Search

We consider the problem of communicating over a channel that randomly “tears” the message block into small pieces of different sizes and shuffles them. For the binary torn-paper channel with block length $n$ and pieces of length ${\mathrm{ Geometric}}(p_{n})$ , we characterize the capacity as $C = e^{-\alpha }$ , where $\alpha = \lim _{n\to \infty } p_{n} \log n$. Our results show that the case of ${\mathrm{ Geometric}}(p_{n})$ -length fragments and the case of deterministic length- $(1/p_{n})$ fragments are qualitatively different and, surprisingly, the capacity of the former is larger. Intuitively, this is due to the fact that, in the random fragments case, large fragments are sometimes observed, which boosts the capacity. [ABSTRACT FROM AUTHOR]

Recently, there has been a lot of success in using the deterministic approach to provide approximate characterization of Gaussian network capacity. In this paper, we take a deterministic view and revisit the problem of wiretap channel with side information. A precise characterization of the secrecy capacity is obtained for a linear deterministic model, which naturally suggests a coding scheme which we show to achieve the secrecy capacity of the degraded Gaussian model (dubbed as “secret writing on dirty paper”) to within half a bit. [ABSTRACT FROM PUBLISHER]

An $(r, \delta)$ -locally repairable code ($(r, \delta)$ -LRC for short) was introduced by Prakash et al. for tolerating multiple failed nodes in distributed storage systems, which was a generalization of the concept of $r$ -LRCs produced by Gopalan et al.. An $(r, \delta)$ -LRC is said to be optimal if it achieves the Singleton-like bound. Recently, Chen et al. generalized the construction of cyclic $r$ -LRCs proposed by Tamo et al. , and constructed several classes of optimal $(r, \delta)$ -LRCs of length $n$ for $n\, |\, (q-1)$ or $n\,|\, (q+1)$ , respectively in terms of a union of the set of zeros controlling the minimum distance and the set of zeros ensuring the locality. Following the work of , , this paper first characterizes $(r, \delta)$ -locality of a cyclic code via its zeros. Then we construct several classes of optimal cyclic $(r, \delta)$ -LRCs of length $n$ for $n\, |\, (q-1)$ or $n\,|\, (q+1)$ , respectively from the product of two sets of zeros. Our constructions include all optimal cyclic $(r,\delta)$ -LRCs proposed in , , and our method seems more convenient to obtain optimal cyclic $(r, \delta)$ -LRCs with flexible parameters. Moreover, many optimal cyclic $(r,\delta)$ -LRCs of length $n$ for $n\, |\, (q-1)$ or $n\,|\, (q+1)$ , respectively with $(r+\delta -1)\nmid n$ can be obtained from our method. [ABSTRACT FROM AUTHOR]

The work reported in this paper aims at developing an algorithm for system operators to solve a robust security-constrained AC optimal power flow (R-SC-ACOPF) problem, which also optimizes the settings of discrete controllers including load tap changers (LTCs) and shunt capacitor banks (SCBs). An iterative algorithm involving a master problem and a number of sub-problems is proposed. The master problem selects the optimal discrete controllers’ settings by taking into account primal SC-ACOPF constraints based on the single-component worst contingencies identified by the sub-problems. Each sub-problem is a bi-level max-min problem used to find the current worst contingency. The overall objective is to minimize the total cost, including generation and load shedding cost, while satisfying all relevant constraints. The master problem and sub-problems allow representing LTC tap positions, SCB status, and contingencies using binary variables. Two case studies are presented as applications of this novel technique. [ABSTRACT FROM AUTHOR]

In this paper, the sequence families of which maximum correlation is determined by the Weil bound of exponential sums are revisited. Using the same approach, two new constructions with large family sizes and low maximum correlation are given. The first construction is an analog of one recent result derived from the interleaved structure of Sidel'nikov sequences. For a prime p and an integer M\vert (p-1), the new M-ary sequence families of period p are obtained from irreducible quadratic polynomials and known power residue-based sequence families. The new sequence families increase family sizes of the known power residue-based sequence families, but keep the maximum correlation unchanged. In the second construction, the sequences derived from the Weil representation are generalized, where each new sequence is the elementwise product of a modulated Sidel'nikov sequence and a modulated trace sequence. For positive integers d

The existing Science Citation Index only counts direct citations, whereas PageRank disregards the number of direct citations. We propose a new Comprehensive Citation Index (CCI) that evaluates both direct and indirect intellectual influence of research papers, and show that CCI is more reliable in discovering research papers with far-reaching influence. [ABSTRACT FROM AUTHOR]

