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The 17 papers in this special section focus on resilience in networked robotic systems. This collection of articles aims to provide a deeper understanding of resilience as it pertains to multirobot systems, and to disseminate the current advances in designing and operating networked robotic systems. We understand resilience to be a characteristic that enables amultirobot system to withstand or overcome unexpected adverse conditions or shocks, and unknown, unmodeled disturbances. It refers to the contingent nature of the robots' behaviors that is aimed at preserving their functionality or minimizing the time periods during which their functionality is compromised. The papers explore new algorithmic and mathematical foundations toward resilience. [ABSTRACT FROM AUTHOR]

This paper validates two approximate dynamic programming approaches on a maritime interdiction problem involving the allocation of multiple heterogeneous assets over a large area of responsibility to interdict multiple drug smugglers using heterogeneous types of transportation on the sea with varying contraband weights. The asset allocation is based on a probability of activity surface, which represents spatio-temporal target activity obtained by integrating intelligence data on drug smuggler whereabouts/waypoints for contraband transportation, behavior models, and meteorological and oceanographic information. We validate the proposed architectural and algorithmic concepts via several realistic mission scenarios. We conduct sensitivity analyses to quantify the robustness and proactivity of our approach, as well as to measure the value of information used in the allocation process. The contributions of this paper have been transitioned to and are currently being tested by Joint Interagency Task Force—South, an organization tasked with providing the initial line of defense against drug trafficking in the East Pacific and Caribbean Oceans. [ABSTRACT FROM AUTHOR]

In this paper, adaptive quantized state estimation fusion is deeply studied. To approach the model mismatching problem induced by random quantization, some quantized Kalman filters have been presented in the previous work, such as the quantized Kalman filter with strong tracking filtering (QKF-STF), the variational Bayesian adaptive quantized Kalman filter (VB-AQKF), and a centralized fusion frame-based complex quantized filter called variational Bayesian adaptive QKF-STF (VB-AQKF-STF). Based on the previous work for the single sensor system, a distributed complex quantized filter is designed in this paper. A novel quantized Kalman filter based on multiple-method fusion scheme (QKF-MMF) is proposed. Similar to the VB-AQKF-STF, the QKF-MMF can also realize joint estimation on the state and the quantization error covariance under the distributed fusion frame. Furthermore, it extends the single sensor results to multisensor tracking systems by using centralized and distributed fusion frames. Two multisensor quantized fusion estimators are proposed for a parallel structure with main-secondary processors in the fusion center. The weighted fusion and embedded integration ways are deeply applied to design the multisensor quantized fusion methods. The proposed work can perfect the quantized estimation algorithms and provide different choices for practical engineering applications. [ABSTRACT FROM AUTHOR]

This paper looks at the case of transgender Britons who tried to correct the gender listed on their government-issued ID cards, but ran up against the British government's increasingly computerized methods for tracking, identifying, and defining citizens. These newly computerizing systems show some of the earliest examples of transphobic algorithmic bias: explicit attempts to program trans people out of the system can be seen in the programming of the early Ministry of Pensions computer system designed to apportion benefits to all tax paying British citizens. Transgender citizens pushed back against these developments, attempting to hack the bureaucratic avenues and categories available to them, laying the groundwork for a coalescing political movement. This paper argues that uncovering the deep prehistory of algorithmic bias and investigating instances of resistance within this history is essential to understanding current debates about algorithmic bias, and how computerized systems have long functioned to create and enforce norms and hierarchies. [ABSTRACT FROM AUTHOR]

This paper presents a heuristic algorithm for minimizing the makespan required to route a set of agents inhabiting a shared guidepath network from their initial locations to their respective destinations while observing a set of regulations that seek to ensure the safety and the integrity of the generated traffic. From an application standpoint, the presented developments are motivated by the traffic coordination challenges that arise in the context of many automated unit-load material handling systems and also in the transport of the ionized atoms that takes place in the context of quantum computing. From a methodological standpoint, our developments constitute a customization of the general “local-search” framework of combinatorial optimization theory to the traffic management problem that is considered in this paper. Hence, the presented results include a rigorous characterization of the considered problem and its solution space, detailed algorithms for the construction of the necessary initial solutions and the improving step for the pursued search, a complexity analysis of these algorithms, and a set of computational experiments that reveal and assess the computational efficiency of the presented algorithms and the efficacy of the derived solutions. The paper concludes with some suggestions for potential extensions of the presented results. Note to Practitioners—In many contemporary applications of automation science and engineering, a number of entities—or “agents”—must be transported expediently from their initial locations to certain destinations using a set of links that define the underlying “guidepath network.” Furthermore, various safety considerations require that the agents must be adequately separated during these transports, and the imposed restrictions turn the corresponding traffic coordination problem into a complex resource allocation problem, where the contested resources are the guidepath-network links. This paper presents a set of algorithms that can provide high-quality schedules for the resulting traffic-scheduling problems in a computationally efficient manner. These properties of our algorithms are established through the necessary theoretical analysis, but they are also demonstrated through a series of numerical experiments where they are shown capable to provide near-optimal solutions for some very complex problem instances in no more than a few seconds. In addition, our algorithms are “complete,” i.e., they will always provide a feasible schedule for any instantiation of the traffic-scheduling problem considered in this paper. Hence, they can effectively address the needs for “real-time” traffic management that arise in the context of the considered applications. [ABSTRACT FROM AUTHOR]

