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Structural performance under seismic loads is necessarily checked after the static load is considered in the design of a structure. If it fails to pass the specified safety requirements, the structure has to be redesigned, resulting in additional time and computational cost. Therefore, in this study, we present four shape optimization problems to obtain rational shapes for free-form shell structures with both high static and dynamic performances. The strain energy under static loads is the measure of static performance, and the lower bound on the fundamental natural frequency or strain energy under seismic loads is the measure of dynamic performance. An optimization problem is first formulated for minimizing the strain energy under frequency constraint, where the self-weight and live loads are applied. The strain energy corresponding to the equivalent seismic static load is next minimized. To prevent unfavorable shape when the design variables are dense, we also consider the surface curvature as the other objective function in a bi-objective optimization problem. Several numerical examples are given to demonstrate the effectiveness of the proposed method.

In this letter, we provide a physical layer supported defense mechanism against traffic analysis attacks by exploring the covert communication capability of digital modulation schemes. We analyze and compare the performances of M-PSK, M-QAM, M-ASK and M-APSK modulations in terms of the probability of detection by a warden and the maximum achievable throughput at the receiver. We obtain a lower bound for the covert transmission probability at Willie, which can be utilized as a covertness limit to maximize the throughput. Numerical studies highlight the tightness of the presented lower bound and show the maximum achievable throughput limits without being detected by a traffic analysis attacker.

How to utilize the limited antennas for tracking multiple targets plays a critical role in the collocated multiple-input multiple-output radar. A dynamic antenna selection strategy is proposed to address this problem. The basis of our strategy is to achieve the optimal antenna selection under the constraint of limited active antennas using the feedback information in the tracking recursion cycle, improving the worst case of estimate accuracy among multiple targets. Since the posterior Cramer–Rao lower bound quantifies the target tracking performance, it is derived and utilized as the optimization criterion. We then propose an efficient algorithm which integrates the convex relaxation technique with the local search to solve the problem. Simulation results show its superior performance compared with the random antenna selection strategy and the heuristic search algorithm. Moreover, the proposed strategy can provide the performance close to the exhaustive search method while maintaining reasonable runtime.

Current metrics for assessing the adequacy of a test-suite plainly focus on the number of components (be it lines, branches, paths) covered by the suite, but do not explicitly check how the tests actually exercise these components and whether they provide enough information so that spectrum-based fault localization techniques can perform accurate fault isolation. We propose a metric, called DDU, aimed at complementing adequacy measurements by quantifying a test-suite's diagnosability, i.e., the effectiveness of applying spectrum-based fault localization to pinpoint faults in the code in the event of test failures. Our aim is to increase the value generated by creating thorough test-suites, so they are not only regarded as error detection mechanisms but also as effective diagnostic aids that help widely-used fault-localization techniques to accurately pinpoint the location of bugs in the system. We have performed a topology-based simulation of thousands of spectra and have found that DDU can effectively establish an upper bound on the effort to diagnose faults. Furthermore, our empirical experiments using the Defects4J dataset show that optimizing a test suite with respect to DDU yields a 34 percent gain in spectrum-based fault localization report accuracy when compared to the standard branch-coverage metric.

This paper investigates the finite-time attitude tracking control problem for rigid spacecraft exposed to the external disturbance and unknown inertial parameters. Initially, the basic controller is developed under the assumption that the inertial parameters and the upper bound of external disturbances are available to the designer. However, this assumption is inapplicable in practical applications. Considering this limitation, adaptive laws and Minimum-learning- parameter (MLP) algorithm are employed in the second controller to estimate the unknown system dynamics. Finite-time stability for tracking errors is achievable under the proposed methods. Though theoretical analysis and numerical simulations, effectiveness of the proposed controllers has been validated.

Inadequate wellbore stability has always been the most important issue during drilling and can cause many drilling complications. The most important tools for studying this phenomenon are the use of geomechanical modeling and failure criteria. Drilling fluid temperature and chemical interactions between drilling fluid and wellbore are also significant factors in wellbore stability. Decreasing the fluid temperature changes the concentration of induced stresses around the well, influencing the wellbore's safe mud window. This study considered a vertical well drilled in a field in southwest of Iran to determine the mud window and find out the optimal path under different conditions. The upper and lower bound of the mud window were calculated separately and simultaneously with three criteria, namely Mohr–Coulomb Failure, Hoek–Brown, and modified Lade. In the modified Lade failure criterion that gives more realistic results, the lower bound under the initial conditions is 7.81–10.51 ppg; by applying the thermal effect, it is 7.68–10.48 ppg, and with a fluid chemistry effect, it is 8.47–10.69 ppg. Given these two factors simultaneously, the lower bound of the mud window changed to 8.34–10.66 ppg. The optimal path in all failure criteria is horizontal drilling of well and maximum in horizontal stress direction. Furthermore, the upper bound of the mud window under mechanical conditions was 21.36–31.13 ppg, and with the effect of temperature was reduced to 21.09–30.84 ppg. Moreover, after applying the chemical effect, it increased to 21.29–31.05 ppg and converted to 21.02–30.76 ppg simultaneously.

Minor-embedding heuristics have become an indispensable tool for compiling problems in quadratically unconstrained binary optimization (QUBO) into the hardware graphs of quantum and CMOS annealing processors. While recent embedding heuristics have been developed for annealers of moderate size (about 2000 nodes), the size of the latest CMOS annealing processor (with 102,400 nodes) poses entirely new demands on the embedding heuristic. This raises the question, if recent embedding heuristics can maintain meaningful embedding performance on hardware graphs of increasing size. Here, we develop an improved version of the probabilistic-swap-shift-annealing (PSSA) embedding heuristic [which has recently been demonstrated to outperform the standard embedding heuristic by D-Wave Systems (Cai et al. in http://arxiv.org/abs/1406.2741 , 2014)] and evaluate its embedding performance on hardware graphs of increasing size. For random cubic and Barabasi–Albert graphs we find the embedding performance of improved PSSA to consistently exceed the threshold of the best known complete graph embedding by a factor of 3.2 and 2.8, respectively, up to hardware graphs with 102,400 nodes. On the other hand, for random graphs with constant edge density not even improved PSSA can overcome the deterministic threshold guaranteed by the existence of the best known complete graph embedding. Finally, we prove a new upper bound on the maximal embeddable size of complete graphs into hardware graphs of CMOS annealers and show that the embedding performance of its currently best known complete graph embedding has optimal order for hardware graphs with fixed coordination number.

