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Bluetooth Low Energy (BLE) has become one of the enabling wireless technologies to facilitate the Internet of Things. Neighbor discovery is critical in BLE communications. BLE uses multiple (three) channels in neighbor discovery. It is challenging to achieve low-latency and low-energy-consumption BLE neighbor discovery due to the lack of analytical models for multichannel neighbor discovery. In this paper, we study BLE multichannel neighbor discovery for two advertising modes specified by BLE: periodic deterministic advertising (PDA) and pseudo-random delay advertising (RDA). We build two generic models, BLE 3-Circle model and BLE 1-Circle model, for characterizing BLE multichannel neighbor discovery. For PDA mode, we present a necessary and sufficient condition for BLE multichannel neighbor discovery, and provide a guideline for parameter setting. With the guideline, we derive the expected discovery latency in closed form, and demonstrate that the expected discovery latency is very close to a theoretical lower bound. For RDA mode, we build an analytical model based on Markov chain to accurately compute the expected discovery latency. Simulation and experimental results show accuracy of our analytical works. Interestingly, our parameter setting guideline works well for both PDA and RDA mode.

In this paper, the physical layer security aspects of a wireless framework over α − κ − μ shadowed (AKMS) fading channel are examined by acquiring closed-form novel expressions of average secrecy capacity, secure outage probability (SOP), and strictly positive secrecy capacity. The lower bound of SOP is derived along with the asymptotic expression of SOP at the high signal-to-noise ratio regime in order to achieve secrecy diversity gain. Capitalizing on these expressions, the consequences due to the simultaneous occurrence of fading and shadowing are quantified. Finally, Monte-Carlo simulations are demonstrated to assess the correctness of the expressions.

In the wideband regime, the performance of many of the popular modulation schemes such as code division multiple access and orthogonal frequency division multiplexing falls quickly without channel state information. Obtaining the amount of channel information required for these techniques to work is costly and difficult, which suggests the need for schemes which can perform well without channel state information. In this work, we present one such scheme, called wideband time frequency coding, which achieves rates on the order of the additive white Gaussian noise capacity without requiring any channel state information. Wideband time frequency coding combines impulsive frequency shift keying with pulse position modulation, which allows for information to be encoded in both the transmitted frequency and the transmission time period. On the detection side, we propose a non-coherent decoder based on a square-law detector, akin to the optimal decoder for frequency shift keying based signals. The impacts of various parameters on the symbol error probability and capacity of wideband time frequency coding are investigated, and the results show that it is robust to shadowing and highly fading channels. When compared to other modulation schemes such as code division multiple access, orthogonal frequency division multiplexing, pulse position modulation, and impulsive frequency shift keying without channel state information, wideband time frequency coding achieves higher rates in the wideband regime, and performs comparably in smaller bandwidths.

Identifying drug-target interactions has been a key step in drug discovery. Many computational methods have been proposed to directly determine whether drugs and targets can interact or not. Drug-target binding affinity is another type of data which could show the strength of the binding interaction between a drug and a target. However, it is more challenging to predict drug-target binding affinity, and thus a very few studies follow this line. In our work, we propose a novel co-regularized variational autoencoders (Co-VAE) to predict drug-target binding affinity based on drug structures and target sequences. The Co-VAE model consists of two VAEs for generating drug SMILES strings and target sequences, respectively, and a co-regularization part for generating the binding affinities. We theoretically prove that the Co-VAE model is to maximize the lower bound of the joint likelihood of drug, protein and their affinity. The Co-VAE could predict drug-target affinity and generate new drugs which share similar targets with the input drugs. The experimental results on two datasets show that the Co-VAE could predict drug-target affinity better than existing affinity prediction methods such as DeepDTA and DeepAffinity, and could generate more new valid drugs than existing methods such as GAN and VAE.

Deep learning models have been shown to be vulnerable to adversarial attacks. Adversarial attacks are imperceptible perturbations added to an image such that the deep learning model misclassifies the image with a high confidence. Existing adversarial defenses validate their performance using only the classification accuracy. However, classification accuracy by itself is not a reliable metric to determine if the resulting image is "adversarial-free". This is a foundational problem for online image recognition applications where the ground-truth of the incoming image is not known and hence we cannot compute the accuracy of the classifier or validate if the image is "adversarial-free" or not. This paper proposes a novel privacy preserving framework for defending Black box classifiers from adversarial attacks using an ensemble of iterative adversarial image purifiers whose performance is continuously validated in a loop using Bayesian uncertainties. The proposed approach can convert a single-step black box adversarial defense into an iterative defense and proposes three novel privacy preserving Knowledge Distillation (KD) approaches that use prior meta-information from various datasets to mimic the performance of the Black box classifier. Additionally, this paper proves the existence of an optimal distribution for the purified images that can reach a theoretical lower bound, beyond which the image can no longer be purified. Experimental results on six public benchmark datasets namely: 1) Fashion-MNIST, 2) CIFAR-10, 3) GTSRB, 4) MIO-TCD, 5) Tiny-ImageNet, and 6) MS-Celeb show that the proposed approach can consistently detect adversarial examples and purify or reject them against a variety of adversarial attacks.