This paper presents F2C, a cooperative resource management system for Infrastructure-as-a-Service (IaaS) clouds. Inspired by group-buying mechanisms in real product and service markets, F2C advocates a group of cloud tenants (called tenant coalition) to buy resource capacity in bulk and share the resource pool in the form of virtual machines (VMs). Tenant coalitions leads to vast opportunities for fine-grained resource sharing among multiple tenants. However, resource sharing, especially for multiple resource types, poses several challenging problems in pay-as-you-use cloud environments, such as sharing incentive, free-riding, lying and economic fairness. To address those problems, we propose Reciprocal Resource Fairness (RRF) , a novel resource allocation mechanism to enable fair sharing on multiple resource types within a tenant coalition. RRF is implemented in two complementary and hierarchical mechanisms: inter-tenant resource trading and intra-tenant weight adjustment. RRF satisfies several highly desirable properties to ensure fairness. We implement F2C in Xen platform. The experimental results show F2C is promising for both cloud providers and tenants. For cloud providers, F2C improves VM density and cloud providers’ revenue by 2.2X compared to the current IaaS cloud models. For tenants, F2C improves application performance by 45 percent and guarantees 95 percent economic fairness among multiple tenants. [ABSTRACT FROM PUBLISHER]

This paper studies the distinctness of primitive sequences over \bf Z/(M) modulo 2, where M is an odd integer that is composite and square-free, and \bf Z/(M) is the integer residue ring modulo M. A new sufficient condition is given for ensuring that primitive sequences generated by a primitive polynomial f\left(x\right) over \bf Z/(M) are pairwise distinct modulo 2. Such result improves a recent result obtained in our previous paper, and consequently, the set of primitive sequences over \bf Z/(M) that can be proven to be distinct modulo 2 is greatly enlarged. [ABSTRACT FROM PUBLISHER]

Functional test sequences can detect defects that are not detected by scan-based tests, but overall, achieve a lower gate-level fault coverage than scan-based tests. Design-for-testability (DFT) approaches for functional test sequences cause the sequences to deviate from functional operation conditions in arbitrary ways over the entire design. This paper introduces the concept of invisible-DFT as a DFT approach for functional test sequences, where the effects of activating the DFT logic are confined to selected logic blocks. This paper develops an invisible-scan approach. Considering a single logic block, the procedure described in this paper inserts scan shift cycles into a functional test sequence while maintaining the same primary input and output sequences for the logic block. This makes the activation of the DFT logic invisible to other logic blocks. The procedure allows a limited number of primary output vectors to be corrected for this purpose. Experimental results are presented to demonstrate the increase in fault coverage that can be achieved by invisible-scan. [ABSTRACT FROM AUTHOR]

The number-theoretic code is a class of codes defined by single or multiple congruences. These codes are mainly used for correcting insertion and deletion errors, and for correcting asymmetric errors. This paper presents a formula for a generalization of the complete weight enumerator for the number-theoretic codes. This formula allows us to derive the weight enumerators and cardinalities for the number-theoretic codes. As a special case, this paper provides the Hamming weight enumerators and cardinalities of the non-binary Tenengolts’ codes, correcting single insertion or deletion. Moreover, we show that the formula deduces the MacWilliams identity for the linear codes over the ring of integers modulo $r$. [ABSTRACT FROM AUTHOR]

This paper studies the downlink of a cloud radio access network (C-RAN) in which a centralized processor (CP) communicates with mobile users through base stations (BSs) that are connected to the CP via finite-capacity fronthaul links. Information theoretically, the downlink of a C-RAN is modeled as a two-hop broadcast-relay network. Among the various transmission and relaying strategies for such model, this paper focuses on the compression strategy, in which the CP centrally encodes the signals to be broadcast jointly by the BSs, then compresses and sends these signals to the BSs through the fronthaul links. We characterize an achievable rate region for a generalized compression strategy with Marton’s multicoding for broadcasting and multivariate compression for fronthaul transmission. We then compare this rate region with the distributed decode-forward (DDF) scheme, which achieves the capacity of the general relay networks to within a constant gap, and show that the difference lies in that DDF performs Marton’s multicoding and multivariate compression jointly as opposed to successively as in the compression strategy. A main result of this paper is that under the assumption that the fronthaul links are subject to a sum capacity constraint, this difference is immaterial; so, for the Gaussian network, the compression strategy based on successive encoding can already achieve the capacity region of the C-RAN to within a constant gap, where the gap is independent of the channel parameters and the power constraints at the BSs. As a further result, for C-RAN under individual fronthaul constraints, this paper also establishes that the compression strategy can achieve to within a constant gap to the sum capacity. [ABSTRACT FROM AUTHOR]

In many applications, top-k query is an important operation to return a set of interesting points in a potentially huge data space. It is analyzed in this paper that the existing algorithms cannot process top- k query on massive data efficiently. This paper proposes a novel table-scan-based T2S algorithm to efficiently compute top-k results on massive data. T2S first constructs the presorted table, whose tuples are arranged in the order of the round-robin retrieval on the sorted lists. T2S maintains only fixed number of tuples to compute results. The early termination checking for T2S is presented in this paper, along with the analysis of scan depth. The selective retrieval is devised to skip the tuples in the presorted table which are not top-k results. The theoretical analysis proves that selective retrieval can reduce the number of the retrieved tuples significantly. The construction and incremental-update/batch-processing methods for the used structures are proposed in this paper. The extensive experimental results, conducted on synthetic and real-life data sets, show that T2S has a significant advantage over the existing algorithms. [ABSTRACT FROM AUTHOR]

