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Gas-insulated equipment such as GIS (Gas-Insulated Substation) and GIL (Gas-Insulated transmission lines) faces critical insulation failure challenges caused by particles inside. However, the particle traps, as key devices available to inhibit the movement of particles, lack theoretical basis and quantitative methodology for trap design especially with high voltage ratings. In this paper, a particle motion excitation and observation platform is established to physically simulate the actual online operating conditions of HVAC/HVDC GIS/GIL and study the capture mechanism of particle traps. Experimental results illustrate that, the collision motion style is a principal factor to affect the particle capturing capability, and the capture behavior of AC and DC particle traps can be classified into three separate modes, depending on variation of the collision process which can be characterized by random probability and the lognormal distribution rebound angle. A particle trajectory simulation model incorporating motion randomness is thereby presented to realize quantitative analysis and further optimize the trap parameters. A hazard index and minimum collision times are defined and calculated to quantitatively evaluate the particle collision effect on trapping capability. Further, a novel trap design topology is proposed to enhance the anti-motion efficiency of the particle traps. Targeted at high objective capture probability, the particle trap parameters are optimized to reduce the hazard index and improve the minimum collision times by the Monte Carlo scheme. The obtained results indicated that, the capture probability of AC traps can reach 86.5% at a gap spacing of 5 mm, while that of DC traps can reach 74.1% with an 8 mm-width slot and a 50-degree inclination angle. Verification of the novel article trap is implemented on a test rig, which gives an increased capture probability by 30% compared with the traditional trap design. The proposed new trap topology and corresponding methodology present quantitative guidance for particle traps optimization and render application potential for different voltage levels. [ABSTRACT FROM AUTHOR]

This papers presents a numerical approach based on computational fluid dynamics (CFD) that was developed to ensure that gravity oil-water separators at Hydro-Québec electrical substations comply with the environmental legislation in place. More precisely, multiphase simulations are performed for two operating conditions of an in service separator, and an experimental setup is used to produce an oil spill and to measure the time-varying oil concentration at the separator outlet. The results show that, for the tested conditions, the oil droplet size at a separator inlet generally follows a log-normal distribution and that the droplet diameter varies between 100 and 1000 µm with a mean value of 475 µm. Furthermore, both the experimental and numerical results show that the time required for the oil droplets to reach the separator outlet is quite shorter than the theoretical mean residence time. Finally, this study demonstrates that the numerical model predicts the separator efficiency within 1% of the measured value, and that it can be a valuable tool to investigate existing separators or to design new ones. [ABSTRACT FROM AUTHOR]

Pneumoconiosis staging has been a very challenging task, both for certified radiologists and computer-aided detection algorithms. Although deep learning has shown proven advantages in the detection of pneumoconiosis, it remains challenging in pneumoconiosis staging due to the stage ambiguity of pneumoconiosis and noisy samples caused by misdiagnosis when they are used in training deep learning models. In this article, we propose a fully deep learning pneumoconiosis staging paradigm that comprises a segmentation procedure and a staging procedure. The segmentation procedure extracts lung fields in chest radiographs through an Asymmetric Encoder-Decoder Network (AED-Net) that can mitigate the domain shift between multiple datasets. The staging procedure classifies the lung fields into four stages through our proposed deep log-normal label distribution learning and focal staging loss. The two cascaded procedures can effectively solve the problem of model overfitting caused by stage ambiguity and noisy labels of pneumoconiosis. Besides, we collect a clinical chest radiograph dataset of pneumoconiosis from the certified radiologist's diagnostic reports. The experimental results on this novel pneumoconiosis dataset confirm that the proposed deep pneumoconiosis staging paradigm achieves an Accuracy of 90.4%, a Precision of 84.8%, a Sensitivity of 78.4%, a Specificity of 95.6%, an F1-score of 80.9% and an Area Under the Curve (AUC) of 96%. In particular, we achieve 68.4% Precision, 76.5% Sensitivity, 95% Specificity, 72.2% F1-score and 89% AUC on the early pneumoconiosis ‘stage-1’. [ABSTRACT FROM AUTHOR]

A Bayesian approach based on the vulnerability distribution is proposed to estimate the confidence limits of the state probability and the threat level of multistate electronic systems interfered by intentional electromagnetic interference (IEMI). The vulnerability distribution is used to describe the state probability function of the multi-state system (MSS) for a given IEMI threat level. When a small number of test samples are under a specific threat level and prior MSS information is known, the posterior estimation of the state probability function can be obtained based on Bayesian theory. Furthermore, to effectively apply the state probability function to assess the vulnerability of the MSS, the uncertainty of the state probability function is discussed. Considering state probabilities under the same threat level as random variables, all these state probabilities also follow the Dirichlet distribution. Thus, the confidence limits of the posteriori state probability and the threat level can be inferred by the marginal distribution of the state probability, which is induced by combining the Dirichlet and vulnerability distributions. Finally, a case study is given to demonstrate the details of the calculation process of the vulnerability distribution, the state probability function, and the confidence limits under a lognormal distribution. [ABSTRACT FROM AUTHOR]

Disordered complex networks are of fundamental interest in statistical physics, and they have attracted recent interest as stochastic models for information transmission over wireless networks. While mathematically tractable, a network based on the regulation Poisson point process model offers challenges vis-a-vis network efficiency. Strongly correlated alternatives, such as networks based on random matrix spectra (the Ginibre network), on the other hand offer formidable challenges in terms of tractability and robustness issues. In this work, we demonstrate that network models based on random perturbations of Euclidean lattices interpolate between Poisson and rigidly structured networks, and allow us to achieve the best of both worlds: significantly improve upon the Poisson model in terms of network efficacy measured by the Signal to Interference plus Noise Ratio (abbrv. SINR) and the related concept of coverage probabilities, at the same time retaining a considerable measure of mathematical and computational simplicity and robustness to erasure and noise. We investigate the optimal choice of the base lattice in this model, connecting it to the celebrated problem optimality of Euclidean lattices with respect to the Epstein Zeta function, which is in turn related to notions of lattice energy. This leads us to the choice of the triangular lattice in 2D and face centered cubic lattice in 3D, whose Gaussian perturbations we consider. We provide theoretical analysis and empirical investigations to demonstrate that the coverage probability decreases with increasing strength of perturbation, eventually converging to that of the Poisson network. In the regime of low disorder, our studies suggest an approximate statistical behaviour of the coverage function near a base station as a log-normal distribution with parameters depending on the Epstein Zeta function of the lattice, and related approximate dependencies for a power-law constant that governs the network coverage probability at large thresholds. In 2D, we determine the disorder strength at which the perturbed triangular lattice (abbrv. PTL) and the Ginibre networks are the closest measured by comparing their network topologies via a comparison of their Persistence Diagrams in the total variation as well as the symmetrized nearest neighbour distances. We demonstrate that, at this very same disorder, the PTL and the Ginibre networks exhibit very similar coverage probability distributions, with the PTL performing at least as well as the Ginibre. Thus, the PTL network at this disorder strength can be taken to be an effective substitute for the Ginibre network model, while at the same time offering the advantages of greater tractability both from theoretical and empirical perspectives. [ABSTRACT FROM AUTHOR]

