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Constrained partially observable Markov decision processes (CPOMDPs) have been used to model various real-world phenomena. However, they are notoriously difficult to solve to optimality, and there exist only a few approximation methods for obtaining high-quality solutions. In this study, grid-based approximations are used in combination with linear programming (LP) models to generate approximate policies for CPOMDPs. A detailed numerical study is conducted with six CPOMDP problem instances considering both their finite and infinite horizon formulations. The quality of approximation algorithms for solving unconstrained POMDP problems is established through a comparative analysis with exact solution methods. Then, the performance of the LP-based CPOMDP solution approaches for varying budget levels is evaluated. Finally, the flexibility of LP-based approaches is demonstrated by applying deterministic policy constraints, and a detailed investigation into their impact on rewards and CPU run time is provided. For most of the finite horizon problems, deterministic policy constraints are found to have little impact on expected reward, but they introduce a significant increase to CPU run time. For infinite horizon problems, the reverse is observed: deterministic policies tend to yield lower expected total rewards than their stochastic counterparts, but the impact of deterministic constraints on CPU run time is negligible in this case. Overall, these results demonstrate that LP models can effectively generate approximate policies for both finite and infinite horizon problems while providing the flexibility to incorporate various additional constraints into the underlying model. [ABSTRACT FROM AUTHOR]

It is one of the most challenging problems in applied mathematics to approximatively solve high-dimensional partial differential equations (PDEs). Recently, several deep learning-based approximation algorithms for attacking this problem have been proposed and tested numerically on a number of examples of high-dimensional PDEs. This has given rise to a lively field of research in which deep learning-based methods and related Monte Carlo methods are applied to the approximation of high-dimensional PDEs. In this article we offer an introduction to this field of research by revisiting selected mathematical results related to deep learning approximation methods for PDEs and reviewing the main ideas of their proofs. We also provide a short overview of the recent literature in this area of research. [ABSTRACT FROM AUTHOR]

Graph matching, the problem of aligning a pair of graphs so as to minimize their edge disagreements, has received widespread attention owing to its broad spectrum of applications in data science. As the problem is NP–hard in the worst-case, a variety of approximation algorithms have been proposed for obtaining high quality, suboptimal solutions. In this article, we approach the task of designing an efficient polynomial-time approximation algorithm for graph matching from a previously unconsidered perspective. Our key result is that graph matching can be formulated as maximizing a monotone, supermodular set function subject to matroid intersection constraints. We leverage this fact to apply a discrete optimization variant of the minorization-maximization algorithm which exploits supermodularity of the objective function to iteratively construct and maximize a sequence of global lower bounds on the objective. At each step, we solve a maximum weight matching problem in a bipartite graph. Differing from prior approaches, the algorithm exploits the combinatorial structure inherent in the problem to generate a sequence of iterates featuring monotonically non-decreasing objective value while always adhering to the combinatorial matching constraints. Experiments on real-world data demonstrate the empirical effectiveness of the algorithm relative to the prevailing state-of-the-art. [ABSTRACT FROM AUTHOR]

Graph edit distance is one of the most flexible and general graph matching models available. The major drawback of graph edit distance, however, is its computational complexity that restricts its applicability to graphs of rather small size. Recently, the authors of the present paper introduced a general approximation framework for the graph edit distance problem. The basic idea of this specific algorithm is to first compute an optimal assignment of independent local graph structures (including substitutions, deletions, and insertions of nodes and edges). This optimal assignment is complete and consistent with respect to the involved nodes of both graphs and can thus be used to instantly derive an admissible (yet suboptimal) solution for the original graph edit distance problem in O(n3) time. For large scale graphs or graph sets, however, the cubic time complexity may still be too high. Therefore, we propose to use suboptimal algorithms with quadratic rather than cubic time for solving the basic assignment problem. In particular, the present paper introduces five different greedy assignment algorithms in the context of graph edit distance approximation. In an experimental evaluation, we show that these methods have great potential for further speeding up the computation of graph edit distance while the approximated distances remain sufficiently accurate for graph based pattern classification. [ABSTRACT FROM AUTHOR]

The hypervolume and hypervolume contributions are widely used in multiobjective evolutionary optimization. However, their exact calculation is NP-hard. By definition, hypervolume is an ${m}$ -D integral (where ${m}$ is the number of objectives). Using polar coordinate, this paper transforms the hypervolume into an (${m}\,\, -$ 1)-D integral, and then proposes two approximation methods for computing the hypervolume and hypervolume contributions. Numerical experiments have been conducted to investigate the performance of our proposed methods. [ABSTRACT FROM AUTHOR]

De novo genome assembly describes the process of reconstructing an unknown genome from a large collection of short (or long) reads sequenced from the genome. A single run of a Next-Generation Sequencing (NGS) technology can produce billions of short reads, making genome assembly computationally demanding (both in terms of memory and time). One of the major computational steps in modern day short read assemblers involves the construction and use of a string data structure called the de Bruijn graph. In fact, a majority of short read assemblers build the complete de Bruijn graph for the set of input reads, and subsequently traverse and prune low-quality edges, in order to generate genomic "contigs"—the output of assembly. These steps of graph construction and traversal, contribute to well over 90 percent of the runtime and memory. In this paper, we present a fast algorithm, FastEtch, that uses sketching to build an approximate version of the de Bruijn graph for the purpose of generating an assembly. The algorithm uses Count-Min sketch, which is a probabilistic data structure for streaming data sets. The result is an approximate de Bruijn graph that stores information pertaining only to a selected subset of nodes that are most likely to contribute to the contig generation step. In addition, edges are not stored; instead that fraction which contribute to our contig generation are detected on-the-fly. This approximate approach is intended to significantly improve performance (both execution time and memory footprint) whilst possibly compromising on the output assembly quality. We present two main versions of the assembler—one that generates an assembly, where each contig represents a contiguous genomic region from one strand of the DNA, and another that generates an assembly, where the contigs can straddle either of the two strands of the DNA. For further scalability, we have implemented a multi-threaded parallel code. Experimental results using our algorithm conducted on E. coli, Yeast, C. elegans, and Human (Chr2 and Chr2+3) genomes show that our method yields one of the best time-memory-quality trade-offs, when compared against many state-of-the-art genome assemblers. [ABSTRACT FROM AUTHOR]