Load profile classification is very important in load forecast, planning and management. Although customers are generally grouped by utilities into residential, commercial classes and respective subclasses, there is a lack of systematic framework that can be used to characterize different classes with signatures that are both human-readable and machine-readable. The work presented in this paper attempts to formulate the theoretical framework for customer classification using the annual load profiles. This paper demonstrates how to extract characteristic attributes in frequency domain (CAFD) and use these CAFDs to formulate a hierarchy of load profiles that can be used as the systematic framework for customer load classification. As signatures for customer classes and subclasses, the CAFDs are obtained by using a data mining method called CART (classification and regression tree). The paper presents a load profile classification test to establish the efficacy of the proposed approach which is significant improvement over current practices that provide mostly qualitative labeling. [ABSTRACT FROM AUTHOR]

This paper describes a test compaction procedure that combines the advantages of using multicycle tests for test compaction with the advantages of using both broadside and skewed-load tests for increasing the fault coverage and achieving test compaction. The procedure is the first to combine these two concepts in a single procedure. The combination is made possible by a definition of a multicycle skewed-load test that is suggested in this paper, and complements the definition of a multicycle broadside test. Experimental results demonstrate the effectiveness of multicycle broadside and skewed-load tests in achieving test compaction for benchmark circuits. [ABSTRACT FROM AUTHOR]

Lists names of various papers, published in the journal "IEE Transaction on Neutral Networks" as of September 01, 2003, which exclusively deal with neural network system and the related works on it.

Scholar name disambiguation remains a hard and unsolved problem, which brings various troubles for bibliography data analytics. Most existing methods handle name disambiguation separately that tackles one name at a time, and neglect the fact that disambiguation of one name affects the others. Further, it is typically common that only limited information is available for bibliography data, e.g., only basic paper and citation information is available in DBLP. In this study, we propose a collective approach to name disambiguation, which takes the connection of different ambiguous names into consideration. We reformulate bibliography data as a heterogeneous multipartite network, which initially treats each author reference as a unique author entity, and disambiguation results of one name propagate to the others of the network. To further deal with the sparsity problem caused by limited available information, we also introduce word-word and venue-venue similarities, and we finally measure author similarities by assembling similarities from four perspectives. Using real-life data, we experimentally demonstrate that our approach is both effective and efficient. [ABSTRACT FROM AUTHOR]

Linear codes achieving or nearly achieving the Singleton bound are interesting in both theory and practice. The objective of this paper is to construct several infinite families of MDS, near MDS and almost MDS codes from some special cyclic subgroups of ${\mathbb {F}}_{q^{2}}^{*}$. To this end, the augmentation and extension techniques are used. The codes in this paper have flexible parameters and their lengths could be large. The minimum linear locality of the codes constructed in this paper is also studied. Some infinite families of optimal linearly locally recoverable codes are obtained. Besides, some codes in this paper are proved to be proper for error detection. [ABSTRACT FROM AUTHOR]

Cycle time, which is of order of ten weeks for most products in semiconductor wafer fabrication facilities (wafer fabs), must be explicitly considered in production planning models. Cycle times depend nonlinearly on the resource workload in a wafer fab. Different data-driven (DD) production planning formulations are studied in this paper. Such formulations are based on a set of system states representing the congestion behavior of a wafer fab with work in process (WIP) and resulting output levels. Different WIP-output relations in the DD formulations are investigated. In addition, different methods are proposed to gather representative sets of system states. The DD formulations are compared with the Allocated Clearing Function (ACF) formulation with respect to profit and cost values using simulation models of a scaled-down and a large-sized multi-product wafer fab, respectively. The simulation results show that some DD variants are able to outperform the ACF formulation under several experimental conditions. [ABSTRACT FROM AUTHOR]

Availability of a common, shared benchmark to provide repeatable, quantifiable, and comparable results is an added value for any scientific community. International consortia provide benchmarks in a wide range of domains, being normally used by industry, vendors, and researchers for evaluating their software products. In this regard, a benchmark of untimed Petri net models was developed to be used in a yearly software competition driven by the Petri net community. However, to the best of our knowledge there is not a similar benchmark to evaluate solution techniques for Petri nets with timing extensions. In this paper, we propose an evaluation framework for the comparative analysis of generalized stochastic Petri nets (GSPNs) simulation techniques. Although we focus on simulation techniques, our framework provides a baseline for a comparative analysis of different GSPN solvers (e.g., simulators, numerical solvers, or other techniques). The evaluation framework encompasses a set of 50 GSPN models including test cases and case studies from the literature, and a set of evaluation guidelines for the comparative analysis. In order to show the applicability of the proposed framework, we carry out a comparative analysis of steady-state simulators implemented in three academic software tools, namely, GreatSPN, PeabraiN, and TimeNET. The results allow us to validate the trustfulness of these academic software tools, as well as to point out potential problems and algorithmic optimization opportunities. [ABSTRACT FROM AUTHOR]