The mixture of wireless and power line communications (PLC) is vital for implementing new applications in smart grid and vehicular communications. In this work, we investigate the performance of non-orthogonal multiple access (NOMA) based dual-hop hybrid communication systems with decode-and-forward relay. The wireless channel is characterized by Nakagami-m fading under an additive white Gaussian noise (AWGN), while the PLC channels are characterized by Log-normal distribution with Bernoulli Gaussian noise including both background and impulsive noise components. New closed-form expressions for the outage probability, the asymptotic outage probability and ergodic capacity are derived and verified via extensive representative simulations. For more insights on the outage performance, we analyze the diversity order. Additionally, we proposed a power allocation optimization technique to achieve an outage-optimal performance. The results show that the system outage probability improves as the impulsive noise index and the arrival probability of the impulsive component of the PLC additive noise decrease, while their effect is negligible on the ergodic capacity. Finally, the performance of the proposed system is compared against a benchmark OMA-based system. [ABSTRACT FROM AUTHOR]

This article deals with the problem of detecting a marine target with coherent radars in a correlated heavy-tailed sea clutter background. The heavy-tailed sea clutter is modeled by a compound-Gaussian model, and the clutter texture is characterized by the lognormal distribution with a new parameterization form. We develop an adaptive coherent detector on the basis of the two-step generalized likelihood ratio test. The proposed detector can achieve adaptation to sea clutter characteristics by using the maximum a posterior estimate of the clutter texture, the constrained approximate maximum likelihood estimator of the speckle covariance matrix, and the proposed negative- and positive-fractional moment estimate of amplitude parameters of sea clutter. Remarkably, the proposed detector inherently ensures a constant false alarm rate with respect to the clutter power mean and the speckle covariance matrix. Finally, numerical experiments using simulated data and real radar data demonstrate that the proposed estimator and adaptive coherent detector outperform their respective competitors. [ABSTRACT FROM AUTHOR]

This article presents a radar cross-section (RCS)-based statistical recognition system for identifying/classifying unmanned aerial vehicles (UAVs) at microwave frequencies. First, the article presents the results of the vertical (VV) and horizontal (HH) polarization RCS measurement of six commercial UAVs at 15 and 25 GHz in a compact range anechoic chamber. The measurement results show that the average RCS of the UAVs depends on shape, size, material composition of the target UAV as well as the azimuth angle, frequency, and polarization of the illuminating radar. Afterward, radar characterization of the target UAVs is achieved by fitting the RCS measurement data to 11 different statistical models. From the model selection analysis, we observe that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively poorly. The best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. The performance of the UAV statistical recognition system is evaluated at different signal noise ratio (SNR) with the aid of Monte Carlo analysis. At an SNR of 10 dB, the average classification accuracy of 97.60% or better is achievable. [ABSTRACT FROM AUTHOR]

A model of exchange bias in a polycrystalline bilayer based on thermal fluctuations was extended to account for an arbitrary magnetization angle of the ferromagnetic (FM) layer. It allows for a more accurate description of the thermally activated reversal of the Néel vector in antiferromagnetic (AFM) grains since the extended model takes into account the variation of barrier height resulting from the magnetization angle of the FM layer. Field-annealing (setting of the exchange bias) and further relaxation of the FM/AFM bilayer was simulated using a sequence of iterations based on this model. An array of AFM grains is generated based on a lognormal distribution of their volume. Chosen parameters of distribution are based on the fit to the experimentally measured temperature dependence of exchange bias in sputter-deposited CoFe/IrMn films. The angular deviation of the exchange bias and the degradation of its amplitude in an applied field, with relative angle varied from 0° to 180°, are calculated. Hysteresis of the exchange bias with field exposure is observed. A temperature–time ($T$ – $\tau$) diagram of the deviation angle, predicting the stability of the exchange bias under specific temperature and time, is presented. [ABSTRACT FROM AUTHOR]

We present a novel statistical model, the generalized-Gaussian–Rician (GG-Rician) distribution, for the characterization of synthetic aperture radar (SAR) images. Since accurate statistical models lead to better results in applications such as target tracking, classification, or despeckling, characterizing SAR images of various scenes including urban, sea surface, or agricultural is essential. The proposed statistical model is based on the Rician distribution to model the amplitude of a complex SAR signal, the in-phase and quadrature components of which are assumed to be generalized-Gaussian (GG) distributed. The proposed amplitude GG-Rician model is further extended to cover the intensity of SAR signals. In the experimental analysis, the GG-Rician model is investigated for amplitude and intensity SAR images of various frequency bands and scenes in comparison to state-of-the-art statistical models that include Weibull, $\mathcal {G}_{0}$ , Generalized gamma, and the lognormal distribution. The statistical significance analysis and goodness-of-fit test results demonstrate the superior performance and flexibility of the proposed model for all frequency bands and scenes, and its applicability on both amplitude and intensity SAR images. [ABSTRACT FROM AUTHOR]