To satisfy the requirement of quality control in printing and packaging industry, a sheet counting apparatus is developed, which adopts a line-scan camera to image the fringes of sheet stack and is able to provide a real-time and noncontact measurement of their quantity. With a brief introduction of the system architecture, our main work focuses on the sheet counting algorithms. The basic principle is to identify each sheet profile from the 1-D image with a robust ridge strength measurement. First, a multiscale bi-Gaussian ridge likelihood measurement and a ridge-valley descriptor are utilized to improve adjacent objects detection by increasing local contrast around sheet fringes. Then, a sheet recognition scheme, which integrates a peak detection algorithm and the ridge region criteria for verification, is proposed to discriminate true sheets from the obtained ridgeness measure. According to experiments and tests in real production lines, our apparatus can reach a very high measuring accuracy for printing papers or cards with a thickness not <0.2 mm. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the consensus problem of general linear dynamic multiagent systems with stochastic sampling. In this paper, the sampling intervals randomly switch between two different values. The communication topology between agents is fixed and directed. Full- and reduced-order observers are designed based on neighbor agents’ relative output information. The algorithms to construct such observers are also provided. By using the estimated states of the agents, the observer-based consensus protocol with stochastic sampling are presented. Sufficient conditions to ensure consensus in mean square are derived by using Lyapunov stability theory. Finally, simulations are given to examine the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper presents a novel two-dimensional real-time modeling approach for a proton exchange membrane fuel cell (PEMFC) based on a tridiagonal matrix algorithm (Thomas algorithm). The Thomas algorithm consists of a forward elimination and a backward substitution, its arithmetic complexity of computations being much lower than the Gaussian elimination. In order to use this advanced numerical solver, the differential equations of reactant gas convection and diffusion phenomena in serpentine channels are transformed into a tridiagonal equations system. In addition, a three-level bisection algorithm has been developed to solve spatial physical quantities distribution for electrochemical domain. The real-time computing methods developed in this paper are then implemented in C language for a fast execution time in a real-time processor. The proposed real-time model is experimentally validated using a 1.2 kW Ballard NEXA fuel cell system, and its practical feasibilities in advanced real-time control for PEMFC systems have been experimentally demonstrated in an RT-LAB real-time simulator. [ABSTRACT FROM PUBLISHER]

This paper introduces an efficient method for calculating the three-phase power flow in a loop-based microgrid. The proposed method incorporates the conventional Newton–Raphson (NR) iterative approach in a backward/forward sweep (BFS) algorithm for power distribution network analyses. Conventional compensation-based approaches are commonly used to account for loop breakpoints (LBPs) and PV nodes. However, the efficiency and the convergence of traditional solutions deteriorate as the number of loops and PV nodes increases. In this paper, we convert microgrid loops into radial structures by breaking up LBPs, when PV nodes connected to distributed generators (DGs) are regulated with scheduled constant voltage magnitudes. Then, we apply a three-phase BFS-based power flow method with an acceptable convergence for radial distribution networks. Next, we use the NR method for power mismatch corrections at LBPs and PV nodes. Finally, the proposed method is extended to islanded microgrids by introducing the system frequency as a variable. We label the proposed loop-based method an NR-BFS power flow calculation scheme, which combines NR and BFS methods for microgrid solutions. The solution of the proposed algorithm, which signifies the application of the improved BFS method, is applicable to active distribution systems with several loops and DGs. The simulation results demonstrate the efficiency of the proposed method in the loop-based microgrid applications. [ABSTRACT FROM AUTHOR]

In recent days, many interacted shape products have been developed by manufacturing industries for different applications in various fields such as defense, aerospace, and space centers. In manufacturing, 30% of time consumption is due to assembly operation compared with the remaining processes in manufacturing. It is very difficult to get optimal sequence because assembly sequence planning is a multimodel optimization problem. As the number of parts in the assembly increases, the possible number of sequences increases exponentially therefore obtaining the optimal assembly sequence becomes more difficult and time consuming. There exist many mathematical algorithms to obtain optimal assembly sequences. But, recent studies state that they perform poorly when it comes to multiobjective optimal assembly sequence. In recent years, researchers have developed several soft computing-based algorithms for solving assembly sequence problems. In this paper, assembly subset detection method has been introduced. The proposed method is applied for the first time to solve assembly sequence problems. This method eliminates those assembly sets that have more directional changes and require more energy. The method is compared with other algorithms, namely, genetic algorithm (GA), enhanced GA, ant colony optimization (ACO), memetic algorithm, imperialistic harmonic search algorithm, and flower pollination algorithm (FPA), and is found to be successful in achieving the optimal assembly sequence for an industrial product with smaller number of iterations. Note to Practitioners—This paper is motivated by the redesign of helicopter cowling of a Canadian aircraft company using concepts of design for assembly. Though we could reduce the number of parts using advanced composite materials and manufacturing processes, obtaining a feasible assembly for the new assembly structure required a lot of computation time. Hence, the researchers studied the existing literature on assembly sequence generation methods and their limitations, and came up with efficient automated optimal sequence generation method. [ABSTRACT FROM AUTHOR]

Hoist scheduling in automated electroplating lines has been extensively studied in a static environment. However, practical electroplating lines are subject to diversified unforeseen disruptions that require frequent rescheduling to maintain or optimize system performance. This paper addresses a hoist scheduling problem, where randomly arriving jobs need to be inserted into an existing schedule without changing the sequence of hoist moves already scheduled. The objective is to minimize the total completion time of all the jobs in the existing schedule and a newly inserted job. We develop a polynomial-time heuristic that adjusts the starting times of the existing hoist moves to a limited extent but does not bring about a severe disturbance of the existing hoist moves. We compare our algorithm with two existing approaches with different rescheduling policies (i.e., partial and zero adjustment of the existing schedule). We empirically analyze the productivity and the stability of the schedules generated by the three approaches. Computational results demonstrate that our algorithm can generate more productive and stable schedules than the two existing approaches. Note to Practitioners—Electroplating and chemical surface treatment lines with automated material handling hoists are commonplace in electronics, semiconductor, and many other manufacturing industries. In an uncertain environment, hoist rescheduling plays an important role in improving the productivity and reducing the impact of disruptions. This paper presents a hoist scheduling algorithm to deal with dynamic job arrivals by considering the impact of the disturbance incurred by rescheduling. Our algorithm can generate a better schedule with smaller total completion time and slighter disturbance than the existing algorithms. The proposed algorithm runs in polynomial time and can be used to control hoist operations in practical electroplating lines. A comparative analysis provides useful insights on the implementation of rescheduling approaches and policies to industry practitioners. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of learning undirected graphical models from data generated according to the Glauber dynamics (also known as the Gibbs sampler). The Glauber dynamics is a Markov chain that sequentially updates individual nodes (variables) in a graphical model and it is frequently used to sample from the stationary distribution (to which it converges given sufficient time). Additionally, the Glauber dynamics is a natural dynamical model in a variety of settings. This paper deviates from the standard formulation of graphical model learning in the literature, where one assumes access to independent identically distributed samples from the distribution. Much of the research on graphical model learning has been directed toward finding algorithms with low computational cost. As the main result of this paper, we establish that the problem of reconstructing binary pairwise graphical models is computationally tractable when we observe the Glauber dynamics. Specifically, we show that a binary pairwise graphical model on p nodes with maximum degree d can be learned in time f(d)p^2\log p , for a function f(d) defined explicitly in this paper, using nearly the information-theoretic minimum number of samples. [ABSTRACT FROM AUTHOR]

