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Establishing causal relations between random variables from observational data is perhaps the most important challenge in today’s science. In remote sensing and geosciences, this is of special relevance to better understand the earth’s system and the complex interactions between the governing processes. In this paper, we focus on an observational causal inference, and thus, we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (nondeterministic) additive noise models, which relies on the principle of independence between the cause and the generating mechanism. A practical algorithmic instantiation of such principle only requires: 1) two regression models in the forward and backward directions and 2) the estimation of statistical independence between the obtained residuals and the observations. The direction leading to more independent residuals is decided to be the cause. We instead propose a criterion that uses the sensitivity (derivative) of the dependence estimator, the sensitivity criterion allows to identify samples most affecting the dependence measure, and hence, the criterion is robust to spurious detections. We illustrate the performance in a collection of 28 geoscience causal inference problems, a database of radiative transfer models simulations and machine learning emulators in vegetation parameter modeling involving 182 problems, and assessing the impact of different regression models in a carbon cycle problem. The criterion achieves the state-of-the-art detection rates in all cases, and it is generally robust to noise sources and distortions. The presented approach confirms the validity in observational bivariate problems in the earth sciences. [ABSTRACT FROM AUTHOR]

Model checking is a formal method for verifying whether the system satisfies a user-defined specification. Compared to simulation, model checking is restricted in capacity. On the other hand, simulation is weak in detecting bugs that require long and complex sequences of events to be exposed. This paper combines model checking and simulation in an abstraction-refinement scheme to mitigate the problems of both methods. Abstraction refinement iteratively constructs a simplified model to verify the original model. While a simplified model mitigates the weakness of model checking, the set of simplified error traces model helps guide simulation toward deep bugs. In abstraction refinement, concretization—a process of deriving an error trace in the original model from the abstract ones—is used to invalidate spurious abstract error traces or to refute a property. In this paper, we describe a novel concretization algorithm that combines simulation with satisfiability to efficiently refute properties with very long error traces. [ABSTRACT FROM AUTHOR]

This paper presents an online procedure for the automatic search of the maximum-torque-per-ampere operating region for a synchronous reluctance motor. The algorithm is based on a signal-injection method with a random-based perturbation pattern applied to a common direct-torque-controlled drive. Among motor parameters, only the stator resistance is required to perform the automatic procedure. Simulations and experimental results are presented in the paper, demonstrating the benefits of the proposed algorithm. The solution is easily extended to any ac drive. [ABSTRACT FROM PUBLISHER]

In this paper, a mathematical model for the beacon-enabled mode of the IEEE 802.15.4 medium-access control (MAC) protocol is provided. A personal area network (PAN) composed of multiple nodes, which transmit data to a PAN coordinator through direct links or multiple hops, is considered. The application is query based: Upon reception of the beacon transmitted by the PAN coordinator, each node tries to transmit its packet using the superframe structure defined by the IEEE 802.15.4 protocol. Those nodes that do not succeed in accessing the channel discard the packet; at the next superframe, a new packet is generated. The aim of the paper is to develop a flexible mathematical tool able to study beacon-enabled 802.15.4 networks organized in different topologies. Both the contention access period (CAP) and the contention-free period defined by the standard are considered. The slotted carrier-sense multiple access with collision avoidance (CSMA/CA) algorithm used in the CAP portion of the superframe is analytically modeled. The model describes the probability of packet successful reception and access delay statistics. Moreover, both star and tree-based topologies are dealt with; a suitable comparison between these topologies is provided. The model is a useful tool for the design of MAC parameters and to select the better topology. The mathematical model is validated through simulation results. The model differs from those previously published by other authors in the literature as it precisely follows the MAC procedure defined by the standard in the context of the application scenario described. [ABSTRACT FROM AUTHOR]

The frequency scaling algorithm (FSA) was proposed to process the synthetic aperture radar (SAR) data acquired via the dechirp-on-receive approach. Some aspects of improving the FSA are investigated in this paper, based on which an extended FSA (EFSA) is presented. The general purpose of the EFSA is to reduce the effect of range spectrum shift of the intermediate processing results, which occurs during the scaling operation in the FSA, so as to achieve a more effective utilization of the processed bandwidth. The EFSA is implemented through time shifting the scaling and the inverse scaling functions used in the FSA and also the adjustment of the scaling factor. The derivation of the EFSA is detailed in this paper. Point target simulation in squinted imaging geometry indicates that the presented algorithm is more suitable for large-squint applications. [ABSTRACT FROM AUTHOR]

