queueing model to evaluate the waiting time of each passenger in SSCS. The parameters of the model are calibrated and validated using the field observation data collected in 24 subway stations of Beijing, which serve as the input of simulation studies. We conduct two simulation studies to evaluate three indices of SSCS: passengers' waiting time in SSCS, K-systematic time of SSCS, and density flow map, and draw three conclusions. (a) 1X2D, 2X3D, 2X4D configurations are suitable for small, medium and large passenger flows, respectively, where mXnD denotes the configuration of m X-ray machines and n detector doors; (b) acceptable efficiency of SSCS is achieved when the ratio between the numbers of X-ray machines and detector doors is in the range of 1:2-1:1, and the efficiency achieves highest at the ratio 1:2; (c) Increasing additional detector doors for no-bag channels can improve the efficiency of SSCS. However, switching existing doors to no-bag channels leads to opposite results. Our findings are beneficial to improving both the capacity and efficiency of SSCS, as well as allocating security check facilities reasonably.

Availability of the big data from Bluetooth MAC Scanners (BMS) over the network provides opportunities to trace the movement of the individual Bluetooth-equipped vehicles on the network. However, BMS might not perfectly detect all the devices within its detection zone. For dense urban networks, the scanner scanning zones can significantly overlap, which complicates the detailed reconstruction of the vehicle trajectory. Addressing the need, this paper proposes a Slit based Trajectory Reconstruction (STATER) algorithm where for each BMS, a slit is defined that considers the overlap and connectivity with other BMS, and thereafter the trajectory is reconstructed considering the shortest path over the observed sequence of slits. A numerical simulation framework is proposed to thoroughly test the proposed STATER algorithm at various levels of ambiguity and randomness in the input dataset. The testing results indicate that the reconstructed trajectories could capture more than 90% (true positive) of the actual path and an average error (false positive) of 11.3% at different randomness levels considered in the experiments. As proof of concept, STATER is applied on one-day data from the entire Brisbane network with 0.56m trips, the computational performance for which supports its practical applicability.

When used in engineering applications, most existing chaotic systems may have many disadvantages, including discontinuous chaotic parameter ranges, lack of robust chaos, and easy occurrence of chaos degradation. In this article, we propose a two-dimensional (2-D) parametric polynomial chaotic system (2D-PPCS) as a general system that can yield many 2-D chaotic maps with different exponent coefficient settings. The 2D-PPCS initializes two parametric polynomials and then applies modular chaotification to the polynomials. Setting different control parameters allows the 2D-PPCS to customize its Lyapunov exponents in order to obtain robust chaos and behaviors with desired complexity. Our theoretical analysis demonstrates the robust chaotic behavior of the 2D-PPCS. Two illustrative examples are provided and tested based on numeral experiments to verify the effectiveness of the 2D-PPCS. A chaos-based pseudorandom number generator is also developed to illustrate the applications of the 2D-PPCS. The experimental results demonstrate that these examples of the 2D-PPCS can achieve robust and desired chaos, have better performance, and generate higher randomness pseudorandom numbers than some representative 2-D chaotic maps.

In early modern Europe information circulated in the form of regular manuscript newsletters. However, most of the once existing news sheets did not survive. This study will present novel methods (random sampling with replacement, aka bootstrapping, and Bayesian statistics) to harness this lost corpus and to analyze its gender dimensions.

Chance-constrained optimization provides a promi-sing framework for solving control and planning problems with uncertainties, due to its modeling capability to capture randomness in real-world applications. In this paper, we consider a UAV trajectory planning problem with probabilistic geo-fence, building on the chance-constrained optimization approach. In the considered problem, randomness of the model, such as the uncertain boundaries of geo-fences, is incorporated in the formulation. By solving the formulated chance-constrained optimization with a novel sampling based solution method, the optimal UAV trajectory is achieved while limiting the probability of collision with geo-fences to a prefixed threshold. Furthermore, to obtain a totally collision-free trajectory, i.e., avoiding the collision not only at the discrete time-steps but also within the entire time horizon, we build on the idea of an iterative scheme. That is, to iterate the solving of the chance-constrained optimization until the collision with probabilistic geo-fence is avoided at any time within the time horizon. At last, we validate the effectiveness of our method via numerical simulations.