For outdoor millimeter-wave mobile radio systems, the main service area is expected to be in line-of-sight (LOS) and semi-line-of-sight (SLOS) environments. The modeling of propagation in a mixed LOS/SLOS environment is thus important in system design. For modeling propagation in an SLOS environment, the application of the Loo model developed for land mobile satellite service with roadside shadowing is promising because this model reflects the physical mechanism of radio propagation. However, the model is inconvenient in practice because its calculation involves numerical integration over an infinite range. In this article, we approximate the Loo distribution using three basic distributions, namely the Nakagami-Rice distribution (or Rician distribution), Nakagami-m distribution, and lognormal distribution. Based on the evaluation results, we propose the use of the Nakagami-m distribution in place of the Loo distribution. We demonstrate the conversion of Loo distribution parameters to Nakagami-m distribution parameters. Finally, we examine a multistate model that expresses environments composed of LOS, SLOS, and non-LOS conditions. [ABSTRACT FROM AUTHOR]

This paper presents an approach to determine the DGA warning values for a transformer experimental platform by using the normal data from on-line DGA. In order to collect normal data and PD-defect data, an experimental platform of a 10 kV transformer with a capacity of 100 kVA is established for long-term operation. Five sets of DGA data are obtained, 1 set from the normal data, 2 sets from the PD-defect in developing-discharge stage (D1-1) and 2 sets from PD-defect in severe-discharge stage (D1-2). All sets of data are analyzed and found better suitable for the lognormal distribution, in which there are 2 parameters, µ and σ. It is found that the position parameters µ for 5 sets of data are nearly linear with the PD values and the scale parameters σ for 5 sets of data are nearly equal for certain discharges. Therefore, PD-defect data distribution can be approximately deduced from the normal data. In general, there is a common domain between the distributions of either PD-defect data and normal data or D1-1 and D1-2. A ROC curve, widely used in medical diagnosis, is employed to determine the warning values in common domain data. The optimal cut-point in common domain between the normal data and the deductive data of D1-1 is chosen as the Typical Value, and the cut-point between the deductive data of D1-1 and that of D1-2 is chosen as the Alarm Value. [ABSTRACT FROM AUTHOR]

Usually, researchers adopt Weibull distribution as the best fitting lifetime model of several electronics/ optoelectronics devices, including organic light-emitting diodes (OLEDs). They justify the adoption based on a few statistical goodness-of-fit (GoF) tests, which are not always applicable due to violating some of the assumptions of the tests. In this article, several standard and valid GoF tests show that the lognormal distribution is a better fitting model than the Weibull distribution for OLEDs and performs better when a censoring scheme is applied. [ABSTRACT FROM AUTHOR]

The impact of linear correlation between lognormal distribution grain size mean and sigma along the polysilicon channel on threshold voltage (Vth) variability has been investigated in three dimensional (3D) NAND flash. The variety of grain size mean and sigma results in the unstable Vth variability. To obtain a stable Vth distribution with various grain size mean, the grain size mean dependent Vth variability sensitivity to the grain size sigma was used to optimize the linear correlation between grain size mean and sigma via TCAD simulation. The optimized linear correlation with stable Vth variability is obtained except for the “unbalance region” affected by the combination of grain boundaries and positions with these grain size mean and sigma values resulting in the slightly shrinking Vth variability. Our results strongly suggest that this approach could guide the direction of polysilicon crystallization optimization to obtain stable Vth distribution with the predicted linear correlation between grain size mean and sigma.

Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that traffic obeys the log-normal distribution which is a better fit than the Gaussian distribution commonly claimed in the literature. We also investigate an alternative heavy-tailed distribution (the Weibull) and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which exhibit a poor fit for all distributions tried and show that this is often due to traffic outages or links that hit maximum capacity. We demonstrate that the data we look at is stationary if we consider samples of 15-minute long or even 1-hour long. This gives confidence that we can use the distributions for estimation and modelling purposes. We demonstrate the utility of our findings in two contexts: predicting that the proportion of time traffic will exceed a given level (for service level agreement or link capacity estimation) and predicting 95th percentile pricing. We also show that the log-normal distribution is a better predictor than Gaussian or Weibull distributions in both contexts. [ABSTRACT FROM AUTHOR]

In this article, the random vibration test and finite-element simulation analysis of SAC305 solder joints in BGA packaged electronic chips were carried out. A failure model for BGA solder joints was established based on the life data. And vulnerable parts in electronic chips were analyzed. First, the random vibration test was conducted. Second, a lognormal distribution model of solder joints by means of the life data was established and analyzed based on the fracture mechanics theory. Finally, the finite-element simulation model of BGA packaged electronic chip was established and discussed. Failure characteristics of solder joints at different positions in electronic chips were compared and the failure mode of the solder joint was discussed using the simulation result and the observation result with scanning electron microscope (SEM). The results show that the life of each single solder joint in BGA packaged electronic chips follows a logarithmic normal distribution under the random vibration. Solder balls located at the four outermost corners of the chip are subjected to the greatest stress, which are the weakest parts in electronic chips. Cracks in solder joints are generated near the package side and emerged from the edge of the intermetallic compound (IMC) layer with the largest curvature. [ABSTRACT FROM AUTHOR]

This paper analyses the experimentally-assessed dual-polarized (DP) mobile channel in a tunnel environment at 1.35 GHz under traffic conditions. We investigate the impact of antenna polarization and radiation pattern on the non-stationary vehicle-to-infrastructure (V2I) channel. Basic channel evaluation metrics are examined including path gain, co-polarization ratio (CPR), and cross-polarization discrimination (XPD). In addition, the stationarity region is estimated using the channel correlation function approach, and used to calculate the time-varying delay and Doppler power profiles. Statistical models are presented for parameters like CPR, XPD, RMS delay and Doppler spreads, where the lognormal distribution provides the best fit. The polarization and the opening angle of the antennas into the propagation channel are found to strongly influence the observed non-stationarity of the channel. They impact the degree of multipath richness that is captured, thus providing different path gain, delay and Doppler spreads. Based on our analysis, the directional antenna with vertical polarization provides the longest stationarity time of 400 ms at 90 km/h, as well as the highest path gain and lowest dispersion. Furthermore, the DP channel capacity is calculated and its dependence on different normalization approaches is investigated. We propose a more accurate normalization for the DP channels that takes the conservation of energy into account. Moreover, the subchannels correlation coefficients are determined. While the condition number is found to be low on average, the correlation results show high correlation among the DP subchannels. As conclusion, we show how the CPR and XPD play the main role in providing multiplexing gain for DP tunnel channels. [ABSTRACT FROM AUTHOR]