Channel order estimation is a critical step in most blind single-input multiple-output (SIMO) channel identification/equalization algorithms. Several methods for estimating either the true channel order or its most significant part (the so-called effective channel order) have been recently proposed, but a solution able to work in practical scenarios (low or moderate signal-to-noise ratios (SNR5) and channels with small leading and/or trailing coefficients) has not been found yet. In this paper, a new criterion for effective channel order detection of SIMO channels is presented. The method is based on the fact that the cost function typically used in blind identification algorithms decreases monotonically with the estimated channel order, whereas for blind equalization algorithms, the cost function increases monotonically. In this paper, it is shown that a straightforward combination of both cost functions attains its minimum at the correct channel order even for moderate SNRS. The proposed method is able to work with small data sets, colored signals, and channels with small head and tail taps, which is a common problem in communication applications. The improvement of the proposed criterion over a number of existing algorithms is demonstrated through simulations. [ABSTRACT FROM AUTHOR]

The Warp code (and its framework of associated tools) was initially developed for particle-in-cell simulations of space-charge-dominated ion beams in accelerators, for heavy-ion-driven inertial fusion energy, and related experiments. It has found a broad range of applications, including nonneutral plasmas in traps, stray electron clouds in accelerators, laser-based acceleration, and the focusing of ion beams produced when short-pulse lasers irradiate foil targets. We summarize novel methods used in Warp, including: time-stepping conducive to diagnosis and particle injection; an interactive Python-Fortran-C structure that enables scripted and interactive user steering of runs; a variety of geometries (3-D $x$ , $y$ , $z$ ; 2-D $r$ , $z$ ; 2-D $x$ , $y$ ); electrostatic and electromagnetic field solvers; a cut-cell representation for internal boundaries; the use of warped coordinates for bent beam lines; adaptive mesh refinement, including a capability for time-dependent space-charge-limited flow from curved surfaces; models for accelerator lattice elements (magnetic or electrostatic quadrupole lenses, accelerating gaps, etc.) at user-selectable levels of detail; models for particle interactions with gas and walls; moment/envelope models that support sophisticated particle loading; a drift-Lorentz mover for rapid tracking through regions of strong and weak magnetic field; a Lorentz-boosted frame formulation with a Lorentz-invariant modification of the Boris mover; an electromagnetic solver with tunable dispersion and stride-based digital filtering; and a pseudospectral electromagnetic solver. Warp has proven useful for a wide range of applications, described very briefly herein. It is available as an open-source code under a BSD license. This paper describes material presented during the Prof. Charles K. (Ned) Birdsall Memorial Session of the 2013 IEEE Pulsed Power and Plasma Science Conference. In addition to our overview of the computational methods used in Warp, we summarize a few aspects of Ned's contributions to plasma simulation and to the careers of those he mentored. [ABSTRACT FROM PUBLISHER]

This paper focuses on a lot merging–splitting problem in a semiconductor wafer fabrication facility in which a relatively large number of wafer types are produced according to orders with different due dates. In the fab, two or more lots can be merged into a single lot if routes and all processing conditions of the lots are the same for a number of subsequent operations, and the merged lot is split into the original lots at the point where the routes or processing conditions become different. We suggest lot merging–splitting algorithms to reduce the total tardiness of the orders and the cycle times of the lots. The suggested algorithms are evaluated through a series of simulation experiments and the result shows that the algorithms work better than a method used in a real fab. [ABSTRACT FROM PUBLISHER]

In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study. [ABSTRACT FROM AUTHOR]

