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This article proposes an ultrawide output range ${LLC}$ resonant converter based on adjustable turns ratio transformer and reconfigurable bridge. Due to the reconfiguration of the transformer by two four ${\bf -}$ quadrant switches, the transformer exhibits three different effective turns ratios, which correspond to three output ranges. Moreover, in case of lower output voltage, the primary side is reconfigured as a half-bridge to further extend the output voltage range. The design of resonant tank parameters is less constrained by the voltage gain requirement. The switching frequency range in each mode can be squeezed close to the resonant frequency, which contributes to an improved soft switch performance. The operational principles, design considerations, and mode transition are analyzed. A 4 A rated prototype that converts 390 V input to a 60–450 V output is designed and tested to validate the concept. A smooth mode transition is achieved without transient issues. The design prototype demonstrates 97.50 $\%$ peak efficiency and good overall efficiency performance over this ultrawide output range.

Energy beamforming takes center stage in radio frequency (RF)-enabled wireless energy transfer (WET) systems. In this letter, we study multi-user energy beamforming to minimize the overall charging time for energy harvesting users (EHUs) with different energy requirements. In order to lessen the cost and power consumption by reducing the required RF chains, it is of consequence to figure out low-rank optimal energy beamforming. By analyzing an upper bound on the rank of a minimum-rank solution, we develop a method to figure out low-rank optimal energy beamforming from the formulated semi-definite programming problem. Our simulation results exhibit the proposed energy beamforming considerably reduces the charging time required until all the requests are met. We also reveal that the capacity achieving precoder for multicasting transmission is a special case of the proposed energy beamforming solution.

Learning disentangled and interpretable representations is an important aspect of information understanding. In this paper, we propose a novel deep learning model representing both discrete and continuous latent variable spaces which can be used in either supervised or unsupervised learning. The proposed model is trained using an optimization function employing the mutual information maximization criterion. For the unsupervised learning setting we define a lower bound to the mutual information between the joint distribution of the latent variables corresponding to the real data and those generated by the model. The maximization of this lower bound during the training induces the learning of disentangled and interpretable data representations. Such representations can be used for attribute manipulation and image editing tasks.

Controller Area Network with Flexible Data-rate (CAN FD) is beneficial for the in-vehicle communication of Internet of Connected Vehicles (IoCVs) because of its high bandwidth and data field length. However, CAN FD lacks a security authentication mechanism, making it extremely vulnerable to masquerade attacks. This study proposes the security enhancement for a real-time parallel in-vehicle application adopting a two-stage method. The first stage obtains the lower bound of an in-vehicle application by quickly abandoning most of sequences, while the second stage enhances security by adding Message Authentication Codes (MACs) to messages taking advantage of the laxity interval from the lower bound to the deadline. Experiments with an example and the adaptive cruise control in-vehicle application show the advantage of the proposed two-stage method in increasing the total byte size of MACs.

The detection and tracking performance of passive radar systems can be enhanced by exploiting multiple emitters and one receiver site by allowing for multiple bistatic geometries. This article investigates the measurement accuracy of passive radar systems that employ the advanced television system committee (ATSC 1.0) signal as a signal of opportunity. The monostatic and bistatic modified Cramer–Rao lower bound for the range and velocity measurements are derived as a quantitative way to evaluate the performance variation when exploiting multiple transmitters. The effect of 3D target position on the signal-to-noise ratio, with respect to stationary transmitters and receiver, is highlighted. Furthermore, signal-to-interference-and-noise ratio modeling is included by considering the direct signal interference suppression performance for a set of transmitters. An example in Columbus, Ohio, for a single receiver and multiple digital television transmitters that are spatially distributed is presented to find the optimal emitter that yields the best range and velocity measurement in the analyzed area. Target-tracking experiments show an agreement between the optimal emitter map and the tracking quality for the set of transmitters.

To address the tracking control problem for n-DOF parallel robots in presence of the lumped disturbance, including modeling errors, friction and external disturbance, a finite-time stable robust sliding mode dynamic control (FRSMDC) for parallel robots is explored. From the implementation condition of the FRSMDC for parallel robots, the limitation on the change rate of the lumped disturbance is relaxed for easy realization. From the results of the FRSMDC for parallel robots, the finite-time stability of the sliding variable is proved and the settling time is derived; the switching gain required is only larger than the upper bound of the disturbance estimation error, instead of the upper bound of the disturbance, due to the feed-forward compensation to the lumped disturbance via a disturbance observer. Consequently, the system robustness is improved and the chattering of FRSMDC is alleviated. The finite-time stability of the closed-loop system is confirmed with Lyapunov theory. Besides, the application of the proposed method is extended to a general multi-input multi-output nonlinear system with the relative degree m by analogy. Finally, the case of the dynamic control of a 6-DOF parallel robot for automobile electro-coating conveying is studied for simulation and experiment, so as to attest the validity of the FRSMDC for parallel robots.

The energy limitation of wireless sensors limits the lifetime of the traditional wireless sensor networks. The Battery-Free Sensor Network (BF-WSN) is a new network architecture proposed in recent years to address the limitation of wireless sensor networks. In a BF-WSN, the battery-free node can harvest energy from the ambient environment, and thus the lifetime of a BF-WSN is unlimited in terms of energy. The coverage quality is an important measurement of BF-WSNs. Considering the specific features of BF-WSNs, we propose a new deployment concept for BF-WSNs, named Joint Deployment. It aims to determine the locations and working schedules of sensor nodes to maximize network coverage quality. Based on the joint deployment concept, we propose a new deployment problem of battery-free sensor nodes. We prove that this problem is at least NP-Hard. We also analyze the upper bound of this problem. Furthermore, we propose an approximated algorithm to solve this problem and analyze the time complexity and the ratio bound of the algorithm. Extensive simulations are carried out to examine the performance of the proposed algorithm. The simulation results show that the algorithm is efficient and effective.