Multistate series-parallel system (MSSPS) is a widely used model for representing engineering systems, whose reliability has been extensively analyzed. Universal generating function (UGF) is an efficient method for evaluating the reliability of MSSPS. However, when facing the large-scale MSSPS, where the number of system components and possible states are enormous, calclating the exact system reliability can be rather time-consuming. To evaluate the reliability of large-scale MSSPS more efficiently, this paper proposes an approximation method, named continuization discretization approximation (CDA) method. The CDA approach consists of continuization and discretization processes. The continuization process applies Gaussian approximation method based on the central limit theory and the UGF technique to evaluate parallel subsystems. While the discretization process discretizes the continuous distribution to a discrete one, and proposes an algorithm to evaluate the series subsystems efficiently. The efficiency and accuracy performance of the CDA method can be adjusted by parameters according to the computational resource and the system scale. The newly proposed method is compared to the existing methods in evaluating the large-scale MSSPS. Numerical examples show that the CDA method has evident advantage in computational efficiency with satisfactory accuracy performance. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for evaluating the performance of a closed-loop spare machines supply system when the time between failures as well as the repair times is generally distributed. The closed-loop system with finite number of machines and the generic time distribution assumption well represents real-life spare machines supply systems but at the cost of higher complexity. The increased complexity prohibits the use of existing models, which evaluate the performance of closed-loop systems assuming exponentially distributed failure and repair times. The Extended Bottleneck (EBOTT) algorithm in the literature estimates the performance measures of a closed-loop system with generally distributed repair service times. However, as our analysis indicates, the EBOTT algorithm may fall into an infinite loop even in assessing the performance of a simple spare machines supply system. The Modified EBOTT algorithm presented in this paper overcomes the shortcomings of the EBOTT algorithm and exhibits superior performance. Finally, through tradeoff analysis between spare machines inventory level and repair shop capacities, insights into the performance of such systems are presented. [ABSTRACT FROM AUTHOR]

In stochastic programming, statistics, or econometrics, the aim is in general the optimization of a criterion function that depends on a decision variable ϑ and reads as an expectation with respect to a probability P. When this function cannot be computed in closed form, it is customary to approximate it through an empirical mean function based on a random sample. On the other hand, several other methods have been proposed, such as quasi-Monte Carlo integration and numerical integration rules. In this paper, we propose a general approach for approximating such a function, in the sense of epigraphical convergence, using a sequence of functions of simpler type which can be expressed as expectations with respect to probability measures Pn's that, in some sense, approximate P. The main difference with the existing results lies in the fact that our main theorem does not impose conditions directly on the approximating probabilities but only on some integrals with respect to them. In addition, the Pn's can be transition probabilities, i.e., are allowed to depend on a further parameter, ξ, whose value results from deterministic or stochastic operations, depending on the underlying model. This framework allows us to deal with a large variety of approximation procedures such as Monte Carlo, quasi--Monte Carlo, numerical integration, quantization, several variations on Monte Carlo sampling, and some density approximation algorithms. As by-products, we discuss convergence results for stochastic programming and statistical inference based on dependent data, for programming with estimated parameters, and for robust optimization; we also provide a general result about the consistency of the bootstrap for M-estimators. [ABSTRACT FROM AUTHOR]

Approximation algorithms with linear complexities are required in the treatments of big data, however, present algorithms cannot output the diameter of a set of points with arbitrary accuracy and near-linear complexity. By introducing the partition technique, we introduce a very simple approximation algorithm with arbitrary accuracy ε and a complexity of O(N + ε−1 log ε−1) for the cases that all points are located in an Euclidean plane. The error bounds are proved strictly, and are verified by numerical tests. This complexity is better than existing algorithms, and the present algorithm is also very simple to be implemented in applications. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose an approximation method to study the regularity of solutions to the Isaacs equation. This class of problems plays a paramount role in the regularity theory for fully nonlinear elliptic equations. First, it is a model-problem of a non-convex operator. In addition, the usual mechanisms to access regularity of solutions fall short in addressing these equations. We approximate an Isaacs equation by a Bellman one, and make assumptions on the latter to recover information for the former. Our techniques produce results in Sobolev and Hölder spaces; we also examine a few consequences of our main findings. [ABSTRACT FROM AUTHOR]

In this paper, we present a new low-complexity iterative joint phase estimation and decoding algorithm which can be applied to low-density-parity-check (or turbo) codes for channels affected by strong phase noise. The algorithm exhibits a very good performance and a very low complexity even in strong phase noise, high code rate, and high-order constellations. The proposed algorithm works by first obtaining a preliminary pilots-aided phase estimation. Next, the expressions for the belief propagation messages are approximated around that reference and reduced to a Gaussian canonical form. This results in a simple closed form of a recursive update. Simulations results for phase-shift-keying modulation of size 8 and quadrature-amplitude modulation of size 64 are presented and compared with other algorithms in the literature. [ABSTRACT FROM AUTHOR]

Social awareness and social ties are becoming increasingly popular with emerging mobile and handheld devices. Social trust degree describing the strength of the social ties has drawn lots of research interests in many fields in wireless communications, such as resource sharing, cooperative communication, and so on. In this paper, we propose a social ties based hybrid cooperative beamforming and jamming scheme to secure wireless transmissions under a stochastic geometry framework, where the friendly nodes are categorized into relays and jammers according to their locations and social trust degrees with the source node. Connection outage probability (COP) and secrecy outage probability (SOP) of such a scheme in a random network have been analyzed. To facilitate a more convenient performance evaluation, we propose a double Gamma ratio approach through the Gamma approximation method. Based on this, the COP and SOP are tractably obtained in closed-forms. We further consider the SOP in the presence of Poisson point process distributed eavesdroppers and derive an upper bound. The simulation results verify our theoretical findings, and validate that the social trust degree has dramatic influences on the security performance in the networks. [ABSTRACT FROM AUTHOR]

Best investment in the road infrastructure or the network design is perceived as a fundamental and benchmark problem in transportation. Given a set of candidate road projects with associated costs, finding the best subset with respect to a limited budget is known as a bilevel Discrete Network Design Problem (DNDP) of NP-hard computationally complexity. We engage with the complexity with a hybrid exact-heuristic methodology based on a two-stage relaxation as follows: (i) the bilevel feature is relaxed to a single-level problem by taking the network performance function of the upper level into the user equilibrium traffic assignment problem (UE-TAP) in the lower level as a constraint. It results in a mixed-integer nonlinear programming (MINLP) problem which is then solved using the Outer Approximation (OA) algorithm (ii) we further relax the multi-commodity UE-TAP to a single-commodity MILP problem, that is, the multiple OD pairs are aggregated to a single OD pair. This methodology has two main advantages: (i) the method is proven to be highly efficient to solve the DNDP for a large-sized network of Winnipeg, Canada. The results suggest that within a limited number of iterations (as termination criterion), global optimum solutions are quickly reached in most of the cases; otherwise, good solutions (close to global optimum solutions) are found in early iterations. Comparative analysis of the networks of Gao and Sioux-Falls shows that for such a non-exact method the global optimum solutions are found in fewer iterations than those found in some analytically exact algorithms in the literature. (ii) Integration of the objective function among the constraints provides a commensurate capability to tackle the multi-objective (or multi-criteria) DNDP as well. [ABSTRACT FROM AUTHOR]