This paper presents a novel stochastic event-based super-twisting controller design for addressing the formation control problem in networked multi-agent system in the presence of external disturbance. The stochasticity in the system is introduced by randomness of network uncertainties i.e. losses and delays of data packets. The proposed design has three parts. The first part derives event-triggering condition for control input updates of each agent in the system. The second part guarantees desired formation in finite time by deriving an upper bound on reaching time to the designed sliding surface of each agent. The third part ensures admissibility of event based control updates of the agents such that they achieve stable desired formation. The proposed stochastic design has been tested on Pioneer P3-DX mobile robots. The results show the threefold effectiveness of stochastic design. They are a) retaining the advantages of event-triggering strategy, b) retaining the advantages of super-twisting sliding mode controller, and c) robust towards network uncertainties.

Two synchronization problems, synchronization with probability one and synchronization in probability, are investigated for an array of coupled probabilistic Boolean networks (CPBNs). Compared with the former one, the in-probability problem considers a more general situation, in which synchronization can be achieved with a positive probability, instead of strictly 100%. It reflects the intrinsic randomness of biological systems. For both problems, some necessary and sufficient conditions are proposed, based on which two feasible algorithms are presented for checking two kinds of synchronization, respectively. CPBNs can be seemed as a combination of coupled Boolean networks (CBNs) with assigned probabilities. There are also detailed discussions on the impact of the synchronism of CBNs on the two addressed synchronization problems. The existence of invariant synchronization subsets is studied, which deepens our understanding on the difference and difficulty of these problems. Finally, numerical simulations show the effectiveness of the theoretical results.

The capability of deep learning (DL) techniques for dealing with non-linear, dynamic and correlated data has paved the way for DL-based fault detection and diagnosis (FDD). Among them, autoencoders (AEs) have shown their potential to serve as the fault detection network. However, misclassifying faulty samples that share similar patterns to normal samples is a common drawback of AEs. In this work, a source-aware autoencoder (SAAE) is proposed as an extension of AEs to incorporate faulty samples in the training stage. In SAAE, flexibility in tuning recall and precision trade-off, ability to detect unseen faults and applicability in imbalanced data sets are achieved. Bidirectional long short-term memory (BiLSTM) with skip connections SAAE is designed as the structure of the fault detection network. Further, a deep network with BiLSTM and residual neural network (ResNet) is proposed for the subsequent fault diagnosis step to avoid randomness imposed by the order of the input features. A framework for combining fault detection and fault diagnosis networks is also presented without the assumption of having a perfect fault detection network. A comprehensive comparison among relevant existing techniques in the literature and SAAE-ResNet is also conducted on the Tennessee-Eastman process, which shows the superiority of the proposed FDD method.

In this article, a novel control algorithm is developed for a class of nonlinear stochastic systems subject to multiple disturbances, including exogenous dynamic disturbance and general non-Gaussian noise. An observer is designed to estimate the exogenous disturbance, and then the disturbance compensation is incorporated into a feedback control strategy for the non-Gaussian system. Considering the ability of entropy in randomness quantification, a performance index is established based on the generalized entropy optimization principle. Furthermore, it is adjusted to be available for the controller solution, which also solves the coupling between two kinds of disturbances. On this basis, the optimal controller is provided in a recursive way, with which the closed-loop stability and good antidisturbance ability can be guaranteed simultaneously. Compared with the existing studies on the non-Gaussian stochastic systems, the proposed control algorithm has merits in multiple disturbances decoupling and enhanced antidisturbance performance. Finally, a simulation example is given to demonstrate the effectiveness of theoretical results.

Dynamic and non-linear systems are emerging as potential candidates for random bit generation. In this context, chaotic systems, which are both dynamic and stochastic, are particularly suitable. This paper introduces a new continuous chaotic system along with its corresponding implementation, which targets field-programmable gate arrays (FPGAs). This chaotic system has five dimensions, which exhibit complex chaotic dynamics, thus enabling the utilization of chaotic signals in cryptography. A mathematical analysis is presented to demonstrate the dynamic characteristics of the proposed hyperchaotic system. A novel digital implementation of the proposed system is presented. Moreover, a data scrambling circuit is implemented to eliminate the bias effect and increase the randomness of the bitstream generated from the chaotic signals. We show that the proposed random bit generator has high randomness. The generated bits successfully pass well-known statistical randomness test-suites, i.e., NIST SP800-22, Diehard, and TestU01. The ready-to-use random bit generator is deployed on a Xilinx Zynq-7000 SoC ZC702 Evaluation Kit. Experimental results show that the proposed random bit generator can achieve a maximum throughput of 6.78 Gbps, which is over 3.6 times greater than state-of-the-art designs while requiring under 4% of the resources available on the targeted FPGA.