The benefits of GNSS have created dependencies on navigation in modern day aviation systems. Many of these systems operate with no backup navigation source. This makes the capabilities supported by precise navigation vulnerable. This paper investigates a contemporary approach to terrain-referenced navigation (TRN), used to preserve an aircraft’s navigation solution during periods of GNSS denial. Traditionally, TRN has been accomplished using a single measurement sensor pointed nadir to an aircraft. Although shown to be effective, this approach limits the achievable navigation accuracy by restricting the measurable terrain gradients to those below an aircraft. This paper explores an alternative approach to TRN that maximizes navigation information through optimal steering of a gimbaled laser. A Cramer-Rao lower bound analysis as well as a high-fidelity simulation establishes the utility of optimal steering while employing TRN. This original approach to TRN shows the order of magnitude improvements in navigation performance over existing methods.

Two-sided assembly lines are widely utilized to assemble large-sized products such as cars and trucks. Recently, these types of assembly lines have been applied to assemble different types of products due to a large variety of customer demands and strong market competition. This paper presents two simple local search methods, the iterated greedy algorithm and iterated local search algorithm, to deal with type I mixed-model two-sided assembly line balancing problems. These two algorithms utilize new precedence-based local search functions with referenced permutation and two neighborhood structures to emphasize intensification while preserving high search speed. Additionally, these local search methods are enhanced by utilizing the best decoding scheme amongst nine candidates and a new station-oriented evaluation to guide the search direction. New lower bound calculations are also presented to check the optimality of the achieved solutions. Eleven recent and high-performing metaheuristic algorithms are re-implemented to test the performance of the proposed algorithms. A comprehensive study on a set of benchmark problems demonstrates the advantages of the improvements and the superiority of the two proposed methods. Experimental results show that the proposed algorithms obtain 23 new upper bounds compared with two recently published algorithms, among which 19 cases are proven to be optimal for the first time.

In this paper, upper bound method of limit analysis is used to obtain the vertical and inclined bearing capacity of rock masses containing two orthogonal joint sets. The spacing of the joint sets is assumed to be large compared to the footing width. Different orientation angles are considered for the joints, and the Mohr–Coulomb failure criterion is employed for both the intact rock and the joint sets. Analytical expressions are derived for the bearing capacity of a strip footing resting on a jointed rock subjected to vertical loading. Then, the effect of load inclination on the bearing capacity is incorporated into the analytical equations by means of inclination coefficients. The findings are compared to the other solutions existing in the literature. The results of this study show that increasing the joint friction angle leads to decreasing the inclination coefficient. Moreover, increasing the joint orientation angle with respect to the horizontal direction results in decreasing the inclination coefficients. The obtained results are presented in tabular form that can be used easily for practical purposes.

Many modern symmetric ciphers apply MDS or almost MDS matrices as diffusion layers. The performance of a diffusion layer depends on its diffusion property measured by branch number and implementation cost which is usually measured by the number of XORs required. As the implementation cost of MDS matrices of large dimensions is high, some symmetric ciphers use binary matrices as diffusion layers to trade-off efficiency versus diffusion property. In the current paper, we investigate cyclic binary matrices (CBMs for short), mathematically. Based upon this theorical study, we provide efficient matrices with provable lower bound on branch number and minimal number of fixed-points. We consider the product of sparse CBMs to construct efficiently implementable matrices with the desired cryptographic properties.

Interval prediction is essential to improve the scheduling and planning of wind power systems. In this study, a novel lower upper bound estimation model based on the gated recurrent unit was proposed for the clustered wind power forecasting. Different from existing research, the proposed model directly realizes interval prediction based on the point prediction results and the corresponding error interval coefficients, and an unsupervised learning strategy is introduced to construct the error interval coefficients. In addition, loss functions related to the characteristics of the prediction interval are designed, and an effective gradient descent algorithm is adopted to optimize the entire model. In the comparative experiments, two clustered data were collected as experimental data, and seven representative models were selected as benchmark models, which fully proved the superiority of the proposed model.

Photon counting detectors (PCDs) are classically described as being either paralyzable or nonparalyzable. When the PCD is paralyzed, it is no longer sensitive to the detection of additional flux. A recent strategy in PCD design has been to compensate for detector paralysis by embedding specialized paralysis compensation electronics into the application-specific integrated circuit (ASIC). One such compensation mechanism is the pileup trigger, which places an additional energy bin at very high energy that is triggered only during pileup. Another compensation mechanism is the retrigger architecture, which converts a paralyzable PCD into a nonparalyzable PCD. We propose a third mechanism that modifies the retrigger architecture using dedicated secondary counters. We studied the incremental benefit of these three paralysis compensation mechanisms in simulation. We modeled the spectral response using Monte Carlo simulations and then estimated the variance in basis material decomposition of a single pixel using the Cramér-Rao lower bound (CRLB). In the absence of paralysis compensation, noise in basis material images shows sharp increases at moderate flux (near the characteristic count rate) due to contrast inversion and again at high flux. The pileup trigger reduces noise at high flux but does not eliminate contrast inversion. The retrigger architecture eliminates contrast inversion but does not reduce noise at high flux. Our proposed retrigger architecture with dedicated secondary counters reduce noise at both moderate and high flux.

We consider a wireless source localization network in which a target node emits localization signals that are used by anchor nodes to estimate the target node position. In addition to target and anchor nodes, there can also exist eavesdropper nodes and jammer nodes which aim to estimate the position of the target node and to degrade the accuracy of localization, respectively. We first propose the problem of eavesdropper selection with the goal of optimally placing a given number of eavesdropper nodes to a subset of possible positions in the network to estimate the target node position as accurately as possible. As the performance metric, the Cramer-Rao lower bound (CRLB) related to the estimation of the target node position by eavesdropper nodes is derived, and its convexity and monotonicity properties are investigated. By relaxing the integer constraints, the eavesdropper selection problem is approximated by a convex optimization problem and algorithms are proposed for eavesdropper selection. Moreover, in the presence of parameter uncertainty, a robust version of the eavesdropper selection problem is developed. Then, the problem of jammer selection is proposed where the aim is to optimally place a given number of jammer nodes to a subset of possible positions for degrading the localization accuracy of the network as much as possible. A CRLB expression from the literature is used as the performance metric, and its concavity and monotonicity properties are derived. Also, a convex optimization problem and its robust version are derived after relaxation. Moreover, the joint eavesdropper and jammer selection problem is proposed with the goal of placing certain numbers of eavesdropper and jammer nodes to a subset of possible positions. Simulation results are presented to illustrate performance of the proposed algorithms., Comment: 16 pages, 10 figures