This paper considers a general stochastic resource allocation problem that arises widely in wireless networks, cognitive radio, networks, smart-grid communications, and cross-layer design. The problem formulation involves expectations with respect to a collection of random variables with unknown distributions, representing exogenous quantities such as channel gain, user density, or spectrum occupancy. We consider the constant step-size stochastic dual subgradient descent (SDSD) method that has been widely used for online resource allocation in networks. The problem is solved in dual domain, which results in a primal resource allocation subproblem at each time instant. The goal here is to characterize the non-asymptotic behavior of such stochastic resource allocations in an almost sure sense. It is well known that with a step size of \epsilon , SDSD converges to an \mathcal {O}(\epsilon) -sized neighborhood of the optimum. In practice, however, there exists a trade-off between the rate of convergence and the choice of \epsilon $ . This paper establishes a convergence rate result for the SDSD algorithm that precisely characterizes this trade-off. Toward this end, a novel stochastic bound on the gap between the objective function and the optimum is developed. The asymptotic behavior of the stochastic term is characterized in an almost sure sense, thereby generalizing the existing results for the stochastic subgradient methods. For the stochastic resource allocation problem at hand, the result explicates the rate with which the allocated resources become near-optimal. As an application, the power and user-allocation problem in device-to-device networks is formulated and solved using the SDSD algorithm. Further intuition on the rate results is obtained from the verification of the regularity conditions and accompanying simulation results. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a characterization of elementary trapping sets (ETSs) for irregular low-density parity-check (LDPC) codes. These sets are known to be the main culprits in the error floor region of such codes. The characterization of ETSs for irregular codes has been known to be a challenging problem due to the large variety of non-isomorphic ETS structures that can exist within the Tanner graph of these codes. This is a direct consequence of the variety of the degrees of the variable nodes that can participate in such structures. The proposed characterization is based on a hierarchical graphical representation of ETSs, starting from simple cycles of the graph, or from single variable nodes, and involves three simple expansion techniques: degree-one tree ( dot ), path , and lollipop , thus, the terminology dpl characterization. A similar dpl characterization was proposed in an earlier work by the authors for the leafless ETSs of variable–regular LDPC codes. The present paper generalizes the prior work to codes with a variety of variable node degrees and to ETSs that are not leafless. The proposed dpl characterization corresponds to an efficient search algorithm that, for a given irregular LDPC code, can find all the instances of (a, b) ETSs with size a and b \leq b\max , exhaustively. Although branch-&-bound exhaustive search algorithms for finding ETSs of irregular LDPC codes exist, to the best of our knowledge, the proposed search algorithm is the first of its kind, in that, it is devised based on a characterization of ETSs that makes the search process efficient. For a constant degree distribution and range of search, the worst-case complexity of the proposed dpl algorithm increases linearly with the block length $n$ . The average complexity, excluding the search for the input simple cycles, is constant in $n$ . Extensive simulation results are presented to show the versatility of the search algorithm, and to demonstrate that, compared to the literature, significant improvement in search speed can be obtained. [ABSTRACT FROM PUBLISHER]

This paper presents an approach to obtain high torque density in induction motor (IM) design by optimizing the search algorithms and processes. Optimal design of electrical machines has many sub-optimal peaks and requires significant computation time. To solve this problem, this paper proposes a new cost pattern value method (CPVM) for local search algorithms employed in optimal design. The CPVM concept is an intelligent strategy that digitizes the gradient over the searching region by sharing information between neighboring points. When the algorithm converges quickly to local optimum by a normalized weight method, it provides a mechanism to escape from that region. The validity of the proposed approach was evaluated using a benchmark function, and it was applied to optimal design of a 260 kW IM to maximize torque production based on a finite-element analysis. For better accuracy, we employ slip-frequency analysis compatible with vector control for an IM numerical analysis. [ABSTRACT FROM AUTHOR]

Finding the sparse representation of a signal in an overcomplete dictionary has attracted a lot of attention over the past years. This paper studies ProSparse, a new polynomial complexity algorithm that solves the sparse representation problem when the underlying dictionary is the union of a Vandermonde matrix and a banded matrix. Unlike our previous work, which establishes deterministic (worst-case) sparsity bounds for ProSparse to succeed, this paper presents a probabilistic average-case analysis of the algorithm. Based on a generating-function approach, closed-form expressions for the exact success probabilities of ProSparse are given. The success probabilities are also analyzed in the high-dimensional regime. This asymptotic analysis characterizes a sharp phase transition phenomenon regarding the performance of the algorithm. [ABSTRACT FROM AUTHOR]

Following the vision of future Supergrids, the existing power systems are expected to evolve into AC/DC systems containing voltage source converters with multiterminal configurations. This paper presents a new sequential power flow algorithm for such power systems. The algorithm can be applied to systems with multiple AC and DC grids of any grid topology. Because of its pure AC/DC network decomposition, the simplification of elements within the converter stations is not required, as is the case with other proposals in the literature. The paper also addresses the convergence properties of the proposed algorithm. A convergence factor has been designed that allows controlling the exit criterion within each inner loop of the sequential AC/DC load flow problem. It is shown that by modulating the convergence factor, the number of inner loop iterations needed to achieve a solution can be diminished. [ABSTRACT FROM AUTHOR]

Filtered orthogonal frequency division multiplexing (F-OFDM) system is a promising waveform for 5G and beyond to enable the multi-service system and spectrum efficient network slicing. However, the performance for F-OFDM systems has not been systematically analyzed in the literature. In this paper, we first establish a mathematical model for an F-OFDM system and derive the conditions to achieve the interference-free one-tap channel equalization. In the practical cases (e.g., insufficient guard interval, asynchronous transmission, and so on), the analytical expressions for inter-symbol interference, inter-carrier interference, and adjacent-carrier interference are derived, where the last term is considered as one of the key factors for asynchronous transmissions. Based on the framework, an optimal power compensation matrix is derived to make all of the subcarriers having the same ergodic performance. Another key contribution of this paper is that we propose a multi-rate F-OFDM system to enable low-complexity low-cost communication scenarios, such as narrow-band Internet of Things, at the cost of generating inter-subband interference (ISubBI). Low computational complexity algorithms are proposed to cancel the ISubBI. The result shows that the derived analytical expressions match the simulation results, and the proposed ISubBI cancelation algorithms can significantly save the original F-OFDM complexity (up to 100 times) without significant performance loss. [ABSTRACT FROM PUBLISHER]