For a given graph $G$, the metric and edge metric dimensions of $G$, $\dim(G)$ and ${\rm edim}(G)$, are the cardinalities of the smallest possible subsets of vertices in $V(G)$ such that they uniquely identify the vertices and the edges of $G$, respectively, by means of distances. It is already known that metric and edge metric dimensions are not in general comparable. Infinite families of graphs with pendant vertices in which the edge metric dimension is smaller than the metric dimension are already known. In this article, we construct a 2-connected graph $G$ such that $\dim(G)=a$ and ${\rm edim}(G)=b$ for every pair of integers $a,b$, where $4\le b, Comment: 12 pages

Many data sources, such as human poses, lie on low-dimensional manifolds that are smooth and bounded. Learning low-dimensional representations for such data is an important problem. One typical solution is to utilize encoder-decoder networks. However, due to the lack of effective regularization in latent space, the learned representations usually do not preserve the essential data relations. For example, adjacent video frames in a sequence may be encoded into very different zones across the latent space with holes in between. This is problematic for many tasks such as denoising because slightly perturbed data have the risk of being encoded into very different latent variables, leaving output unpredictable. To resolve this problem, we first propose a neighborhood geometric structure-preserving variational autoencoder (SP-VAE), which not only maximizes the evidence lower bound but also encourages latent variables to preserve their structures as in ambient space. Then, we learn a set of small surfaces to approximately bound the learned manifold to deal with holes in latent space. We extensively validate the properties of our approach by reconstruction, denoising, and random image generation experiments on a number of data sources, including synthetic Swiss roll, human pose sequences, and facial expression images. The experimental results show that our approach learns more smooth manifolds than the baselines. We also apply our approach to the tasks of human pose refinement and facial expression image interpolation where it gets better results than the baselines.

Much work has already been studied on the interrelation between the epidemic spreading and awareness spreading to prevent infections in a social network. By selecting seed users to spread awareness, we can control epidemic spreading. However, selecting seed users with the maximum influential users may not be the best solution in location-based social networks. Therefore, it is challenging to determine users to spread the information (the awareness of prevention) in these networks. The minimized epidemic infection (MEI) problem aims to find a seed set with k seed users such that the infection users can be minimized. In this article, we propose a piecewise function to measure the probability of each user being infected, which considers the distance and time. Then, we propose an algorithm called location-infected-greedy (LIG) to solve the MEI problem by finding the seed nodes that consider the probability of infection, time of check-in, location information, and influence of users. In the meantime, LIG can obtain an upper bound of the data-dependent approximate ratio, and it runs in O(kn²), where n is the total number of nodes and k is the number of seed nodes. Finally, extensive contrast experiments on real-world location-based social networks show that our algorithm is efficient and effective.

This paper studies the coverage performance of unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) networks under the nonlinear and linear energy harvesting (EH) models in the rich scattering scenarios including smart farming and smart ranching, where the none-line-of-sight (NLoS) links are the dominate component of the wireless channel. Multiple UAVs are equipped with directional antennas to transfer information and energy to ground users (GUs). Power splitting (PS) or time switching (TS) architecture is employed at GUs. In order to evaluate the system performance in fading channels, the information and energy coverage probabilities of the system are discussed, and by using the stochastic geometry approach and the approximate scaling method, the general and lower bound explicit expressions of the coverage probabilities are derived. Numerical results show that the coverage performance of PS-based systems is superior to that of TS-based systems. Moreover, although the linear EH model yields better results than the nonlinear one, as the linear EH model is too ideal, its yielded results may mismatch practical EH circuits, and the ones yielded by the nonlinear EH model is much closer to practice, as the nonlinear EH model is based on real data measurement. Additionally, the nonlinear EH model has a relatively small effect on the harvested energy coverage probability, and the linear EH model introduces greater bias for TS-based system than that for PS-based one.

We derive the upper and lower bounds on the coverage of a 2-D deployment of static sensors. We use these bounds in constructing a method of estimating the coverage of deployment by assuming that there are only pairwise intersections between the disks representing the range of each sensor. The speed of this approximation allows it to be built into a local search technique, as part of a memetic algorithm (MA) that tries to deploy a given set of sensors with maximum coverage. We show that this MA outperforms the previous techniques in terms of both speed and coverage achieved.

This paper investigates the event-triggered attitude cooperative control problem for spacecraft formation flying subject to unknown external disturbances and model uncertainties. Considering that only a subset of formation spacecraft have access to the desired state information, a distributed event-triggered control strategy is proposed to achieve attitude synchronization. The proposed controller is only updated when the triggering condition is satisfied, which greatly reduces the frequency of the controller update. It is shown that the trajectories of state errors are able to remain in a small region around the origin. Subsequently, in order to decrease energy consumption and computational burden, an event-triggered mechanism that only requires the last neighbors state information is designed. It has been proved that the triggering interval has a strictly positive lower bound, which ensures that Zeno behavior can be avoided. Finally, numerical simulations are come out to verify the effectiveness of the developed control law.

Multikey homomorphic encryption (MKHE) supports arbitrary homomorphic evaluation on the ciphertext of different users and thus can be applied to scenarios involving multiusers (e.g., cloud computing and artificial intelligence) to protect user privacy. CDKS19 is the current most efficient MKHE scheme, and its relinearization process consumes most of the time of homomorphic evaluation. In this study, an optimized relinearization algorithm of CDKS19 is proposed. This algorithm reorganizes the evaluation key during the key generation process, decreases the complexity of relinearization, and reduces the error growth rate during homomorphic evaluation. First, we reduce the scale of the evaluation key by increasing its modulus instead of using a gadget vector to decompose the user's public key and extend the ciphertext of homomorphic multiplication. Second, we use rescaling technology to optimize the relinearization algorithm; thus, the error bound of the ciphertext is reduced, and the homomorphic operation efficiency is improved. Lastly, the average-case error estimation on the variances of polynomial coefficients and the upper bound of the canonical embedding map are provided. Results show that our scheme reduces the scale of the evaluation key, the error variance, and the computational cost of the relinearization process. Our scheme can effectively perform the homomorphic multiplication of ciphertexts.