This article proposes a method for identifying potential self-adequate sub-networks in the existing power distribution grid. These sub-networks can be equipped with control and protection schemes to form microgrids capable of sustaining local loads during power systems contingencies, thereby mitigating disasters. Towards identifying the best microgrid candidates, this work formulates a chance-constrained optimal distribution network partitioning (ODNP) problem addressing uncertainties in load and distributed energy resources; and presents a solution methodology using the sample average approximation (SAA) technique. Practical constraints like ensuring network radiality and availability of grid-forming generators are considered. Quality of the obtained solution is evaluated by comparison with- a) an upper bound on the probability that the identified microgrids are supply-deficient, and b) a lower bound on the objective value for the true optimization problem. Performance of the ODNP formulation is illustrated through case-studies on a modified IEEE 37-bus feeder. It is shown that the network flexibility is well utilized; the partitioning changes with risk budget; and that the SAA method is able to yield good quality solutions with modest computation cost. Virginia Tech Open Access Subvention Fund Published version This work was supported in part by the Virginia Tech Open Access Subvention Fund.

This paper proposes a co-design adaptive defense scheme against a class of zero-day buffer over-read attacks that follow unknown stationary probability distributions. In particular, the co-design scheme integrates an improved UCB algorithm and a customized server. The improved UCB algorithm adaptively allocates guard pages on a heap based on induced damage of the guard pages so as to minimize the accumulated damage over time. The security damages of the improved UCB algorithm are proven to be always below a temporal bound without knowing which attack is launched when the buffer allocation follows a certain stationary probability distribution. Then an efficient server modification is introduced to randomly allocate buffers. Moreover, the damages of our scheme asymptotically converge to those of the optimal defense policy where the launched attacks and their distributions are known in advance. Further, the co-design scheme is evaluated with several real-world Heartbleed attacks. The experiment results demonstrate the validity of the upper bound and show that the adaptive defense is effective against all the attacks of interest with runtime overheads as low as 5%.

Using the real wireless spectrum occupancy status in 2.4 and 5 GHz bands collected at a railway station as representative of a heavy wireless LAN (WLAN) traffic environment, this paper studies the modeling of durations of busy/idle (B/I) status and its predictability based on predictability theory. We first measure and model the channel status in the heavy traffic environment over almost all of the WLAN channels at 2.4 GHz and 5 GHz bands in a busy (rush hour) period and non-busy period. Then, using two selected channels at 2.4 GHz and 5 GHz bands, we analyze the upper bound (UB) and lower bound (LB) of predictability of the busy/idle durations based on predictability theory. The analysis shows that the LB predictability of durations can be easily increased by changing their probability distribution. Based on this property, we introduce the data categorization (DC) method. By categorizing the busy/idle durations into different streams, the proposed data categorization can improve the prediction performance of some streams with large LB predictability, even if it employs a simple low-complexity auto-regressive (AR) predictor.

Reconfigurable Intelligent Surface (RIS) has emerged as a promising technology in wireless networks to achieve high spectrum and energy efficiency. RIS typically comprises a large number of low-cost nearly passive elements that can smartly interact with the impinging electromagnetic waves for performance enhancement. However, optimally configuring massive number of RIS elements remains a challenge. In this article, we present a machine learning (ML) based modeling approach that learns the interactions between the phase shifts of the RIS elements and receiver (Rx) location attributes and uses them to predict the achievable rate directly without using channel state information (CSI) or received pilots. Once learned, our model can be used to predict optimal RIS configuration for any new receiver locations in the same wireless network. We leverage deep learning (DL) techniques to build our model and study its performance and robustness. Simulation results demonstrate that the proposed DL model can recommend near-optimal RIS configurations for test receiver locations which achieved close to an upper bound performance that assumes perfect channel knowledge. Our DL model was trained using less than 2% of the total receiver locations. This promising result represents great potential in developing a practical solution for the optimal phase shifts of RIS elements without requesting CSI from the wireless network infrastructure.

The demands for deterministic network services are getting significant. IEEE Time sensitive networking (TSN) task group has aimed for deterministic services through IEEE 802 networks. TSN’s solutions rely on the time-synchronization and the slot scheduling coordinated throughout a network. The former requires costly hardware implementations for accuracy and scalability. The latter requires a schedulability analysis, which is proven to be NP-complete and not scalable. For modest-to-large scale dynamic networks, a more flexible solution is necessary. We propose a scalable framework that guarantees any desired jitter upper bound, including zero jitter. The framework is composed of; packets with relative time-stamps, a network that guarantees latency bounds, and buffers at the network egress. The latency bounds of a network can be of any value. The buffer holds packets for predetermined intervals to reproduce arbitrary inter-arrival intervals to inter-departure intervals, based on the network’s latency bound. The idea behind the proposed framework is to minimize the latency first with work-conserving schedulers, and then adjust the jitter at the egress of a network. We argue that this is simpler than minimizing latency and jitter at the same time. A direct benefit of such simplicity is its scalability. We examine the network solutions that guarantee latency bound, such as the DiffServ and the Flow aggregate-interleave regulator (FAIR), as the candidates for the component in the framework. We show with a case study and simulations that the proposed framework can guarantee smaller latency bounds, with zero jitter, than those of the TSN’s synchronous approach.

This paper proposes a novel framework for analyzing the localization accuracy of data fusion for fingerprinting approaches in non-line-of-sight (NLOS) environments. Using simulation data generated for two very different NLOS environments (a suburban area of $3.3km\times 3.3km$ in Santa Clara, California, and a mountainous area of $11.4km\times 11.4km$ in the Caspian region), we establish novel channel models for measurement differences of three data types (received signal strength indicator (RSS), time of arrival (TOA) and direction of arrival (DOA)) at $K$ neighboring nodes of an arbitrary node. The crucial point is that the modeling errors for each of the three data types are shown to be jointly Gaussian distributed. Based on these measurement difference models, Cramer-Rao Lower Bound (CRLB) is used as a benchmark to evaluate $K$ -nearest neighbor (KNN) and Weighted $K$ -Nearest Neighbor (WKNN). It is shown that the proposed CRLB analyses can be employed to evaluate fingerprinting systems with various designs (such as different data types and fusion options) and different configurations (such as densities of reference nodes and numbers of anchor nodes) in diverse NLOS environments(such as suburban and mountainous regions).