Most applications of underwater acoustic sensor networks (UASNs) rely upon reliable location information of sensor nodes. However, the harsh underwater environment makes localization more challenging as compared to terrestrial sensor networks. This paper is concerned with a localization issue for UASNs, subject to clock asynchronization and node mobility. A hybrid architecture including autonomous underwater vehicles (AUVs), active and passive sensor nodes is designed, where AUVs act as anchor nodes to provide localization information for sensor nodes. In order to eliminate the effect of asynchronous clocks and compensate the mobility of sensor nodes, an asynchronous localization algorithm with mobility prediction is provided for active and passive sensor nodes. Then, two localization optimization problems are formulated as minimizing the sum of all measurement errors. To solve the localization optimization problems, iterative least squares estimators are designed. In addition, the convergence analysis and Cramé r–Rao lower bound for the localization errors are also provided. Finally, simulation results show that the proposed localization approach can reduce the localization time by comparing with the exhaustive search-based localization method. Meanwhile, the asynchronous algorithm in this paper can effectively eliminate the impact of the clock asynchronization and node mobility. [ABSTRACT FROM AUTHOR]

In this paper we present design and analysis of scalable hardware architectures for training learning parameters of RBFNN to classify large data sets. We design scalable hardware architectures for K-means clustering algorithm to training the position of hidden nodes at hidden layer of RBFNN and pseudoinverse algorithm for weight adjustments at output layer. These scalable parallel pipelined architectures are capable of implementing data sets with no restriction on their dimensions. This paper also presents a flexible and scalable hardware accelerator for realization of classification using RBFNN, which puts no limitation on the dimension of the input data is developed. We report FPGA synthesis results of our implementations. We compare results of our hardware accelerator with CPU, GPU and implementations of the same algorithms and with other existing algorithms. Analysis of these results show that scalability of our hardware architecture makes it favorable solution for classification of very large data sets. [ABSTRACT FROM AUTHOR]

In this paper, a novel augmented complex-valued least-mean kurtosis (ACLMK) algorithm is proposed for processing complex-valued signals. The negated kurtosis of the complex-valued error signal is defined as a cost function by using augmented statistics. As a result of the minimization of this cost function, the ACLMK algorithm containing all second-order statistical properties is obtained for processing noncircular complex-valued signals. Moreover, in this paper, convergence and misadjustment conditions of the proposed ACLMK algorithm are derived from the steady-state analysis. The simulation results on complex-valued system identification, prediction, and adaptive noise cancelling problems show that the use of the cost function defined by the negated kurtosis of the complex-valued error signal based on augmented statistics enables the processing of the noncircular complex-valued signals, and significantly improves the performance of the proposed ACLMK algorithm in terms of the mean square deviation, the mean square error, the prediction gain and the convergence rate when compared to other algorithms. [ABSTRACT FROM AUTHOR]

As electric power system operators shift from conventional energy to renewable energy sources, power distribution systems will experience increasing fluctuations in supply. These fluctuations present the need to not only design online decentralized power allocation algorithms, but also characterize how effective they are given fast-changing consumer demand and generation. In this paper, we present an online decentralized dual descent (OD3) power allocation algorithm and determine (in the worst case) how much of observed social welfare can be explained by fluctuations in generation capacity and consumer demand. Convergence properties and performance guarantees of the OD3 algorithm are analyzed by characterizing the difference between the online decision and the optimal decision. We demonstrate validity and accuracy of the theoretical results in the paper through numerical experiments using real power generation data. [ABSTRACT FROM PUBLISHER]

Many power systems applications such as power flow and short-circuit analysis require very large sparse matrix computations. With the increase in reliance on our electric infrastructure, power systems are continually growing in size, creating greater computational complexity in solving these large linear systems within reasonable time. For sparse matrix applications, it is desirable to have an algorithm with low runtime complexity in terms of the number of nonzeros in the matrix. There have been several recent advances in computational methods in other fields that, if applied to power system, could make real-time dynamic simulation a reality. Much work has been done for specific types of these problems where the system is symmetric and diagonally dominant, similar to the form of power system matrices. This paper details an expansion on the current work in fast linear solvers to develop power system specific methods that show potential for accurate solutions in $O(m \log ^2 n)$ run times, where $n$ represents the number of nodes and $m$ represents the number of nonzeros in the power system matrix. This paper presents the simulation validation of a recently developed recursively solved iterative chain method for sparse matrices using a low stretch spanning tree preconditioner. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate spectrum sharing with power-constrained multiradio secondary users (SUs) in cognitive radio networks. The scenario under consideration consists of a primary spectrum owner who runs auctions for leasing her idle channels and multiple SU bidding for winning the usage of spectrum channels. Different from existing works in the literature with an assumption of single-minded SUs, in this paper, SUs can benefit from flexible quantity of channels. In addition, since each SU is ordinarily equipped with a fixed number of radios, she cannot utilize the amount of channels that exceed her radio capacity. Moreover, each SU has a certain power limitation so that the quality of service (QoS) of her transmission may also be constrained, even though the number of allocated channels is increased. To jointly address all these challenges, a novel ascending-price progressive auction algorithm is proposed, where the spectrum allocation decisions are made by gradually increasing the unit channel price. Theoretical analyses prove that the proposed algorithm meets the properties of QoS satisfaction, individual rationality, and incentive compatibility and achieves Pareto optimality. Simulation results further demonstrate that the proposed auction algorithm can improve both the auction revenue and the social welfare, and increase the number of winning SUs compared to the counterparts. [ABSTRACT FROM PUBLISHER]