This paper formulates a consensus filter-based distributed variational Bayesian (CFBDVB) algorithm for density approximation of traffic flow and average traffic speed in a freeway. This algorithm uses traffic measurements, including volume, occupancy rate, and average velocity, collected by some inductive loop sensors. These traffic sensors are settled in sealed spaces in the freeway network, such that they demonstrate a distributed sensor network. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. Using the suggested algorithm, a challenging problem is solved: selection of the right number of components. The algorithm begins with a large number of initialized components. In this algorithm, each sensor client severally computes local sufficient statistics by using local observations. A consensus filter is then used to spread out local sufficient statistics to neighbors and approximate global sufficient statistics in each client. Then, using the global sufficient statistics, irrelevant components are determined and omitted. Then, the remaining components' parameters are estimated. The suggested CFBDVB algorithm is scalable and robust. It estimates the parameters of mixtures and simultaneously selects the number of components. Various simulations of sensor nets have been done to confirm the promising performance of the CFBDVB algorithm. [ABSTRACT FROM PUBLISHER]

While the multiple sequence alignment output by an aligner strongly depends on the parameter values used for the alignment scoring function (such as the choice of gap penalties and substitution scores), most users rely on the single default parameter setting provided by the aligner. A different parameter setting, however, might yield a much higher-quality alignment for the specific set of input sequences. The problem of picking a good choice of parameter values for specific input sequences is called parameter advising. A parameter advisor has two ingredients: (i) a set of parameter choices to select from, and (ii) an estimator that provides an estimate of the accuracy of the alignment computed by the aligner using a parameter choice. The parameter advisor picks the parameter choice from the set whose resulting alignment has highest estimated accuracy. In this paper, we consider for the first time the problem of learning the optimal set of parameter choices for a parameter advisor that uses a given accuracy estimator. The optimal set is one that maximizes the expected true accuracy of the resulting parameter advisor, averaged over a collection of training data. While we prove that learning an optimal set for an advisor is NP-complete, we show there is a natural approximation algorithm for this problem, and prove a tight bound on its approximation ratio. Experiments with an implementation of this approximation algorithm on biological benchmarks, using various accuracy estimators from the literature, show it finds sets for advisors that are surprisingly close to optimal. Furthermore, the resulting parameter advisors are significantly more accurate in practice than simply aligning with a single default parameter choice. [ABSTRACT FROM PUBLISHER]

The low-rank approximation method is one of the most effective approaches recently proposed for attenuating random noise in seismic data. However, the low-rank approximation approach assumes that the seismic data has low rank for its $f-x$ domain Hankel matrix. This assumption is seldom satisfied for the complicated seismic data. Besides, the low-rank approximation approach is usually implemented in local windows in order to satisfy the principal assumption required by the algorithm itself. When implemented in local windows, the rank is even more difficult to choose because the seismic data is highly nonstationary in both time and spatial dimensions and the optimal rank for different local windows is not consistent with each other. In order to preserve enough useful energy, one needs to set a relatively large rank when implementing the low-rank approximation method, which makes the traditional method incapable of attenuating enough noise. Considering such difficulties described above, we propose an empirical low-rank approximation approach. We adaptively decompose the input data into several components that have truly low ranks via empirical mode decomposition. An interpretation of the proposed empirical low-rank approximation method is that we empirically decompose a multi-dip seismic image that is not of low rank into multiple single-dip seismic images that are low-rank individually. We use both synthetic and field data examples to demonstrate the superior performance of the proposed approach over traditional alternatives. [ABSTRACT FROM PUBLISHER]

In this paper, we devise a scheduling algorithm for ordering transmission of synchrophasor data from the substation to the control center in as short a time frame as possible, within the real-time hierarchical communications infrastructure in the electric grid. The problem is cast in the framework of the classic job scheduling with precedence constraints. The optimization setup comprises the number of phasor measurement units (PMUs) to be installed on the grid, a weight associated with each PMU, processing time at the control center for the PMUs, and precedence constraints between the PMUs. The solution to the PMU placement problem yields the optimum number of PMUs to be installed on the grid, while the processing times are picked uniformly at random from a predefined set. The weight associated with each PMU and the precedence constraints are both assumed known. The scheduling problem is provably NP-hard, so we resort to approximation algorithms, which provide solutions that are suboptimal yet possessing polynomial time complexity. A lower bound on the optimal schedule is derived using branch and bound techniques, and its performance evaluated using standard IEEE test bus systems. The scheduling policy is power grid-centric, since it takes into account the electrical properties of the network under consideration. [ABSTRACT FROM PUBLISHER]

The usage of linear transformations has great relevance for data decorrelation applications, like image and video compression. In that sense, the discrete Tchebichef transform (DTT) possesses useful coding and decorrelation properties. The DTT transform kernel does not depend on the input data and fast algorithms can be developed to real-time applications. However, the DTT fast algorithm presented in literature possess high computational complexity. In this paper, we introduce a new low-complexity approximation for the DTT. The fast algorithm of the proposed transform is multiplication free and requires a reduced number of additions and bit-shifting operations. Image and video compression simulations in popular standards show good performance of the proposed transform. Regarding hardware resource consumption for FPGA shows a 43.1% reduction in configurable logic blocks and ASIC place and route realization shows a 57.7% reduction in the area-time figure compared with the 2D version of the exact DTT. [ABSTRACT FROM PUBLISHER]

This paper studies the estimation of Dirichlet process mixtures over discrete incomplete rankings. The generative model for each mixture component is the generalized Mallows (GM) model, an exponential family model for permutations which extends seamlessly to top-$t$   rankings. While the GM  is remarkably tractable in comparison with other permutation models, its conjugate prior is not. Our main contribution is to derive the theory and algorithms for sampling from the desired posterior distributions under this DPM. We introduce a family of partially collapsed Gibbs samplers, containing as one extreme point an exact algorithm based on slice-sampling, and at the other a fast approximate sampler with superior mixing that is still very accurate in all but the lowest ranks. We empirically demonstrate the effectiveness of the approximation in reducing mixing time, the benefits of the Dirichlet process approach over alternative clustering techniques, and the applicability of the approach to exploring large real-world ranking datasets. [ABSTRACT FROM PUBLISHER]