Role assignment is an important and complex task in collaboration and management. Group role assignment (GRA) pursues the optimal team performance by assigning pertinent roles to agents in a team from the team’s viewpoint. There are many factors that should be considered when conducting role assignment. Budgets are such a factor. This paper presents a challenging role assignment problem in collaboration, called GRA with budget constraints (GRABC). This paper contributes a thorough investigation of the GRABC problem including: 1) the first formalization of the proposed problem; 2) a set of practical solutions to different forms of this problem by using the IBM ILOG CPLEX optimization package (CPLEX); 3) a set of necessary conditions for these problems to possess feasible solutions to improve the CPLEX solutions; and 4) a synthesized process to deal with GRABC problems. Contribution 2) actually clarifies a list of requirements in solving the GRABC problem. Simulations, experiments and a case study are used to verify the practicability and efficiency of the proposed solutions. [ABSTRACT FROM AUTHOR]

This paper presents a planning procedure that allows an anthropomorphic dual-arm robotic system to perform a manipulation task in a natural human-like way by using demonstrated human movements. The key idea of the proposal is to convert the demonstrated trajectories into attractive potential fields defined over the configuration space and then use an RRT*-based planning algorithm that minimizes a path-cost function designed to bias the tree growth toward the human-demonstrated configurations. This paper presents a description of the proposed approach as well as results from a conceptual and a real application example, the latter using a real anthropomorphic dual-arm robotic system. A path-quality measure, based on first-order synergies (correlations between joint velocities) obtained from real human movements, is also proposed and used for evaluation and comparison purposes. The obtained results show that the paths obtained with the proposed procedure are more human-like. [ABSTRACT FROM AUTHOR]

Mean hitting time for random walks on a network, defined as the average of hitting times over all possible pairs of nodes, has found a large variety of applications in many areas. In this paper, we first prove that among all $N$ -node networks, the complete graph has the minimum mean hitting time $N-1$ , which scales linearly with network size. We then study a random walk mobility model with location heterogeneity, modeled by scale-free networks, which are ubiquitous in realistic systems such as P2P networks. For this purpose, we consider random walks on two sparse scale-free small-world networks. By using the connection between random walks and electrical networks, we derive explicit formulas about mean hitting time for both networks, the dominant scaling of which exhibits the same behavior as that of complete graphs. This paper demonstrates that heterogeneous sparse networks can have low-mean hitting time with a behavior similar to that of dense complete graphs, which is instrumental in designing networks, where search is fast between any pair of nodes. [ABSTRACT FROM AUTHOR]

In an interesting paper, Prof. C. Ding provided three constructions of cyclic codes of length being a product of two primes. Numerical data shows that many codes from these constructions are best cyclic codes of the same length and dimension over the same finite field. However, not much is known about these codes. In this paper, we explain some of the numerical data by developing a general method on cyclic codes of composite length and on estimating the minimum distance. We also provide a general construction of cyclic codes of composite length which are related to Ding’s constructions. Numerical data shows that it produces many best cyclic codes as well. Finally, we point out how these cyclic codes can be used to construct convolutional codes with large free distance. [ABSTRACT FROM AUTHOR]

List decoding of insertions and deletions in the Levenshtein metric is considered. The Levenshtein distance between two sequences is the minimum number of insertions and deletions needed to turn one of the sequences into the other. In this paper, a Johnson-like upper bound on the maximum list size when list decoding in the Levenshtein metric is derived. This bound depends only on the length and minimum Levenshtein distance of the code, the length of the received word, and the alphabet size. It shows that polynomial-time list decoding beyond half the Levenshtein distance is possible for many parameters. Further, we also prove a lower bound on list decoding of deletions with the well-known binary Varshamov–Tenengolts codes, which shows that the maximum list size grows exponentially with the number of deletions. Finally, an efficient list decoding algorithm for two insertions/deletions with VT codes is given. This decoder can be modified to a polynomial-time list decoder of any constant number of insertions/deletions. [ABSTRACT FROM AUTHOR]

Let m be an even positive integer. A Boolean bent function ƒ on F2m-1 × F2 is called a cyclic bent function if for any a ≠ b ∈ F2m-1 and ε ∈ F2, ƒ(ax1, x2)+ ƒ(bx1, x2 + ε) is always bent, where x1 ∈ F2m-1, x2 ∈ F2. Cyclic bent functions look extremely rare. This paper focuses on cyclic bent functions on F2m-1 × F2 and their applications. The first objective of this paper is to establish a link between quadratic cyclic bent functions and a special type of prequasifields, and construct a class of quadratic cyclic bent functions from the Kantor-Williams prequasifields. The second objective is to use cyclic bent functions to construct families of optimal sequences. The results of this paper show that cyclic bent functions have nice applications in several fields such as coding theory, symmetric cryptography, and CDMA communication. [ABSTRACT FROM AUTHOR]