This paper introduces a new method of estimating the variation interval and the expected value of optimal economic operation cost for the energy hub (EH) model considering load and renewable energy uncertainties in the residential district. Firstly, renewable energy and load uncertainties are defined as uncertain sets of historical data. Due to dispatch factors, the traditional EH model is non-linear and difficult to calculate and analyze. In this paper, a multi-step modeling method is developed for the breakdown of a complex EH model (CEH) into several simple EH (SEH) models and variable substitutions in each SEH, to eliminate the non-linearity of CEH. The upper and lower bound of the operating costs are then obtained based on the duality theorem and the expected value is calculated by expected value model. Finally, the case study in Beijing residential district is carried out to confirm the efficiency of the proposed method.

We study a family of adversarial (a.k.a. nonstochastic) multi-armed bandit (MAB) problems, wherein not only the player cannot observe the reward on the played arm (self-unaware player) but also it incurs switching costs when shifting to another arm. We study two cases: In Case 1, at each round, the player is able to either play or observe the chosen arm, but not both. In Case 2, the player can choose an arm to play and, at the same round, choose another arm to observe. In both cases, the player incurs a cost for consecutive arm switching due to playing or observing the arms. We propose two novel online learning-based algorithms each addressing one of the aforementioned MAB problems. We theoretically prove that the proposed algorithms for Case 1 and Case 2 achieve sublinear regret of O(√⁴KT³ln K) and O(√³(K-1)T²ln K), respectively, where the latter regret bound is order-optimal in time, K is the number of arms, and T is the total number of rounds. In Case 2, we extend the player's capability to multiple m>1 observations and show that more observations do not necessarily improve the regret bound due to incurring switching costs. However, we derive an upper bound for switching cost as c ≤ 1/√³m² for which the regret bound is improved as the number of observations increases. Finally, through this study, we found that a generalized version of our approach gives an interesting sublinear regret upper bound result of O(Ts+1/s+2) for any self-unaware bandit player with s number of binary decision dilemma before taking the action. To further validate and complement the theoretical findings, we conduct extensive performance evaluations over synthetic data constructed by nonstochastic MAB environment simulations and wireless spectrum measurement data collected in a real-world experiment.

Realizing fundamental cryptographic primitives with unconditional security is a central topic in information-theoretic cryptography. These primitives can be realized based on physical assumptions, such as the existence of noisy channels, an upper bound on the storage capacity, or the laws of quantum mechanics. Palmieri and Pereira [1] demonstrated that delays in communication channels can be used as a reasonable and effective assumption to obtain an unconditionally secure oblivious transfer protocol against honest-but-curious adversaries. While any oblivious transfer protocol secure against malicious adversaries can be used to implement commitment, the reduction does not work if the oblivious transfer protocol is only secure against honest-but-curious adversaries. Thus, the question of obtaining a secure commitment protocol based on channel delays is still open. In this paper, we provide a concrete protocol for implementing string commitments based on packet reordering – a consequence of channel delays in packet networks.

Searchable symmetric encryption (SSE) enables a client to store a database on an untrusted server while supporting keyword search in a secure manner. Despite the rapidly increasing interest in SSE technology, experiments indicate that the performance of the known schemes scales badly to large databases. Somewhat surprisingly, this is not due to their usage of cryptographic tools, but rather due to their poor locality (where locality is defined as the number of non-contiguous memory locations the server accesses with each query). The only known schemes that do not suffer from poor locality suffer either from an impractical space overhead or from an impractical read efficiency (where read efficiency is defined as the ratio between the number of bits the server reads with each query and the actual size of the answer). We construct the first SSE schemes that simultaneously enjoy optimal locality, optimal space overhead, and nearly-optimal read efficiency. Specifically, for a database of size N, under the modest assumption that no keyword appears in more than N1 − 1/loglogN documents, we construct a scheme with read efficiency O(loglogN). This essentially matches the lower bound of Cash and Tessaro (EUROCRYPT ’14) showing that any SSE scheme must be sub-optimal in either its locality, its space overhead, or its read efficiency. In addition, even without making any assumptions on the structure of the database, we construct a scheme with read efficiency O(logN). Our schemes are obtained via a two-dimensional generalization of the classic balanced allocations (“balls and bins”) problem that we put forward. We construct nearly-optimal two-dimensional balanced allocation schemes, and then combine their algorithmic structure with subtle cryptographic techniques.

High penetration of renewables in the distribution network brings significant uncertainties, especially during volatile weather conditions. Hence, the network controllers should be designed to account for these uncertainties, and respond to unpredictable events like voltage-dips for reliable voltage control. This paper proposes a control scheme, where inverter-based Distributed Energy Resources (DERs) respond locally with Q(V) control and adapt to set-points assigned by the centralized controller (CC). The Robust Constrained Model Predictive Control (RCMPC) scheme is proposed for centralized voltage control. It robustly deploys control resources from DERs and tap-changers to regulate the lower/upper bound of node voltages within the targeted limit. Moreover, RCMPC ensures minimum resource utilization by relaxing the targeted voltage limit whenever it anticipates significant uncertainties. The CC is implemented in Python, which communicates with the RMS model of the UKGDS network for measurements and dispatching set-points. The performance of RCMPC is compared with deterministic MPC (DMCP) at 5, 10, and 15-minute time-steps of CC. The proposed RCMPC can regulate the node voltage even at a higher degree of uncertainty seen at a 15-minute time-step. In contrast, the DMPC could not contain the node voltages under the targeted limit and worsened at a larger time-step.

Mutually orthogonal complementary sets (MOCSs) have found a number of practical applications in wireless communications and radar owing to their perfect aperiodic auto-correlation and cross-correlation properties. Recently, toward the challenge of designing MOCSs with non-power-of-two lengths, direct constructions were presented by Wu et al. using generalized Boolean functions (GBFs). In this letter, a new construction of MOCSs is proposed based on GBFs, which leads to MOCSs with flock size $2^{k+1}$ and length $2^m+2^t$ , whose set size is $1/2$ of the flock size, where integers $0\leq t . In addition, the constructed MOCSs can yield column sequence peak-to-mean envelope power ratio (PMEPR) of at most 2.