Recently, Shabtay considered a scheduling problem on a single serial-batching machine with rejection to minimize the dual criteria of total completion time and total rejection cost, where the number of jobs to be included in each batch is not restricted. He studied four variants of the problem: the first is to minimize the sum of the two criteria; the second and third are to minimize one criterion, subject to the other criterion not exceeding a given value; and the last is to find the Pareto-optimal solutions for the bicriterion problem. Shabtay provided an O ( \textitn^\mathbf 5 ) algorithm for the first variant and an O ( n ^\mathbf 6/\boldsymbol \varepsilon ^\mathbf 2 ) fully polynomial-time approximation scheme (FPTAS) for the fourth variant. In this paper, we provide an alternative O ( n ^\mathbf 4 ) algorithm to solve the first variant and an O ( n ^\mathbf 5/\boldsymbol \varepsilon ) FPTAS for the fourth variant, which are more efficient than those developed by Shabtay from a theoretical perspective. However, when the size of each batch is bounded by a given number b~\boldsymbol >1 , the corresponding time complexities of our algorithms for the first and fourth variants reduce to O (bn ^\mathbf 3 ) and O (bn ^\mathbf 4/\boldsymbol \varepsilon ), respectively. [ABSTRACT FROM AUTHOR]

In many applications, it is a basic operation for the sink to periodically collect reports from all sensors. Since the data gathering process usually proceeds for many rounds, it is important to collect these data efficiently, that is, to reduce the energy cost of data transmission. Under such applications, a tree is usually adopted as the routing structure to save the computation costs for maintaining the routing tables of sensors. In this paper, we work on the problem of constructing a data aggregation tree that minimizes the total energy cost of data transmission in a wireless sensor network. In addition, we also address such a problem in the wireless sensor network where relay nodes exist and consider the cases where the link quality is not perfect. We show that these problems are NP-complete and propose O(1) -approximation algorithms for each of them. Simulations show that the proposed algorithms have good performance in terms of energy cost. [ABSTRACT FROM AUTHOR]

Channel rendezvous is a vital step to form a cognitive radio network (CRN). It is intractable to guarantee rendezvous for secondary users (SUs) within a short finite time in asynchronous, heterogeneous, and anonymous CRNs. However, most previous heterogeneous algorithms rely on explicit SUs' identifiers (IDs) to guide rendezvous, which is not fully distributed. In this paper, we exploit the mathematical construction of sunflower sets to develop a single-radio sunflower set (SSS)-based pairwise rendezvous algorithm. We propose an approximation algorithm to construct disjoint sunflower sets. Then, the SSS leverages the variant permutations of elements in sunflower sets to adjust the order of accessing channels instead of SUs' IDs, which is more favorable for anonymous SUs in distributed environments. We also propose a multiradio-sunflower-set-based pairwise rendezvous algorithm to bring additional rendezvous diversity and accelerate the rendezvous process. Moreover, for the case with more than two SUs, we propose a multiuser collaborative scheme in which SUs cooperatively exchange and update their channel-hopping sequences until rendezvous. We derive the theoretical upper and lower bounds of rendezvous latency of the proposed algorithms. Extensive simulation comparisons with the state-of-the-art blind-rendezvous algorithms are conducted, incorporating the metrics of maximum and expected time-to-rendezvous. The simulation results show that our algorithms can achieve rendezvous faster than previous works. [ABSTRACT FROM PUBLISHER]

Mobile cloud computing is emerging as a main ubiquitous computing platform to provide rich cloud resources for various applications of mobile devices. Although most existing studies in mobile cloud computing focus on energy savings of mobile devices by offloading computing-intensive jobs from mobile devices to remote clouds, the access delays between mobile users and remote clouds usually are long and sometimes unbearable. Cloudlet as a new technology is capable to bridge this gap, and can enhance the performance of mobile devices significantly while meeting the crisp response time requirements of mobile users. In this paper, we study the cloudlet placement problem in a large-scale Wireless Metropolitan Area Network (WMAN) consisting of many wireless Access Points (APs). We first formulate the problem as a novel capacitated cloudlet placement problem that places $K$ cloudlets to some strategic locations in the WMAN with the objective to minimize the average access delay between mobile users and the cloudlets serving the users. We then propose an exact solution to the problem by formulating it as an Integer Linear Programming (ILP). Due to the poor scalability of the ILP, we instead propose an efficient heuristic for the problem. For a special case of the problem where all cloudlets have identical computing capacities, we devise novel approximation algorithms with guaranteed approximation ratios. We also devise an online algorithm for dynamically allocating user requests to different cloudlets, if the $K$ cloudlets have already been placed. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising and scalable. [ABSTRACT FROM AUTHOR]

Adaptive fuzzy controllers (AFCs), i.e., fuzzy controllers using heuristic expert knowledge and equipped with adaption ability, are widely used in the industry in the present days as they are capable of dealing with ill-formulated nonlinear systems (where exact mathematical formulas are not known) or partially described nonlinear systems. AFCs have been widely deployed to control many nonlinear systems with different types of critical nonlinearities. Over the last two decades, many different approaches toward adaptive fuzzy control have been reported. In this paper, we recapitulate the latest research works related to AFCs and provide an up-to-date survey on the latest development made in adaptive fuzzy control theory and applications based on different types of fuzzy systems considered and nonlinearities associated with the nonlinear system to be controlled. We categorize the different approaches based on different types of nonlinearities and consider basic nonlinear systems, nonlinear systems with external disturbances, nonlinear systems with some special type of nonlinearities, nonaffine nonlinear systems, and strict-feedback nonlinear systems. It turns out that many existing methods are subject to various limitations, and none of them is capable of controlling a general nonlinear systems. Hence, it is still an open problem to design and develop a universal AFC for a general class of nonlinear systems. [ABSTRACT FROM PUBLISHER]

Graph matching (GM) is a fundamental problem in computer science, and it plays a central role to solve correspondence problems in computer vision. GM problems that incorporate pairwise constraints can be formulated as a quadratic assignment problem (QAP). Although widely used, solving the correspondence problem through GM has two main limitations: (1) the QAP is NP-hard and difficult to approximate; (2) GM algorithms do not incorporate geometric constraints between nodes that are natural in computer vision problems. To address aforementioned problems, this paper proposes factorized graph matching (FGM). FGM factorizes the large pairwise affinity matrix into smaller matrices that encode the local structure of each graph and the pairwise affinity between edges. Four are the benefits that follow from this factorization: (1) There is no need to compute the costly (in space and time) pairwise affinity matrix; (2) The factorization allows the use of a path-following optimization algorithm, that leads to improved optimization strategies and matching performance; (3) Given the factorization, it becomes straight-forward to incorporate geometric transformations (rigid and non-rigid) to the GM problem. (4) Using a matrix formulation for the GM problem and the factorization, it is easy to reveal commonalities and differences between different GM methods. The factorization also provides a clean connection with other matching algorithms such as iterative closest point; Experimental results on synthetic and real databases illustrate how FGM outperforms state-of-the-art algorithms for GM. The code is available at http://humansensing.cs.cmu.edu/fgm. [ABSTRACT FROM AUTHOR]