This article proposes a new fastener model and validate the stochastic process for the lightning current assessment in the aircraft assemblies proposed in the previous parts. In particular, the stochastic method concerns the finite-difference time domain (FDTD) fastener modeling for which the variability of the contact resistance has to be taken into account. The principle is to merge an electromagnetic fastener model with an uncertainty model. The new fastener model proposed in this article is a wire with a lumped resistor on a low conductive plate. This model is an improvement of the model of Part I, where the fastener is only represented by a wire with a lumped resistor. The proposed model in this article is able to ensure a good shielding effectiveness without current distribution disruptions provided that the conductivity of the plate is weak in contrast to the lumped resistor. Moreover, it has the benefits to be able to represent any fastener without location constraint in the FDTD grid. A log-normal distribution has been previously established as uncertainty model that represents the variability of the resistive parameter. The fastener model is included in the FDTD modeling of a realistic fuel tank with each lumped resistor taking a probabilistic value according to the log-normal law. To take into account, the variability of resistors, several simulations with different sets of resistance values are performed. The aim is to surround current measurements with simulated currents at various locations of the fuel tank for a lightning injection performed directly on a fuel tank fastener. This article demonstrates that a specific attention must be given to the modeling of fasteners close to the lightning injection point for the current assessment. Moreover, the results highlight the relevance of the use of an uncertainty model and its reliability for the lightning current assessment. [ABSTRACT FROM AUTHOR]

Motivated by an accelerated degradation test (ADT) on the power gain of microwave power amplifiers, in this article we propose a model-ranking approach for the estimation of some important reliability characteristics. Different degradation models and statistical lifetime distributions are applied to model the data obtained from the ADT. We study the effect of model misspecification in estimating the reliability characteristics when the behavior of the degradation process and the underlying degradation-data-generating mechanism are unknown. We then propose a model-ranking approach with weighted estimation procedures when multiple candidate models are under consideration. Through a Monte Carlo simulation study, we show that the proposed approach is robust and insensitive to model misspecification. Finally, the ADT data from the motivating example are used to illustrate the proposed methodologies. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new statistical model parameter extraction method for compact power device models. While existing parameter extraction methods are only applicable for the parameters that follow normal distributions, the proposed method can simultaneously incorporate the model parameters that follow lognormal distributions. In addition, a set of dominant model parameters, which largely contributes to the characteristic variation of power MOSFETs, has been studied on the basis of the proposed statistical parameter modeling. Through the numerical analysis upon measured drain currents of planar and trench SiC power MOSFETs, we demonstrate that the fluctuation of the current characteristics can be represented by a small number of dominant parameters. In our example, threshold voltage and current scaling factor are identified to be particularly important to approximate the fluctuation of the current characteristics in both structures. [ABSTRACT FROM AUTHOR]

High availability is one of the core properties of Infrastructure as a Service (IaaS) and ensures that users have anytime access to on-demand cloud services. However, significant variations of workflow and the presence of super-tasks, mean that heterogeneous workload can severely impact the availability of IaaS clouds. Although previous work has investigated global queues, VM deployment, and failure of PMs, two aspects are yet to be fully explored: one is the impact of task size and the other is the differing features across PMs such as the variable execution rate and capacity. To address these challenges we propose an attribute-based availability model of large scale IaaS developed in the formal modeling language CARMA. The size of tasks in our model can be a fixed integer value or follow the normal, uniform or log-normal distribution. Additionally, our model also provides an easy approach to investigating how to arrange the slack and normal resources in order to achieve availability levels. The two goals of our work are providing an analysis of the availability of IaaS and showing that the use of CARMA allows us to easily model complex phenomena that were not readily captured by other existing approaches. [ABSTRACT FROM AUTHOR]

This article presents details about an extensive channel measurement campaign and subsequent statistical channel models for the characterization of 300 GHz channels for wireless rack-to-rack (R2R) and blade-to-blade (B2B) communications in a data center-like environment. Measurements were conducted in various scenarios such as R2R line-of-sight (LoS), R2R obstructed-LoS (OLoS), R2R reflected-non-LoS (RNLoS), R2R obstructed-RNLoS (ORNLoS), B2B RNLoS, B2B ORNLoS, and B2B LoS scenarios. In the aforementioned scenarios, we explored the impact of transmitter (Tx)/receiver (Rx) misalignment and obstructions such as cables, metal cabinets, and mesh structures on terahertz (THz) propagation, as well as feasibility of using existing metal objects as reflectors for NLoS links. For the R2R LoS scenario, an optical lens was used to extend the Tx-Rx separation distance. This led to a waveguide effect in the channels measured thereby resulting into a path loss exponent (PLE) of 1.48 with a shadowing gain of 0.7 dB. When obstructions of cables are present, ORNLoS link outperforms OLoS link with 2.5 dB lower shadowing gain and weaker multipath. Reflector in the RNLoS link has reflection coefficients very close to 1 for all incident angles. For the B2B scenario, a dual-reflector THz transceiver rack system is proposed to enable wireless links across vertically stacked servers and allow easy maintenance and repair of servers. The measured path loss closely follows the Friis values in the LoS link and in the RNLoS link with hollow vertical ground plane. When obstructions of cables are present, the ORNLoS link experiences 5–10 dB higher path loss and on average 0.25 GHz lower coherence bandwidth than the RNLoS link. The measured statistical channel properties show that the shadowing gain caused by cable clusters follows the log-normal distribution. [ABSTRACT FROM AUTHOR]

This correspondence considers the inverse Gaussian distribution, which is a tractable and accurate alternative to the log-normal distribution that represents not only shadowing in wireless communications but also turbulence in free-space optical communications. We first derive the generalized moment generating function (G-MGF) of the inverse Gaussian distribution, and then make use of the derived formula to effectively analyze several performance metrics, such as area under the receiver operating characteristics (ROC) curve, average error rate, outage probability, etc. Finally, we provide some numerical results to corroborate the accuracy of our evaluations. [ABSTRACT FROM AUTHOR]