In this paper, we consider a cooperative non-orthogonal multiple access (NOMA) aided underwater optical wireless system in which the source transmits to two users where the near user serves as a relay node to the far user. Our proposed system consists of multiple narrow-angle light-emitting diode (LED)/photodiode (PD) elements at the source, near user, and far user. In order to achieve communication, our system selects a single LED/PD at each node. We propose several low complexity LED/PD selection schemes that aim to maximize the link throughput and in addition consider optimal and random LED/PD selection for benchmarking. In order to characterize the performance of each scheme, bounds and closed-form tight approximations on the average achievable sum rates are presented. The use of multi element nodes and NOMA increase the average sum rate significantly over conventional orthogonal access. Moreover, near-optimal throughput can be achieved using channel gain based and line-of-sight based LED/PD selection schemes in the medium-to-high transmit power regimes. The derived expressions are also useful to investigate the impact of key system and channel parameters such as the source transmit power, power allocation factor, node placement, and the number of elements at each node.

Effective interference management in the multiuser interference channel strongly hinges on the channel state information’s availability at the transmitters (CSIT). In a broad range of emerging large-scale and distributed networks (e.g., the Internet of Things), acquiring the CSIT is prohibitive due to the extensive information exchange that it imposes. As a result, the interference management approaches that rely on the CSIT lose their effectiveness in such circumstances. This article focuses on the two-user interference channel and proposes a broadcast approach to interference management. Its hallmark is that the transmitters, unlike the receivers, are entirely oblivious to instantaneous channel states. Each transmitter splits its message into multiple superimposed encoded information layers, where each layer is adapted to a given possible state for the combined states of all channels. Depending on the relative gain between the direct and interfering channels, each receiver opportunistically decodes a subset of both transmitters’ received layers. An average achievable rate region is delineated, serving as an inner bound on the Gaussian interference channel’s average capacity region in the absence of CSIT. Finally, an upper bound on the gap between the achievable sum-rate and the sum-rate capacity is established.

This paper addresses the resilient distributed filtering problem for a class of time-varying multirate systems over sensor networks subject to time-correlated fading channels. The time-correlated fading channel, whose fading coefficient is described by a dynamical process, is introduced to better capture the time-correlation property of the wireless channels. Moreover, the phenomenon of stochastic gain fluctuations is taken into account to accommodate the potential implementation errors, thereby achieving resilient filtering performance. The lifting technique is employed to cast the underlying multirate system into a general signal-rate one, and then an augmented system is formulated to account for the dynamics from both the plant and the fading coefficients. Our aim is to design a distributed filter such that certain upper bound is first guaranteed on the filtering error covariance and then minimized at each sampling instant.With the help of trace operations, the gain parameters of the desired distributed filter are obtained in terms of the solutions to two sets of recursive equations. Finally, the effectiveness of the developed filtering algorithm is verified via an illustrative example.

In the interference-limited communication scenarios, the interference impairs the data transmission and further degrades the delay performance. In this letter, the stochastic delay guarantee for a wireless dual-hop network with an interference-limited relay is investigated. Considering the randomness of wireless fading in both data links and interference links, the stochastic service process provided by the network is characterized in terms of the moment generation function (MGF). Due to the stochastic service process, the delay should be a random variable (RV). The delay-outage probability is defined as the probability that the actual transmission delay exceeds a threshold value. To evaluate the stochastic delay guarantee, we derive an upper bound on the minimum acceptable delay threshold and a lower bound on the maximum supportable transmitted data size with the constraint of the delay-outage probability, respectively. Simulation results show that the bounds obtained by the theoretical analysis are reasonably tight and well reflect the effect of the interference on the stochastic delay guarantee.

With the rapid evolution of communication technologies, high-speed optical wireless applications under the water surface as a replacement or complementary to the conventional radio frequency (RF) and acoustic technologies are attracting significant attention from the researchers. Since underwater turbulence (UWT) is an inevitable impediment for a long distance underwater optical wireless communication (UOWC) link, mixed RF-UOWC is being considered as a more feasible solution by the research community. This article deals with the secrecy performance of a variable gain relay-based mixed dual-hop RF-UOWC framework under the intercepting attempt of a potential eavesdropper. The RF link undergoes Generalized Gamma (GG) fading distribution, whereas the UOWC link is subjected to mixture Exponential Generalized Gamma (mEGG) distribution. The eavesdropper is capable of wiretapping via a RF link that also experiences the GG fading. The secrecy analysis incorporates the derivations of closed-form expressions for strictly positive secrecy capacity, average secrecy capacity, and exact as well as lower bound of secrecy outage probability in terms of univariate and bivariate Meijer’s $G$ and Fox’s $H$ functions. Based on these expressions, impacts of heterodyne and intensity modulation/direct detection techniques along with weak, moderate, and severe UWT conditions due to air bubbles, temperature, and salinity gradients are quantified. To the best of authors’ knowledge, the proposed model is the first of its kind that addresses the secrecy analysis of a temperature gradient RF-UOWC system along with air bubbles, as opposed to the existing models that considered thermally uniform scenarios only. Finally, the derived expressions are verified via Monte-Carlo simulations.

Localization is of great importance in several fields such as healthcare and security. To achieve localization, GPS technologies are common for outdoor localization but are insufficient for indoor localization. This is because the accuracy and precision of the users’ indoor locations are influenced by many factors (e.g., multi-path signal propagations and signal fading). As a result, the methodologies and technologies for indoor localization services need continuous refinement. Another challenge is the time complexity of the methodologies which impacts the performance of mobile phones’ limited resources. To address these challenges, a new set of fingerprinting algorithms called Fingerprinting Line-Based Nearest Neighbour is designed and implemented. The new set shifts some points such grid points possibly towards target nodes based on a deterministic range of percentages. Furthermore, the new algorithms run in 0(j * d) (i.e., the term y refers to the number of grid points and the term d refers to the number of Received Signal Strength Indicators collected at the online stage). This paper presents the following: 1) the accuracy of the new set of algorithms has a theoretical upper bound in terms of the distance errors, 2) the new set of algorithms (e.g., 90% shifting percentage) improves the accuracy compared to those for several existing Nearest Neighbour (NN)-based algorithms such as the Dual-Scanned Fingerprint Localization and the Soft-Range-Limited KNN (SRL-KNN) by 106% and 68%, respectively, 3) the algorithm improves the precision compared to those for the NN-based algorithms such as the Path-Loss-Based Fingerprint Localization and the SRL-KNN 76% and 73%, respectively, and 4) the new set has lower probabilities related to the position errors compared to those for the existing NN-based algorithms. Therefore, the new set of algorithms is considered reliable and efficient for indoor location services.