Plane strain compression of cylindrical billets with flat dies has been studied in this paper. A deformation model has been proposed and the dimensions of the deformed billet cross-section for a given press stroke have been determined. To analyze the process, two types of theoretical approaches, i.e., the upper bound technique and the slab method of analysis have been applied and the required forming load has been estimated. Analytical results of billet cross-section deformed geometry and compression load have been compared with the finite element data obtained by the DEFORM 2D ver11.0 finite element package. It is shown that there is a satisfactory agreement between the theoretical predictions and the FE simulation data. The solution can be used in industrial applications for evaluating the required press load to deform the long circular cross-section billets, which is essential for selecting the forming machine with enough capacity.

In our current fast-paced era, customers are often willing to pay extra premium for shorter lead times, which motivates further research on the scheduling problem with due-date assignment and customer-specified lead times. As part of this effort, we extend the classic minsum ‘DIF’ scheduling model to allow optional job-rejection, thus adding an important component of real-life applications, namely, the possibility that the scheduler decides to process only a subset of the jobs and outsource the disjoint set. The scheduler is penalised for rejecting certain jobs by setting job-dependent rejection costs, and he is limited by a given upper bound on the total rejection cost. The most general version of the minsum DIF problem includes job-dependent cost parameters and lead-times, and it is strongly NP-hard. Therefore, we study six variants of the problem, where either only the cost parameters or the lead-times are job dependent. All alternatives are extended by optional job-rejection that possibly bounds the constraints or the underlying cost functions. We establish that all studied problems are NP-hard in the ordinary sense and present pseudo-polynomial dynamic programming algorithms and extensive numerical studies for most solutions.

Simultaneous wireless information and power transfer (SWIPT) is a major breakthrough in the field of low-power wireless information transmissions. In this paper, the secrecy performance of the SWIPT-enabled relay network with full-duplex destination-aided jamming is assessed, where both the power-splitting (PS) and time-switching (TS) schemes at the relay are considered with the linear and nonlinear energy harvesting models. The relay harvests energy from the confidential signal and artificial noise sent by the source and destination, respectively, and forwards the amplified signal to the destination, in the presence of an eavesdropper. The analytical closed-form expressions of the connection outage probability (COP), secrecy outage probability (SOP), and transmission outage probability (TOP) for PS- and TS-based schemes are derived, and the closed-form expression of the lower bound of ergodic secrecy capacity (ESC) is calculated. The asymptotic-form expressions of the COP, SOP, TOP, and ESC are further analyzed to capture the valuable information in the high SNR regime. Numerical results verify the correctness of analytical results, reveal the effects of the PS/TS ratio, and transmit the signal-to-noise ratio on secrecy performance.

The Rao-Nam cryptosystem is a symmetric version of the McEliece code-based cryptosystem proposed to get rid of the shortcomings inherent in the first symmetric code-based encryption schemes. Almost immediately after the publication of this cryptosystem, attacks on it based on selected plaintexts appeared, which led to the emergence of various improvements and modifications of the original cryptosystem. The secret key in the traditional Rao-Nam scheme is a certain Boolean matrix and a set of binary vectors used to generate distortions during encryption. Such vectors must have different syndromes, that is, be different modulo of the code generated by the rows of the specified matrix. The original work of Rao and Nam considered two methods of forming the set of these vectors, the first of which consists in using predetermined vectors of sufficiently large weight, and the second is random selection of these vectors according to the equiprobable scheme. It is known that the first option does not provide the proper security of the Rao – Nam cryptosystem (due to the small number and simple structure of these vectors), but the second option is more meaningful and requires additional research. The purpose of this paper is to obtain estimates of the effectiveness (time complexity for a given upper bound of the error probability) of attacks on a cryptosystem, which generalizes the traditional Rao – Nam scheme to the case of a finite Abelian group (note that the need to study such versions of the Rao – Nam cryptosystem is due to their consideration in recent publications). Two attacks, based on selected plaintext, are presented. The first of them is not mentioned in the works known to the authors of this article and, under certain well-defined conditions, it allows recovering the secret key of the cryptosystem with quadratic complexity. The second attack is a generalized and simplified version of the well-known Struik-van Tilburg attack. It is shown that the complexity of this attack depends on the power of the stabilizer of the set of vectors, which forms the second part of the key, in the translation group of the Abelian group, over which the Rao – Nam cryptosystem is considered. In this paper, a bound is obtained for the probability of triviality of the stabilizer under the condition of random choice of this set. From the obtained bound, it follows that Struik-van Tilburg attack is, on average, noticeably more efficient than the worst case considered earlier.

Due to the intermittent and stochastic nature of solar power, solar power interval prediction is of great importance for grid management and power dispatching. A combination interval prediction model based on the lower and upper bound estimation (LUBE) is proposed to efficiently quantify the solar power prediction uncertainty. In the proposed model, the upper and lower bounds are separately predicted by two prediction engines. The extreme learning machine (ELM) is selected as the basic prediction engine. The auto-encoder technique is used to initialize the input weight matrix of ELM for efficient feature learning. A novel biased convex cost function is developed for ELM to predict the interval boundary. The output weight matrix of ELM can be solved via the convex optimization technique instead of the conventional heuristic algorithm. The proposed interval prediction model can be formulated as a bi-level optimization problem. In the lower-level problem, the lower and upper ELMs are trained under different candidate hyper-parameters of the biased cost function. In the upper-level problem, the optimal combination of the lower and upper prediction engines is determined by evaluating the interval prediction performance. Comprehensive experiments based on public data set are conducted to validate the superiority of the proposed interval prediction model.