To support massive connectivity for sporadically active devices is a challenging task, as the randomness of the channel and the large number of users lead to enormous increase of communication overhead. Different to the existing methods that differentiate users in resources including time, frequency and code, we propose a new joint activity detection and channel estimation framework for massive multiple-input multiple-output (MIMO) systems, where angular domain information of active users is exploited to enhance activity detection and channel estimation. By exploiting the sporadic activity of users and the angular spread of the wireless signals, the activity detection and channel estimation is formulated as a compressive sensing problem with multiple measurement vectors, which has a simultaneously row-sparse and clustered sparse structure. The sizes and positions of the nonzero clusters are arbitrary, which brings new challenges for algorithm derivation. To this end, we develop new algorithms based on sparse Bayesian learning, where novel hyper-priors are proposed to capture the structural signal characteristics, and appropriate approximations are employed to facilitate algorithm derivations. Numerical experiments demonstrate the improved activity detection and channel estimation performance of the proposed approach in comparison to the existing methods.

Synchronization of a class of nonlinear singularly perturbed complex networks (SPCNs) with semi-Markov jump topologies and impulsive effects is studied in this article. A complex network with a kind of random switching topologies is considered, where the randomness is depicted by a semi-Markov chain. A method is put forward to obtain the upper bound of singularly perturbed parameter (SPP) with different coupling strengths, and the concept of average impulsive interval is introduced to regulate the frequency of impulses. By utilizing the SPP-dependent semi-Markovian Lyapunov function, some sufficient conditions are derived for achieving synchronization of an SPCN. The effectiveness and validity of the proposed synchronization strategy are verified by two numerical examples.

Morse complexes are gradient-based topological descriptors with close connections to Morse theory. They are widely applicable in scientific visualization as they serve as important abstractions for gaining insights into the topology of scalar fields. Data uncertainty inherent to scalar fields due to randomness in their acquisition and processing, however, limits our understanding of Morse complexes as structural abstractions. We, therefore, explore uncertainty visualization of an ensemble of 2D Morse complexes that arises from scalar fields coupled with data uncertainty. We propose several statistical summary maps as new entities for quantifying structural variations and visualizing positional uncertainties of Morse complexes in ensembles. Specifically, we introduce three types of statistical summary maps – the probabilistic map, the significance map, and the survival map – to characterize the uncertain behaviors of gradient flows. We demonstrate the utility of our proposed approach using wind, flow, and ocean eddy simulation datasets.

Area coverage and network lifetime are two contradictory issues to the architecture development of a wireless sensor network (WSN). A satisfactory balance could be achieved by deploying abundant sensor nodes randomly and dividing them into k exclusive cover sets. Toward self-organized partition with higher efficiency, we address the problem from the perspective of networked potential games and propose a memorial mixed-response algorithm (MMRA), which is implemented in a distributed and synchronous manner. Being viewed as a game player, each sensor node first updates its memory using a temporary action, which is generated by following a mixed response rule. After this, the coordination evolves into the next iteration by each player randomly drawing an action from its memory with equal probabilities. We prove that our algorithm converges with probability 1 to a convention of Nash equilibria, with the worst approximation ratio strictly larger than 0.5. Moreover, it is also found that a tradeoff between solution efficiency and computation time could be achieved via the adjustment of the amount of randomness introduced via the memory length m as well as the probability pₘ, where better partition results are more likely to be generated using a larger m and smaller pₘ. Comparisons with existing distributed methods demonstrate the superiority of our method in terms of solution refinement as well as convergence speed.

This article presents a design-for-test methodology for embedded memories. The methodology relies on a fully random fault model of post-fabrication errors, which results in a low-overhead test strategy. The methodology's effectiveness is demonstrated on an embedded system with faulty memories.