Ensuring data freshness is important for industrial Internet of things (IIoT). In this paper, we consider a typical IIoT application where multiple sensors monitor some time-varying physical processes and report measurements to a central base station through an unreliable wireless channel. In order to save energy, each sensor may switch to sleep mode for a while after successfully transmitting a packet. Since only awake sensors are able to transmit data, we propose a novel function of age of information with penalty (or AoI-penalty) to capture the eagerness of the active sensors to provide fresh information. We formulate a new AoI-penalty minimization problem for scheduling the sensors’ transmissions. We theoretically derive a necessary condition for the system’s AoI-penalty to converge to a finite value, and further obtain a lower bound of the AoI-penalty. Moreover, we develop a max-weight based scheduling policy and theoretically prove that it is the optimal policy when the network is symmetric and the channel is error-free. Simulation results demonstrate that the proposed policy achieves AoI performance near to the lower bound and that with such sleep-wake sensors, the achieved AoI performance is close to that with non-sleeping sensors but at a much lower energy cost.

We present a new branch-and-bound algorithm for the Quadratic Multiple Knapsack Problem. A key component of our algorithm is a new upper bound that divides the pairwise item values among individual items, estimates the maximum potential value contributed by each individual item, and calculates the upper bound via a transportation model. A local search is used to adjust the division of pairwise item values in order to improve the upper bound. Reduced costs from the solution of the transportation problem are used to forbid some item-to-knapsack assignments. Information from the upper bound is also used in selecting the item to branch on next. Computational experiments are carried out on three sets of benchmark instances from the literature, two sets of smaller instances with 20–35 items and 3–10 knapsacks per instance, and one set of larger instances with 40–60 items and 3–10 knapsacks per instance. Our algorithm finds and verifies optimal solutions for all small benchmark instances in less than one hour of CPU time apiece and outperforms the best previously proposed algorithms for the problem in terms of both the average computation time and the number of instances for which optimality is verified. For the larger benchmark instances, our algorithm obtains smaller average gaps than the best previously proposed methods.

Focusing on improving the operation efficiency and riding comfort of metro rail lines in the peak hours, this article investigates the real-time train regulation and passenger load control problem with respect to frequent disturbances. To better illustrate the relationship between the train timetable and the on-board passengers, the variations of the departure time and the passenger load are elaborated in the form of a state-space model. Based on the Lyapunov stability theory, the problem of minimizing an upper bound on the quadratic performance function is transformed to a dynamic optimization problem with a set of linear matrix inequalities (LMIs), and a predictive control strategy is designed to guarantee the actual train schedule and number of in-vehicle passengers track the nominal timetable and the expected passenger load with a given disturbance attenuation level. With the objective to reduce the computational workloads and cut down the utilization of wireless transmitting resources, an event-triggered strategy is developed to implement the proposed stabilizing feedback controller only when the measurement error exceeds certain threshold, which has better adaptability to the application in large-scale metro networks. Some numerical examples based on the Beijing Yizhuang Metrol Line are provided for illustration of the effectiveness of the proposed scheme.

In this paper, the performance of a secure massive multiple-input multiple-output (MIMO) system adopting low-resolution digital-to-analog converters (DACs) is analyzed over spatially correlated wireless channels. A tight lower bound for the achievable secrecy rate is derived with artificial noise (AN) transmitted in the null space of the user channels. Using the analytical results, the impact of spatial correlation on the secrecy rate is explicitly evaluated in the presence of low-resolution DACs. The analytical observations reveal that using low-resolution DACs can be beneficial to the secrecy performance compared with ideal DACs, when the channels are strongly correlated and optimal power allocation is not employed., Comment: accepted by IEEE Wireless Communication Letters

Caching systems have long been crucial for improving the performance of a wide variety of network and web based online applications. In such systems, end-to-end application performance heavily depends on the fraction of objects transferred from the cache, also known as the cache hit probability. Many caching policies have been proposed and implemented to improve the hit probability. In this work, we propose a new method to compute an upper bound on hit probability for all non-anticipative caching policies, i.e., for policies that have no knowledge of future requests. Our key insight is to order the objects according to the ratio of their Hazard Rate (HR) function values to their sizes and place in the cache the objects with the largest ratios till the cache capacity is exhausted. Under some statistical assumptions, we prove that our proposed HR to size ratio based ordering model computes the maximum achievable hit probability and serves as an upper bound for all non-anticipative caching policies. We derive closed form expressions for the upper bound under some specific object request arrival processes. We also provide simulation results to validate its correctness and to compare it to the state-of-the-art upper bounds. We find it to be tighter than state-of-the-art upper bounds for a variety of object request arrival processes., IFIP WG 7.3 Performance

Reconfigurable intelligent surface (RIS) has drawn worldwide attention because of its attractive capability to improve the electromagnetic propagation environment and surprising advantages of low cost and low power consumption. Recently, RIS is further advanced to realize the independent control of two orthogonal polarizations in real time. In this paper, we study the dual-polarized RIS-assisted mobile communication system and investigate its performance under practical polarization imperfections. Specifically, we provide a tight upper bound of the ergodic spectrum efficiency. Through the bound, we find that the performance is greatly dependent on the polarization imperfections and RIS phase shifts of the RIS-assisted link, when the number of base station antennas is large enough and the deterministic component exists in the RIS-assisted link. On basis of this tight bound, we propose an approximate optimal RIS phase shift design to maximize the ergodic spectrum efficiency upper bound and emphasize the necessity to make phase adjustment between two polarizations of the RIS. Theoretical analysis further gives guidance for the configuration of RIS. Numerical results demonstrate that the upper bound is very tight and the proposed phase shift design with adjustment is approximate optimal.

This paper investigates an event triggered control problem of connected vehicles under multiple cyber-attacks, including denial-of-service attacks (DoS) and deception attacks, which occur aperiodically. Then, a novel event triggered control scheme is proposed in the presence of multiple cyber-attacks. The upper bound of attack frequency, attack length rate of DoS attacks and the conditions of controller gains are obtained. It is proven that the proposed distributed control scheme can realize the asymptotic tracking performance of connected vehicles while avoiding Zeno behavior. A string stability analysis under multiple cyber-attacks is also provided. Simulation results confirm the effectiveness of the event triggered control schemes under multiple cyber-attacks.