As the training dataset size and the model size of machine learning increase rapidly, more computing resources are consumed to speedup the training process. However, the scalability and performance reproducibility of parallel machine learning training, which mainly uses stochastic optimization algorithms, are limited. In this paper, we demonstrate that the sample difference in the dataset plays a prominent role in the scalability of parallel machine learning algorithms. We propose to use statistical properties of dataset to measure sample differences. These properties include the variance of sample features, sample sparsity, sample diversity, and similarity in sampling sequences. We choose four types of parallel training algorithms as our research objects: (1) the asynchronous parallel SGD algorithm (Hogwild! algorithm), (2) the parallel model average SGD algorithm (minibatch SGD algorithm), (3) the decentralization optimization algorithm, and (4) the dual coordinate optimization (DADM algorithm). Our results show that the statistical properties of training datasets determine the scalability upper bound of these parallel training algorithms.

This article is primarily concerned with finite-time convergence (FTC) and predefined-time convergence (PTC) design for a class of general zeroing neural network (ZNN) by constructing different activation functions (AFs). Based on the limit comparison test for improper integrals, some useful theoretical criteria are proposed to determine whether a nonlinear-activated ZNN model has FTC, PTC, or not. This novel method can avoid the valuation loss of the zoom method and the unsolvable barrier of the direct integration method that are widely used in the previous ZNN design. According to these convergence criteria, some instructive corollaries are derived to design valuable AFs to make ZNN models with FTC or PTC more easily. By taking a matrix-inversion ZNN model, some commonly used AFs are used to verify the usability of the criteria. In addition, some new AFs are constructed to further design some better ZNN models with superior FTC or PTC. Finally, convergence types of the ZNN model based on different AFs are visualized in numerical experiments.

The performance of simultaneous wireless information and power transfer (SWIPT) is investigated for an intelligent reflective surface (IRS)-aided wireless system. Tight bounds for the achievable rate and harvested energy are derived in closed-form for the time-switching (TS) and power-splitting (PS) protocols by adopting linear/non-linear energy harvesting models. The achievable rate-energy trade-off of IRS-aided SWIPT is quantified, and the effects of phase-shift quantization at the IRS are investigated. The impact of TS/PS factors, the number of reflective elements, reflective coefficients, and linear versus non-linear energy harvesting models is also investigated through analytical results and Monte-Carlo simulations.

Variable renewable energy sources introduce significant amounts of short-term uncertainty that should be considered when making investment decisions. In this work, we present a method for representing stochastic power system operation in day-ahead and real-time electricity markets within a capacity expansion model. We use Benders’ cuts and a stochastic rolling-horizon dispatch to represent operational costs in the capacity expansion problem (CEP) and investigate different formulations for the cuts. We test the model on a two-bus case study with wind power, energy storage, and a constrained transmission line. The case study shows that cuts created from the day-ahead problem gives the lowest expected total cost for the stochastic CEP. The stochastic CEP results in 3% lower expected total cost compared to the deterministic CEP capacities evaluated under uncertain operation. The number of required stochastic iterations is efficiently reduced by introducing a deterministic lower bound, while extending the horizon of the operational problem by persistence forecasting leads to reduced operational costs.

In this paper, we address the fixed-time consensus tracking problem of second-order leader-follower multi-agent systems with nonlinear dynamics under directed topology. The consensus tracking algorithm consists of distributed observer and observer-based decentralized controller. The fixed-time distributed observer guarantees that each follower estimates the leader’s state under directed topology within a fixed time, where the upper bound of convergence time is independent on the initial conditions. The fixed-time decentralized controller makes each follower converge to the leader’s state in fixed time via tracking the distributed observer’s state and overcome the nonlinear dynamics without adding linear control terms. Finally, the numerical example is provided to illustrate the effectiveness of the results.

Lead extrusion dampers are supplemental energy-dissipation devices that are used to mitigate seismic structural damage. Small volumetric sizes and high force capacities define high-force-to-volume (HF2V) devices, which can absorb significant response energy without sacrificial damage. However, the design of such devices for specific force capacities has proven difficult based on the complexities of their internal reaction mechanisms, leading to the adoption of empirical approaches. This study developed upper- and lower-bound force capacity estimates from analytical mechanics based on direct and indirect metal extrusion for guiding design. The derived equations are strictly functions of HF2V device geometric parameters, lead material properties, and extrusion mechanics. The upper-bound estimates from direct and indirect extrusion are denoted as (FUB,1, FUB,2) and (FUB,3, FUB,4), respectively, and the lower-bound estimates are denoted as (FLB, FLB,1) based on the combination of extrusion and friction forces. The proposed models were validated by comparing the predicted bounds to experimental force capacity data from 15 experimental HF2V device tests. The experimental device forces all lie above the lower-bound estimates (FLB, FLB,1) and below the upper-bound estimates (FUB,1, FUB,2, FUB,4). Overall, the (FLB, FUB,2) pair provides wider bounds and the (FLB,1, FUB,4/FUB,1) pair provides narrower bounds. The (FLB,1, FUB,1) pair has a mean lower-bound gap of 36%, meaning the lower bound was 74% of the actual device force on average. The mean upper-bound gap was 33%. The bulge area and cylinder diameter of HF2V devices are key parameters affecting device forces. These relatively tight bounds provide useful mechanics-based predictive design guides for ensuring that device forces are within the targeted design range after manufacturing.

International audience; Route planning represents a major challenge with a substantial impact on safety, economy, and even climate. An ever-growing urban population caused a significant increase in commuting times, therefore, stressing the prominence of efficient real-time route planning. In essence, the goal is to compute the fastest route to reach the target location in a realistic environment where traffic conditions are time-evolving. Consequently, a large volume of traffic data is potentially required and the route continuously updated. We thereby address the rerouting problem to answer questions such as when, how often, and where is rerouting worthwhile. We base our study on a real dataset, comprising the travel times of the road segments of New York, London, and Chicago, collected over three months. By exploiting this dataset, we implement an optimal algorithm, able to mimic ideal predictions of road segment speeds in the network. Thereby, allowing us to compute the lower bound of travel-time to serve as a reference against other routing techniques. Mainly, we quantify the achieved travel-time gain of a static, no rerouting , and continuous rerouting strategies. Surprisingly, we find that traffic conditions are sufficiently stable for short time windows, and rerouting a vehicle is very seldom useful when exploiting accurate statistics at departure time. Typically, real-time rerouting should only be triggered during rush hours, for long routes, passing through well-identified road segments.