Polarimetric whitening filter (PWF) can be used to filter polarimetric synthetic aperture radar (PolSAR) images to improve the contrast between ships and sea clutter background. For this reason, the output of the filter can be used to detect ships. This paper deals with the setting of the threshold over PolSAR images filtered by the PWF. Two parameter-constant false alarm rate (2P-CFAR) is a common detection method used on whitened polarimetric images. It assumes that the probability density function (PDF) of the filtered image intensity is characterized by a log-normal distribution. However, this assumption does not always hold. In this paper, we propose a systemic analytical framework for CFAR algorithms based on PWF or multi-look PWF (MPWF). The framework covers the entire log-cumulants space in terms of the textural distributions in the product model, including the constant, gamma, inverse gamma, Fisher, beta, inverse beta, and generalized gamma distributions ($\text{G}\Gamma $ Ds). We derive the analytical forms of the PDF for each of the textural distributions and the probability of false alarm (PFA). Finally, the threshold is derived by fixing the false alarm rate (FAR). Experimental results using both the simulated and real data demonstrate that the derived expressions and CFAR algorithms are valid and robust. [ABSTRACT FROM AUTHOR]

It is challenging to quantify the left tail behavior of the cumulative distribution function of a sum of lognormal random variables, especially when they are correlated and non-identically distributed. The mathematical intractability hampers the analysis on diversity receptions over lognormal fading channels. In this paper, a novel asymptotic technique is exploited to study non-identically distributed dual-branch lognormal fading channels with correlation. Closed-form asymptotic outage probability expressions are derived for maximum-ratio combining (MRC), equal-gain combining (EGC), and selection combining (SC). Three counterintuitive facts are revealed: 1) one of the two channels contributes no asymptotic performance gain to the diversity reception systems under certain conditions, implying that a link can be discarded without causing asymptotic performance loss; 2) SC outperforms EGC under certain conditions; and 3) the outage probability of SC is arbitrarily close to that of MRC under certain conditions. The new result reveals insights into the long-standing problem of asymptotic analysis for correlated lognormal fading channels, paving the way for analysis on more general channel models. This paper also provides an efficient approach to evaluate the non-identically distributed lognormal fading channels at a high signal-to-noise ratio. Practically, the analytical results prevent some redundant units that contribute no performance gain. [ABSTRACT FROM AUTHOR]

In this paper, bias and CRB analysis for both proposed line-of-sight (LoS) and conventional radio-signal-strength (RSS) based positioning methods are derived. In contrast to the conventional lognormal based analysis, the derived error models take into account the environmental multipath fading effect. The proposed LoS-based scheme improves positioning accuracy by eliminating the effect of non-range dependent portion of powers from the received signal. It suppresses non-line-of-sight (NLoS) disturbance to the positioning result through estimating the LoS component from fading signal. Hence, it is robust to environmental fading disturbance. The derived error models are validated by simulation. Both analytical model and simulation results show that the accuracy and robustness of the proposed LoS-based positioning are superior to that of the conventional RSS-based methods. [ABSTRACT FROM AUTHOR]

Outage probability analysis of diversity reception over lognormal fading channels is challenging because the cumulative distribution function (CDF) of a sum of lognormal random variables (RVs) does not yield a closed-form expression. Furthermore, traditional asymptotic analysis techniques fail for lognormal fading channels. The well-applied approximation techniques, such as Fenton, Wilkinson, and Schwartz and Yeh approximations, become unreliable when the signal-to-noise ratio (SNR) is high. In this paper, we derive closed-form bounds of outage probability for dual-branch equal-gain combining system over non-identically distributed lognormal fading channels. The bounds are shown tight from low-to-high SNR regimes, which provides an efficient way to evaluate the EGC system without resorting to expensive Monte Carlo simulation or numerical integration. Furthermore, the new analytical expressions can be used to test at what SNR the existing approximation techniques become inaccurate, and can also be used as criteria for system design. [ABSTRACT FROM AUTHOR]

We perform a study on the buffer occupancy 1 The term buffer occupancy refers to the amount (or number) of data (or packets) present in the buffer. We shall be using the terms buffer occupancy and system size interchangeably as deemed fit. and its steady-state properties in a mobile opportunistic network (MON) for different forwarding strategies. Nodes in MON adhere to replication based strategy in order to ensure reliable delivery. Such replications occur in bulks during intermittent and very short contact events, which often lead to buffer congestion in the relay nodes, thereby affecting the network performance. The state-of-the-art on buffer management in MON relies on local information exchange to measure buffer occupancy, which leads to overhead. Therefore, to find the average buffer occupancy, we first study the aggregated bulk transfer size using real-life contact traces and find that it follows a log-normal distribution. Since the contact rate has been observed to follow a Poisson distribution, we model the packet arrival process in each node as a compound Poisson process. Furthermore, the departure process is modeled as packets dropped due to time-to-live expiry, which is shown to have an exponential distribution. Using the above model, we derive the steady-state probability distribution of buffer occupancy of an arbitrary node for three different classes of replication scheme, viz., epidemic routing, probabilistic routing, and time-varying probabilistic routing. The results of this paper help in assessing how fast a node buffer gets depleted under different routing algorithms, which will help in designing better buffer management techniques and routing algorithms. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communications have received a lot of attention as it can significantly improve efficiency and safety of road traffic. In general, V2V scenarios have some special features such as high mobility of transceivers, low antenna height, and small communication ranges, which significantly affect the propagation characteristics. Therefore, an accurate channel model is required to characterize V2V propagation channel. However, some important features of V2V channels have not been well characterized, for example, most existing V2V channel models tend to only consider the distribution of multipath components (MPCs) in a horizontal dimension, whereas ignoring vertical dimension, which is inconsistent with the distribution of MPCs in the actual channel. Moreover, the dynamic clusters of MPCs have not been well modeled in the existing V2V models. Therefore, in order to better model the V2V channel, a cluster-based three-dimensional (3D) channel model is proposed in this paper, which is based on the measurements conducted at 5.9 GHz in urban and suburban scenarios. In the proposed model, the distribution of MPCs clusters in both horizontal and vertical dimensions is considered. The space-alternating generalized expectation-maximization algorithm is used to extract MPCs, and the clustering and tracking algorithms are used to identify and track the clusters of dynamic MPCs. In the proposed model, all MPC clusters are divided into two categories: global-clusters and scatterer-clusters, and the distribution of the two clusters are characterized by a series of inter- and intra-cluster parameters. It is found that both the azimuth spread and the elevation spread follow the lognormal distribution. In addition, the power of MPCs within a cluster has the truncated-Gaussian distribution, whereas the angle of MPCs within a cluster has the Laplacian distribution. Finally, the accuracy of the model is verified by comparing the measurements and simulations. [ABSTRACT FROM AUTHOR]