Volt-VAR optimization (VVO) has been investigated extensively in power systems. However, under the era of integrated energy systems (IES), the growing interdependencies between different energy systems complicate traditional VVO. This is further hardened by incurred gas quality problems due to the hydrogen injection in IES, produced by widely applied power-to-gas (P2G) facilitates that couple between power and gas systems. This paper develops a two-stage volt-VAR-pressure optimization (VVPO) model for PV-penetrated IES to manage the variation of system voltages while managing gas quality indices. In addition to the traditional voltage regulating devices, P2G facilities, which can mitigate fluctuating PV output via converting the surplus generation into hydrogen, are also used for voltage management. A two-stage distributionally robust optimization (DRO) based on moment information is utilized to model PV uncertainty. A semidefinite programming model is formulated and finally solved by the constraint generation algorithm. A 33-bus-20-node IES is used to verify the effectiveness of the proposed VVPO on voltage management, ensured gas quality with high economic efficiency. The proposed VVPO is applicable to injecting other green gases into gas systems while ensuring power quality and enable system operators to provide low-cost but high-quality multi-energy to customers. [ABSTRACT FROM AUTHOR]

This paper proposes a Principal Component Analysis (PCA)-based method to construct composite indices to rapidly assess power system transient stability. By assigning appropriate weights to a few selected sub-indicators, a composite index can be constructed to guarantee a better coverage of worst-case stability scenarios of the power system. This paper provides an automatic method to construct a composite index with four sub-indicators calculated by the integral of acceleration, speed deviation and rotor angle of generators over a certain integration period using PCA. The effectiveness of the sub-indicators is validated first, followed by a comprehensive example of process of composite index construction, and the assessment of the influence of the effect of integration period on the performance of the composite index. High accuracy of classification has been demonstrated using composite index with appropriate settings of the integration periods of the sub-indicators. [ABSTRACT FROM AUTHOR]

Ride-sourcing services are now reshaping the way people travel by effectively connecting drivers and passengers through mobile internets. Online matching between idle drivers and waiting passengers is one of the most key components in a ride-sourcing system. The average pickup distance or time is an important measurement of system efficiency since it affects both passengers’ waiting time and drivers’ utilization rate. It is naturally expected that a more effective bipartite matching (with smaller average pickup time) can be implemented if the platform accumulates more idle drivers and waiting passengers in the matching pool. A specific passenger request can also benefit from a delayed matching since he/she may be matched with closer idle drivers after waiting for a few seconds. Motivated by the potential benefits of delayed matching, this paper establishes a two-stage framework which incorporates a combinatorial optimization and multi-agent deep reinforcement learning methods. The multi-agent reinforcement learning methods are used to dynamically determine the delayed time for each passenger request (or the time at which each request enters the matching pool), while the combinatorial optimization conducts an optimal bipartite matching between idle drivers and waiting passengers in the matching pool. Four tailored reinforcement learning methods, delayed multi-agent deep Q learning (Delayed-M-DQN), delayed multi-agent actor-critic (Delayed-M-A2C), delayed multi-agent Proximal Policy Optimization (Delayed-M-PPO), and delayed multi-agent actor-critic with experience replay (Delayed-M-ACER), are developed. Through extensive empirical experiments with a well-designed simulator, we show that the proposed framework is able to remarkably improve system performances, by well balancing the trade-off among pick-up time, matching time, successful matching rate. [ABSTRACT FROM AUTHOR]

As the rate at which scientific work is published continues to increase, so does the need to discern high-impact publications. In recent years, there have been several approaches that seek to rank publications based on their expected citation-based impact. Despite this level of attention, this research area has not been systematically studied. Past literature often fails to distinguish between short-term impact, the current popularity of an article, and long-term impact, the overall influence of an article. Moreover, the evaluation methodologies applied vary widely and are inconsistent. In this work, we aim to fill these gaps, studying impact-based ranking theoretically and experimentally. First, we provide explicit definitions for short-term and long-term impact, and introduce the associated ranking problems. Then, we identify and classify the most important ideas employed by state-of-the-art methods. After studying various evaluation methodologies of the literature, we propose a specific benchmark framework that can help us better differentiate effectiveness across impact aspects. Using this framework we investigate: (1) the practical difference between ranking by short- and long-term impact, and (2) the effectiveness and efficiency of ranking methods in different settings. To avoid reporting results that are discipline-dependent, we perform our experiments using four datasets from different scientific disciplines. [ABSTRACT FROM AUTHOR]

This paper deals with randomized polling of a social network. In the case of forecasting the outcome of an election between two candidates A and B, classical intent polling asks randomly sampled individuals: who will you vote for? Expectation polling asks: who do you think will win? In this paper, we propose a novel neighborhood expectation polling (NEP) strategy that asks randomly sampled individuals: what is your estimate of the fraction of votes for A? Therefore, in NEP, sampled individuals will naturally look at their neighbors (defined by the underlying social network graph) when answering this question. Hence, the mean squared error (MSE) of NEP methods rely on selecting the optimal set of samples from the network. To this end, we propose two NEP algorithms for the following cases: (i) the social network graph is not known but, random walks (sequential exploration) can be performed on the graph, and (ii) the social network graph is unknown but, uniformly sampled nodes from the network are available. For both cases, algorithms based on a graph theoretic consequence called friendship paradox are proposed. Theoretical results on the dependence of the MSE of the algorithms on the properties of the network are established. Numerical results on real and synthetic data sets are provided to illustrate the performance of the algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an evolution of control systems of Modular Multilevel Converters (MMCs) focusing on the internal voltages and currents dynamics. MMCs have passive components inside the converter that create extra dynamics compared to conventional VSCs. Some control schemes that do not consider these internal dynamics may still stabilize the system asymptotically thanks to the linearisation in the modulation step. However these control schemes are less robust because they are prone to poor damped oscillations on the dc side of the converter. The MMC circuit and energy relationships are presented in this paper. Along with a gradual development of the energy based control, the important roles of each internal dynamics are clearly demonstrated. Experimental results are presented to show the impacts of the linearisation in the modulation step on the system behaviour. [ABSTRACT FROM AUTHOR]