Although the recently proposed single-implicit-equation-based input voltage equations (IVEs) for the independent double-gate (IDG) MOSFET promise faster computation time than the earlier proposed coupled-equations-based IVEs, it is not clear how those equations could be solved inside a circuit simulator as the conventional Newton–Raphson (NR)-based root finding method will not always converge due to the presence of discontinuity at the G-zero point (GZP) and nonremovable singularities in the trigonometric IVE. In this paper, we propose a unique algorithm to solve those IVEs, which combines the Ridders algorithm with the NR-based technique in order to provide assured convergence for any bias conditions. Studying the IDG MOSFET operation carefully, we apply an optimized initial guess to the NR component and a minimized solution space to the Ridders component in order to achieve rapid convergence, which is very important for circuit simulation. To reduce the computation budget further, we propose a new closed-form solution of the IVEs in the near vicinity of the GZP. The proposed algorithm is tested with different device parameters in the extended range of bias conditions and successfully implemented in a commercial circuit simulator through its Verilog-A interface. [ABSTRACT FROM PUBLISHER]

Electronically controlled air suspension systems have been used in vehicles to improve ride comfort and handling safety by adjusting vehicle height. This paper proposes a new nonlinear controller to adjust the height of the vehicle sprung mass (height control) and to regulate the roll and pitch angles of the vehicle body (leveling control) by an air suspension system. A sliding mode control algorithm is designed to improve the tracking accuracy of the control and to overcome nonlinearities and uncertainties in the air suspension system. A mathematical model of the air suspension system is formulated in a nonlinear affine form to describe the dynamic behavior of the system and to derive the control algorithm. The sliding mode observer is also designed to estimate the pressures inside four air springs. The effectiveness and performance of the proposed control algorithm are verified by simulations and actual vehicle tests. [ABSTRACT FROM PUBLISHER]

In this paper, a model-based fault prognosis method is developed for hybrid systems with multiple incipient faults. The concept of augmented global analytical redundancy relations is proposed for the identification of degradation of components, such as sensors and actuators, which cannot be described by physical parameters. In addition, multiple incipient faults are considered in a complex hybrid system, and these faults can develop during a mode when the faults are not detectable. The unknown degradation characteristic of each incipient fault is identified with the closest matching one of some prescribed dynamic models. The resultant degradation model will serve as a base for prognosis. In the process of fault detection and isolation, the degradation models and faults are identified using a multiple-adaptive-hybrid-particle-swarm-optimization algorithm. The proposed methodology and algorithm are verified with simulation as well as experiments. [ABSTRACT FROM PUBLISHER]

This correspondence paper presents an algorithm in which the independent components of link lengths are used as a medium to analyze the forward kinematics of a six-degree-of-freedom Stewart platform. The link lengths are firstly transformed into independent components through independent component analysis. Then, the value of positional variables is computed by using the Nelder–Mead algorithm by taking advantage of the relationships between the independent components and the positional variables. Simulations have been conducted to test the proposed algorithm. The experimental results show that the proposed algorithm can achieve a better performance than the other published algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we consider distributed estimation of a noise-corrupted deterministic parameter in energy-constrained wireless sensor networks from energy-distortion perspective. Given a total energy budget allowable to be used by all sensors, there exists a tradeoff between the subset of active sensors and the energy used by each active sensor in order to minimize the estimation MSE. To determine the optimal quantization bit rate and transmission energy of each sensor, a concept of equivalent unit-energy MSE function is introduced. Based on this concept, an optimal energy-constrained distributed estimation algorithm for homogeneous sensor networks and a quasi-optimal energy-constrained distributed estimation algorithm for heterogeneous sensor networks are proposed. Moreover, the theoretical energy-distortion performance bound for distributed estimation is addressed and it is shown that the proposed algorithm is quasi-optimal within a factor 2 of the theoretical lower bound. Simulation results also show that the proposed method can achieve a significant reduction in the estimation MSE when compared with other uniform schemes. Finally, the proposed algorithm is easy to implement in a distributed manner and it adapts well to the dynamic sensor environments. [ABSTRACT FROM AUTHOR]