An electrocardiogram (ECG) consists of complex P-QRS-T waves. Detecting long-term ECG recordings is time-consuming and error-prone for cardiologists . Deep neural networks (DNNs) can learn deep representations and empower automatic arrhythmia detection. However, when applying DNNs in practice, they usually suffer from domain shift that exits between the training data and testing data. Such shift can be caused by the high variability contained in ECG signals between patients and internal-variability of heartbeats for same patients, leading to degrading performance and impeding generalization of DNNs. To tackle this problem, we propose an unsupervised semantic-aware adaptive feature fusion network (USAFFN) to reduce such shift by alleviating the semantic distribution discrepancy between the feature spaces of two domains. Furthermore, an ECG contains rich information from different angles (beat, rhythm, and frequency levels), which is essential for arrhythmia detection. Therefore, a multi-perspective adaptive feature fusion (MPAFF) module is introduced to extract informative ECG representations. The experimental results show that the detection performance of our approach is highly competitive with the upper bound of alternative methods on the ARDB, and the generalization is confirmed on the INCART and LTDB.

This brief studies the adaptive finite-time attitude tracking control of rigid spacecraft systems with full-state constraints and external disturbance. Based on the barrier Lyapunov function, a novel finite-time command filtered backstepping control algorithm is proposed, which can not only avoid computing the differential of virtual control signal, but also guarantee the predefined state constraints are not violated. The adaptive control technique is applied to estimate the upper bound of disturbance, and the designed controller with adaptive updating laws can make the tracking error converge to an adjustable neighborhood of the origin in finite time. Simulation results show the effectiveness of the present control method.

Spectrum sensing is one of the main components of the cognitive radio (CR) system, based on which secondary users (SUs) can get access to spectrum holes available. On the other hand, a malicious adversary can also attack the primary user (PU) system and the legitimate CR system via spectrum sensing, which can lead to serious security issue for both systems. In this article, we study an online attacking strategy, referred to as PU emulation attacker (PEA), which transmits forged PU signals over available channels to deteriorate the spectrum sensing performance of the SUs. We propose an online learning based attacking scheme for both the single attacker and the multiple-attacker cases, and analyze the regret upper bound of the proposed algorithm. The proposed PEA strategy can work in both stationary and non-stationary CR networks where the statistical characteristics of channels and the access strategy of SUs change over time. Numerical results show that it is more efficient than others in two different performance metrics: successful accesses of SUs and effective attacks of PEAs. Our proposal raises an interesting open question on how to develop CR networks with security guarantee.

The main objective of this study is to employ a probabilistic approach to determine the appropriate value of the strong column-weak beam ratio (SCWBR) for three mid- to high-rise moment frames. These buildings are subjected to pulse-type ground motions. The nonlinear soil-structure interaction (SSI) is also involved as another seismic energy dissipation mechanism. A set of incremental nonlinear dynamic analyses are performed for 91 pulse-like ground motions. The proposed approach includes global and local performance criteria. Park-Ang damage index is utilized as the damage measure for columns. In this regard, simple mathematical equations are also derived to quantify the impact of the SCWBR. This framework introduces an upper bound on the SCWBR beyond which further increase of this parameter would not be required to limit the damage of columns. The results indicate that for the 4-story building, the applicability of the SCWBR extends to values as large as 2.4, while for the 8 and 12-story buildings, this is restricted to 1.8 and 1.6, respectively. However, these values substantially depend on the pulse period in such a way that the SCWBR of 1.2 would be sufficient when the pulse period is approaching the fundamental period of structures. The SSI may improve the collapse probability of high-rise structures to a larger extent compared to SCWBR. Nevertheless, its effect can be diminished by more damage of columns at the lower portion of buildings.

Nowadays, using Blockchain Technology (BCT) is growing faster in each country. It is essential to apply BCT in Supply Chain Network Design (SCND) and is considered by the designer and manager of SC. This research indicates Viable Supply Chain Network Design (VSCND) by applying BCT. A new form of two-stage robust optimization is suggested. Facility locations and activation BCT for VSCND is the first stage of decisions; finally, we determine flow transshipment between components in the next stage. The GAMS-CPLEX is used for solving the model. The results show that running BCT will decrease 0.99% in costs. There is an economic justification for using BCT when demand is high. A fix-and-optimize and Lagrange relaxation (LR) generate lower and upper bound to estimate large scale in minimum time. The gap between the main model and fix-and-optimize is better than the LR algorithm. Finally, this research suggests equipping VSCND by BCT that becomes more resilient against demand fluctuation, sustainable, and agile.

Estimating the heat transfer in deep underground geothermal systems is a challenging task. The concept of explicitly describing the heat transfer between phases has been shown superior to the conventional approach but the heat transfer coefficient of fractures is still ambiguous. In this work, a definition of the lower and upper bound of the heat transfer coefficient between rock and flowing fluid in a single rock fracture is derived and validated with almost 250 experiments from other studies. The resulting values range between 8 and 260 W/m2 °C, with upper bounds roughly a factor 10 larger. Further, as shown in this work, the assumption of local thermal equilibrium can result in lower production temperatures than the assumption of heat transfer between phases depending on reservoir conditions. A dimensionless number is derived to distinct those cases and to determine the lower limit of the outflow temperature in any scenario. The results of this work enable a quantification of the value range of the heat transfer coefficient from laboratory experiments and allow a tabulation of the heat transfer coefficient for all kind of scenarios. The presented results can be used in discrete fracture models for an improved prognosis of production temperatures.