A substantial body of literature on global design optimization techniques is based on an implicit assumption that a system under optimization is designed by a perfectly centralized design team with all team members committed to a global optimum solution. This assumption enables the system engineers to minimize all the objective functions simultaneously while controlling all design decisions at the same time. Recent research suggested that this inherent assumption may need to be revisited. This paper proposes a novel framework for design optimization using game theory, which represents a design process as a normal-form game and unifies two fundamental decisions of discipline designers, namely a system-global or a discipline-local optimum solution. This paper provides a general mathematical formulation of the framework for a design process with any number of discipline designers. The framework introduces the notion of the Nash front, which is a set of design solutions consisting of the Nash equilibria derived through the reallocation of design authority between discipline designers. This paper then tests the framework on a numerical optimization case study of a Stirling micro combined heat and power plant with two discipline designers. The result indicates that for the system capital cost and the system power output the difference between global Pareto-optimum designs and the Nash front designs on average across five different scenarios of design authority allocation was 22% and 16%, correspondingly. The application of the novel framework in different fields of engineering could help to build models that are more realistic and prevent impractical technical objectives. [ABSTRACT FROM AUTHOR]

This paper describes a method of finding a constrained optimum excitation of a generic transmitting antenna subarray. Constraint of a maximum power coupled from the transmitting to the receiving subarray is discussed. Optimum realized gain, optimum gain, and optimum directivity goals are covered. The proposed method can operate on any generic multi-port antenna, with no assumptions taken on the geometry, the constituent elements construction, or the interelement coupling. This paper in particular investigates dense linear arrays and analyses the applicability of several typical transmitter/receiver partitionings to the proposed method. Achieving a below −30 dB TX-to-RX total coupling with less than 1 dB realized gain penalty over a 135° scanning angle is demonstrated for below-half-wavelength spaced 73 GHz $E$ -band PCB-based linear arrays. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem for which lengths a maximum distance separable (MDS) Euclidean self-dual code over $\mathbb {F}_{q}$ exists. This problem is completely solved for the case where $q$ is even. For $q$ is odd, some $q$ -ary MDS Euclidean self-dual codes were obtained in the literature. In this paper, we construct six new classes of $q$ -ary MDS Euclidean self-dual codes by using generalized Reed–Solomon (GRS for short) codes and extended GRS codes. [ABSTRACT FROM AUTHOR]

In this paper, a general framework of constructing the optimal frequency-hopping sequence (FHS) sets is presented based on the designated direct product. Under this framework, we obtain infinitely many new optimal FHS sets by combining some one-coincide (OC) sequence sets that are newly constructed in this paper with some known optimal FHS sets. Our constructions are also based on extension method. However, our constructions give new and flexible parameters due to the free choice of OC sequence set. As a result, our constructions allow a great flexibility of choosing parameters of FHS sets for a given frequency-hopping spread spectrum system. [ABSTRACT FROM AUTHOR]

In a paper by Guruswami et al., it was shown that the length $n$ of a $q$ -ary linear locally recoverable code with distance $d \geqslant 5$ is upper bounded by $O(dq^{3})$. Thus, it is a challenging problem to construct $q$ -ary locally recoverable codes with distance $d \geqslant 5$ and length approaching the upper bound. The same paper also gave an algorithmic construction of $q$ -ary locally recoverable codes with locality $r$ and length $n=\Omega _{r}(q^{2})$ for $d=5$ and 6, where $\Omega _{r}$ means that the implicit constant depends on locality $r$. In this paper, we present an explicit construction of $q$ -ary locally recoverable codes of distance $d= 5$ and 6 via binary constant weight codes. It turns out that 1) our construction is simpler and more explicit and 2) the length of our codes is greater than previously known. [ABSTRACT FROM AUTHOR]

The construction of a quasi-cyclic low density parity-check (QC-LDPC) matrix is usually carried out in two steps. In the first step, a prototype matrix is defined according to certain criteria (size, girth, check and variable node degrees, and so on). The second step involves the expansion of the prototype matrix. During this last phase, an integer value is assigned to each non-null position in the prototype matrix corresponding to the right-rotation of the identity matrix. The problem of determining these integer values is complex. The state-of-the-art solutions use either some mathematical constructions to guarantee a given girth of the final QC-LDPC code, or a random search of values until the target girth is satisfied. In this paper, we propose an alternative/complementary method that reduces the search space by defining large equivalence classes of topologically identical matrices through row and column permutations using additive, structural, and multiplicative transformations. Selecting only a single element per equivalence class can reduce the search space by a few orders of magnitude. Then, we use the formalism of constraint programming to list the exhaustive sets of solutions for a given girth and a given expansion factor. An example is presented in all sections of the paper to illustrate the methodology. [ABSTRACT FROM AUTHOR]