We study the problem of minimum-number full-view area coverage in camera sensor networks, i.e., how to select the minimum number of camera sensors to guarantee the full-view coverage of a given region. Full-view area coverage is challenging because the full-view coverage of a 2-D continuous domain has to be considered. To tackle this challenge, we first study the intrinsic geometric relationship between the full-view area coverage and the full-view point coverage and prove that the full-view area coverage can be guaranteed, as long as a selected full-view ensuring set of points is full-view covered. This leads to a significant dimension reduction for the full-view area coverage problem. Next, we prove that the minimum-number full-view point coverage is NP-hard and propose two approximation algorithms to solve it from two different perspectives, respectively: 1) By introducing a full-view coverage ratio function, we quantify the “contribution” of each camera sensor to the full-view coverage through which we transform the full-view point coverage into a submodular set cover problem and propose a greedy algorithm (GA); and 2) by studying the geometric relationship between the full-view coverage and the traditional coverage, we propose a set-cover-based algorithm (SCA). We analyze the performance of these two approximation algorithms and characterize their approximation ratios. Furthermore, we devise two distributed algorithms that obtain the same approximation ratios as GA and SCA, respectively. Finally, we provide extensive simulation results to validate our analysis. [ABSTRACT FROM PUBLISHER]

Mobile crowdsensing has shown elegant capacity in data collection and has given rise to numerous applications. In the sense of coverage quality, marginal works have considered the efficient (less cost) and effective (considerable coverage) design for mobile crowdsensing networks. We investigate the optimal quality-aware coverage in mobile crowdsensing networks. The difference between ours and the conventional coverage problem is that we only select a subset of mobile users so that the coverage quality is maximized with constrained budget. To address this new problem, which is proved to be NP-hard, we first prove that the set function of coverage quality is nondecreasing submodular. By leveraging the favorable property in submodular optimization, we then propose an (\text1-(\text1/e)) approximation algorithm with O(n^k+2) time complexity, where $k$ is an integer that is greater than or equal to 3. Finally, we conduct extensive simulations for the proposed scheme, and the results demonstrate that ours outperforms the random selection scheme and one of the state of the art in terms of total coverage quality by, at most, 2.4× and 1.5× and by, on average, 1.4× and 1.3×, respectively. Additionally, ours achieves a near-optimal solution, compared with the brute-force search results. [ABSTRACT FROM PUBLISHER]

In heterogeneous networks (HetNets), the load between macrocell base stations (MBSs) and small-cell base stations (SBSs) is imbalanced due to their different transmission powers and locations. This load imbalance significantly impacts system performance and affects the experience of mobile users (MUs) with different priorities. In this paper, we aim to distributively optimize the user association in HetNets with various user priorities to solve the load balancing problem. Since the user association is a binary matching problem, which is NP-hard, we propose a distributed belief propagation (BP) algorithm to approach the optimal solution. We first develop a factor graph model, using the network topology, to represent this user association problem. With this factor graph, we propose a novel distributed BP algorithm by adopting the proportional fairness as the objective. Next, we theoretically prove the existence of the fixed point in our BP algorithm. To be more practical, we develop an approximation method to significantly reduce the computational and communication complexity of the BP algorithm. Furthermore, we analyze some properties of the factor graph relevant to the performance of the BP algorithm using the stochastic geometry. Simulation results show that 1) the proposed BP algorithm well approaches the optimal system performance and achieves a much better performance compared with other association schemes and that 2) the analytical results on the average degree distribution and sparsity of the factor graph match with those obtained from the Monte Carlo simulations. [ABSTRACT FROM PUBLISHER]

The Booth multiplier has been widely used for high performance signed multiplication by encoding and thereby reducing the number of partial products. A multiplier using the radix-4 (or modified Booth) algorithm is very efficient due to the ease of partial product generation, whereas the radix-8 Booth multiplier is slow due to the complexity of generating the odd multiples of the multiplicand. In this paper, this issue is alleviated by the application of approximate designs. An approximate 2-bit adder is deliberately designed for calculating the sum of 1× and 2× of a binary number. This adder requires a small area, a low power and a short critical path delay. Subsequently, the 2-bit adder is employed to implement the less significant section of a recoding adder for generating the triple multiplicand with no carry propagation. In the pursuit of a trade-off between accuracy and power consumption, two signed 16 × 16 bit approximate radix-8 Booth multipliers are designed using the approximate recoding adder with and without the truncation of a number of less significant bits in the partial products. The proposed approximate multipliers are faster and more power efficient than the accurate Booth multiplier. The multiplier with 15-bit truncation achieves the best overall performance in terms of hardware and accuracy when compared to other approximate Booth multiplier designs. Finally, the approximate multipliers are applied to the design of a low-pass FIR filter and they show better performance than other approximate Booth multipliers. [ABSTRACT FROM AUTHOR]

Apart from spectral efficiency (SE), energy efficiency (EE) has become an important metric for future wireless communication systems. However, how to achieve balance between SE and EE for a multicell multiuser coordinated beamforming system remains an open problem. In this paper, we propose a new paradigm for EE–SE balancing, namely, using a new metric called resource efficiency (RE) as the objective function to achieve a reasonable tradeoff between SE and EE. The considered optimization problem is more complex than traditional problems and, in general, is NP-hard for finding the globally optimal solution. To obtain insight into the problem, we first equivalently transform the original problem into a parameterized subtractive form optimization problem with some auxiliary variables by using the fraction programming theory. Then, a successive convex approximation method and the classical second-order cone programming method are jointly used to solve the equivalent problem. Finally, an effective algorithm with proven convergence is proposed to obtain the solution to the considered RE optimization. In addition, the algorithm is further extended to consider imperfect channel state information (CSI) using the worst-case design. Extensive numerical simulations are given to validate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