Adaptive gradient learning methods such as Adam, RMSProp, and AdaGrad play an essential role in training a very deep neural network. The learning rates of these optimizers are adaptively changed to accelerate the training process. And the convergence speed is much faster than SGD in many deep learning tasks such as classification and NLP tasks. However, recent works have pointed out that adaptive learning methods can not converge to a critical point under some situations and suffer poor generalization in many deep learning tasks. In our study, we propose AdaDB, an adaptive learning optimizer with data-dependent bound on the learning rate. Every element in the learning rate vector is constrained between a dynamic upper bound and a constant lower bound. And the upper bound is dependent on the data. We also give a theoretical proof of the convergence of AdaDB in the non-convex setting. Our experiments show that AdaDB is capable of eliminating the generalization gap between Adam and SGD. Experiments also reveal the convergence speed of AdaDB is much faster than Adam.

The problem of distributed representation learning is one in which multiple sources of information $X_1,\ldots,X_K$ are processed separately so as to learn as much information as possible about some ground truth $Y$. We investigate this problem from information-theoretic grounds, through a generalization of Tishby's centralized Information Bottleneck (IB) method to the distributed setting. Specifically, $K$ encoders, $K \geq 2$, compress their observations $X_1,\ldots,X_K$ separately in a manner such that, collectively, the produced representations preserve as much information as possible about $Y$. We study both discrete memoryless (DM) and memoryless vector Gaussian data models. For the discrete model, we establish a single-letter characterization of the optimal tradeoff between complexity (or rate) and relevance (or information) for a class of memoryless sources (the observations $X_1,\ldots,X_K$ being conditionally independent given $Y$). For the vector Gaussian model, we provide an explicit characterization of the optimal complexity-relevance tradeoff. Furthermore, we develop a variational bound on the complexity-relevance tradeoff which generalizes the evidence lower bound (ELBO) to the distributed setting. We also provide two algorithms that allow to compute this bound: i) a Blahut-Arimoto type iterative algorithm which enables to compute optimal complexity-relevance encoding mappings by iterating over a set of self-consistent equations, and ii) a variational inference type algorithm in which the encoding mappings are parametrized by neural networks and the bound approximated by Markov sampling and optimized with stochastic gradient descent. Numerical results on synthetic and real datasets are provided to support the efficiency of the approaches and algorithms developed in this paper., Comment: 35 pages, 10 figures, submitted for possible publication

In this paper, we propose various quantitative information-theoretic bit security reduction measures that correlate the statistical difference between two probability distributions with the security level gap for two cryptographic schemes. We derive tighter relations between information-theoretic measures for quantifying the precision and guarantee the security level of the cryptographic scheme implemented over a precision-restricted environment. Further, the generalized versions of previous security reductions are devised by relaxing the constraints on the upper bounds of the information-theoretic measures. This makes it possible to estimate bit security more reliable and improves the security level. We also estimate the effects on the security level when the $\kappa $ -bit secure original scheme is implemented on a $p$ -bit precision system. In previous studies, $p$ was fixed as $\frac {\kappa }2$ ; however, the proposed schemes are generalized such that the security level $\kappa $ and precision $p$ can vary independently. This results in a significant difference. Moreover, previous results cannot provide the exact lower bound of the security level for $p\ne {\frac {\kappa }{2}}$ . However, the proposed results can provide the exact lower bound of the estimation value of the security level as long as the precision $p$ satisfies certain conditions. We provide diverse types of security reduction formulas for the six types of information-theoretic measures. The proposed schemes can provide information-theoretic guidelines regarding the difference between the security levels of two identical cryptographic schemes when extracting randomness from two different probability distributions. In particular, the proposed schemes can be used to quantitatively estimate the effect of the statistical difference between the ideal and real distributions on the security level.

Due to the appearance of quantum computers, which will significantly reduce the time of solving certain problems, the security of many standardized cryptosystems is under threat. This prompted NIST to launch an open competition to create new post-quantum standards in 2016. In the summer of 2020, the NTRU algorithm, one of the fastest post-quantum algorithms based on lattices in Euclidean space (1996), was entered the seven finalists of this competition. However, only in 2017 was proposed an analog of this encryption scheme – symmetric encryption scheme NTRUCipher. Preliminary researches of this encryption scheme have been conducted but it’s security to chosen-plaintext attack, which consists of compiling a system of linear equations corrupted by noise (over a finite field of simple order) and solving it using a generalized BKW algorithm, have not been analyzed. For the first time, the NTRUCipher + cipher scheme is proposed in this article. Its main difference is the usage of an additional random polynomial when encrypting. The security of NTRUCipher cipher scheme and its modification NTRUCipher+ against BKW-attack is researched. Such an attack is possible for symmetric NTRU-like cipher schemes but it has not been considered before. Analytical (upper and lower) bounds of the BKW attack’s complexity on NTRUCipher and NTRUCipher + are obtained. The comparison of these cipher schemes on the encrypted messages’ length against BKW-attack at certain identical fixed parameters is carried out. It is shown that the security increase of the NTRUCipher cipher scheme against BKW-attack due to the usage of an additional additive in encryption is almost completely leveled by increasing the upper bound of the decryption failure probability. Research allows to compare these cipher schemes in terms of security and practicality and to conclude that it is inexpedient to use NTRUCipher+ to increase the security of the NTRUCipher cipher scheme to BKW attack. In the future, it is planned to develop methods for constructing symmetric analogs of the NTRU cryptosystem based on other general lattice-based structures.