Cryptographic random number generation is critical for any quantum safe encryption. Based on the natural uncertainty of some quantum processes, variety of quantum random number generators or QRNGs have been created with physical quantum processes. They generally generate random numbers with good unpredictable randomness. Of course, physical QRNGs are costic and require physical integrations with computing systems. This paper proposes a pseudo quantum random number generator with a quantum algorithm called quantum permutation pad or QPP, leveraging the high entropy of quantum permutation space its bijective transformation. Unlike the Boolean algebra where the size of information space is 2n for an n-bit system, an n-bit quantum permutation space consists of 2n! quantum permutation matrices, representing all quantum permutation gates over an n-bit computational basis. This permutation space holds an equivalent Shannon information entropy log_2(2^n!). A QPP can be used to create a pseudo QRNG or pQRNG capable integrated with any classical computing system or directly with any application for good quality deterministic random number generation. Using a QPP pad with 64 8-bit permuation matrices, pQRNG holds 107,776 bits of entropy for the pseudo random number generation, comparing with 4096 bits of entropy in Linux /dev/random. It can be used as a deterministic PRNG or entropy booster of other PRNGs. It can also be used as a whitening algorithm for any hardware random number generator including QRNG without discarding physical bias bits., 6 pages, 2 figures, 8 tables, presented and published in QCE2021

In this paper we present a novel, ultra-compact, True Random Number Generator (TRNG) architecture and its FPGA implementation. The proposed Latched Ring Oscillator (LRO) TRNG allows the generation of a TRNG bit from a single FPGA Slice. Despite its very compact structure, the proposed LRO-TRNG relies on both meta-stability and accumulated jitter as entropy sources, and exhibits very good results in terms of unpredictability and randomness. The proposed architecture has been implemented on Xilinx Spartan-6 devices and the TRNG performances have been extensively validated under supply voltage and temperature variations. Measurements results have shown that the LRO-TRNG exhibits an estimated entropy of about 7.99834 per bit (according to T8 test of the AIS-31) and a throughput of 0.76 Mbits/s with a 50MHz clock. A comparison against the state of the art shows that the proposed LRO-TRNG outperforms most of the previously published TRNGs, in terms of the ratio between throughput and FPGA resources usage.

Model predictive control (MPC) using randomized optimization is expected to solve different control problems. However, it still faces various challenges for real-world applications. This paper attempts to solve those challenges and demonstrates a successful implementation of randomized MPC on the autonomous driving using a radio-controlled (RC) car. First of all, a sample generation technique in the frequency domain is discussed. This prevents undesirable randomness which affect the smoothness of the steering operation. Second, the proposed randomized MPC is implemented on a Graphics Processing Unit (GPU). The expected GPU acceleration in calculation speed at various problem sizes is also presented. The results show the improved control performance and computational speed that was not achievable using CPU based implementation. Besides, the selection of parameters for randomized MPC is discussed. The usefulness of the proposed scheme is demonstrated by both simulation and experiments. In the experiments, a 1/10 model RC car is used for collision avoidance task by autonomous driving.

True random number generators (TRNGs) play an important role in encryption systems. In this paper, a novel method of generating true random numbers on a field-programmable gate array (FPGA) is proposed based on the random jitter of a multi-stage feedback ring oscillator(MSFRO) as the entropy source. Based on the traditional ring oscillator, a multi-stage feedback structure is added to enlarge the range of clock jitter, and improve the frequency of clock sampling and the randomness of the entropy source. Different from the traditional clock sampling structure, we use the clock jitter signal generated by the MSFRO to sample the clock signal generated by the phase-locked loop (PLL) of the FPGA. The obtained output value is operated by XOR to reduce the deviation of the output value and improve its randomness. The TRNG is implemented in Xilinx Virtex-6 FPGA, which has low hardware resource consumption and high throughput. The entropy source classification, hardware resources and throughput are compared with those of existing TRNGs. The results showed that the proposed TRNG only consumed 24 LUTs and 2 DFFs. Compared with other TRNGs, this design has very low hardware resource consumption and its throughput is up to 290Mbps. The random bit sequence generated by this TRNG passes the NIST SP800-22 test and the NIST SP800-90B test.

This letter proposes an analytical framework to assess the network performance of a wireless network adopting statistical priority-based multiple access (SPMA) scheme with nodes equipping with directional antennas. By taking into account the spatial randomness of nodes and the impact of directional antenna, we first derive the analytical expressions of medium access probability (MAP), burst success probability, and packet success probability with tools from stochastic geometry. We then verify the correctness of the analytical framework by adopting three typical directional antenna models. By comparing with the scenario where nodes are equipped with omnidirectional antenna, we show the superiority of the directional antennas in limiting the aggregated network interference and enhancing the packet success probability.