To improve the energy efficiency of a direct expansion air conditioning (DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The control strategy consists of an open loop optimization controller and a closed-loop guaranteed cost periodically intermittent-switch controller (GCPISC). The error dynamics system of the closed-loop control is modelled based on the GCPISC principle. The difference, compared to the previous DX A/C system control methods, is that the controller designed in this paper performs control at discrete times. For the ease of designing the controller, a series of matrix inequalities are derived to be the sufficient conditions of the lower-layer closed-loop GCPISC controller. In this way, the DX A/C system output is derived to follow the optimal references obtained through the upper-layer open loop controller in exponential time, and the energy efficiency of the system is improved. Moreover, a static optimization problem is addressed for obtaining an optimal GCPISC law to ensure a minimum upper bound on the DX A/C system performance considering energy efficiency and output tracking error. The advantages of the designed hierarchical controller for a DXA/C system with uncertain parameters are demonstrated through some simulation results.

The expansion complexity is a new figure of merit for cryptographic sequences. In this paper, we present an explicit formula of the (irreducible) expansion complexity of ultimately periodic sequences over finite fields. We also provide improved upper and lower bounds on the $N$ th irreducible expansion complexity when they are not explicitly determined. In addition, for some infinite sequences with given nonlinear complexity, a tighter upper bound of their $N$ th expansion complexity is given.

In this paper, non-orthogonal multiple access (NOMA) networks assisted by multiple intelligent reflecting surfaces (IRSs) with discrete phase shifts are investigated, in which each user device (UD) is served by an IRS to improve the quality of the received signal. Two scenarios are considered according to whether there is a direct link between the base station (BS) and each UD, and the outage performance is analyzed for each of them. Specifically, the asymptotic expressions for the upper and lower bounds of the outage probability in the high signal-to-noise ratio (SNR) regime are derived. Following that, the diversity order is obtained. It is shown that the use of discrete phase shifts does not degrade diversity order. More importantly, simulation results reveal that a 3-bit resolution for discrete phase shifts is sufficient to achieve near-optimal outage performance. Simulation results also imply the superiority of IRSs over full-duplex decode-and-forward relays., Comment: Accepted for publication in the IEEE Transactions on Wireless Communications

Slabs and plate-shaped concrete structures are extensively used for the construction of floors and bridges. Nowadays, engineers make use of plenty of methods and techniques for assessing the capacity of slabs, but many such methods are either over-conservative, inadequate for daily uses or non-automated. A new pseudo-lower bound method for the assessment of concrete slabs, in which such shortcomings are addressed, is proposed in this paper. The technique is based on a linear-elastic finite-element method considering moment redistributions due to yielding. The solution is achieved through an automated local optimisation procedure with a monotonically increasing load. As outputs, the method provides a lower bounded estimate of the intensity of load causing global failure and a configuration of yielded elements in the slab. Different examples are reviewed and the results were found to estimate load-bearing capacities well.

Routing is a fundamental problem and has been extensively studied in various networks. However, in highly dynamic networks (e.g., wireless ad hoc networks), nodes have limited transmission opportunities due to high mobility, noise and interference, where traditional routing is often not the best approach. Opportunistic routing (OR) , on the other hand, can effectively minimize the routing cost (e.g., the number of hops) and improve the success of routing by utilizing link metrics. However, the link metrics are usually unknown in advance and changing. In this paper, we design an adaptive algorithm called Thompson sampling-based opportunistic routing (TSOR) motivated by the distributed Bellman-Ford algorithms. TSOR is able to learn the link metrics and route packets simultaneously to reduce the overall cost. Theoretically, we show a lower bound and an upper bound of the cumulative regret (i.e., performance gap) between TSOR and the optimal routing algorithm that knows all link metrics in advance. The regret increases sublinearly with respect to the number of packets, and has a lower order in terms of the network size than the best-known results. Furthermore, we compare TSOR with the state-of-the-art algorithms, and the evaluation results show that TSOR has a lower regret and a faster convergence rate to the optimal policy than the state-of-the-art algorithms.

International audience; In many networks, a user has to allocate the link capacity between upload and download. When such networks are used for cooperative computing, the user needs to maintain the division of upload and download capacities at an optimal value to receive the maximum utility. To determine this optimal value, we model upload-download partitioning as a resource maximization game. We show that a Nash equilibrium (NE) obtained for this game is socially optimal. Thus this NE acts as an upper bound on capacity partitioning and serves as a benchmark to analyze the efficiency and performance of various capacity partitioning algorithms. Specifically, using this upper bound and simulations, we examine the performance of different partitioning algorithms while considering the dynamics of resource requests.

Intelligent reflecting surface (IRS) has emerged as a promising and low-cost technology for improving wireless communications by collecting dispersed radio waves and redirecting them to the intended receivers. In this letter, we characterize the achievable rate when multiple IRSs are utilized in the manner of decode-and-forward (DF) relaying. Our performance analysis is based on the Nakagami- $m$ fading model with perfect channel state information (CSI). Tight upper bound expressions for the ergodic rate are derived. Moreover, we compare the performance of the multi-IRS DF relaying system with that of the one with a single IRS and confirm the gain. We then optimize the IRS configuration considering the numbers of IRSs and IRS reflecting elements, which provides useful insights for practical design.

In this paper, a novel training beam sequence design for multiuser millimeter wave tracking systems is proposed. For each receiver, a single-path channel model is firstly investigated, where we introduce a maximum a posteriori (MAP) criterion to estimate the time-varying angle of departure (AoD), followed by an extended Kalman filter to update the stale complex path gain. We then employ training beam sequence design to minimize the estimated AoD’s average mean squared error (AMSE), which however has no explicit expression. We firstly derive a closed-form upper bound for the AMSE and then simplify this upper bound into a tractable form, based on which a nonlinear optimization problem (NLP) is formulated. By solving this NLP optimally using its corresponding Karush-Kuhn-Tucker conditions, we obtain an efficient training beam sequence. The proposed MAP criterion and its associated training beam sequence design are further extended to multi-path scenarios, where a joint estimation of the multiple paths is firstly discussed, followed by a sequential estimation as a low-complexity alternative. Numerical results demonstrate the superiority of our proposed scheme over the existing benchmark methods, especially in the case when the receivers’ channels change rapidly.