Spectral efficiency analysis in presence of correlated desired and interfering signals is very important in densely deployed access points scenarios of modern generation wireless networks. However, most works available in literature either address the effect of correlated interfering signals or include interferer activity. Further, available literature has also addressed the effect of large-scale fading (shadowing and distance-dependent path loss) only however, they have fallen short of including the composite effect of line-of-sight and nonline-of-sight multipath small-scale fading. The correlation of desired signals with interfering signals because of shadowing has also not been considered in existing literature either. In this paper, we present a comprehensive analytical signal to interference power ratio evaluation framework addressing all the above-mentioned important propagation effects in a holistic manner. In this analysis, we extend and apply the moment generating function-matching method to such systems to achieve high accuracy. We compare the analytical results against realistic channel model based extensive Monte-Carlo simulation for millimeter Wave and sub-6 GHz in both indoor and outdoor scenarios. The performance of the model is depicted in terms of mean, $\alpha \%$ outage spectral efficiency, and Kullback–Leibler divergence and Kolmogorov–Smirnov distance. [ABSTRACT FROM AUTHOR]

It is challenging to derive closed-form performance expressions for composite channels considering both the large-scale lognormal fading and small-scale fading, thus most existing analyses on the composite channels are based on numerical approaches such as the Gauss–Hermite quadrature. In this paper, we develop a simple framework to achieve closed-form asymptotic expressions for the outage probabilities and the error rates of the composite lognormal-X fading channels, where the “X” can be Rayleigh, Rician, Nakagami- $m$ or any other fading channels that have finite diversity orders. It is proved that the lognormal fading contributes an exponential factor to the asymptotic expressions, and the exponential factor is related to the diversity order of the X channel and the lognormal parameters. The new analytical tool is shown to be versatile in evaluating the performance of radio-frequency communications suffering both the large-scale and small-scale fading, including diversity reception systems, relaying systems, and distributed antenna systems. It is also shown to be useful in evaluating free-space optical communication systems with pointing error, and in deriving asymptotic signal-to-noise ratio gaps between different modulation formats over lognormal fading channels. The elegant asymptotic expressions reveal insights into the composite fading channels and can be a criterion for various system designs. [ABSTRACT FROM AUTHOR]

In this paper, an analytical model for slow and fast fading effects in maritime container terminals is derived, from fitting distributions to the results of measurements performed in an actual operational environment. The proposed model is composed of a set of equations, enabling to evaluate fading statistical distribution parameters for different system and environments conditions, as a function of frequency, base station antenna height, and average height of container stacks in a deepwater container terminal. The model's coefficients were obtained from a multivariate linear regression of the fitted distributions parameters, allowing to evaluate the different statistical distribution parameters with a negligible mean error and a standard deviation of the error less than 0.65 dB. It is observed that a good fitting is obtained for slow fading with the Lognormal Distribution, the standard deviation ranging in [1.40, 4.81] dB, while fast fading is well modeled by the Nakagami Distribution with the standard deviation ranging in [0.43, 1.26]. Hence, in a conservative perspective, values of 5 and 1.3 dB are proposed to characterize slow and fast fading, respectively. [ABSTRACT FROM AUTHOR]

In order to fulfill different demands between ultralow energy consumption and high performance, integrated circuits are being designed to operate across a large range of supply voltages, in which resiliency to timing violation is the key requirement. Unfortunately, traditional timing analysis which focuses on setup timing tolerance for higher performance cannot model the hold violation efficiently across different voltages. In this paper, we proposed a complete flow of computationally efficient methodology for guaranteeing hold margin, which is particularly important for low-power devices, e.g., Internet-of-Things devices. Leveraging both the conventional static timing analysis and a most probable point (MPP) theory, we develop a new hold-timing closure methodology across voltages eliminating expensive Monte Carlo simulation. To improve the efficiency of locating MPP, a novel MPP search method is proposed that employs a set of approximation eliminating the time-consuming iterative search. Several novel modeling techniques, such as the incorporation of nonlinear behaviors of circuit operations and correlation coefficient modeling, are also proposed in this paper to significantly improve the accuracy of the design. With the proposed modeling techniques, a novel hold resilient design technique equipped with the proposed variation-aware timing resilient flip-flop is developed. The proposed design technique eliminates the excessive hold-fixing operation at low voltage and its associated performance degradation at high voltage, whereas still being compatible with the conventional design closure flow. Design example on a voltage-scalable digital signal processor was used to demonstrate the potential of the technique. The result in a 45-nm technology shows that the elimination of more than 20 000 hold buffers, 23% performance improvement at high voltages, and 7% area saving are achieved using the proposed technique compared with the conventional digital design technique. [ABSTRACT FROM AUTHOR]