In this paper, we consider the joint power allocation and base station (BS) assignment in nonorthogonal downlink transmission code-division multiple-access (CDMA) communication systems. In addition to the popular sum-power criterion, we introduce three novel criteria that better take into account the network requirements and particularly the separate power limit of each of the BSs. In our model, we allow each user to be connected to more than one BS and show that such a scheme can improve the system capacity (while currently this mechanism is only used to support soft handoff). Nevertheless, we show that in any optimal solution, the actual number of connections used by each user is small. We propose a unified approach to optimization, which allows the use of linear programming (LP)-based iterative algorithms to solve all of the presented optimization problems. The proposed scheme is verified by simulations for several scenarios. The simulation results show that by taking into account the power constraints of each of the BSs, the system capacity can be increased by up to 33%. The results can also approximate the performance of orthogonal CDMA networks with precision that increases with system bandwidth. [ABSTRACT FROM AUTHOR]

In this paper, we present a theory of quantum serial turbo codes, describe their iterative decoding algorithm, and study their performances numerically on a depolarization channel. Our construction offers several advantages over quantum low-density parity-check (LDPC) codes. First, the Tanner graph used for decoding is free of 4-cycles that deteriorate the performances of iterative decoding. Second, the iterative decoder makes explicit use of the code's degeneracy. Finally, there is complete freedom in the code design in terms of length, rate, memory size, and interleaver choice. We define a quantum analogue of a state diagram that provides an efficient way to verify the properties of a quantum convolutional code, and in particular, its recursiveness and the presence of catastrophic error propagation. We prove that all recursive quantum convolutional encoders have catastrophic error propagation. In our constructions, the convolutional codes have thus been chosen to be noncatastrophic and nonrecursive. While the resulting families of turbo codes have bounded minimum distance, from a pragmatic point of view, the effective minimum distances of the codes that we have simulated are large enough not to degrade the iterative decoding performance up to reasonable word error rates and block sizes. With well-chosen constituent convolutional codes, we observe an important reduction of the word error rate as the code length increases. [ABSTRACT FROM AUTHOR]

This paper deals with beamforming in wireless relay networks with perfect channel information at the relays, receiver, and transmitter if there is a direct link between the transmitter and receiver. It is assumed that every node in the network has its own power constraint. A two-step amplify-and-forward protocol is used, in which the transmitter and relays not only use match filters to form a beam at the receiver but also adaptively adjust their transmit powers according to the channel strength information. For networks with no direct link, an algorithm is proposed to analytically find the exact solution with linear (in network size) complexity. It is shown that the transmitter should always use its maximal power while the optimal power of a relay can take any value between zero and its maxima. Also, this value depends on the quality of all other channels in addition to the relay's own. Despite this coupling fact, distributive strategies are proposed in which, with the aid of a low-rate receiver broadcast, a relay needs only its own channel information to implement the optimal power control. Then, beamforming in networks with a direct link is considered. When the direct link exists during the first step only, the optimal power control is the same as that of networks with no direct link. For networks with a direct link during the second step only and both steps, recursive numerical algorithms are proposed. Simulation shows that network beamforming achieves the maximal diversity order and outperforms other existing schemes. [ABSTRACT FROM AUTHOR]

Abstract-This paper presents a modulation technique based on the generalized pulsewidth-modulation strategy for matrix converters. The proposed technique uses a discontinuous modulation to clamp each output leg of the converter during 1200 of the output voltage period, achieving a reduced number of switchings compared with the traditional modulation techniques. Aside from that, the major attraction of the proposed technique is an additional algorithm that lags the clamping of each output leg of the converter to synchronize it with the peak of the corresponding output current (load current), avoiding high switching losses (switching at high currents). Therefore, this technique reduces the number of switchings as well as guarantees only medium and low current switchings. Simulation and experimental results show the efficiency of the proposed technique. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new maximum-likelihood (ML) target localization approach which uses quantized sensor data as well as wireless channel statistics in a wireless sensor network. The novelty of our approach comes from the fact that statistics of imperfect wireless channels between sensors and the fusion center along with some physical layer design parameters are incorporated in the localization algorithm. We call this approach "channel-aware target localization." ML target location estimators are derived for different wireless channel models and receiver architectures. Furthermore, we derive the Cramér-Rao lower bounds (CRLBs) for our proposed channel-aware ML location estimators. Simulation results are presented to show that the performance of the channel-aware ML location estimators are quite close to their theoretical performance bounds even with relatively small number of sensors and their performance is superior compared to that of the channel-unaware ML estimators. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new robust congestion- control strategy for a differentiated-services (DiffServ) network. A sliding-mode-based variable structure control technique is utilized to design a congestion controller on the basis of a fluid flow model. The performance of our proposed scheme is then demonstrated on a DiffServ network. Our proposed algorithm is developed for an interconnected network as a hop-by-hop distributed-control approach. A formal analysis of the controlled network is conducted, where it is shown that the error dynamics of the overall time-delay-dependent system is ℒ∞ stable. Numerical simulations are provided to demonstrate the capabilities of our proposed methodology. [ABSTRACT FROM AUTHOR]