Future Internet of Things (IoTs) networks will consist of a Hybrid Access Point (HAP) that powers devices via Radio Frequency (RF) signals. These devices harvest RF-energy and are tasked with monitoring target(s) such as vehicles at key locations. In order to maximize their expected targets monitoring time, this paper considers the problem of optimizing the HAP's transmit power, and the active time of devices. We formulate a Stochastic Program (SP) and solve it using the Sample Average Approximation method. We also present a sequential Monte-Carlo based reinforcement learning method or SMC-L to learn sensor activation time. Our results show that (i) the confidence interval upper bound of the solution derived by the SP is affected by the number of samples that represent targets appearance time, (ii) increasing the sensing range of devices affects the total expected target monitoring time, (iii) the learning speed of SMC-L is affected by its exploration rate, and (iv) SMC-L does not utilize the energy of devices fully under poor channel conditions.

© 1967-2012 IEEE. Relays have been deployed to provide connectivity to heavily blocked and isolated radio environments, but suffer from severe data rate losses due to the half-duplex nature of relays. This paper exploits non-orthogonal multiple access (NOMA) to enhance the sum-rate of two heavily blocked users to and from a base station (BS) via two-way relays (TWRs). By carefully arranging the NOMA transmissions, only two time slots are needed to complete a round of two-way transmissions between the BS and the two users. Unlike existing works where users associated with the same relay form a NOMA pair, the two users are connected to two different relays in the proposed scheme, due to the blockage. We analyze the sum-rate of the proposed NOMA-based TWR scheme with two popular relaying modes, namely, amplify-and-forward (AF) and decode-and-forward (DF) relaying, and derive closed-form expressions for the sum-rate upper bounds of the scheme. We also analyze the asymptotic sum-rate of the NOMA-based TWR schemes in high signal-to-noise ratio (SNR) regions. Simulations verify the analysis, and show that the proposed schemes outperform existing TDMA-based alternatives and a NOMA-based benchmark scheme. It is shown that the sum-rate of the schemes is independent of the transmit power of the BS in the high SNR regions, given the transmit powers of the relays and users.

This study investigates the impact of co-channel interference caused by external terminals on multi-user bi-directional amplify-and-forward based wireless relaying network. The investigation considers multi-user pair and a single bi-directional relay terminal and a finite number of co-channel interference in the system model. A lower bound on the end-to-end outage probability is derived for the simultaneous transmission and the opportunistic source-pair selection strategy. According to analytical, asymptotic, and Monte-Carlo based computer simulation results, the opportunistic source-pair selection strategy achieves a near-optimal performance for the inter-cell interference caused by other user-pairs within the system. Results also show that the opportunistic source-pair selection strategy also achieves optimal diversity order, which is equal to N, in high signal-to-noise ratio regimes. Results also show that the co-channel interference degrades the achievable diversity order from N to 0 while also affecting the system coding gain.

Fast time-varying channel parameters and large penetration losses for signals passing through train carriages are two well-known challenges for wireless communications on high speed rails (HSRs). In this paper, we introduce, for the first time, backscatter technology into HSR wireless communications, which can address these two challenges and yet have low complexity of signal processing and low cost of circuit implementation compared with traditional solutions such as relaying or beamforming. Specifically, we propose a backscatter aided wireless transmission (BAWT) scheme and demonstrate that it outperforms the existing direct wireless transmission (DWT) scheme. We derive the upper and lower bounds of channel capacity for BAWT and prove that it exceeds that of DWT on certain conditions. We also propose the transceiver design for both BAWT and DWT, including joint carrier frequency offset and channel estimator, and signal detector. We show that BAWT, rather than DWT, can obtain the channel statistical information in practical applications due to fixed train antennas and unchanged tracks, which can be utilized to facilitate channel estimation. Finally, simulation results are provided to corroborate the proposed solutions.

The recently proposed Minimal Complexity Machine (MCM) finds a hyperplane classifier by minimizing an upper bound on the Vapnik–Chervonenkis (VC) dimension. The VC dimension measures the capacity or model complexity of a learning machine. Vapnik’s risk formula indicates that models with smaller VC dimension are expected to show improved generalization. On many benchmark datasets, the MCM generalizes better than SVMs and uses far fewer support vectors than the number used by SVMs. In this paper, we describe a neural network that converges to the MCM solution. We employ the MCM neurodynamical system as the final layer of a neural network architecture. Our approach also optimizes the weights of all layers in order to minimize the objective, which is a combination of a bound on the VC dimension and the classification error. We illustrate the use of this model for robust binary and multi-class classification. Numerical experiments on benchmark datasets from the UCI repository show that the proposed approach is scalable and accurate, and learns models with improved accuracies and fewer support vectors.

Reducing energy consumption is of paramount importance in wireless networks, particularly now with the increasing popularity of the Internet of Things. One of the most effective tools for that is duty cycling the radio. In particular, asynchronous methods have been repeatedly studied in the literature. In this letter, we consider the asymmetrical case of asynchronous duty cycling, i.e., when nodes operate under different duty cycles because of different energy requirements. More specifically, we derive upper and lower bounds, as well as an approximate model for the neighbor discovery time of nodes operating under asymmetrical block designs. We further validate our model by comparing it to empirical data.

An adaptive ranging technique for maintaining high-accuracy ranging between nodes in coherent distributed antenna arrays is presented. Coherent distributed antenna arrays are networks of wireless systems coordinated coherently at the level of the wavelength of the wireless signal. Enabling coherent operation between separate mobile nodes for active and passive microwave remote sensing requires accurate knowledge of the relative positions of the nodes in the array. In this article, a novel adaptive ranging technique based on the near-optimal waveform for high-accuracy ranging, a two-tone waveform, is designed and demonstrated in software-defined radio platforms representing array nodes. Ranging accuracy is dependent on both signal-to-noise ratio and the separation of the two tones in the waveform; however, in realistic environments, factors such as attenuation or antenna misalignment are not easily predicted, which can lead to degradation of the ranging measurement. Selecting one appropriate waveform for range measurements is, thus, not feasible unless the bandwidth assigned to it is always higher than required for the needed range accuracy. Rather than allocating such an unnecessarily wide bandwidth, this article presents a controller that regulates the spectral resources adaptively to meet the desired reference accuracy while minimizing the total occupied bandwidth. The controller continuously monitors the statistical parameters of the received signal, such as signal-to-noise ratio, in a perception stage and adapts the spectral characteristics of the transmitted waveform in an action stage. The adaptive action, based on a Cramer–Rao lower bound analysis, maintains the signal statistical characteristics below a specified bound to maintain high coherent gain. Experimental results demonstrate the ability to maintain ranging standard deviation of 1.5 mm (standard deviation of time delay estimates equals $10^{-11}$ s), which yields 90% or more of the possible achievable coherent gain at carrier frequencies up to 13.33 GHz.