This study hybridizes the Fuzzy Quality Function Deployment (FQFD) and Evidential Reasoning (ER) approaches to prioritize the Transportation Design (TDs) based on Transportation Requirements (TRs) in Road High-demand Public Transportation (RHDPT). This six-step approach identifies the TRs and their Trapezoidal Weights (TWs) using the FQFD approach and based on Trapezoidal Fuzzy Numbers (TFNs) assigned by decision-makers. The second step identifies the TDs based on TRs aided to the FQFD approach. In the third step, the relation matrix between TDs and TRs is configured. Then, the fuzzy interaction weights of TDs are calculated, where TFNs consider the fulfillment of each one of the TDs on TRs. The output of the mentioned step is the Interaction Trapezoidal Weights (ITWs) of the TDs. Finally, the fourth step calculates the TWs of TDs. At the fifth step, we configure the Belief Structure (BS) of ER in order to convert the TWs of TDs to BS of ER. In fact, this step converts the output of the FQFD approach to input on ER. Finally, the priority of TDs is calculated based on the upper and lower bounds of expected values of TDs. Moreover, the superiority of TDs over each other is calculated. This paper not only smooths the chaos and ambiguity aspects of decision makers’ opinions via an efficient Lemma, but it also considers the weights of decision-makers, along with trapezoidal fuzzy numbers. A case study is implemented for the prioritization of TDs in Arba'een event as an RHDPT. The case study results and sensitivity analyses showed the excellent performance of our hybrid FQFD-ER approach to prioritize the TDs in RHDPT.

Providing Ultra-Reliable Low-Latency Communications (URLLC) is one of the key challenges for 5G systems, which requires the redesign of both radio access technologies and the architectures. While 3GPP and the research community focus mainly on radio low layer functionality and architectural changes that enable URLLC, this article proposes a novel cross-layer framework that extends the already standardized 5G solutions to support a very massive number of users/flows in the system. The framework consists of a scheduler, a congestion avoidance algorithm, and a new traffic management algorithm, which together achieve the maximum capacity of 5G systems for URLLC, i.e., they maximize the load at which the reliability and latency requirements are satisfied for at least 99 percent of the users. Extensive performance evaluation results demonstrate that the proposed framework achieves the network capacity very close to the upper bound.

The focus of this study is a joint analysis of the stability of formation control and the capacity of wireless communications between agents moving in a formation. The main results derived in this study show a connection between the second moment stability/convergence of formation control and the expected capacity of the data communications for multiagent systems in which the collective behavior of the multiple agents is dominated by formation control with random errors. More specifically, by developing a joint control-communication framework for the performance analysis, we investigate whether random errors in the formation control have a destructive influence on the data communications between agents. We prove that the expected capacity of the multiagent data communication link has a nonvanishing lower bound that can be evaluated through second moment performance analysis. The results from simulations indicate that the theoretical analysis provides reasonable lower bounds for different system parameters.

Network slicing is expected to become an integral part of future 5G systems providing a simple mechanism for physical network operators to diversify their business models. New Radio (NR) technology operating in millimeter wave (mmWave) band is one of the critical bearers for this functionality, providing extraordinary capacity at the air interface. This paper provides a mathematical tool for assessing the upper and lower bounds of NR BS density needed to maintain the requested slice rate guarantees. The upper bound corresponds to the full traffic isolation between slices while the lower one — to the full mixing of traffic from the slices. To this aim, we unite the tools of stochastic geometry and queuing theory formulating a performance evaluation framework that allows assessing the rate violation metrics in a dynamic network slicing environment. The developed framework captures specifics of mmWave NR technology, including antenna directivity at the UE and NR BS sides, propagation and blockage losses, as well as the service process with location-dependent resource requirements. Our results show that for considered schemes, the operational regime of the system changes abruptly with respect to the density of NR BSs. The difference between full isolation and full mixing schemes becomes bigger in environments with high session arrival intensities that naturally require dense deployments. Thus, at the initial market penetration phase, full isolation can be used without compromising the network performance. However, at mature stages, more complex schemes are needed to reduce the capital expenditures of the operators. acceptedVersion

This work presents a numerical study on the use of multi-resolution strategies for the computationally challenging problem of optimal design of high-resolution nonlinear electro-active shell-type devices using Topology Optimization methods. This paper puts forward the following novelties. First, it presents a tailor-made in-plane multi-resolution technique where a higher level of resolution for the density field is permitted within the in-plane surface of the electro-active material. Second, a numerical study is carried out to elucidate the lower bound for the critical ratio between the density resolution level (or the number of density voxels within every element of the coarse analysis mesh) and the filtering length that can be used to avoid the presence of QR-patterns in the unexplored scenario of nonlinear electromechanics, without compromising the computational efficiency of the multi-resolution scheme. The numerical experiments illustrate the benefits of the proposed methodology to obtain high-resolution designs with a reasonable computational cost and reveal the possibility for more flexible bounds for the critical ratio as those reported in linear elasticity.

We use distributionally-robust optimization for machine learning to mitigate the effect of data poisoning attacks. We provide performance guarantees for the trained model on the original data (not including the poison records) by training the model for the worst-case distribution on a neighbourhood around the empirical distribution (extracted from the training dataset corrupted by a poisoning attack) defined using the Wasserstein distance. We relax the distributionally-robust machine learning problem by finding an upper bound for the worst-case fitness based on the empirical sampled-averaged fitness and the Lipschitz-constant of the fitness function (on the data for given model parameters) as regularizer. For regression models, we prove that this regularizer is equal to the dual norm of the model parameters.