It is a well-known fact that the Discrete Fourier Transform (DFT) \vec X of an arbitrary n -length input signal \vec {x} , can be computed from all the n time-domain samples in O( n\log n) operations via a Fast Fourier Transform (FFT) algorithm. If the spectrum k -sparse (where k\ll n ), can we do better? We show that asymptotically in k and n , when k ), where 0 \leq \delta <1 ), and the support of the non-zero DFT coefficients is uniformly random, the fast fourier aliasing-based sparse transform (FFAST) algorithm, proposed in this paper, computes, with asymptotically high probability, the k non-zero DFT coefficients of from O( k) samples of \vec x in O( k\log k) arithmetic operations. Further, the constants in the big Oh notation for both sample and computational cost are small, e.g., when \delta < 0.99 , which essentially covers a wide range of sub-linear sparsity cases, the sample cost is less than 4 k . Although, in this paper we assume that the samples of the signal \vec x observed by the FFAST are noise-free, a noise-robust extension of the FFAST is provided in a companion (Part II) paper [1]. Our approach is based on filter-less sub-sampling of the input signal using a set of carefully chosen uniform sub-sampling patterns guided by the Chinese Remainder Theorem (CRT). The idea is to cleverly exploit, rather than avoid, the resulting aliasing artifacts induced by sub-sampling. Specifically, the sub-sampling operation on the time-domain signal \vec x is designed to create aliasing patterns of the non-zero coefficients of the spectrum \vec X to “look like” parity-check constraints of a good erasure-correcting sparse-graph code. Next, we show that computing the sparse DFT \vec X is equivalent to decoding of an appropriate sparse-graph code. The sparse-graph codes further permit a fast peeling-style decoding. Consequently, the resulting DFT computation is low in both the sample and the decoding complexity. We analytically connect our proposed CRT-based aliasing framework to random sparse-graph codes, and analyze the performance of our algorithm using density evolution techniques from coding theory. We provide extensive simulation results, that are in tight agreement with our theoretical findings. [ABSTRACT FROM PUBLISHER]

In this paper, we present a novel high-throughput anti-collision algorithm for passive ultrahigh-frequency (UHF) radio-frequency identification (RFID) systems. Our algorithm utilizes signal recovery techniques of collided tag signals to both recover tag communications and obtain an accurate count of all tags in the field. Passive UHF RFID systems are used for long-range passive communications for applications including supply chain management and electronic tolling. Anti-collision algorithms are used to ensure successful RFID tag communications due to the likelihood of multiple tags being in the field and attempting to communicate simultaneously. The limited on-tag functionality necessitates the use of simple anti-collision algorithms such as a dynamic frame slotted Aloha (DFSA) algorithm. With our novel collision detection and signal recovery anti-collision algorithm, the RFID reader can retrieve multiple valid communications from each collided slot in a DFSA-based anti-collision protocol, while our algorithm allocates an optimal number of slots resulting in more collided but recoverable slots and fewer empty slots. Our algorithm achieves a nearly 100% throughput improvement with an expected throughput of 0.85 compared with an expected throughput of 0.426 for a standard DFSA algorithm. The reader receiver with the proposed algorithm is implemented in a field-programmable gate array and the whole reader system is verified using the communication tests with commercial tags. According to the synthesized results in an SMIC 0.13- \mu \textm CMOS technology, the collision detection and signal recovery module consumes about 135k GE.

Note to Practitioners—Signal recovery methods are capable of extracting information from collided signals. In this paper, we introduce and analyze a signal recovery method based on the voltage histogram of the received signal. Both the original signals and the number of collided signals are recovered. The information of tags is used by our novel algorithm to increase the system throughput in DFSA-based anti-collision algorithms. The recovered signals allow our algorithm to directly obtain the tag identifier. By determining the number of collided signals, our algorithm is able to calculate an accurate estimate of the total number of tags in the reader’s field. With signal recovery and an accurate tag count, our algorithm is able to reduce the total number of slots needed to identify all tags, thereby increasing the throughput. We present a novel frame length optimization method that increases throughout by shrinking the number of slots, thereby intentionally causing collisions and reducing the total number of empty slots. In addition, we present a hardware implementation of our novel method that is suitable for integration into RFID readers. When the signal strengths of the tags in collision in the in-phase or quadrature path are not close to each other, the performance of our method is much better than that of the traditional method in which the collision signal is treated as ineffective and ignored. [ABSTRACT FROM PUBLISHER]

This paper presents an efficient algorithm to test whether a planar object can be caged by a formation of point agents (point fingertips or point mobile robots). The algorithm is based on a space mapping between the 2-D work space ( \mathcal W space) and the 3-D configuration space ( \mathcal C space) of the given agent formation. When performing caging test on a planar object, the algorithm looks up the space mapping to recover the \mathcal C space of the given agent formation, labels the recovered \mathcal C space, and counts the number of labeled surfaces to judge the success of caging. The algorithm is able to work with various planar shapes, including objects with convex boundaries, concave boundaries, or holes. It can also respond quickly to varying agent formations and different object shapes. Experiments and analysis on different objects and fingertip formations demonstrate the completeness, robustness, and efficiency of our proposal.

Note to Practitioners—This paper proposes an algorithm to solve a geometric problem—find whether a given formation of planar points can constrain (or cage) a planar shape. Users can use the proposed algorithm to actuate a formation of robotic fingertips to perform caging-based grasping tasks or use the proposed algorithm to actuate a formation of mobile robots to perform cooperative transportation tasks. The algorithm inherits the merits of caging and helps users to avoid explicit force analysis. It offers robustness to avoid uncertainty in the tasks. The code of our work is in the supplementary material. [ABSTRACT FROM AUTHOR]

It becomes more and more critical for manufacturing enterprises to improve energy efficiency because of the escalating energy prices, increasing global competitions, and more rigorous government regulations. In this paper, a systematic method is developed to improve the energy efficiency of a multistage manufacturing system through production control. The method aims at reducing energy consumption with minimal negative impact on production. We start from the analysis of system dynamics and develop quantitative methods to estimate energy saving opportunities. A supervisory control algorithm is developed to improve system energy efficiency by periodically taking the saving opportunities. Simulation case studies are performed to validate the effectiveness of the control algorithm.