In this paper, we consider a simple coding scheme for spatial modulation, where the same set of active transmit antennas is repeatedly used over consecutive multiple transmissions. Based on a Gaussian approximation, an approximate maximum likelihood (ML) detection problem is formulated to detect the indices of active transmit antennas. We show that the solution to the approximate ML detection problem can achieve a full coding gain. Furthermore, we develop a low-complexity iterative algorithm to solve the problem with low complexity based on a well-known machine learning approach, i.e., variational inference. Simulation results show that the proposed algorithm can have a near ML performance. A salient feature of the proposed algorithm is that its complexity is independent of the number of active transmit antennas, whereas an exhaustive search for the ML problem requires a complexity that grows exponentially with the number of active transmit antennas. [ABSTRACT FROM AUTHOR]

Constacyclic codes are well-known generalizations of cyclic and negacyclic codes. Due to their rich algebraic structure, constacyclic codes are used to construct quantum codes and symbol-pair codes. Let ${\mathbb {F}}_{q}$ be a finite field with order $q$ , where $q$ is a positive power of a prime $p$. Suppose that $n$ is a positive integer and the product of distinct prime factors of $n$ divides $q-1$ , i.e., $rad(n)\mid (q-1)$. In this paper, we explicitly factorize the polynomial $x^{n}-\lambda $ for each $\lambda \in {\mathbb {F}}_{q}^{*}$. As applications, first, we obtain all repeated-root $\lambda $ -constacyclic codes and their dual codes of length $np^{s}$ over ${\mathbb {F}}_{q}$ ; second, we determine all simple-root LCD cyclic codes and LCD negacyclic codes of length $n$ over ${\mathbb {F}}_{q}$ ; third, we list all self-dual repeated-root negacyclic codes of length $np^{s}$ over ${\mathbb {F}}_{q}$. In contrast to known results, the lengths of constacyclic codes in this paper have more flexible parameters. [ABSTRACT FROM AUTHOR]

The earth’s magnetosphere is an intricate input–output system, where the inputs are the solar wind parameters and the output measure are the geomagnetic. The disturbance storm time (DST) index is one such quantity measure for the intensity of disturbance in the geomagnetic field due to the magnetic storm. The geomagnetic activities have catastrophic effects on several communication networks and harmful for biological livings. Therefore, it is significant to develop a model for the prediction of disturbance in the geomagnetic field. This paper presents linear as well as nonlinear parametric techniques to model the complex magnetosphere dynamics. Neural network (NN)-based modeling techniques, such as feed-forward NN (FFNN), NN integrated with nonlinear autoregression with exogenous (NARX) inputs, adaptive neuro-fuzzy inference system (ANFIS), and recurrent NN are developed for the prediction of the magnetic storm. Global search algorithms, such as particle swarm optimization and genetic algorithm, also train the developed FFNNs. All the models are trained on 15 years’ real-time series data of solar wind parameters and DST index on a frequency of 1 h. The accuracy of the predicted DST index for multiple intensity levels of the magnetic storm using all developed linear and nonlinear modeling techniques is presented, compared, and analyzed thoroughly. The performance of NN integrated with NARX inputs and ANFIS is higher than rest of methods developed in the paper. Both techniques have the capability to predict mild, moderate, and intense magnetic storm with high degree of accuracy. The comparative analysis with state of the art shows the enhanced accuracy and robustness of developed models in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we study two families of codes with availability, namely, private information retrieval (PIR) codes and batch codes. While the former requires that every information symbol has $k$ mutually disjoint recovering sets, the latter imposes this property for each multiset request of $k$ information symbols. The main problem under this paradigm is to minimize the number of redundancy symbols. We denote this value by $r_{P}(n,k)$ and $r_{B}(n,k)$ , for PIR codes and batch codes, respectively, where $n$ is the number of information symbols. Previous results showed that for any constant $k$ , $r_{P}(n,k) = \Theta (\sqrt {n})$ and $r_{B}(n,k)= {\mathcal{ O}}(\sqrt {n}\log (n))$. In this paper, we study the asymptotic behavior of these codes for non-constant $k$ and specifically for $k=\Theta (n^\epsilon)$. We also study the largest value of $k$ such that the rate of the codes approaches 1 and show that for all $\epsilon < 1$ , $r_{P}(n,n^\epsilon) = o(n)$ and $r_{B}(n,n^\epsilon) = o(n)$. Furthermore, several more results are proved for the case of fixed $k$. [ABSTRACT FROM AUTHOR]