In high-end data processing systems, such as databases, the execution concurrency level rises continuously since the introduction of multicore processors. This happens both on premises and in the cloud. For these systems, a buffer pool management of high scalability plays an important role on overall system performance. The scalability of buffer pool management is largely determined by its data replacement algorithm, which is a major component in the buffer pool management. It can seriously degrade the scalability if not designed and implemented properly. The root cause is its use of lock-protected data structures that incurs high contention with concurrent accesses. A common practice is to modify the replacement algorithm to reduce the contention on the lock(s), such as approximating the LRU replacement with the CLOCK algorithm or partitioning the data structures and using distributed locks. Unfortunately, the modification usually compromises the algorithm's hit ratio, a major performance goal. It may also involve significant effort on overhauling the original algorithm design and implementation. This paper provides a general solution to improve the scalability of a buffer pool management using any replacement algorithms for the data processing systems on physical on-premises machines and virtual machines in the cloud. Instead of making a difficult trade-off between the high hit ratio of a replacement algorithm and the low lock contention of its approximation, we design a system framework, called BP-Wrapper, that eliminates almost all lock contention without requiring any changes to an existing algorithm. In BP-Wrapper, we use a dynamic batching technique and a prefetching technique to reduce lock contention and to retain high hit ratio. The implementation of BP-Wrapper in PostgreSQL adds only about 300 lines of C code. It can increase the throughput by up to two folds compared with the replacement algorithms with lock contention when running TPC-C-like and TPC-W-like workloads. [ABSTRACT FROM AUTHOR]

We consider optimal distributed computation of a given function of distributed data. The input (data) nodes and the sink node that receives the function form a connected network that is described by an undirected weighted network graph. The algorithm to compute the given function is described by a weighted directed acyclic graph and is called the computation graph. An embedding defines the computation communication sequence that obtains the function at the sink. Two kinds of optimal embeddings are sought, the embedding that: 1) minimizes delay in obtaining function at sink, and 2) minimizes cost of one instance of computation of function. This abstraction is motivated by three applications—in-network computation over sensor networks, operator placement in distributed databases, and module placement in distributed computing. We first show that obtaining minimum-delay and minimum-cost embeddings are both NP-complete problems and that cost minimization is actually MAX SNP-hard. Next, we consider specific forms of the computation graph for which polynomial-time solutions are possible. When the computation graph is a tree, a polynomial-time algorithm to obtain the minimum-delay embedding is described. Next, for the case when the function is described by a layered graph, we describe an algorithm that obtains the minimum-cost embedding in polynomial time. This algorithm can also be used to obtain an approximation for delay minimization. We then consider bounded treewidth computation graphs and give an algorithm to obtain the minimum-cost embedding in polynomial time. [ABSTRACT FROM PUBLISHER]

Auction mechanisms have recently attracted substantial attention as an efficient approach to pricing and allocating resources in cloud computing. This work, to the authors’ knowledge, represents the first online combinatorial auction designed for the cloud computing paradigm, which is general and expressive enough to both: 1) optimize system efficiency across the temporal domain instead of at an isolated time point; and 2) model dynamic provisioning of heterogeneous virtual machine (VM) types in practice. The final result is an online auction framework that is truthful, computationally efficient, and guarantees a competitive ratio \approx 3.30 in social welfare in typical scenarios. The framework consists of three main steps: 1) a tailored primal-dual algorithm that decomposes the long-term optimization into a series of independent one-shot optimization problems, with a small additive loss in competitive ratio; 2) a randomized subframework that applies primal-dual optimization for translating a centralized cooperative social welfare approximation algorithm into an auction mechanism, retaining the competitive ratio while adding truthfulness; and 3) a primal-dual algorithm for approximating the one-shot optimization with a ratio close to e. We also propose two extensions: 1) a binary search algorithm that improves the average-case performance; 2) an improvement to the online auction framework when a minimum budget spending fraction is guaranteed, which produces a better competitive ratio. The efficacy of the online auction framework is validated through theoretical analysis and trace-driven simulation studies. We are also in the hope that the framework can be instructive in auction design for other related problems. [ABSTRACT FROM PUBLISHER]

In ad hoc wireless networks, a Connected Dominating Set (CDS) has been extensively used as a Virtual Backbone (VB) for routing. The majority of approximation algorithms for constructing a small CDS in wireless ad hoc networks follow a general two-phased approach. The first phase is to construct a Dominating Set (DS) and the second phase is to connect the nodes in it. Generally, in the first phase, a Maximum Independent Set (MIS) is used as the DS. The relation between the size of a Maximum Independent Set and a Minimum Connected Dominating Set (MCDS) plays the key role in the performance analyses of these two-phased algorithms. In homogeneous wireless ad hoc networks modeled as Unit Disk Graphs (UDG) and Unit Ball Graphs (UBG), the relation between them has been well studied. However, in heterogeneous wireless networks which generally modeled as Disk Graphs with Bidirectional links (DGB) and Ball Graphs with Bidirectional links (BGB), upper bounds for the size of MISs have seldom been studied. In this paper, we give tighter upper bounds for the size of MISs in heterogeneous wireless ad hoc networks. When the maximum and minimum transmission range are relatively close, our result is much better. In DGB, when the transmission range ratio is (1,1.152], (1.152,1.307], (1.307,1.407], (1.407,1.462], (1.462,1.515], (1.515,1.618], (1.618,1.932], we prove that the size of any Maximal Independent Set (MIS) is upper bounded by 6opt + 1, 7opt + 1, 8opt + 1, 9opt + 1, 10opt + 1, 11opt + 1, 16.7778opt + 1.2222, where opt denotes the size of an optimal solution of the CDS problem. [ABSTRACT FROM AUTHOR]

Nonlinear dimensionality reduction methods have demonstrated top-notch performance in many pattern recognition and image classification tasks. Despite their popularity, they suffer from highly expensive time and memory requirements, which render them inapplicable to large-scale datasets. To leverage such cases we propose a new method called “Path-Based Isomap”. Similar to Isomap, we exploit geodesic paths to find the low-dimensional embedding. However, instead of preserving pairwise geodesic distances, the low-dimensional embedding is computed via a path-mapping algorithm. Due to the much fewer number of paths compared to number of data points, a significant improvement in time and memory complexity with a comparable performance is achieved. The method demonstrates state-of-the-art performance on well-known synthetic and real-world datasets, as well as in the presence of noise. [ABSTRACT FROM PUBLISHER]