Prior asymptotic performance analyses are based on the series expansion of the moment-generating function (MGF) or the probability density function (PDF) of channel coefficients. However, these techniques fail for lognormal fading channels because the Taylor series of the PDF of a lognormal random variable is zero at the origin and the MGF does not have an explicit form. Although lognormal fading model has been widely applied in wireless communications and free-space optical communications, few analytical tools are available to provide elegant performance expressions for correlated lognormal channels. In this paper, we propose a novel framework to analyze the asymptotic outage probabilities of selection combining (SC), equal-gain combining (EGC), and maximum-ratio combining (MRC) over equally correlated lognormal fading channels. Based on these closed-form results, we show: 1) the outage probability of EGC or MRC becomes an infinitely small quantity compared to that of SC at high signal-to-noise ratio (SNR); 2) channel correlation can cause an infinite performance loss at high SNR; and 3) negatively correlated lognormal channels can outperform the independent lognormal channels. The analyses reveal insights into the long-standing problem of asymptotic performance analyses over correlated lognormal channels, and circumvent the time-consuming Monte Carlo simulation and numerical integration. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a tractable semi-analytical approach for the network performance analysis of uplink (UL) cellular networks, which is based on a deterministic network analysis using a Gaussian approximation (DNA-GA). The key contribution of this paper is to investigate the UL signal-to-interference ratio (SIR) performance using the DNA-GA analysis. In particular, the SIR is modeled as a ratio of two random variables (RVs), representing the signal power and the aggregate interference power, respectively. The signal power is further characterized by a product of two RVs, i.e., a lognormal RV and an RV with an arbitrary distribution. The former RV comes from a common assumption of lognormal shadow fading, and the latter one takes the rest of random factors into account, such as random user positions, arbitrary types of multi-path fading, and so on. The aggregate interference power is approximated by an RV with a power lognormal distribution. The proposed DNA-GA analysis has several desirable features: 1) it naturally considers lognormal shadow fading; 2) it can treat arbitrary shape and/or size of cell coverage areas; 3) it can handle non-uniform user distributions; 4) it can cope with any type of multi-path fading; and 5) it can be applied to multi-antenna base stations. These features make the DNA-GA analysis very useful for the network performance analysis of the 5th generation systems with general cell deployment and user distribution. [ABSTRACT FROM AUTHOR]

Reliability analysis of multicomponent repairable systems with dependent component failures is challenging for two reasons. First, the failure mechanism of one component may depend on other components when considering component failure dependence. Second, imperfect repair actions can have accumulated effects on the repaired components and these accumulated effects are difficult to measure. In this paper, we propose a parametric statistical model to capture the failure dependence information with general component repair actions. We apply the maximum likelihood method to estimate the model parameters by utilizing the historical failure data. Statistical hypothesis tests are developed to determine the dependence structure of the component failures based on the proposed reliability model. The proposed methodology is demonstrated by a simulation study and case studies of a car body assembly process and a forklift vehicle system. [ABSTRACT FROM PUBLISHER]

A fast and stochastic analysis methodology for electromigration (EM) assessment of power distribution networks is presented in this paper. We examine the impact of variability on EM time-to-failure (TTF), considering altered current densities due to global/local process variations as well as the fundamental factors that cause the conventional EM TTF distribution. Through the novel variations-aware current density model based on Hermite polynomial chaos, we demonstrate significant margins in EM lifetime when compared with the traditional worst case approach. On the other hand, we show that the traditional approach is altogether incompetent in handling transistor-level local variations leading to significantly optimistic lifetime estimates for lower metal level interconnects of power delivery network. Subsequently, we attempt to bridge the conventional, component-level EM verification method to the system level failures, inspired by the extreme order statistics. We make use of asymptotic order models to determine the TTF for the $k$ th component failure due to EM, and demonstrate application of this approach in developing IR drop aware system-level failure criteria. [ABSTRACT FROM PUBLISHER]

We present a phenomenological theory of the log-normal statistics commonly observed in filamentary resistive memory and related devices. Based on the central limit theorem that statistics are shown to emerge regardless of the underlying material properties when the processes are dominated by thermal activation or tunneling. That takes place in particular for the read-out resistances in the high-resistive (OFF) state, and the random telegraph noise amplitudes, but can be observed in the low-resistive (ON) state as well. We show that the statistics of switching times becomes log-normal when the switching is due to the field induced nucleation. [ABSTRACT FROM AUTHOR]

In 1973, Goldstein introduced his log-t detector, which attained the constant false alarm rate property for a class of clutter models including the lognormal and Weibull distributions. This paper shows that under the assumption of Pareto-distributed clutter, false alarm regulation can also be attained. A modification of the log- t detector is shown to enhance its performance in terms of managing interference. [ABSTRACT FROM PUBLISHER]

We present a series of closed-form upper bounds of the differential entropy of univariate continuous distributions based on the maximum entropy principle. We apply those bounds to Gaussian mixture models, and study their tightness properties. [ABSTRACT FROM PUBLISHER]

As a vital component for a communication payload transponder, the reliability of a traveling wave tube (TWT) plays a significant role in the satellite payload configuration design. Decisions regarding the level of transponder redundancy required are highly dependent on the predicted failure rate of the TWT. Based on observation of on-orbit performance, the existing MIL-HDBK-217 failure rate prediction model is deemed to be too conservative for the TWT. Derivation of a more accurate TWT failure rate value based on on-orbit flight data is preferred. To account for uncertainty of the on-orbit data, the failure rate value is typically estimated to its upper confidence bound. Since the data could change over time, a direct estimate from the on-orbit data may fluctuate severely. To avoid such severe fluctuation, and recognizing that the space industry has performed such estimation over a quite lengthy period of time, this paper proposes a Bayesian posterior analysis approach for the TWT failure rate estimation. Its confidence interval and bounds are derived. Prior distribution is established based on historic TWT failure rate estimates and special cases with assumed prior distributions are discussed. Results of examples indicate clear benefits by using the Bayesian posterior analysis when additional failures are observed. To evaluate the impact of each prior distribution parameter to the posterior estimate, a sensitivity analysis is performed and results are discussed. [ABSTRACT FROM PUBLISHER]

A performance analysis of VDSL2 technologies in FttC or FttC/FttDp hybrid scenarios requires knowledge of the access network geometry, propagation, and crosstalk interference models. A simulation-based approach is commonly adopted for performance assessment. In this paper, we introduce a performance evaluation framework based on the log-normal approximation for the probabilistic description of the signal-to-interference plus noise ratio (SINR), which is then used to obtain the statistics of the achievable bit rate per user. A very good agreement has been observed between the exact and the approximated calculations of the bit rate up to 600 m from the Cabinet. In addition, the log-normal approximation has allowed to define a simple procedure, based on the equivalent number of co-located interferers, to evaluate the bit rate per user for generic access network geometry. Furthermore, two approximated formulas for bit rate have been obtained under high SINR and dominant far-end crosstalk (FEXT) conditions. They evidence the logarithmic decrease of the bit rate with the distance from the Cabinet and provide the accurate results for distances up to 400 m. Finally, when FEXT fluctuations are modeled in accordance with Beta distribution, we have observed the log-normal approximation still provides valid probabilistic description of the SINR. [ABSTRACT FROM AUTHOR]