Note to Practitioners—Manufacturing systems are facing increasing pressure to reduce energy consumption, as global competition, sustainability, and green processes are becoming more prevalent. Although most of the research efforts on manufacturing energy saving have focused on developing individual energy efficient machines, it can be more cost-effective to improve energy efficiency through better control of the energy usage of the whole production system. Therefore, this paper presents a systematic method to improve system energy efficiency with a minimal negative impact on production. This paper continues the work by Chang et al. by extending the scope of energy saving opportunity theory from serial production systems to general serial–parallel production systems. It also develops analytical methods based on Markov chain models to estimate the energy saving opportunity accurately. [ABSTRACT FROM AUTHOR]

This paper presents an extension of the integrated time-chirp rate analysis technique, known as the parameterized centroid frequency-chirp rate distribution (PCFCRD), for noisy multicomponent linear frequency modulated signals analysis. The PCFCRD is based on a newly defined correlation function and its auto term accumulation is two-dimensionally coherent. The principle, cross term characteristic, implementation, properties, parameter selection criterion, antinoise performance, and estimation accuracy are analyzed for the PCFCRD. In this paper, comparisons with the coherently integrated cubic phase function, maximum likelihood method, and Lv's distribution are performed. With mathematic analyses and numerical simulations, we demonstrate that the PCFCRD outperforms these three representative counterparts. Further, by combining with analyses of the PCFCRD, several unclear mechanisms of the constant delay introduction are mathematically interpreted, especially quantitative influences on the chirp rate resolution and theoretical antinoise performance. [ABSTRACT FROM AUTHOR]

This paper presents an improved approach to the recently proposed multiple-resonator-based method for the harmonic analysis that has been provided in the previous papers. Previously, two inherent particular cases have been considered. In these cases, reference points in which estimation is performed are located either in the middle or at the end of the observation interval. The first case exhibits a good noises and unwanted harmonics attenuation but possesses a large delay time. In the second case, the filters are able to form a zero-flat phase response about the operation frequency and hence able to provide instantaneous estimates, but with large overshoots caused by resonant frequencies at the edges of the passband, and the high level of the sidelobes, that also makes it susceptible to interharmonics and noise interference. The aim of this paper is to propose a compromised solution provided by the tradeoff between those opposite requirements by shifting this point along the observation interval. This way the frequency responses of the estimator are reshaped. A maximally flatness of the frequency response in the operation harmonic frequency is kept in all cases, but only locating the reference point in a fraction around the center of the observation interval provides flat-top frequency responses. The effectiveness of the proposed estimation technique is shown through simulations. [ABSTRACT FROM AUTHOR]

Role assignment is a critical element in the role-based collaboration process. There are many different requirements to be considered when undertaking this task. This correspondence paper formalizes the group multirole assignment (GMRA) problem; proves the necessary and sufficient condition for the problem to have a feasible solution, provides an improved IBM ILOG CPLEX optimization package solution, and verifies the proposed solution with experiments. The contributions of this paper include: 1) the formalization of an important engineering problem, i.e., the GMRA problem; 2) a theoretical proof of the necessary and sufficient condition for GMRA to have a feasible solution; and 3) an improved ILOG solution to such a problem. [ABSTRACT FROM AUTHOR]

This paper presents a generic methodology to optimize constellations based on their geometrical shaping for bit-interleaved coded modulation (BICM) systems. While the method can be applicable to any wireless standard design it has been tailored to two delivery scenarios typical of broadcast systems: 1) robust multimedia delivery and 2) UHDTV quality bitrate services. The design process is based on maximizing the BICM channel capacity for a given power constraint. The major contribution of this paper is a low complexity optimization algorithm for the design of optimal constellation schemes. The proposal consists of a set of initial conditions for a particle swarm optimization algorithm, and afterward, a customized post processing procedure for further improving the constellation alphabet. According to the broadcast application cases, the sizes of the constellations proposed range from 16 to 4096 symbols. The BICM channel capacities and performance of the designed constellations are compared to conventional quadrature amplitude modulation constellations for different application scenarios. The results show a significant improvement in terms of system performance and BICM channel capacities under additive white Gaussian noise and Rayleigh independently and identically distributed channel conditions. [ABSTRACT FROM PUBLISHER]

This paper presents a cost-effective motion-planning method for robots operating in complex and realistic environments. While sampling-based path-planning algorithms, such as a rapidly exploring random tree (RRT) and its variants, have been highly effective for general path-planning problems, it is still difficult to find the minimum cost path in a complex space efficiently since RRT-based algorithms extend a search tree locally, requiring a large number of samples before finding a good solution. This paper presents an efficient nonmyopic path-planning algorithm by combining RRT* and a stochastic optimization method, called cross entropy. The proposed method constructs two RRT trees: The first tree is a standard RRT* tree, which is used to determine the nearest node in the tree to be extended to a randomly chosen point, and the second tree contains the first tree with additional long extensions. By maintaining two separate trees, we can grow the search tree nonmyopically to improve the efficiency of the algorithm while ensuring the asymptotic optimality of RRT*. From an extensive set of simulations and experiments using mobile and humanoid robots, we demonstrate that the proposed method consistently finds low-cost paths faster than the existing algorithms. [ABSTRACT FROM PUBLISHER]

The task of feature selection is to find the most representative features from the original high-dimensional data. Because of the absence of the information of class labels, selecting the appropriate features in unsupervised learning scenarios is much harder than that in supervised scenarios. In this paper, we investigate the potential of locally linear embedding (LLE), which is a popular manifold learning method, in feature selection task. It is straightforward to apply the idea of LLE to the graph-preserving feature selection framework. However, we find that this straightforward application suffers from some problems. For example, it fails when the elements in the feature are all equal; it does not enjoy the property of scaling invariance and cannot capture the change of the graph efficiently. To solve these problems, we propose a new filter-based feature selection method based on LLE in this paper, which is named as LLE score. The proposed criterion measures the difference between the local structure of each feature and that of the original data. Our experiments of classification task on two face image data sets, an object image data set, and a handwriting digits data set show that LLE score outperforms state-of-the-art methods, including data variance, Laplacian score, and sparsity score. [ABSTRACT FROM PUBLISHER]

This paper describes the application of discrete-wavelet transform (DWT)-based algorithms to compress optical emission spectral intensity data collected during wafer fabrication. The major goal is to seek computationally efficient compression algorithms that can significantly reduce the data storage requirement but also are capable of retaining enough data authenticity critical for diagnostic purposes. The potential benefits will provide a promising foundation for an integrated data collection and compression tool. The optical emission data are treated in this paper either as images for the whole spectrum or bands of time series and are treated accordingly using appropriate DWT compression approaches. We have found through representative simulation examples that using the set partitioning in hierarchical trees compression algorithm it is achievable to obtain better than 99% storage reduction for optical emission spectrum (OES) images. For OES time series we have achieved around 95% storage reduction with Daubechies and Haar wavelets. The storage reductions are achieved while maintaining sufficient authenticity to retain the dynamic nature of OES data for diagnostic purposes. [ABSTRACT FROM AUTHOR]