In a sensor network, data might be stored in so-called storage nodes, which receive raw data from other nodes, compress them, and send them toward a sink. We consider the problem of locating $k$ storage nodes in order to minimize the energy consumed for converging the raw data to the storage nodes as well as to converge the compressed data to the sink. This is known as the minimum k$ -storage problem . In general, the problem is NP$ -hard. However, we are able to devise a polynomial-time algorithm that optimally solves the problem in bounded-tree width graphs. We then characterize the minimum k$ -storage problem from the approximation viewpoint. We first prove that it is NP . We then propose a local search algorithm that guarantees a constant approximation factor. We conducted extended experiments to show that the algorithm performs very well, exhibiting very small deviation from the optimum and computational time. It is worth to note that our problem is a generalization to the well-known metric $k$ -median problem and then the obtained results also hold for this case. [ABSTRACT FROM PUBLISHER]

This paper studies an optimal channel assignment problem for passive monitoring in multi-channel wireless networks, where a set of sniffers capture and analyze the network traffic to monitor the wireless network. The objective of this problem is to maximize the total amount of traffic captured by sniffers by judiciously assigning the radios of sniffers to a set of channels. This problem is NP-hard, with the computational complexity growing exponentially with the number of sniffers. We develop distributed online solutions for large-scale and dynamic networks. The dynamism in the network may arise from mobility of the nodes being monitored. Our algorithm is guaranteed to achieve at least 1 - \frac1e times the optimum, regardless of the network topology and the channel assignment of nodes to be monitored, while providing a distributed solution amenable to online implementation. Further, our algorithm is cost-effective, in terms of communication and computational overheads, due to the use of purely local communication and the incremental adaptation to network changes. We present two operational modes of our algorithm for two types of networks that change at different rates; one is a proactive mode for fast-varying networks, while the other is a reactive mode for slowly-varying networks. Simulation results demonstrate the effectiveness of the two modes of our algorithm and compare it to the theoretically optimal algorithm. [ABSTRACT FROM PUBLISHER]

We study constrained general-sum stochastic games with unknown Markovian dynamics. A distributed constrained no-regret Q -learning scheme (CNR Q ) is presented to guarantee convergence to the set of stationary correlated equilibria of the game. Prior art addresses the unconstrained case only, is structured with nested control loops, and has no convergence result. CNR Q is cast as a single-loop three-timescale asynchronous stochastic approximation algorithm with set-valued update increments. A rigorous convergence analysis with differential inclusion arguments is given which draws on recent extensions of the theory of stochastic approximation to the case of asynchronous recursive inclusions with set-valued mean fields. Numerical results are given for the exemplary application of CNR Q to decentralized resource control in heterogeneous wireless networks. [ABSTRACT FROM PUBLISHER]

It has been known that scheduling algorithms designed to achieve throughput optimality and good delay performance often require solving the Maximum Weighted Independent Set (MWIS) problem. However, under most realistic network settings, the MWIS problem is known to be NP-hard. In non-fading environments, low-complexity scheduling algorithms have been provided that converge either to the MWIS solution in time or to a solution that achieves at least a provable fraction of the achievable throughput. However, in more practical systems the channel conditions can vary at faster time-scales than convergence occurs in these lower-complexity algorithms. Hence, these algorithms cannot take advantage of opportunistic gains, and may no longer result in achieving good performance. In this paper, we propose a low-complexity scheduling scheme that performs provably well under fading channels and is amenable to implement in a distributed manner. To the best of our knowledge, this is the first scheduling scheme under fading environments that requires only local information, has a low complexity that grows logarithmically with the network size (provided that the conflict graph has bounded maximum vertex degree), and achieves provable performance guarantees (arbitrarily close to that of the well-known centralized Greedy Maximal Scheduler). We verify that the throughput and the delay of our proposed scheme are close to those of the optimal MaxWeight that solves MWIS at each time. Further, we implement our algorithm in a testbed by modifying the existing IEEE 802.11 DCF. The experiment results show that our implementation successfully accounts for wireless fading, attains the short-term opportunistic gains in practice, and hence substantially outperforms IEEE 802.11 DCF. [ABSTRACT FROM PUBLISHER]

Relay assignment significantly affects the performance of the cooperative communication, which is an emerging technology for the future mobile system. Previous studies in this area have mostly focused on assigning a dedicated relay to each source-destination pair for one-to-one (121) traffic. However, many-to-one (M21) traffic, which is also common in many situations (for example, several users associate with one access point in a wireless access network such as a WLAN), hasn't been well studied. This paper addresses the shared relay assignment (SRA) problem for M21 traffic. We formulate two new optimization problems: one is to maximize the minimum throughput among all the sources (hereafter called M21-SRA-MMT), and the other is to maximize the total throughput over all the sources while maintaining some degree of fairness (hereafter called M21-SRA-MTT). As the optimal solutions to the two problems are hard to find, we propose two approximation algorithms whose performance factors are 5.828 and 3, respectively, based on the rounding mechanism. Extensive simulation results show that our algorithms for M21-SRA-MMT can significantly improve the minimum throughput compared with existing algorithms, while our algorithm for M21-SRA-MTT can achieve the close-to-optimal performance. [ABSTRACT FROM AUTHOR]

Identifying a preferable route is an important problem in location-based service. A user may want to find “an optimal route such that it passes by mall, restaurant, and bank, meanwhile go through bank before mall, and arrive at the restaurant at mealtime.” However, none of the algorithms in the existing work on route planning can be used to answer such queries that exist in real life. Motivated by this, we define the problem of temporal-sensitive optimal route discovery (TORD), which is an NP-hard problem. Since the rise of GPS-equipped mobile devices has led to the emergence of big trajectory data, we propose a hybrid evaluation indicator of routes called preference combined by the distance from the road network and popularity from trajectories. To achieve this goal, we first develop an improved compact minimum bound rectangle algorithm to get the association between trajectory data and road network, which can update the association conveniently for new trajectories. Then, we construct a two-layer preference network that is used for mining the preference and travel time. Finally, we devise three approximation algorithms to answer TORD queries. The results of empirical studies show that all the proposed algorithms are capable of answering TORD queries efficiently. [ABSTRACT FROM PUBLISHER]

Visualization of population in a high-dimensional objective space throughout the evolution process presents an attractive feature that could be well exploited in designing many-objective evolutionary algorithms (MaOEAs). In this paper, a new visualization method is proposed. It maps individuals from a high-dimensional objective space into a 2-D polar coordinate plot while preserving Pareto dominance relationship, retaining shape and location of the Pareto front, and maintaining distribution of individuals. From it, a decision-maker can observe the evolution process, estimate location, range, and distribution of Pareto front, assess quality of the approximated front and tradeoff between objectives, and easily select preferred solutions. Furthermore, its applications can be scalable to any dimensions, handle a large number of individuals on front, and simultaneously visualize multiple fronts for comparison. Based on this visualization tool, a performance metric, named polar-metric, is designed. The convergence of the approximate front is measured by radial values of all population members on that front. Meanwhile, the diversity performance is mainly determined by niche count of each subregion in a high-dimensional objective space. Experimental results show that it can provide a comprehensive and reliable comparison among MaOEAs. [ABSTRACT FROM PUBLISHER]