Support vector guided dictionary learning, as a discriminative dictionary learning method combining with support vector machine (SVM), embodies the margin maximization principle and achieves good generalization performances in many practical applications. However, this method ignores the key fact that the generalization performance of the SVM classifier depends not only on the margin between two classes of training samples, but also on the radius of the smallest sphere covering them. In the paper, we propose a novel method called generalization-error-bound-based discriminative dictionary learning (GEBDDL). The basic insight of GEBDDL is that the coding vectors, which are used to build the SVM classifier, are not fixed during the learning process. As a result, the radius of the smallest sphere changes with the learned coding vectors. The key feature of GEBDDL is that it explicitly incorporates the radius-margin-bound, which is directly related to the upper bound of the leave-one-out error of SVM, into its objective function to guide learning the dictionary and the coding vectors, and building the SVM classifier. In the paper, we first elaborate our motivation and propose the optimization model and then discuss how to solve it in detail. Further, we explore how to approximate the radius of the smallest sphere in our methodology. This can enhance the computational efficiency by bypassing the quadratic programming problem of computing the radius, while yielding a close performance to GEBDDL. Finally, the comprehensive experiments are conducted on several benchmark datasets, and the results demonstrate the superiority of the proposed methods over the other competing methods.

This paper addresses the relative position coordinated control problem for spacecraft formation flying under an undirected communication graph, whilst considering mass uncertainties, external disturbances, and limited communication resources. A new event-triggered information transmission mechanism is first presented, where each spacecraft only requires accessing to the states of neighbors intermittently. Subsequently, a novel event-based coordinated control scheme is proposed by combining a smooth adaptive projection rule that confines the parameter estimations to well-defined bounded convex hypercubes. Under the proposed control framework, the information exchange among spacecraft occurs only when the specified event is triggered, thereby significantly reducing the communication load and saving the onboard resources. Furthermore, a positive lower bound on interevent time intervals is guaranteed to exclude Zeno behavior. By virtue of Lyapunov stability analysis and graph theory, it is proved that the relative position tracking errors can converge to small invariant sets around the origin, and that all closed-loop signals are bounded, even in the presence of mass uncertainties and external disturbances. Finally, numerical simulations are given to evaluate the effectiveness and highlight the advantages of the developed control algorithm.

Intelligent reflecting surface (IRS) is a revolutionizing technology to boost the energy and spectrum efficiency by reconfiguring wireless propagation environment. In this letter, IRS is leveraged to improve the performance of the cell-edge user as well as the coordinated multi-point transmission (CoMP). Particularly, an uplink power minimization problem is formulated to jointly optimize the power allocation and the phase shifts in a two-cell IRS-aided non-orthogonal multiple access (NOMA) system with joint detection. To render the original problem tractable, the joint optimization problem of power allocation and phase shift is converted into a pure phase shift determination problem. Furthermore, a low-complexity method with closed-form solution is developed to address the transformed problem. It is shown by simulation that the power consumption of the proposed scheme is quite close to the lower bound and is less than those of various benchmarking schemes.

In this paper, a novel adaptive fast nonsingular terminal sliding mode (FNTSM) control approach is proposed for the robust adaptive finite-time prescribed performance attitude tracking control of spacecraft subject to inertia uncertainties, external disturbances, and input saturation. First, a simple error transformation is introduced to guarantee the attitude tracking errors always stay within the predefined performance bounds. Then, a FNTSM surface is presented based on the transformed attitude tracking errors. Finally, an adaptive FNTSM controller is designed by using the adaptive updating law to estimate the square of the norm of the lumped uncertain term. Rigorous theoretical analysis for the practical finite-time stability of the resulting closed-loop system is provided. The proposed adaptive FNTSM controller can guarantee the attitude tracking errors converge to the arbitrarily small region about zero in finite time within the predefined performance bounds. Benefiting from the adaptive estimation technique, the proposed adaptive FNTSM controller is continuous and the unexpected chattering phenomenon is significantly reduced. Moreover, the prior knowledge on the upper bound of the lumped uncertain term is no longer needed in the control design. Simulation experiments illustrate the effectiveness and superiority of the proposed control approach.

For the first time, the lower bound shakedown theorem is integrated into a level set–based topology optimization framework to identify lightweight elastoplastic designs. Shakedown is a cyclic elastoplastic behavior in which, upon cycling beyond the elastic limit, the accumulation of plastic strain arrests and purely elastic behavior is recovered. In contrast to most elastoplastic topology optimization, the use of a lower bound shakedown limit allows elastoplastic shakedown limits to be rigorously estimated using only the elastic solution. Under small deformation assumptions, this amounts to solving one simple partial differential equation, avoiding the non-linearity associated with plasticity, and thus simplifying the resolution process. Numerical results are provided for several benchmark examples. The results highlight the design performance enhancements attributed to allowing elastoplastic shakedown to occur instead of designing to first yield. In particular, up to 10% reduction in weight is found for the simple structures considered.

This paper addresses the challenge of synchronized multiple spacecraft attitude reorientation in presence of pointing and boundary constraints with limited inter-spacecraft communication link. Relative attitude pointing constraint among the fleet of spacecraft has also been modeled and considered during the attitude maneuvers toward the desired states. Formation fling control structure that consists of decentralized path planners based on virtual structure approach joint with discrete time optimal local controller is designed to achieve the mission’s goals. Due to digital computing of spacecraft’s onboard computer, local optimal controller based on discrete time prediction and correction algorithm has been utilized. The time step of local optimal algorithm execution is designed so that the spacecraft track their desired attitudes with appropriate error bound. The convergence of the proposed architecture and stability of local controller’s tracking error within appropriate upper bound are proved. Finally, a numerical simulation of a stereo imaging scenario is presented to verify the performance of the proposed architecture and the effectiveness of the algorithm.