A schedule-based system is a system that operates on or contains within a schedule of events and breaks at particular time intervals. Entities within the system show presence or absence in these events by entering or exiting the locations of the events. Given radio frequency identification (RFID) data from a schedule-based system, what can we learn about the system (the events and entities) through data mining? Which data mining methods can be applied so that one can obtain rich actionable insights regarding the system and the domain? The research goal of this paper is to answer these posed research questions, through the development of a framework that systematically produces actionable insights for a given schedule-based system. We show that through integrating appropriate data mining methodologies as a unified framework, one can obtain many insights from even a very simple RFID dataset, which contains only very few fields. The developed framework is general, and is applicable to any schedule-based system, as long as it operates under certain basic assumptions. The types of insights are also general, and are formulated in this paper in the most abstract way. The applicability of the developed framework is illustrated through a case study, where real world data from a schedule-based system is analyzed using the introduced framework. Insights obtained include the profiling of entities and events, the interactions between entity and events, and the relations between events. [ABSTRACT FROM AUTHOR]

Existing research on a simultaneous wireless information and power transfer (SWIPT) system is based on the assumption of Gaussian inputs. However, the optimal design on Gaussian inputs may lead to dramatic performance loss for practical systems with finite-alphabet inputs. This paper focuses on the precoder design for a SWIPT system with finite-alphabet inputs and instantaneous channel state information over a multiple-input multiple-output channel. We formulate the optimal precoder design as an optimization problem, in which the objective is to maximize the mutual information over the channel from the transmitter to the information receiver under the constraints of transmit power and harvested energy threshold. The formulated problem is NP-hard, so a global optimal solution cannot be found within the polynomial time. The main contributions of this paper are as follows: 1) By using its structure, the NP-hard problem is relaxed to a semidefinite programming problem. Then, a general solving framework for both co-located and separated receiver cases, based on the semidefinite relaxation (SDR) technique, is developed to achieve a near optimal precoder. 2) For the case of co-located receivers, we show that the optimal precoder design is a concave problem with respect to power allocation; then, a specific algorithm for co-located receivers is proposed. Compared with the general SDR-based method, the specific algorithm for co-located receivers exhibits almost the same performance but much lower complexity. 3) The performance of several practical co-located receiver designs is analyzed in SWIPT systems with finite-alphabet inputs. Finally, we provide simulation results to show the efficacy of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

In spectral analysis, achieving coherent sampling, especially when signals have large distortion, has been a challenge for many years. This paper introduces three algorithms to resolve this issue. In comparison to previous algorithms, and two widely used methods in industry: windowing and four-parameter sine wave fit, these new algorithms are capable of obtaining accurate spectral results of the signal, while achieving high accuracy as well as computational efficiency. The novel contribution of this paper is that not only does it propose two new algorithms, but also it analyzes their advantages and limitations in detail, providing their trade-offs and different fields of applications. Extensive simulations and measurements are performed to validate these algorithms. Combined with their high accuracy, computational efficiency, and robustness against signal purity, these algorithms are readily available to be implemented for bench or on-chip testing. In addition, it is suitable for data converter spectral testing when noncoherent sampling is present, and spectrally pure test signal source is not available. [ABSTRACT FROM PUBLISHER]

Several methods have been proposed and investigated so far on mitigation of very fast transient overvoltages (VFTO) in gas-insulated switchgear (GIS). The state-of-the-art methods are primarily based on dissipation of the energy associated with electromagnetic waves that the VFTO originate from and are composed of. Present paper reports on an alternative concept of VFTO mitigation based on the principle of controlling voltage conditions preceding voltage breakdown in SF6 gas that leads to VFTO generation. The paper introduces different control algorithms and shows how the algorithms can limit VFTO maximum value and total number of voltage breakdowns during operation of the GIS disconnector. The concept is applied for mitigation of VFTO in ultra-high voltage GIS. As the study case, an 1100 kV test set-up is used as recently reported for Wuhan (China) GIS station, with the disconnector characteristics obtained from 1100 kV development tests. [ABSTRACT FROM PUBLISHER]

The continuous globalization of the semiconductor industry has significantly raised the vulnerability of chips under hardware Trojan (HT) attacks. It is extremely challenging to detect HTs in fabricated chips due to the existence of process variations (PVs), since PVs may cause larger impacts than HTs. In this paper, we propose a novel framework for HT detection in digital integrated circuits. The goal of this paper is to detect HTs inserted during fabrication. The HT detection problem is formulated as an under-determined linear system by a sparse gate profiling technique, and the existence of HTs is mapped to the sparse solution of the linear system. A Bayesian inference-based calibration technique is proposed to recover PVs for each chip for the sparse gate profiling technique. A batch of under-determined linear systems are solved together by the well-studied simultaneous orthogonal matching pursuit algorithm to get their common sparse solution. Experimental results show that even under big measurement errors, the proposed framework gets quite high HT detection rates with low measurement cost. [ABSTRACT FROM PUBLISHER]

Motivated by circumstances with severe computational resource limits (e.g., settings with strong constraints on memory or communication), this paper addresses the problem of concisely representing and processing information for estimation and planning tasks. In this paper, conciseness is a measure of explicit representational complexity: for filtering, we are concerned with maintaining as little state as possible to perform a given task; for the planning case, we wish to generate the plan graph (or policy graph) with the fewest vertices that is correct and also complete. We present hardness results showing that both filtering and planning are NP-hard to perform in an optimally concise way, and that the related decision problems are NP-complete. We also describe algorithms for filter reduction and concise planning, for which these hardness results justify the potentially suboptimal output. The filter-reduction algorithm accepts as input an arbitrary combinatorial filter, expressed as a transition graph, and outputs an equivalent filter that uses fewer I-states to complete the same filtering task. The planning algorithm, using the filter-reduction algorithm as a subroutine, generates concise plans for planning problems that may involve both nondeterminism and partial observability. Both algorithms are governed by parameters that encode tradeoffs between computational efficiency and solution quality. We describe implementation of both algorithms and present a series of experiments evaluating their effectiveness. Note to Practitioners—The reduced filters and plans explored in this paper are of practical interest in several contexts, including: 1) on robot platforms with severely limited computational power; 2) communication over low-bandwidth noisy channels; 3) a special instance of the previous case includes human-robot interaction settings where interfaces constrain information transfer; and 4) understanding the size and the structure of concise plans or filters for given problems provides insights into those problems (e.g., to assess the value of a particular sensor by comparing the size of filters with or without it.) [ABSTRACT FROM AUTHOR]