In this paper, we investigated how magnetic field influenced Trichel discharges by performing a statistical analysis of the current amplitude and pulse interval of Trichel discharges at various magnetic field intensities. Experimental results indicate that the amplitude and pulse intervals always obey lognormal functions. As the magnetic field intensity increased, the current amplitudes almost decreased and the pulse intervals increased significantly. The paths of negative ions produced in the Trichel discharge were curved by the magnetic field, weakening their shielding effect. A magnetic field can enhance the possibility of Trichel discharge with small energy release. Moreover, the characteristics of the Trichel pulse could be controlled with only magnetic field and without any energy addition. [ABSTRACT FROM AUTHOR]

This paper considers density evolution for low-density parity-check (LDPC) and multi-edge type LDPC (MET-LDPC) codes over the binary input additive white Gaussian noise channel. We first analyze three single-parameter Gaussian approximations for density evolution and discuss their accuracy under several conditions, namely, at low rates, with punctured and degree-one variable nodes. We observe that the assumption of symmetric Gaussian distribution for the density-evolution messages is not accurate in the early decoding iterations, particularly at low rates and with punctured variable nodes. Thus, single-parameter Gaussian approximation methods produce very poor results in these cases. Based on these observations, we then introduce a new density evolution approximation algorithm for LDPC and MET-LDPC codes. Our method is a combination of full density evolution and a single-parameter Gaussian approximation, where we assume a symmetric Gaussian distribution only after density-evolution messages closely follow a symmetric Gaussian distribution. Our method significantly improves the accuracy of the code threshold estimation. Additionally, the proposed method significantly reduces the computational time of evaluating the code threshold compared with full density evolution thereby making it more suitable for code design. [ABSTRACT FROM PUBLISHER]

We consider the problem of reconstructing a low-rank matrix from a subset of its entries and analyze two variants of the so-called alternating minimization algorithm, which has been proposed in the past. We establish that when the underlying matrix has rank one, has positive bounded entries, and the graph underlying the revealed entries has diameter which is logarithmic in the size of the matrix, both algorithms succeed in reconstructing the matrix approximately in polynomial time starting from an arbitrary initialization. We further provide simulation results which suggest that the second variant which is based on the message passing type updates performs significantly better. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a single probability density function for the distributions of the delay in the presence of the process variation for different regions of operation. The delay variation model is inspired by considering the analytical current models for each operating region. Based on these models, we suggest using the log-skew-normal distribution for modeling the delay variation for a wide range of supply voltages from the subthreshold to above-threshold regions. To assess the accuracy of the proposed delay distribution, the mean, standard deviation, skewness, 99th percentile, and yield of the proposed distribution are compared with those of the normal and log-normal distributions using the Monte Carlo (MC) simulations for different circuits in both bulk and FinFET technologies. The results show a higher accuracy for the proposed distribution in all regions of operation. Also, the proposed model enables us to obtain the 3 \sigma yield of the distribution using up to 3.4 times less MC simulation time. [ABSTRACT FROM PUBLISHER]

This paper presents an investigation of how to model the statistical properties of radio channels arising in industrial environments over long time horizons, e.g., hours and days. Based on extensive measurement campaigns, conducted at three different factory buildings, it is shown that for mobile transceivers the fading characteristics are Rayleigh or close to Rayleigh. However, for transceivers mounted at fixed locations, the use of conventional single fading distributions is not sufficient. It is shown that a suitable model structure for describing the fading properties of the radio channels, as measured by power, is a mixture of gamma and compound gamma-lognormal distributions. Furthermore, the complexity of the model generally increases with the observation interval. A model selection approach based on a connection between Kullback’s mean discrimination information and the log-likelihood provides a robust choice of model structure. We show that while a (semi)-Markov chain constitute a suitable model for the channel dynamics the time dependence of the data can be neglected in the estimation of the parameters of the mixture distributions. Neglecting the time dependence in the data leads to a more efficient parametrization. Moreover, it is shown that the considered class of mixture distributions is identifiable for both continuous and quantized data under certain conditions and under those conditions a maximum likelihood under independence assumption estimator is shown to give consistent parameters also for data which are not independent. The parameter estimates are obtained by maximizing the log likelihood using a genetic and a local interior point algorithm. [ABSTRACT FROM AUTHOR]

For a lognormal distribution, in Bayesian framework, we consider estimation of unknown parameters and prediction of future observable when it is known that samples are hybrid censored. We derive Bayes estimates with respect to the squared error loss function under both informative and non-informative prior situations. These estimates are then computed using Lindley method, importance sampling, and OpenBUGS software. For comparison purposes, we also obtain maximum-likelihood estimates using expectation-maximization (EM) algorithm, and compute Fisher information matrix as well. Predictive estimates of future observable are obtained under the setup of one- and two-sample prediction problems. We further compute equal-tail and highest posterior density predictive intervals, the corresponding average interval length and coverage percentage. Proposed methods of estimation and prediction are compared numerically, and comments are made based on a simulation study. Two real data sets are also analyzed for illustration purposes. [ABSTRACT FROM AUTHOR]

The bit error rate (BER) performance loss of $M$-ary differential phase-shift keying (MDPSK) with respect to M$-ary phase-shift keying (MPSK) in wireless fading channels has been extensively studied, but the understanding of this problem in lognormal fading channels has been limited. The most recent work has shown the asymptotic BER performance losses of BDPSK and QDPSK with respect to BPSK and QPSK, respectively, in the lognormal fading channels. However, it is not known how the modulation order increases. In this paper, we derive the general expression of the relative BER performance loss of MDPSK with respect to MPSK for arbitrary M$ over the lognormal fading channels in high signal-to-noise ratio regimes. We start our analysis by investigating the asymptotic BER performance losses of MDPSK with respect to MPSK for . Using the regularity of these results, we obtain the general closed-form expression of the relative BER performance loss in the lognormal fading channels. [ABSTRACT FROM AUTHOR]