Cache-aided wireless device-to-device (D2D) networks have demonstrated more promising performance improvement for video distribution than conventional distribution methods; thus, understanding the fundamental scaling behavior of such networks is highly important. However, the existing scaling laws for multi-hop networks are not optimal even in the case of Zipf popularity distributions (gaps between upper and lower bounds are not constants); furthermore, there are no scaling law results for such networks for the more practical case of a Mandelbrot-Zipf (MZipf) popularity distribution. We thus in this work investigate the throughput-outage performance for cache-aided wireless D2D networks adopting multi-hop communications, with the MZipf popularity distribution for file requests and users distributed according to Poisson point process. We propose an achievable content caching and delivery scheme, and then analyze its performance. We obtain the optimal scaling law by showing that the achievable performance is tight to the proposed outer bound. Since the Zipf distribution is a special case of the MZipf distribution, the optimal scaling law for the networks considering the Zipf popularity distribution is also obtained, which closes the gap in literature.

Radio frequency identification (RFID) has been widely used in many fields such as object tracking and inventory management. For RFID systems, grouping is a fundamental issue which can support efficient multicast transmissions, dynamic tag management, and accurate aggregate queries. Existing grouping protocols have drawbacks of unknown theoretical communication time, high computational cost on the server end and inability to deal with unexpected tags which are those tags whose IDs have not been collected by readers. In this paper, we would like to address the above limitations and consider a more general grouping problem that allows an arbitrary number of unexpected tags to present. Our objective is to design a protocol that guarantees the reader to efficiently and correctly notify each known tag of its group-ID, while the probability that an unexpected tag is mistakenly notified of any group-ID is smaller than a pre-determined value. In this paper, we first obtain a lower bound on the communication time for solving this generalized grouping problem. Then, we propose a near-optimal protocol, called OPT-G, and prove that its communication time approximately equals the lower bound. Finally, we report extensive simulation results that demonstrate OPT-G's near-optimal performance and its superiority over existing baseline schemes.

Elliptic localization where a transmitter actively sending out a signal to locate an object from its echo appears in many practical systems including multiple-input-multiple-output (MIMO) radars and wireless communications. In this article, the elliptic localization problem when the transmitter position is not known is addressed. We propose an effective method for joint estimation of the object and transmitter positions. Using the range measurements from the direct paths between the transmitter and the receivers together with the indirect paths through the object, we formulate a non-convex weighted least squares (WLS) minimization problem. The non-convex nature of this problem makes it difficult for an iterative search algorithm to reach the optimal solution, implying that good estimate is not guaranteed. To handle this difficulty, we propose to solve the formulated WLS localization problem by applying the semidefinite relaxation (SDR) technique, resulting in a convex semidefinite program (SDP). The proposed SDR method is then extended to the more general case when multiple transmitters at unknown positions are used and the receiver positions are subject to errors. Simulation results show that the proposed method can attain the Cramer-Rao lower bound (CRLB) accuracy under Gaussian noise and has superior performance over the existing method.

Distributed scheduling is an attractive approach for the Multiple-Access Channel (MAC). However, when a subset of the users access the channel simultaneously, distributed rate coordination is necessary, and is a major challenge, since the achievable rate of each user highly depends on the channels of other active users. That is, given a detection technique, e.g., Zero-Forcing (ZF), the rate at which a user can transmit depends on the channels other transmitting users have, a knowledge which is usually unavailable in distributed schemes. Fixing a rate and accepting some outage probability when this rate is too high is common practice in these cases. In this paper, we analyze the outage probability of a distributed, asymptotically optimal threshold-based scheduling algorithm under ZF. We rigorously evaluate the distribution of the relevant projections and give upper and lower bounds on the outage probability as a function of the algorithm parameters. At the limit of a large number of users, the bounds match, resulting in the true asymptotic characterization of the outage probability.

Big data can help with providing valuable perceptions into business activities and disclosing the potential benefits. Advances in machine learning and deep learning technologies make it easier to achieve significant performance in a wide range of domains from city planning and marketing analysis to credit evaluation and identity theft detection. However, it still requires great efforts in selecting efficient learning algorithms and precise model parameters that are deemed confidential in the light of experience. Also worth noting is that there is a fundamental gap between impracticable business requirements and the available value of data reflected. The data holder or data service provider may not have a clear understanding of data interference. The solution to these two problems depends on the capability of predicting the data utility in advance, which raises a fundamental question: to what degree is the data utility predictable? In this work, we present a primary analytical framework for information-theoretic bounds of data utility and utilize the current state-of-the-art and representative algorithms to obtain the achievable lower bounds on a real-world data set. The gap between theoretical upper bounds and achievable lower bounds indicates that the achievable lower bounds can still be optimized for performance.

This letter presents a detailed investigation on the performance of reconfigurable intelligent surface (RIS)-assisted communication system with user scheduling. Depending on the availability of channel state information (CSI) at the RIS, two separate scenarios are considered, namely without CSI and with CSI. Closed-form expressions are derived for the ergodic capacity of the system in both scenarios. It is found that CSI has a significant impact on the performance of the system. Without CSI, the RIS provides an array gain of N, where N is the number of reflecting elements, and user scheduling provides an multi-user gain of $\log \log \text {M}$ , where M is the number of users. With CSI, the RIS provides an array gain of $\text {N}^{2}$ , while no multi-user diversity gain can be obtained.