In this paper, we address the problem of distributed adaptive estimation of node-specific signals for signal enhancement or noise reduction in wireless sensor networks with multi-sensor nodes. The estimation is performed by a multi-channel Wiener filter (MWF) in which a low-rank approximation based on a generalized eigenvalue decomposition (GEVD) is incorporated. In non-stationary or low-SNR conditions, this GEVD-based MWF has been demonstrated to be more robust than the original MWF. In a centralized realization where a fusion center has access to all the nodes’ sensor signal observations, the network-wide sensor signal correlation matrices and the low-rank approximation can be directly estimated and used to compute the network-wide GEVD-based MWF. However, in this paper, we aim to avoid centralizing the sensor signal observations, in which case the network-wide sensor signal correlation matrices cannot be estimated. To this end, we start from the so-called distributed adaptive node-specific signal estimation (DANSE) algorithm, and include GEVD-based low-rank approximations in the per-node local computations. Remarkably, the new algorithm is able to significantly compress the signal observations transmitted between the nodes, while still converging to the network-wide GEVD-based MWF as if each node would have access to all sensor signal observations, even though the low-rank approximations are applied locally at each node. We provide a theoretical convergence analysis, which shows that the algorithm converges to the network-wide GEVD-based MWF under conditions that are less strict than in the original DANSE algorithm. The convergence and performance of the algorithm are further investigated via numerical simulations. [ABSTRACT FROM AUTHOR]

Travel planning and recommendation are important aspects of transportation. We propose and investigate a novel Collective Travel Planning (CTP) query that finds the lowest-cost route connecting multiple sources and a destination, via at most $k$ meeting points. When multiple travelers target the same destination (e.g., a stadium or a theater), they may want to assemble at meeting points and then go together to the destination by public transport to reduce their global travel cost (e.g., energy, money, or greenhouse-gas emissions). This type of functionality holds the potential to bring significant benefits to society and the environment, such as reducing energy consumption and greenhouse-gas emissions, enabling smarter and greener transportation, and reducing traffic congestions. The CTP query is Max SNP-hard. To compute the query efficiently, we develop two algorithms, including an exact algorithm and an approximation algorithm. The exact algorithm is capable finding the optimal result for small values of $k$ (e.g., $k = 2$ ) in interactive time, while the approximation algorithm, which has a $5$ -approximation ratio, is suitable for other situations. The performance of the CTP query is studied experimentally with real and synthetic spatial data. [ABSTRACT FROM PUBLISHER]

The emerging of mobile social networks opens opportunities for viral marketing. However, before fully utilizing mobile social networks as a platform for viral marketing, many challenges have to be addressed. In this paper, we address the problem of identifying a small number of individuals through whom the information can be diffused to the network as soon as possible, referred to as the diffusion minimization problem. Diffusion minimization under the probabilistic diffusion model can be formulated as an asymmetric $k$ -center problem which is NP-hard, and the best known approximation algorithm for the asymmetric $k$ -center problem has approximation ratio of $\log ^*n$ and time complexity $O(n^5)$ . Clearly, the performance and the time complexity of the approximation algorithm are not satisfiable in large-scale mobile social networks. To deal with this problem, we propose a community based algorithm and a distributed set-cover algorithm. The performance of the proposed algorithms is evaluated by extensive experiments on both synthetic networks and a real trace. The results show that the community based algorithm has the best performance in both synthetic networks and the real trace compared to existing algorithms, and the distributed set-cover algorithm outperforms the approximation algorithm in the real trace in terms of diffusion time. [ABSTRACT FROM AUTHOR]

Diagnosis of lower urinary tract dysfunction with urodynamics has historically relied on data acquired from multiple sensors using nonphysiologically fast cystometric filling. In addition, state-of-the-art neuromodulation approaches to restore bladder function could benefit from a bladder sensor for closed-loop control, but a practical sensor and automated data analysis are not available. We have developed an algorithm for real-time bladder event detection based on a single in situ sensor, making it attractive for both extended ambulatory bladder monitoring and closed-loop control of stimulation systems for diagnosis and treatment of bladder overactivity. Using bladder pressure data acquired from 14 human subjects with neurogenic bladder, we developed context-aware thresholding, a novel, parameterized, user-tunable algorithmic framework capable of real-time classification of bladder events, such as detrusor contractions, from single-sensor bladder pressure data. We compare six event detection algorithms with both single-sensor and two-sensor systems using a metric termed Conditional Stimulation Score, which ranks algorithms based on projected stimulation efficacy and efficiency. We demonstrate that adaptive methods are more robust against day-to-day variations than static thresholding, improving sensitivity and specificity without parameter modifications. Relative to other methods, context-aware thresholding is fast, robust, highly accurate, noise-tolerant, and amenable to energy-efficient hardware implementation, which is important for mapping to an implant device. [ABSTRACT FROM AUTHOR]

In this paper, we develop and analyze three limited-feedback resource allocation algorithms suitable for uplink transmission in heterogeneous wireless networks (HetNets). In this setup, one macro-cell shares the spectrum with a set of underlay cognitive small-cells via the orthogonal frequency-division multiple access (OFDMA), where the interference from small-cells to the macro-cell should be kept below a predefined threshold. The resource allocation algorithms aim to maximize the weighted sum of instantaneous rates of all users over all cells by jointly optimizing power and subcarrier allocation under power constraints. Since in practice, the HetNet backhaul capacity is limited, reducing the amount of channel state information (CSI) feedback signaling passed over the backhaul links is highly desirable. To reach this goal, we apply the Lloyd algorithm to develop the limited-feedback two-phase resource allocation scheme. In the first offline phase, an optimal codebook for power and subcarrier allocation is designed and sent to all nodes. In the second online phase, based on channel realizations, the appropriate codeword of the designed codebook is chosen for transmission parameters, and the macro-cell only sends the codeword index represented by a limited number of bits for subcarrier and power allocation to its own users and small-cells. Then, each small-cell informs its own users by their related codewords. The offline phase involves a mixed-integer nonconvex resource allocation problem encountering high computational complexity. To solve it efficiently, we apply the general iterative successive convex approximation (SCA) approach, where the nonconvex optimization problem is transformed into the approximated convex optimization problem in each iteration. The simulation results reveal that the Lloyd algorithm can offer a performance close to the perfect-CSI case (without a limited number of feedback bits). [ABSTRACT FROM AUTHOR]