In this paper, we investigate a hybrid multicast/unicast scheme for a multiple-input single-output cache-aided non-orthogonal multiple access (NOMA) vehicular scenario in the face of rapidly fluctuating vehicular wireless channels. Considering a more practical situation, imperfect channel state information is taking into account. In this paper, we formulate an optimization problem to maximize the unicast sum rate under the constraints of the peak power, the peak backhaul, the minimum unicast rate, and the maximum multicast outage probability. To solve the formulated non-convex problem, a lower bound relaxation method is proposed, which enables a division of the original problem into two convex sub-problems. Computer simulations show that the proposed caching-aided NOMA is superior to the orthogonal multiple access counterpart.

Owing to their short communication range, wireless nodes in wireless sensor networks (WSNs) can exchange information with devices in their vicinity only. Thus, in sparse networks, the full connectivity of the network is rarely achieved. This renders a centralised approach towards localisation in WSNs useless. Moreover, the exploitation of a centralised algorithm for localisation compromises the scalability in dense networks. Thus, a decentralised, location-aware network with partial connectivity and hybrid (range and direction) measurements obtained between known sensors (reference sensors) and sensors at unknown locations (target sensors) is under focus. The decentralised location estimation is obtained using a linear least squares (LLS) approach and performance enhancements are achieved by introducing a weighing strategy to produce weighted least squares (WLS) estimates. This distributed estimation is made possible by designing a map stitching technique that forms the global map of the network from individual local maps of the wireless nodes without compromising the distributed nature of the network. In the analytical section of the study, theoretical mean squares error expression is derived for LLS estimation, and a Cramer–Rao lower bound is derived to bind the performance of the WLS solution. The algorithm's performance validation is conducted both theoretically and via simulations.

In 1985, Ben-Or and Linial (Advances in Computing Research 1989) introduced the collective coin flipping problem, where n parties communicate via a single broadcast channel and wish to generate a common random bit in the presence of adaptive Byzantine corruptions. In this model, the adversary can decide to corrupt a party in the course of the protocol as a function of the messages seen so far. They showed that the majority protocol, in which each player sends a random bit and the output is the majority value, tolerates O(√n) adaptive corruptions. They conjectured that this is optimal for such adversaries. We prove that the majority protocol is optimal (up to a poly-logarithmic factor) among all protocols in which each party sends a single, possibly long, message. Previously, such a lower bound was known for protocols in which parties are allowed to send only a single bit (Lichtenstein, Linial, and Saks, Combinatorica 1989), or for symmetric protocols (Goldwasser, Kalai, and Park, ICALP 2015).

5G cellular network will drive towards higher energy efficiency, lower latency and higher reliable wireless networks. The key contributions can summarize as follows: (1) this paper proposes a feasible method to compute the expected rate and approach the optimal capacity region by upper and lower bounds in 5G cellular network, (2) develops linear network coding and dynamic rate adaptation to improve the throughput and reliability of LAA network, (3) proposes a new E-PDP-MNLC (Efficient Protocol Priority-based Distributed Paired Multicast Network Linear Coding) scheme for LAA network coding. • Networks → Network layer protocols → Routing protocols • Networks → Network algorithms → Data path algorithms → Packet scheduling → Control path algorithms → Network control algorithms.

International audience; Within an industrial manufacturing process, tolerancing is a key player. The dimensions uncertainties management starts during the design phase, with an assessment on variability of parts not yet produced. For one assembly step, we can gain knowledge from the tolerance range required for the parts involved. In order to assess output uncertainty of this assembly in a reliable way, this paper presents an approach based on the deviation of the sum of uniform distributions. As traditional approaches based on Hoeffding inequalities do not give accurate results when the deviation considered is small, we propose an improved upper bound. We then discuss how the stack chain geometry impacts the bound definition. Finally, we show an application of the proposed approach in tolerance design of an aircraft sub-assembly. The main interest of the technique compared to existing methodologies is the management of the confidence level and the emphasis of the explicit role of the balance within the stack chain.

In the last decade remarkable progress has been made in development of suitable proof techniques for analysing randomised search heuristics. The theoretical investigation of these algorithms on classes of functions is essential to the understanding of the underlying stochastic process. Linear functions have been traditionally studied in this area resulting in tight bounds on the expected optimisation time of simple randomised search algorithms for this class of problems. Recently, the constrained version of this problem has gained attention and some theoretical results have also been obtained on this class of problems. In this paper we study the class of linear functions under uniform constraint and investigate the expected optimisation time of Randomised Local Search (RLS) and a simple evolutionary algorithm called (1+1) EA. We prove a tight bound of $\Theta(n^2)$ for RLS and improve the previously best known upper bound of (1+1) EA from $O(n^2 \log (Bw_{\max}))$ to $O(n^2\log B)$ in expectation and to $O(n^2 \log n)$ with high probability, where $w_{\max}$ and $B$ are the maximum weight of the linear objective function and the bound of the uniform constraint, respectively. Also, we obtain a tight bound of $O(n^2)$ for the (1+1) EA on a special class of instances. We complement our theoretical studies by experimental investigations that consider different values of $B$ and also higher mutation rates that reflect the fact that $2$-bit flips are crucial for dealing with the uniform constraint., Comment: Journal version to appear in Algorithmica

In supervised learning, we often face with ambiguous (A) samples that are difficult to label even by domain experts. In this paper, we consider a binary classification problem in the presence of such A samples. This problem is substantially different from semi-supervised learning since unlabeled samples are not necessarily difficult samples. Also, it is different from 3-class classification with the positive (P), negative (N), and A classes since we do not want to classify test samples into the A class. Our proposed method extends binary classification with reject option, which trains a classifier and a rejector simultaneously using P and N samples based on the 0-1-$c$ loss with rejection cost $c$. More specifically, we propose to train a classifier and a rejector under the 0-1-$c$-$d$ loss using P, N, and A samples, where $d$ is the misclassification penalty for ambiguous samples. In our practical implementation, we use a convex upper bound of the 0-1-$c$-$d$ loss for computational tractability. Numerical experiments demonstrate that our method can successfully utilize the additional information brought by such A training data., Comment: 20 pages, 6 figures, accepted at the 12th Asian Conference on Machine Learning (ACML 2020)