A linear frequency-modulated continuous-wave (LFMCW) synthetic aperture radar (SAR) is a highly miniaturized SAR that is suitable for use on a small unmanned aerial vehicle. In squint stripmap mode, the LFMCW SAR image quality is degraded by the presence of nonlinearities in the transmitted signal, combined with the large bandwidth introduced by the frequency scaling in the nonlinear frequency scaling (NLFS) algorithm. This paper proposes an extension of the NLFS algorithm called the Doppler-centroid-dependent frequency scaling algorithm. The algorithm introduces a new Doppler-centroid-dependent scaling factor to decrease the introduced bandwidth, effectively removing the range frequency aliasing, without extra computational load. Nonlinearity correction is performed at the same time as range cell migration correction. The validity of the proposed algorithm is demonstrated with point target simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel way of performing real-valued optimization in the complex domain. This framework enables a direct complex optimization technique when the cost function satisfies the Brandwood's independent analyticity condition. In particular, this technique has been used to derive three algorithms, namely, kurtosis maximization using gradient update (KM-G), kurtosis maximization using fixed-point update (KM-F), and kurtosis maximization using Newton update (KM-N), to perform the complex independent component analysis (ICA) based on the maximization of the complex kurtosis cost function. The derivation and related analysis of the three algorithms are performed in the complex domain without using any complex-real mapping for differentiation and optimization. A general complex Newton rule is also derived for developing the KM-N algorithm. The real conjugate gradient algorithm is extended to the complex domain similar to the derivation of complex Newton rule. The simulation results indicate that the fixed-point version (KM-F) and gradient version (KM-G) are superior to other similar algorithms when the sources include both circular and noncircular distributions and the dimension is relatively high. [ABSTRACT FROM AUTHOR]

Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient fastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most one of them is Gaussian, and each is independent and identically distributed (i.i.d.) in time. Likewise, weights-adjusted second-order blind identification (WASOBI) is asymptotically optimal when all the sources are Gaussian and can be modeled as autoregressive (AR) processes with distinct spectra. Nevertheless, real-life mixtures are likely to, contain both Gaussian AR and non-Gaussian i.i.d. sources, rendering WASOBI and EFICA severely suboptimal. In this paper, we propose a novel scheme for combining the strengths of EFICA and WASOBI in order to deal with such hybrid mixtures. Simulations show that our approach outperforms competing algorithms designed for separating similar mixtures. [ABSTRACT FROM AUTHOR]

While the theory of estimation of monocomponent polynomial phase signals is well established, the theoretical and methodical treatment of multicomponent polynomial phase signals (mc-PPSs) is limited. In this paper, we investigate several aspects of parameter estimation for mc-PPSs and derive the Cramér-Rao bound. We show the limits of existing techniques and then propose a nonlinear least squares (NLS) approach. We also motivate the use the Nelder-Mead simplex algorithm for minimizing the nonlinear cost function. The slight increase in computational complexity is a tradeoff for improved mean square error performance, which is evidenced by simulation results. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm that acquires the intermediate goals between the initial and goal states is proposed for an agent executing multiple tasks. We demonstrate the algorithm in the problem of rearranging multiple objects. The result shows that the moving distance to transfer the entire objects to their goal configuration is 1/15 of that without using intermediate goals. We experiment using a real robot to confirm that the intermediate goal can be adapted to a real environment. Our experimental results showed that an agent could adapt the intermediate goals, which were acquired in the simulation, to the experimental environment. [ABSTRACT FROM AUTHOR]