Although intelligent reflecting surface (IRS) is regarded as a promising solution to enhance the efficiency of wireless energy transfer (WET), the acquiring of channel state information is a crucial challenge for the system in which a training sequence for channel estimation is sent by low-power Internet-of-Things (IoT) devices. In this article, an IRS-aided multidevice WET system is considered. To overcome the limitation in channel estimation, we propose a received power-based channel estimation scheme that can be easily implemented and scalable in wirelessly empowered IoT devices. Specifically, at every single time slot, each device measures the received power of a randomly generated radio-frequency signal and feeds it back to the transmitter. We formulate a channel estimation problem to use the history of received power measurements based on the maximum-likelihood estimation using the phase retrieval framework and temporal channel evolution model. Moreover, we propose an algorithm that can be employed to obtain the stationary solution for the channel estimation problem, which is based on the inexact block coordinate descent method. We also perform algorithm modification to deal with the special case in which the transmitter-IRS channel is available. The simulation results show that the performance of the proposed algorithm approaches the upper bound as the channel slowly changes, although the proposed channel estimation protocol requires only one scalar value feedback.

Cloud Radio Access Network is a candidate solution in 5G and beyond where we intend to serve as many users as possible with minimum transmit power and minimum fronthaul data transmission. In this study, we aim to maximize a mixed expression of the number of users served, the number of fronthaul links used, and the total transmitted power by beamforming optimization under imperfect channel state information. We find a theoretical upper bound and propose a method based on the bound derivation. We compare its performance with heuristic search and norm approximation methods solving a series of convex sub-problems. The detailed simulations show that the proposed method outperforms other baseline techniques in terms of the number of users served, fronthaul data rate, power consumption, and computational complexity.

In many real-world applications, it is the fairness, not the accuracy, of a machine learning (ML) classifier that is the crucial factor. Post-processing approaches are widely considered as successful tools to improve the fairness of black-box ML classifiers. These aim to learn a relabeling function to modify initial predicted labels provided by a pre-trained “unfair” classifier, resulting in fair classification on a given test set. However, many post-processing methods require a training set with true labels to learn the relabeling function. To the best of our knowledge, there have been only two methods that learn the relabeling function without requiring the true labels of training samples. However, both of these methods require access to the predictions of the pre-trained classifier when performing on the test set, even after they learned the optimal relabeling function, and neither offers theoretical guarantees on the trade-off between accuracy loss and fairness improvement. In this paper, we propose a novel post-processing method based on Gaussian process (GP). We first train a GP with unlabeled samples, and use its posterior mean function to approximate the predictions of the pre-trained classifier. We then adjust the mean function (i.e. the relabeling function) to achieve two goals: (1) maximize the fairness and (2) minimize the difference between the relabeling function and the pre-trained classifier. By doing this, our method can improve fairness while maintaining high accuracy. We provide a theoretical analysis to derive an upper bound on accuracy loss for our method. We demonstrate our method on four real-world datasets, comparing with state-of-the-art baselines, to demonstrate its ability to achieve both fairness and accuracy.

The seismic protection of r.c. buildings by base-isolation systems is often achieved using elastomeric devices like the high-damping-rubber bearings (HDRBs). When these buildings are subjected to severe ground motions which can occur at sites located near active faults, the seismic demand of their superstructure can be amplified in a significant way and the interaction between shear and axial laws of the elastomeric bearings can become quite complex. Sophisticated models of the axial, shear and rotational behaviour of HDRBs, supported by experimental tests and numerical simulations, are proposed in the literature to describe their nonlinear response. However, few numerical studies are available to compare the influence of such models on the nonlinear response of base-isolated r.c. buildings under near-fault earthquakes. In this work, nonlinear incremental dynamic analyses of two typical r.c. framed structures, one retrofitted and the other one new, are carried out considering simplified and refined force–displacement laws of HDRBs. To this end, nine earthquakes, corresponding to three strong near-fault seismic events, extracted from the Pacific Earthquake Engineering Research center database, are selected to highlight effects of correlation between their horizontal and vertical components. Numerical results suggest that the nonlinear behaviour of HDRBs can be described in an equivalent way by using an upper and lower bound approach.

This paper focuses on the synchronization of fractional-order complex-valued neural networks (FOCVNNs) with reaction–diffusion terms in finite-time interval. Different from the existing complex-valued neural networks (CVNNs), the reaction–diffusion phenomena and fractional derivative are first considered into the system, meanwhile, the parameter switching (the system parameters will switch with the state) is considered, which makes the presented model more comprehensive. By choosing an appropriate Lyapunov function, the driver and response systems achieve Mittag-Leffler synchronization under a suitable controller. In addition, based on the fractional calculus theorem and the basic inequality methods, a criterion of synchronization for the error system in finite-time interval is derived and the upper bound of the corresponding finite synchronization time can be obtained. Finally, two examples are provided, one is a numerical example to explain the effectiveness of the main results, and the other shows that the results of this paper can be applied to image encryption for any size with high-security coefficient.

Infallible creep rupture life prediction of high temperature steel needs long hours of robust testing over a domain of stress and temperature. A substantial amount of effort has been made to develop alternative methods to reduce the time and cost of testing. This study presents a finite element analysis coupled with a ductility based damage model to predict creep rupture time under the influence of multiaxial stress state of ex-service and as-received Grade 91 steel at 873 K. Three notched bar samples with different acuity ratios of 2.28, 3.0 and 4.56 are modelled in commercial Finite Element (FE) software, ABAQUS v6.14 in order to induce different stress state levels at notch throat area and investigate its effect on rupture time. The strain-based ductility exhaustion damage approach is employed to quantify the damage state. The multiaxial ductility of the material that is required to determine the damage state is estimated using triaxiality-ductility Cock and Ashby relation. Further reduction of the ductility due to the different creep mechanisms over a short and long time is also accounted for in the prediction. To simulate the different material conditions: ex-service and as-received material, different creep coefficients (A) have been assigned in the numerical modelling. In the case of ex-service material, using mean best fit data of minimum creep strain rate gives a good life prediction, while for new material, the lower bound creep coefficient should be employed to yield a comparable result with experimental data. It is also notable that ex-service material deforms faster than as-received material at the same stress level. Moreover, higher acuity provokes damage to concentrate on the small area around the notch, which initiates higher rupture life expectancy. It also found out that, the stress triaxiality and the equivalent creep strain influence the location of damage initiation around the notch area.