As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN system can inherit the advantages of neural networks. However, for streaming data regression, EFN systems still have several drawbacks: 1) determining fuzzy sets is not robust to data sequence; 2) determining fuzzy rules is complex due to subspaces that can approximate to a Takagi-Sugeno-Kang (TSK) rule need to be obtained, and many parameters need to be optimized; 3) it is difficult to detect and adapt to changes in the data distribution, i.e., concept drift, if the output is a continuous variable. Hence, in this paper, a novel evolving-fuzzy-neuro system, called the topology learning-based fuzzy random neural network (TLFRNN), is proposed. In TLFRNN, an online topology learning algorithm is designed to self-organize each layer of TLFRNN. Different from current EFN systems, TLFRNN learns multiple fuzzy sets to reduce the impact of noises on each fuzzy set, and a randomness layer is designed, which assigning the probability of each fuzzy set. Also, TLFRNN does not utilize TSK rules instead uses a simple inference which considering fuzzy and random information of data simultaneously. More importantly, in TLFRNN, concept drift can be detected and adapted easily and rapidly. The experiments demonstrate that TLFRNN achieves superior performance compared to other EFSs.

The design parameters of heat pumps are related to each other nonlinearly or in a complicated manner; therefore, it is difficult to determine the optimal combination of design parameters, such as superheat, subcooling, and refrigerant type, analytically. To address this limitation, three representative heuristic algorithms, namely the genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA), are applied to optimize a heat pump under the given process conditions. Heuristic algorithms are driven based on randomness; thus, the consistency of the calculation results and computational time represent the decision criteria for the appropriate optimizer. The GA is unsuitable as a heat pump optimizer because it requires an excessive number of iterations. In contrast, PSO and SA have a similar capability of consistency and calculation time with a rational number of iterations. In conclusion, PSO exhibits a slightly better consistency and use of computational resources; therefore, PSO is selected as the heat pump design optimization algorithm in this study. The novelty of this work lies in that the related design parameters of the heat pump are simultaneously globally optimized with minimal physical background, and the heuristic algorithm that is most applicable to heat pump design optimization is determined.

Designing high-dimensional chaotic maps with expected dynamic properties is an attractive but challenging task. The dynamic properties of a chaotic system can be reflected by the Lyapunov exponents (LEs). Using the inherent relationship between the parameters of a chaotic map and its LEs, this paper proposes an n-dimensional polynomial chaotic system (nD-PCS) that can generate nD chaotic maps with any desired LEs. The nD-PCS is constructed from n parametric polynomials with arbitrary orders, and its parameter matrix is configured using the preliminaries in linear algebra. Theoretical analysis proves that the nD-PCS can produce high-dimensional chaotic maps with any desired LEs. To show the effects of the nD-PCS, two high-dimensional chaotic maps with hyperchaotic behaviors were generated. A microcontroller-based hardware platform was developed to implement the two chaotic maps, and the test results demonstrated the randomness properties of their chaotic signals. Performance evaluations indicate that the high-dimensional chaotic maps generated from nD-PCS have the desired LEs and more complicated dynamic behaviors compared with other high-dimensional chaotic maps. In addition, to demonstrate the applications of nD-PCS, we developed a chaos-based secure communication scheme. Simulation results show that nD-PCS has a stronger ability to resist channel noise than other high-dimensional chaotic maps.

We propose and experimentally demonstrate a novel multi-channel chaos generation scheme, which can simultaneously produce multiple chaotic signals with wideband spectrum and suppressed time-delay signature (TDS). In this scheme, we introduce an external self-phase-modulated feedback (ESPMF) to improve the optical bandwidth of the initial chaos generated by a conventional external-cavity semiconductor laser (ECSL), then optical tunable filters (OTFs) are utilized to simultaneously extract three-channel chaotic outputs. The experimental results show that the proposed scheme has three main advantages. Firstly, it can simultaneously generate multiple chaotic outputs with different central wavelengths and low correlations. Secondly, the bandwidth of original ECSL-based chaos can be improved by several times, and the undesired TDS characteristics in the original chaos can be completely suppressed to an indistinguishable level (lower than 0.02). Thirdly, the bandwidths of these chaotic outputs are flexible and can be adjusted within a wide range of 20 GHz by controlling the filter bandwidths. In addition, we demonstrate an application of the proposed chaos generation scheme in random bit generation (RBG), and multi-channel high-speed random bit sequences with a total generation rate over Tb/s and NIST verified randomness are simultaneously obtained.