Batch process can exhibit significantly different characteristics across different phases, hence it is significant to partition it reasonably and set up corresponding subphase models for online monitoring. Unlike traditional phase-partition algorithms that customarily exploit the result of PCA algorithm for advanced research, an innovative algorithm which directly extracts effective information from the covariance matrix is presented in this paper, which is called multiway beacon exception analysis for maintenance (MBEAM). Its theoretics and statistical characteristics are demonstrated adequately. Based on the accurate capture of the change in variable correlation caused by characteristic variance of the process, the algorithm can separate the process into major phases and transition patterns automatically. The time-varying characteristics will then remain relatively stable in each independent subphase and will be supervised by homologous monitoring model that reflects the inherent phase feature. Due to its simple and intuitive format, MBEAM has superior performance in computation efficiency and fault interpretation, which is illuminated later in this paper. Synthetical illustrations are given concerning the influences of major parameters on the monitoring performance. Comparison with the step-wise sequential phase partition algorithm is conducted for a clearer insight. Experiments are carried out to further confirm the validation of the proposed method. [ABSTRACT FROM PUBLISHER]

Automatic wafer prealignment is an important process in wafer manufacturing, whereby its flat edge is set at a predefined angle and its center is set in a predefined position; translational and rotational movement compensate for deviation of the wafer. However, as the traditional wafer prealignment algorithm depends on marked templates at the training stage when the type of wafer is changed, the templates need to be retrained. This paper proposes a new wafer prealignment algorithm, based on Fourier transform for orientation prealignment and least square regression for position prealignment, which will automatically adapt to different types of wafers in 2-D space. Results from experiments with the proposed algorithm on a laser-scribing machine using two types of wafer show that the orientation prealignment achieved an accuracy of 0.05°, the position prealignment achieved an accuracy of 5 pixels, and the average operation time was approximately 1.5 s. As prealignment algorithm therefore meets real-time efficiency and precision requirements, it is suitable for use with semiconductor devices.

Note to Practitioners—Because deviation appears when a wafer is first put on the working table, the traditional process is to set up a prealignment system when aligning a new type of wafer. However, different templates increase difficulty for machine operators, and decrease automation and reliability of the devices. This paper therefore proposes a new general prealignment algorithm based on using Fourier transform for orientation prealignment and least square regression for position prealignment. The algorithm was tested on two types of wafer with results showing that the orientation prealignment achieved an accuracy of 0.05°, the position prealignment achieved an accuracy of 5 pixels, and the average operation time was about 1.5 s, all of which meet real-time requirements. Although the algorithm can be adapted to different wafers without adjustment, it does not fit nonquadrilateral chips very well. Accordingly, we will develop an improved algorithm with better generality in due course. [ABSTRACT FROM PUBLISHER]

The calibration of phasor measurement units (PMUs) consists of comparing coordinated universal time time-aligned phasors (synchrophasors) measured by the PMU under test, against reference synchrophasors generated through a PMU calibrator. The IEEE Standard C37.118–2011 and its latest amendment (IEEE Std) describe compliance tests for static and dynamic conditions, and indicate the relative limits in terms of accuracy. In this context, this paper focuses on the definition and accuracy assessment of the reference synchrophasors in the test conditions defined by the above IEEE Std. In the first part of this paper, we describe the characterization of a nonlinear least-squares fitting algorithm used to determine the parameters of these reference synchrophasors. For this analysis, we deploy the proposed algorithm in a PMU calibrator and characterize the algorithm performance within the actual hardware implementation for both static and dynamic test conditions. More specifically, we generate reference waveforms through a highly stable high-resolution digital-to-analog converter and evaluate how the algorithm parameters (observation interval length and sampling frequency) affect the solution accuracy. In the second part, we discuss on the appropriateness of the synchrophasor model in the evaluation of PMU performance under step test conditions. In this regard, we propose an alternative time-domain approach to assess the synchrophasor estimate during transient events. [ABSTRACT FROM AUTHOR]

Liveness of Petri nets means all activities in a modeled system can potentially take place and thus implies deadlock freedom. The research on liveness analysis approaches is inadequate. This paper proposes a liveness judgment reachability graph (LJRG) approach to analyze the liveness of \omega -independent Petri nets. Such nets can be loosely explained as a class of unbounded Petri nets in which the changes of tokens in unbounded places are not related to each other. This paper proposes several algorithms to transform a new modified reachability tree to a new modified reachability graph and then transform it to an LJRG. It then develops the application of LJRG into the liveness analysis of \omega -independent unbounded Petri nets. The proposed method provides a new theoretical method and important tool for the liveness analysis of unbounded Petri nets. It is illustrated via some examples. [ABSTRACT FROM AUTHOR]

This paper introduces a battery charging scheme from a solar photovoltaic (SPV) by using a single sensor-based maximum power point tracking (MPPT) strategy. Here, for quick and efficient tracking, a novel hybrid “Cauchy and Gaussian sine cosine optimization” (CGSCO) algorithm is proposed for MPPT, which is based on only a single current sensor. The main objective of the CGSCO algorithm is, maximum extraction of the power from SPV panel and efficiently charging the battery through maximizing the charging current of the battery. Due to the single sensor, the cost of the charging scheme is very low, as well as the algorithm complexity and computational burden are very less, so it can be easily implemented on the low-cost microcontroller. In this paper, a single current sensor-based battery charging scheme by CGSCO algorithm is tested on MATLAB simulator and verified on a developed hardware of the SPV system. The panel condition, with and without shaded as well as dynamic environmental condition (variable temperature and insolation), is considered during simulation as well as on hardware implementation. Moreover, the tracking ability is compared with the most recent state of the art techniques (Grey wolf optimization and Lagrange interpolation particle swarm optimization (LIPSO)) as well as compared with “CGSCO with the conventional dual (voltage and current) sensor-based MPPT scheme.” The efficient battery charging with quick MPPT by CGSCO algorithm w.r.t. all state of the art techniques as well as dual sensor-based MPPT scheme, in steady-state as well as in dynamic conditions meets the motive of the work. [ABSTRACT FROM PUBLISHER]