Spatially-coupled (SC) codes are a family of graph-based codes that have attracted significant attention, thanks to their capacity approaching performance and low decoding latency. An SC code is constructed by partitioning an underlying block code into a number of components and coupling their copies together. In this paper, we first introduce a general approach for the enumeration of detrimental combinatorial objects in the graph of finite-length SC codes. Our approach is general in the sense that it effectively works for SC codes with various partitioning schemes, column weights, and memories. Next, we present a two-stage framework for the construction of high performance binary SC codes optimized for the additive white Gaussian noise channels; we aim at minimizing the number of detrimental combinatorial objects in the error floor region. In the first stage, we deploy a novel partitioning scheme, called the optimal overlap partitioning, to produce the optimal partitioning corresponding to the smallest number of detrimental objects. In the second stage, we apply a new circulant power optimizer to further reduce the number of detrimental objects in the lifted graph. SC codes constructed by our new framework have up to two orders of magnitude error floor performance improvement and up to 0.6 dB SNR gain compared to prior state-of-the-art SC codes. [ABSTRACT FROM AUTHOR]

Joint pushing and caching is recognized as an efficient remedy to the problem of spectrum scarcity incurred by tremendous mobile data traffic. In this paper, we design the optimal joint pushing and caching policy to maximize bandwidth utilization, which is of fundamental importance to mobile telecom carriers. In particular, we consider a multiuser wireless network with multicast opportunities where each user is equipped with a cache of limited size. First, we formulate the stochastic optimization problem as an infinite horizon average cost Markov decision process. By the structural analysis, we show that how the optimal policy achieves a balance between the current transmission cost and the future average transmission cost. We also show that the optimal average transmission cost decreases with the cache sizes, revealing a tradeoff between storage and bandwidth. Then, due to the fact that obtaining a numerical optimal solution suffers the curse of dimensionality and implementing it requires a centralized controller and global system information, we develop a low-complexity decentralized policy (LDP) by using a linear approximation of the value function and transforming challenging discrete optimization problems into difference of convex (DC) problems, which can be efficiently solved by using DC algorithms. We also obtain an upper bound on the performance gap between the average cost of LDP and the minimum average cost, which can be easily evaluated. Next, we propose an online decentralized algorithm to implement the proposed LDP, when priori knowledge of user demand processes is not available. Finally, using numerical results, we demonstrate the advantage of the proposed solutions over some existing designs. The results in this paper offer useful guidelines for designing practical cache-enabled multiuser wireless networks. [ABSTRACT FROM AUTHOR]

This paper introduces the simultaneous partial-inverse problem (SPI) for polynomials and develops its application to decoding interleaved Reed–Solomon codes beyond half the minimum distance. While closely related both to standard key equations and to well-known Padé approximation problems, the SPI problem stands out in several respects. First, the SPI problem has a unique solution (up to a scale factor), which satisfies a natural degree bound. Second, the SPI problem can be transformed (monomialized) into an equivalent SPI problem where all moduli are monomials. Third, the SPI problem can be solved by an efficient algorithm of the Berlekamp–Massey type. Fourth, decoding interleaved Reed–Solomon codes (or subfield-evaluation codes) beyond half the minimum distance can be analyzed in terms of a partial-inverse condition for the error pattern: if that condition is satisfied, then the (true) error locator polynomial is the unique solution of a standard key equation and can be computed in many different ways, including the well-known multi-sequence Berlekamp–Massey algorithm and the SPI algorithm of this paper. Two of the best performance bounds from the literature (the Schmidt–Sidorenko–Bossert bound and the Roth–Vontobel bound) are generalized to hold for the partial-inverse condition and thus to apply to several different decoding algorithms. [ABSTRACT FROM AUTHOR]

Singular divergence-conforming bases have been proposed for the solution of integral equations although they have seen only occasional use in practical applications. The existing singular bases are not hierarchical, which prevents their use in adaptive $ {p}$ -refinement applications. In this paper, a new family of singular hierarchical basis functions is proposed for quadrilateral cells. These functions model the singularities associated with current and charge density at edges and are more convenient for modeling such singularities than triangular bases of the same kind. The basis functions are of the additive kind and combine a hierarchical polynomial representation on quadrilaterals with linearly independent singular terms that incorporate general exponents that may be adjusted for the specific wedge angle of interest. Moreover, the added singular basis functions are computed on the fly. On the basis of various reported numerical results, this paper also illustrates the difficulties, the advantages, the accuracy, and the cost of using such bases in the method of moment solutions of integral equations. [ABSTRACT FROM AUTHOR]

For a function $f$ from $\mathbb {F}_{2}^{n}$ to $\mathbb {F}_{2}^{n}$ , the planarity of $f$ is usually measured by its differential uniformity and differential spectrum. In this paper, we propose the concept of vanishing flats, which supplies a combinatorial viewpoint on the planarity. First, the number of vanishing flats of $f$ can be regarded as a measure of the distance between $f$ and the set of almost perfect nonlinear functions. In some cases, the number of vanishing flats serves as an “intermediate” concept between differential uniformity and differential spectrum, which contains more information than differential uniformity, however less than the differential spectrum. Secondly, the set of vanishing flats forms a combinatorial configuration called partial quadruple system, since it conveys a detailed structural information about $f$. We initiate this study by considering the number of vanishing flats and the partial quadruple systems associated with monomials and Dembowski-Ostrom polynomials. In addition, we present an application of vanishing flats to the partition of a vector space into disjoint equidimensional affine spaces. We conclude the paper with several further questions and challenges. [ABSTRACT FROM AUTHOR]