In this letter, a batch authentication protocol called as accurate bloom-filter-based batch authentication (ABBA) protocol is presented for large-scale radio-frequency identification (RFID) systems. Similar to its predecessor BBA, ABBA can estimate the number of invalid tags contained in an unknown tag set in one communication round by exploiting a bloom filter (BF) vector constructed distributively by the tags. Whereas different from BBA, ABBA adopts a novel BF vector construction method where the randomness caused by valid tags is removed, and thus the estimation is guaranteed to be more accurate and stable than BBA. Both theoretical analysis and extensive simulations confirm the superiority of ABBA over BBA.

We propose a key generation protocol with the aid of a reconfigurable intelligent surface (RIS) to boost secret key rate (SKR) in quasi-static environments. Considering a passive eavesdropper, we derive the closed-form expression of the lower and upper bounds of the SKR. Our findings indicate the SKR is determined by the number of RIS elements, the correlation coefficient, the pilot length and the quality of the reflecting channel. Our protocol fully exploits the randomness from the direct channel and the reflecting channel. Monte Carlo simulations validate the analytical expression of the SKR and demonstrate our protocol outperforms existing work.

A method of space ensemble (SE) and time ensemble (TEs) correlation function technique applied on ground penetration radar (GPR) B-scan raw data proves to result in detailed information for tracking buried objects. These computations help in site allocations by time ensemble correlation functions (TESCs) and time stamps of objects scattering by space ensemble correlation functions (SECFs) which are precisely consistent with the simulation results. While these results are applied for given data stack, randomness in physical parameters of tested ground always occurs. An introduction of randomness to the physical parameters of tested models by creating stochastic collocation (SC) ensembles incorporating randomness to B-scan via successive A-scans, SC-SE, and SC-TE ensembles enables sensitivity analysis (SA) study of GPR raw data which gives a tool to change the physical properties of the ground in search for better ground matching. This method can be added to GPR machines or simulation software to enhance raw data analysis. A field experiment of small non-metallic objects' allocation was carried out as a platform for assessing the realistic performance of the method.

In this letter, we present a novel ship detection method for polarimetric synthetic aperture radar (PolSAR) images. Generalized polarization relative entropy (GPRE) is proposed to measure the differences between the target and clutter in scattering mechanism, randomness, and intensity. Since it is difficult to derive a theoretical closed-form of the GPRE, we employ the kernel density estimation to model the distribution of the GPRE in ocean regions. Then, a constant false alarm rate (CFAR) ship detection method is proposed based on the estimated distribution. Experiments performed on both synthetic and real scene images demonstrate the effectiveness of the proposed method.

In this work, two practical concepts related to private information retrieval (PIR) are introduced and coined full support-rank PIR and strongly linear PIR. Being of full support-rank is a technical, yet natural condition required to prove a converse result for a capacity expression and satisfied by almost all currently known capacity-achieving schemes, while strong linearity is a practical requirement enabling implementation over small finite fields with low subpacketization degree. Then, the capacity of MDS-coded, linear, full support-rank PIR in the presence of colluding servers is derived, as well as the capacity of symmetric, linear PIR with colluding, adversarial, and nonresponsive servers for the recently introduced concept of matched randomness. This positively settles the capacity conjectures stated by Freij-Hollanti et al. and Tajeddine et al. in the presented cases. It is also shown that, further restricting to strongly-linear PIR schemes with deterministic linear interference cancellation, the so-called star product scheme proposed by Freij-Hollanti et al. is essentially optimal and induces no capacity loss.

Transportation services play an increasingly significant role for people's daily lives and bring a lot of benefits to individuals and economic development. The randomness and volatility of traffic flows, however, constrains the effective provision of transportation services to a certain extent. Precise traffic flow forecasting becomes the key and primary task to realize the stability of intelligent transport systems and ensure efficient scheduling of traffic. This paper investigates the application of an ensemble approach based on deep belief networks for short-term traffic flow forecasting. Traffic flow data, collected from the real world, is decomposed into several Intrinsic Mode Functions (IMFs) and a residue with EEMD (Ensemble Empirical Mode Decomposition). Then, for each component, the essential feature subset is extracted by the mRMR (minimum Redundancy Maximum Relevance Feature Selection) method considering weather conditions and day properties. Furthermore, each component is trained by DBN (Deep belief networks) and their forecasting results are summed up as the output of the ensemble model at last. Results indicate that the proposed approach achieves significant performance improvement over the single DBN and other selected methods.