Social networks are becoming important dissemination platforms, and a large body of works have been performed on viral marketing, but most are to maximize the benefits associated with the number of active nodes. In this paper, we study the benefits related to interactions among activated nodes. Furthermore, since the stochasticity caused by the dynamics of influence cascade in the social network, we propose the adaptive seeding strategy where seeds are selected one by one according to influence propagation situation of seeds already selected, and define the adaptive profit maximization problem. We analyze its complexity and prove it is not adaptive submodular. We find the upper and lower bounds which are adaptive submodular and design an adaptive sandwich policy based on the sandwich strategy which could gain a data dependent approximation solution. Through real data sets, we verify the effectiveness of our proposed algorithm.

Mobile video chatting has emerged as an important Internet multimedia application that greatly enriches interpersonal communications. Mobile power efficiency is crucial to the service quality and time of video chatting on battery-limited smartphones. However, the power characteristics of the video coding and data communication are highly complex due to the time-varying network conditions and dynamic mobile energy features. This incurs crucial challenges to maintaining the low power dissipation of mobile chatting application while streaming satisfactory-quality videos. To address these challenges, this paper presents a join T c O ding-tran S mission O ptimization (TOSO) protocol at application layer that performs machine learning based adaptation of the video bit rate and FEC (Forward Error Correction) coding parameters. By taking advantage of analytical and empirical models characterizing the quality-power relationship, TOSO is able to maximize video quality subject to a specified upper bound of power consumption in mobile chat application. This distinguishing feature prevents the video chat from draining battery too quickly. Moreover, it allows the smartphone operating system or the mobile user to define a desired video chat duration given the remaining battery, avoiding unpleasant conversation disruption due to battery depletion. Extensive experiments based on the Linphone platform and Exata network emulator show that TOSO outperforms baseline approaches by 29.3 percent in power conservation while achieving the same video quality level.

Low-density parity-check (LDPC) codes that allow for encoding-decoding have been adopted in the fifth-generation (5G) network to support high-throughput and low-latency communications. To meet new user requirements of the beyond 5G and future sixth-generation (6G) wireless networks, reconfigurable intelligent surface (RIS) has been proposed as a promising solution to enhance the communication quality. In this letter, we investigate the performance of a RIS-assisted wireless communication system where the signal transmitted from the transmitter is protected by LDPC codes. We derive the analytical upper and lower bounds on the bit error probability of the RIS-assisted wireless system with LDPC-coded source signals. Through Monte Carlo simulations, we validate the accuracy of our derived bounds for various numbers of RIS reflecting elements.

This work investigates the security issue of the energy-constrained untrusted relay network with imperfect channel reciprocity, where the relay without the built-in power supply can only scavenge energy from radio-frequency signals radiated by the source and destination. A hybrid power-splitting (PS)- and time-switching (TS)-based relaying (HPTR) protocol is presented to improve the degraded secrecy performance due to the incomplete self-interference cancellation at the destination. To evaluate the secrecy throughput (ST) of the studied system, the analytical expression of the secrecy outage probability under the delay-limited transmission mode and the closed-form lower bound of the ergodic secrecy capacity under delay-tolerant transmission mode are derived. Both linear and nonlinear models for the energy harvester at the relay are compared. The optimal PS and TS ratios are evaluated numerically. The theoretical derivations are validated by numerical results, revealing that the residual jamming has a negative effect on the secrecy performance of untrusted relay networks, which can be alleviated by the HPTR protocol. Besides, we compare the ST performance of the HPTR protocol with that of the PS and TS relaying schemes, and the results show that the HPTR protocol outperforms both PS and TS relaying protocols in terms of the ST.

We study the French-DeGroot opinion dynamics in a social network with two polarizing parties. We consider a network in which the leaders of one party are given, and we pose the problem of selecting the leader set of the opposing party so as to shift the average opinion to a desired value. When each party has only one leader, we express the average opinion in terms of the transition matrix and the stationary distribution of random walks in the network. The analysis shows balance of influence between the two leader nodes. We show that the problem of selecting at most $k$ absolute leaders to shift the average opinion is $\mathbf{NP}$-hard. Then, we reduce the problem to a problem of submodular maximization with a submodular knapsack constraint and an additional cardinality constraint and propose a greedy algorithm with upper bound search to approximate the optimum solution. We also conduct experiments in random networks and real-world networks to show the effectiveness of the algorithm., Comment: 14 pages, 4 figures

We study security of remote state estimation in wireless cyber–physical systems (CPS) where a sensor sends its measurements to the remote state estimator over a multichannel wireless link in presence of a jamming attacker. Most of the existing works study the sensor’s defense scheme by adopting optimization-based methods and rely on the prior knowledge of the attacker’s attack policy. To relax this constraint, we propose a novel online-learning-based policy called joint channel and power selection (J-CAP) for the sensor to dynamically choose transmission channel and power. The proposed method assumes no prior knowledge of the attacker’s attack policy, nor of the channel state information. J-CAP jointly optimizes sensor’s channel selection and power consumption, and guarantees the estimator’s asymptotic stability. We theoretically prove that J-CAP achieves a sublinear learning regret bound. We also show J-CAP’s optimality by deriving and matching its regret lower and upper bound orders. Compared with the solution that directly applies the baseline solution, J-CAP improves the regret upper bound by a factor of $\sqrt {K+L}$ , where $K$ and $L$ denote the number of channels and number of power levels, respectively. Numerical evaluations validate the analytical results under various CPS parameters, and compare the J-CAP’s performance with the state-of-the-art solutions.