Coded distributed computing (CDC) can alleviate the communication load in distributed computing systems by leveraging coding opportunities via redundant computation. While the optimal computation-communication tradeoff has been well studied for homogeneous systems, it remains largely unknown for heterogeneous systems where workers have different computation capabilities. This paper characterizes the upper and lower bounds of the optimal communication load as two linear programming problems for a general heterogeneous CDC system using the MapReduce framework. Our achievable scheme first designs a parametric data shuffling strategy for any given mapping strategy, and then jointly optimizes the mapping strategy and the data shuffling strategy to obtain the upper bound. The parametric data shuffling strategy allows adjusting the size of the multicast message intended for each worker set, so that it can largely decrease the number of unicast messages and hence increase the communication efficiency. Numerical results show that our achievable communication load is lower than those achieved in existing works. Our lower bound is established by unifying an improved cut-set bound and a peeling method. The obtained upper and lower bounds degenerate to the existing result in homogeneous systems, and coincide with each other when the system is approximately homogeneous or grouped homogeneous.

The effect of underground water levels on the stability of soft rock tunnel faces was analyzed in this paper. The underground water levels was divided into three cases, and the Hoek–Brown failure criterion was introduced into the kinematic approach by "tangent method", then three kinds of stability analysis model of tunnel faces under different underground water levels was constructed. Based on the principle of virtual power, the expressions of supporting force on the tunnel faces of soft rock under different underground water levels were derived. Moreover, the optimal upper limit solution of the supporting force on tunnel faces was obtained by using the sequential quadratic programming algorithm with Matlab software. After that, the effect of underground water levels on the supporting force of tunnel faces was analyzed, and the damage range of tunnel faces under different underground water levels was given. This paper provides some reference value for setting parameters of shield tunneling in water-rich section.

The current research on cooperative localization of mobile cluster networks mainly focus on 2D networks with high-quality wireless environment. This study considers the problem in 3D mobile networks, a fault-tolerant cooperative localization algorithm based on two-layer filter Multidimensional Scaling (Filter-MDS) is proposed. First, the non-line-of-sight (NLOS) error in the wireless environment is expanded, and the concept of random error environment is proposed. Secondly, a location framework suitable for 3D mobile networks is constructed and the Cramer-Rao lower bound (CRLB) of 3D location in random error environment is derived. Filters based on multi-time motion state and geometric configuration are introduced into the location framework to construct Filter-MDS. Simulation experiments show that Filter-MDS can effectively identify the abnormal errors in range measurement and improve cooperative localization accuracy of mobile cluster network in random error environment.

The problem of attitude takeover control of spacecraft by using cellular satellites with limited communication, actuator faults and input saturation is investigated. In order to lighten the communication burden of cellular satellites, an event-triggered control strategy is adopted. The filtered attitude information needs to be transmitted only when the defined measurement error reaches the event-triggered threshold in this strategy. Then, to deal with the unknown inertia matrix, actuator faults, external disturbances and the errors caused by event-triggered scheme, fuzzy logic systems is introduced to estimate the uncertainties directly. Combining fuzzy logic control strategy and the event-triggered method, the first event-triggered adaptive fuzzy control law is developed. Then, torque saturation of cellular satellites is further considered in the second control law, where the upper bound of the uncertainties is estimated by fuzzy logic systems. The resulting closed-loop systems under the two control laws are guaranteed to be bounded. Finally, the effectiveness of two proposed control laws is verified by the numerical simulations.

Over the past decades, multiple antenna technologies have appeared in many different forms, most notably as multiple-input multiple-output (MIMO), to transform wireless communications for extraordinary diversity and multiplexing gains. The variety of technologies has been based on placing a number of antennas at fixed locations which dictates the fundamental limit on the achievable performance. By contrast, this paper envisages the scenario where the physical position of an antenna can be switched freely to one of the N positions over a fixed-length line space to pick up the strongest signal in the manner of traditional selection combining. We refer to this system as a fluid antenna system (FAS) for tremendous flexibility in its possible shape and position. The aim of this paper is to study the achievable performance of a single-antenna FAS system with a fixed length and N in arbitrarily correlated Rayleigh fading channels. Our contributions include exact and approximate closed-form expressions for the outage probability of FAS. We also derive an upper bound for the outage probability, from which it is shown that a single-antenna FAS given any arbitrarily small space can outperform an L-antenna maximum ratio combining (MRC) system if N is large enough. Our analysis also reveals the minimum required size of the FAS, and how large N is considered enough for the FAS to surpass MRC., 26 pages, 5 figures

The performance-based seismic design has two primary concerns: (a) appropriate quantification of the uncertainties associated with the performance evaluation process, and (b) satisfactory characterization of the associated structural damage for direct incorporation into the design or performance evaluation methodology. This study attempts to address these primary concerns by evaluating the performance of reinforced concrete frame using nonlinear static procedures. For this, fifteen-moment resisting frames designed following the guidelines of Indian seismic codes were subjected to different lateral load patterns. The seismic performance is investigated in terms of fundamental periods, roof displacements, interstory drift ratio, base shear, and modification factor and was compared with various performance limits. The obtained results showed disagreement with Indian seismic code provisions, especially, towards the fundamental time period, upper and lower bound values of base shear drift ratio and modification factor.

Large number of antennas and radio frequency (RF) chains at the base stations (BSs) lead to high energy consumption in massive MIMO systems. Thus, how to improve the energy efficiency (EE) with a computationally efficient approach is a significant challenge in the design of massive MIMO systems. With this motivation, a learning-based stochastic gradient descent algorithm is proposed in this article to obtain the optimal joint uplink and downlink EE with joint antenna selection and user scheduling in single-cell massive MIMO systems. Using Jensen’s inequality and the characteristics of wireless channels, a lower bound on the system throughput is obtained. Subsequently, incorporating the power consumption model, the corresponding lower bound on the EE of the system is identified. Finally, learning-based stochastic gradient descent method is used to solve the joint antenna selection and user scheduling problem, which is a combinatorial optimization problem. Rare event simulation is embedded in the learning-based stochastic gradient descent method to generate samples with very small probabilities. In the analysis, both perfect and imperfect channel side information (CSI) at the BS are considered. Minimum mean-square error (MMSE) channel estimation is employed in the study of the imperfect CSI case. In addition, the effect of a constraint on the number of available RF chains in massive MIMO system is investigated considering both perfect and imperfect CSI at the BS.