In order to improve the automatic generation control (AGC) performance of thermal generators, this paper presents a stochastic model predictive control (SMPC) approach for a battery/flywheel hybrid energy storage system (HESS) to distribute power. The approach combines an adaptive Markov chain for power demand prediction of HESS, a scenario tree generation and model predictive control strategy. To develop an effective prediction model to deal with the randomness of a thermal generator in response to AGC command, a Markov chain is used to describe the randomness of the HESS power demand, and a posteriori information is used to adapt to the fluctuation of the AGC command. A scenario tree generation approach is proposed to make better use of the Markov probability matrix. Based on these efforts, an SMPC approach is proposed for HESS energy management. Simulation results show that the regulation performance of the proposed approach outperforms conventional approaches, and that its performance is close to the model predictive control strategy with prescient information of the future power demand. In addition, compared with other approaches such as rule-based strategies, it does less damage to the battery and yields higher annual average net benefits in the whole life cycle.

The performance of ball screws degrades with two-stage features due to the deteriorating inner mechanism and environmental stress, which lowers the positioning accuracy of the ball screws. It thus affects the accuracy of computer numerical control machine tools. However, no research has been carried out on the degradation reliability for ball screws and current studies usually treat the performance degradation of ball screws as a deterministic, single-stage degradation process, ignoring the randomness and two-stage features in the degradation progress. To this end, we firstly formulated a framework of degradation reliability for ball screws, and then established a two-stage performance degradation model with eight unknown parameters. To estimate the unknown parameters, we proposed a two-phase, expectation-maximization estimation method. Finally, we established a degradation reliability model based on the degradation test data and validated the model through experimental data, which shows the model can capture the real-degradation behavior of ball screws.

Telemedicine and various tele-medicinal applications are revolutionizing a variety of healthcare departments through its innovative means of remote diagnosing and faster first aid administration. Digital images play an important role in these applications for ensuring better and faster health carfigure. These digital images, which usually contain confidential and diagnostic information about the patients, are usually transmitted through public networks among hospitals, doctors and patients. Consequently, there is a need to secure them while being stored and in transit to guarantee the privacy of the patient. However, unique properties of digital image data, such as high redundancy and correlation between the pixels of the image and their large size, make the conventional cryptographical algorithms insufficient for ensuring proper security while encrypting them. As the conventional algorithms seize to be a reliable solution, the need to develop improved and dedicated image encryption algorithms also arises. Chaotic systems are systems that appear random and unpredictable from the outside but are governed by deterministic equations or rules on the inside. Due to these properties, along with ergodicity and its heightened sensitivity to the initial conditions, chaotic systems are devised to be one of the best candidates for securing the storage and transmission of digital images. However, the security of a chaotic image encryption system depends upon the chaotic behavior demonstrated by the chaotic map applied in the image encryption system. Because of this, different attacks can be used to break a chaotic encryption system if the scheme is not well-structured. This paper introduces a chaotic image encryption scheme that incorporates two chaotic maps, namely, Arnold's Cat Map and 2D Logistic-Sine-Coupling Map(2D-LSCM), for increased randomness and security of the encrypted image. We also analyze the performance and security of our scheme and compare it with other prominent chaotic image encryption schemes.

True random number generator (TRNG) as one essential hardware primitive is widely used in cryptography, Monte Carlo simulation, and gambling. To evaluate the security of TRNG, the entropy of the TRNG’s output is usually estimated by the stochastic model in theory or measured off-chip after fabrication. However, the sufficiency of entropy is difficult to be guaranteed in practice due to the facts: 1) the inaccuracy of the model-based jitter measurement method; 2) the variations of the chip manufacturing process and operating environments (such as supply voltage and temperature); and 3) malicious attacks. In this work, we design a novel TRNG architecture with on-chip entropy assurance to properly solve practical security problems. In the design, we propose an on-chip entropy estimator for measuring independent jitter to quantify true randomness, which enables continuous monitoring of TRNG at runtime. Furthermore, with the cooperation of the proposed on-chip entropy estimator and a rational self-adaptive mechanism, the designed TRNG can steadily generate bitstreams with sufficient entropy (≥ 0.999 per bit) against PVT variations. We implement the TRNG architecture in FPGAs with different technology nodes (45 and 65 nm) and SMIC 130 nm chips. Experimental results validate that the designed TRNG has an excellent performance in terms of technology independence and environmental robustness. The generated bitstreams pass the NIST SP800-22 and Diehard statistical test suites successfully without any post-processing.