In this paper, we propose a novel visible light positioning (VLP) scheme under multi-path propagation, which is a practical scenario that has not been well studied for VLP. The new scheme exploits the so-called dual-mode light-emitting diode (DM-LED) of different radiation lobe mode numbers at the transmitter and a photodiode at the receiver. Specifically, we devise a method that utilizes a radiation angle measurement approach for DM-LED, derive the Cramer-Rao lower bound (CRLB) of the estimated distance and analyze the characteristics of key parameters. In addition, two localization algorithms based on linear and nonlinear least squares methods are developed. Simulation results show that under the ideal light-of-sight scenario, the CRLB of the proposed VLP scheme can be close to that of the conventional received signal strength (RSS) aided VLP scheme. Furthermore, we implement the first ever prototype DM-LED lamp to validate the new DM-LED aided VLP system model. Both simulation and experiment results demonstrate that the proposed system outperforms its RSS-aided VLP counterpart in terms of positioning accuracy in the more realistic, and thus more challenging multi-path scenario, even with a tilting receiver.

Suppose that a transmitter Alice potentially wishes to communicate with a receiver Bob over an adversarially jammed binary channel. An active adversary James eavesdrops on their communication over a binary symmetric channel (BSC(q)), and may maliciously flip (up to) a certain fraction p of their transmitted bits based on his observations. We consider a setting where the communication must be simultaneously covert as well as reliable, i.e., James should be unable to accurately distinguish whether or not Alice is communicating, while Bob should be able to correctly recover Alice's message with high probability regardless of the adversarial jamming strategy. We show that, unlike the setting with passive adversaries, covert communication against active adversaries requires Alice and Bob to have a shared key (of length at least Omega(log n)) even when Bob has a better channel than James. We present lower and upper bounds on the information-theoretically optimal throughput as a function of the channel parameters, the desired level of covertness, and the amount of shared key available. These bounds match for a wide range of parameters of interest. We also develop a computationally efficient coding scheme (based on concatenated codes) when the amount of shared key available is $\Omega(\sqrt{n} \log n)$, and further show that this scheme can be implemented with much less amount of shared key when the adversary is assumed to be computationally bounded., Comment: Accepted for publication in IEEE Transactions on Information Theory

In this article, the secondary frequency restoration as well as active power allocation problem in an ac microgrid (MG) system subject to bounded varying-time delays are addressed. For each distributed generator, a distributed dynamic event-triggered control law is proposed. Besides, benefiting from using dynamic event-triggered mechanisms, the communication burdens can be measurably reduced. By analyzing the resulting system through a Lyapunov function, a sufficient condition is established to ensure stability and achieve asymptotic frequency restoration and active power sharing. Based on the sufficient condition, an explicit tolerable upper bound of all time delays is obtained. The upper bound can be used for the MG system design guideline in the planning phase, which would enhance real time operating safety. Beisides, no Zeno behavior will exist. To test the proposed control method, the experiments are conducted on the real-time simulator OPAL-RT with DSP controllers. The results demonstrate the effectiveness and performance of the proposed controller. Ministry of Education (MOE) This work was supported in part by the funding support from the Ministry of Education of Singapore under Grant MOE2017-T2-1-050, the National Natural Science Foundation of China under Grant 61903333, the Zhejiang Qianjiang Talent Project under Grant QJD1902010, and the Key Research and Development Program of Zhejiang Province under Grant 2019C01149.

International audience; This article deals with event-triggered filter design of positive systems subject to state saturation. An event-triggering condition in the form of 1-norm is established based on the error and the measurable output. Under the presented event-triggering condition, the filter systems are transformed into interval uncertain systems. The positivity of the filter and error dynamics is achieved by considering the lower bound of the interval systems. Using a linear copositive Lyapunov function, the stability is guaranteed by considering the upper bound of the interval systems. By virtue of linear programming, an l 1 -gain filter is designed for the systems subject to exogenous disturbance. Then, multiplicative and additive gain fluctuations are introduced. Two classes of nonfragile filters are proposed, and a cone is constructed to guarantee that the states of the systems with state saturation can keep inside it. Finally, two examples are given to illustrate the effectiveness of the proposed approach.

The security of wireless information transmission in large-scale multi-input and multioutput (MIMO) is the focus of research in wireless communication. Recently, a new artificial noise—SCO-AN which shows no orthogonality to the channel, is proposed to overcome the shortcomings of traditional artificial noise. In the previous research, the optimization function of SCO-AN is not convex, and its extremum cannot be obtained. Usually, nonconvex optimization algorithms or iterative relaxation algorithms are used to get the maximum value of the optimization objective function. Nonconvex optimization algorithms or iterative relaxation algorithms are greatly affected by the initial value, and the extremum cannot be obtained by a nonconvex optimization algorithm or iterative relaxation algorithm. In this paper, we creatively apply the strong law of large numbers to obtain the optimal value of the optimization function of SCO-AN under the condition of large-scale MIMO: the strong law of large numbers is applied to obtain the ergodic lower bound (ELB) expression of SC for SCO-AN. The power allocation (PA) problem of the SCO-AN system is discussed. We use a statistical method to get the formula for calculating the optimal power distribution coefficient of the SCO-AN system. The transmitter can use the optimal power ratio of PA to distribute the transmitted power without using the PA algorithm. The effect of imperfect channel state information is discussed. Through simulation, we found that more power should be generated for SCO-AN if the channel estimation is imperfect and the proposed method can achieve better security performance in the large-scale MIMO system.