Computation of B1 fields is a key issue for determination and correction of intensity nonuniformity in magnetic resonance images. This paper presents a new method for computing transmitted and received B1 fields. Our method combines a modified MRI acquisition protocol and an estimation technique based on the Levenberg-Marquardt algorithm and spatial filtering. It enables accurate estimation of transmitted and received B1 fields for both homogeneous and heterogeneous Objects. The method is validated using numerical simulations and experimental data from phantom and human scans. The experimental results are in agreement with theoretical expectations. [ABSTRACT FROM AUTHOR]

A new class of variable-structure (VS) algorithms for multiple-model (MM) estimation is presented, referred to as expected-mode augmentation (EMA). In the EMA approach, the original set of models is augmented by a variable set of models intended to match the expected value of the unknown true mode. These models are generated adaptively in real time as (globally or locally) probabilistically weighted sums of mode estimates over the model set This makes it possible to cover a large continuous mode space by a relatively small number of models at a given accuracy level. The paper presents new theoretical results for model-set design, a general formulation of the EMA approach, along with theoretical analysis and justification, and three algorithms for its practical implementation. The performance of the proposed EMA algorithms is evaluated via simulation of a generic maneuvering target tracking problem. [ABSTRACT FROM AUTHOR]

Transmission-line modeling (TLM) is an established numerical simulation method for electromagnetics that has been employed in a wide variety of application areas. To date, TLM has been formulated primarily for structured (usually Cartesian) meshes. This paper presents and illustrates the application of a new TLM algorithm suitable for use with unstructured two-dimensional meshes based upon triangular elements. [ABSTRACT FROM AUTHOR]

This paper studies the performance of Non-persistent CSMA/CA protocols with Exponential Backoff scheduling algorithms. A multi-queue single-server system is proposed to model multiple access networks. The input buffer of each access node is modeled as a Geo/G/1 queue, and the service time distribution of head-of-line packets is derived from the Markov chain of underlying scheduling algorithm. The main results include the complete analysis of the throughput and delay distribution, from which we derived the characteristic equation of network throughput and obtained stable regions with respect to the throughput and bounded mean delay of the Exponential Backoff scheme. We show that the stable throughput region of Exponential Backoff can be obtained even for an infinite population. Since the variance of service time of Exponential Backoff can be unbounded due to the capture effect; thus, its bounded delay region is only a sub-set of its stable throughput region, and the maximum achievable throughput of the network within this region is slightly smaller than the absolute maximum throughput. Analytical results presented in this paper are all verified by simulation. [ABSTRACT FROM PUBLISHER]

This paper proposes large-scale transient stability simulation based on the massively parallel architecture of multiple graphics processing units (GPUs). A robust and efficient instantaneous relaxation (IR)-based parallel processing technique which features implicit integration, full Newton iteration, and sparse LU-based linear solver is used to run the multiple GPUs simultaneously. This implementation highlights the combination of coarse-grained algorithm-level parallelism with fine-grained data-parallelism of the GPUs to accelerate large-scale transient stability simulation. Multithreaded parallel programming makes the entire implementation highly transparent, scalable, and efficient. Several large test systems are used for the simulation with a maximum size of 9,984 buses and 2,560 synchronous generators all modeled in detail resulting in matrices that are larger than 20\,000\times 20\,000. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel call admission control scheme named overlap area assisted new call blocking scheme (NCBS-OA) for High Altitude Platform (HAP) based communications systems, which utilizes both the geographical information and the overlap area to intelligently decide when to block a new call to help avoid possible handover failure. An approximate analytical model is derived to estimate the new call blocking performance of the new scheme, which is verified by simulation results. Research results show that the NCBS-OA can improve the new call blocking probability significantly (by a minimum of 25% for the system model with the parameter values chosen) while maintaining a zero handover call dropping probability seen with the time-based channel reservation algorithm (TCRA), and that the greater the cell radius and the more the number of connections considered in the overlap area of a virtual cell, the better the new call blocking performance. [ABSTRACT FROM AUTHOR]