We consider the problem of topology recognition in wireless (radio) networks modeled as undirected graphs. Topology recognition is a fundamental task in which every node of the network has to output a map of the underlying graph i.e., an isomorphic copy of it, and situate itself in this map. In wireless networks, nodes communicate in synchronous rounds. In each round a node can either transmit a message to all its neighbors, or stay silent and listen. At the receiving end, a node v hears a message from a neighbor w in a given round, if v listens in this round, and if w is its only neighbor that transmits in this round. Nodes have labels which are (not necessarily different) binary strings. Each node knows its label and can use it when executing the algorithm. The length of a labeling scheme is the largest length of a label. We concentrate on wireless networks modeled by trees, and we investigate two problems. • What is the shortest labeling scheme that permits topology recognition in all wireless tree networks of diameter D and maximum degree Δ? • What is the fastest topology recognition algorithm working for all wireless tree networks of diameter D and maximum degree Δ, using such a short labeling scheme? We are interested in deterministic topology recognition algorithms. For the first problem, we show that the minimum length of a labeling scheme allowing topology recognition in all trees of maximum degree Δ ≥ 3 is Θ ( log ⁡ log ⁡ Δ ) . For such short labeling schemes, used by an algorithm working for the class of trees of diameter D ≥ 4 and maximum degree Δ ≥ 3 , we show almost matching bounds on the time of topology recognition: an upper bound O ( D Δ ) , and a lower bound Ω ( D Δ ϵ ) , for any constant ϵ 1 . Our upper bounds are proven by constructing a topology recognition algorithm using a labeling scheme of length O ( log ⁡ log ⁡ Δ ) and using time O ( D Δ ) . Our lower bounds are proven by constructing a class of trees for which any topology recognition algorithm must use a labeling scheme of length at least Ω ( log ⁡ log ⁡ Δ ) , and a class of trees for which any topology recognition algorithm using a labeling scheme of length O ( log ⁡ log ⁡ Δ ) must use time at least Ω ( D Δ ϵ ) , on some tree of this class.

In this letter, we propose to exploit an intelligent reflecting surface (IRS) to secure multiuser two-way communications. Specifically, two end users simultaneously transmit their signals, where the signal of one user is exploited as information jamming to disrupt the reception of the other user's signal at an eavesdropper. A simple user scheduling scheme is developed to improve the secrecy rate. Analytical results in terms of a lower bound on the average secrecy rate and its scaling laws are derived to evaluate the secrecy performance and obtain valuable design insights. Simulation results are provided to confirm the accuracy of the theoretical analysis and validate the secrecy improvement of the proposed scheme over other baseline schemes., To appear in IEEE Communications Letters

This paper shows solicitude for the quantization synchronization of delayed chaotic master and slave neural networks under an dynamic event-triggered strategy. In virtue of a generalized Halanay-type inequality, a theoretical criterion for quasi-synchronization of master and slave neural networks is derived. Meanwhile, we can obtain an exact upper bound of synchronization error by using this criterion. Compared with output feedback controller with event triggering and quantization, the case where the controller only affected by quantization is also considered. Then, we exclude the Zeno behavior of the event-triggered controller. A sufficient criterion for the existence of the quantized output feedback controllers is also provided. A numerical example is cited to illustrate the efficiency of our theoretical criteria. In addition, some experiments of secure image communication are conducted under quasi-synchronization of master and slave neural networks.

An effective lower bound on the entropy of some explicit quadratic plane Cremona transformations is given. The motivation is that such transformations (Hénon maps, or Feistel ciphers) are used in symmetric key cryptography. Moreover, a hyperbolic plane Cremona transformation g is rigid, in the sense of [5], and under further explicit conditions some power of g is tight.

Media-based modulation (MBM) with massive multiple-input multiple-output (mMIMO) wireless systems is a viable solution to realize the ever-increasing demand for high-speed data and extensive connectivity in beyond 5G and 6G wireless communications. MBM-mMIMO utilizes less transmit power and radio resources measured against mMIMO to yield high spectral efficiency and high data rate. However, symbol detection in the uplink of MBM-mMIMO is challenging due to the sparse nature of the received signal, and the cumulative effect of inter-user interference and noise. In this letter, support recovery error constraint-based low complexity sequential symbol detection technique is proposed for uplink MBM-mMIMO system. The proposed MBM-mMIMO detection technique exploits the upper bound on support recovery error and iteratively minimizes the residual error associated with the estimated transmit vector. A reduced message space is also introduced to further enrich the exploration capability of the proposed technique. Simulation results reveal the viability of proposed techniques over several state-of-the-art MBM-mMIMO detection techniques as BER performance and computational complexity are concerned.

In this paper, we focus on intelligent reflecting surface (IRS) assisted multi-antenna communications with transceiver hardware impairments encountered in practice. In particular, we aim to maximize the received signal-to-noise ratio (SNR) taking into account the impact of hardware impairments, where the source transmit beamforming and the IRS reflect beamforming are jointly designed under the proposed optimization framework. To circumvent the non-convexity of the formulated design problem, we first derive a closed-form optimal solution to the source transmit beamforming. Then, for the optimization of IRS reflect beamforming, we obtain an upper bound to the optimal objective value via solving a single convex problem. A low-complexity minorization-maximization (MM) algorithm was developed to approach the upper bound. Simulation results demonstrate that the proposed beamforming design is more robust to the hardware impairments than that of the conventional SNR maximized scheme. Moreover, compared to the scenario without deploying an IRS, the performance gain brought by incorporating the hardware impairments is more evident for the IRS-aided communications., Accepted by IEEE Transactions on Communications