Threshold Rule Research Articles

Fusion of Thresholding Rules During Wavelet-Based Noisy Image Compression

The new method for combining semisoft thresholding rules during wavelet-based data compression of images with multiplicative noise is suggested. The method chooses the best thresholding rule and the threshold value using the proposed criteria which provide the best nonlinear approximations and take into consideration errors of quantization. The results of computer modeling have shown that the suggested method provides relatively good image quality after restoration in the sense of some criteria such as PSNR, SSIM, etc.

Habitat choice and complex decision making in a trap-building predator

The selection of a suitable habitat may maximize the survival and reproduction chances of individuals. We examined the habitat choice of wormlions, small sand-dwelling insects that construct pit-traps to capture prey. Unlike the well-studied antlions, which employ a similar hunting strategy, wormlion behavior has not been thoroughly studied. We first determined whether wormlions prefer 1) shaded versus lit, 2) deep versus shallow sand, and 3) surface-obstructed versus clear microhabitats. The first option of each of the three pairs was highly preferred. In unfavorable microhabitats, larvae were also less likely to construct a pit, mainly in shallow sand, and moved over longer distances. Next, we sought to determine whether the choice is additive (taking more than a single factor into account), hierarchical (ranking the factors according to an order of preference), or based on a threshold rule (accepting a microhabitat that crosses a quality threshold). Combining shade with one of 3 environmental factors – surface obstacles, shallow sand, or high conspecific density – led to lower proportions of wormlions choosing shade. In other words, a combination of shade and any factor was no longer preferred over lit and otherwise favorable microhabitats. We thus conclude that the wormlions’ microhabitat choice is additive, taking more than a single factor into account, and that favorable conditions of any sort are traded-off against one another.

Bounded-Reorder Packet Scheduling in Optical Cut-Through Switch

The recently proposed optical cut-through (OpCut) switch holds a great potential in achieving high energy efficiency, as it allows optical packets to cut through the switch in optical domain whenever possible, which avoids power-hungry O/E/O conversion. In the OpCut switch, to ensure in-order transmission, only optical Head-of-Line (HOL) packet of a switch flow, i.e., the stream of packets sharing the same input and output port, is allowed to cut-through the switch, and optical HOL packets are always prioritized over buffered HOL packets to achieve high cut-through ratio, which is measured by the portion of packets cutting through the switch optically. However, under such priority rule, switch flows with buffered packets are at the risk of starvation, and the OpCut switch fails to achieve 100 percent throughput for all admissible i.i.d. traffics due to the unfairness in packet scheduling. To address this two issues, in this paper we propose a delay threshold rule for packet scheduling, in which buffered packets with delays exceeding a preset delay threshold are prioritized over optical packets. In the meanwhile, the cut-through ratio is very low under heavily congested traffic due to maintaining packet order, whereas the Internet is designed to accommodate a certain degree of packet reorder, which is very common in practice due to path multiplicity. In this paper, we design a bounded-reorder packet scheduling algorithm that significantly increases the cut-through ratio of the OpCut switch while allowing a small degree of out-of-order transmission. Our extensive simulation results show that the energy efficiency of OpCut switch can be significantly improved with only a very small degree of packet reordering, which has little adverse impact on the network application performance.

Adaptive management of multimodal biometrics fusion using ant colony optimization

This paper presents a new approach for the adaptive management of multimodal biometrics to meet a wide range of application dependent adaptive security requirements. In this work, ant colony optimization (ACO) is employed for the selection of key parameters like decision threshold and fusion rule, to ensure the optimal performance in meeting varying security requirements during the deployment of multimodal biometrics systems. Particle swarm optimization (PSO) has been widely utilized for the optimal selection of these parameters in the earlier attempts in the literature [Veeramachaneni et al., 2005] and [Kumar et al., 2010]. However, in PSO these parameters are computed in continuous domain while they are assumed to be better represented as discrete variables [Kumar et al., 2010]. This paper therefore proposes the use of ACO, in which discrete biometric verification parameters are computed to ensure the optimal performance from the multimodal biometrics system. The proposed ACO based framework is also extended to the pattern classification approach where fuzzy binary decision tree (FBDT) is utilized for two-class biometrics verification. The experimental results are presented on true multimodal systems from various publicly available databases; IITD databases of palmprint and iris, XM2VTS database of speech and faces, and the NIST BSSR1 databases of faces and fingerprint images. Our experimental results presented in this paper suggest that (i) ACO based approach is capable of operating on significantly small error rates in comparison to the widely employed PSO for automated selection of biometrics fusion rules/parameters, (ii) the score-level fusion yields better performance with lower error rate in comparison to the decision level fusion, and finally (iii) the FBDT based classification approach delivers considerably superior performance for the adaptive biometrics verification.

Customized maximal-overlap multiwavelet denoising with data-driven group threshold for condition monitoring of rolling mill drivetrain

Fault identification timely of rolling mill drivetrain is significant for guaranteeing product quality and realizing long-term safe operation. So, condition monitoring system of rolling mill drivetrain is designed and developed. However, because compound fault and weak fault feature information is usually sub-merged in heavy background noise, this task still faces challenge. This paper provides a possibility for fault identification of rolling mills drivetrain by proposing customized maximal-overlap multiwavelet denoising method. The effectiveness of wavelet denoising method mainly relies on the appropriate selections of wavelet base, transform strategy and threshold rule. First, in order to realize exact matching and accurate detection of fault feature, customized multiwavelet basis function is constructed via symmetric lifting scheme and then vibration signal is processed by maximal-overlap multiwavelet transform. Next, based on spatial dependency of multiwavelet transform coefficients, spatial neighboring coefficient data-driven group threshold shrinkage strategy is developed for denoising process by choosing the optimal group length and threshold via the minimum of Stein’s Unbiased Risk Estimate. The effectiveness of proposed method is first demonstrated through compound fault identification of reduction gearbox on rolling mill. Then it is applied for weak fault identification of dedusting fan bearing on rolling mill and the results support its feasibility.

Improving Efficiency Monitoring Systems for Potentially Dangerous Objects Based on Optimization of Group Detection Sensors

This paper presented the method for improving the effectiveness detecting critical states of potentially dangerous objects on the basis of the sensors group and a joint Bayesian optimization the threshold and majority rule to detection.

Staffing Call Centers with Uncertain Arrival Rates and Co‐sourcing

In a call center, staffing decisions must be made before the call arrival rate is known with certainty. Once the arrival rate becomes known, the call center may be over‐staffed, in which case staff are being paid to be idle, or under‐staffed, in which case many callers hang‐up in the face of long wait times. Firms that have chosen to keep their call center operations in‐house can mitigate this problem by co‐sourcing; that is, by sometimes outsourcing calls. Then, the required staffing N depends on how the firm chooses which calls to outsource in real time, after the arrival rate realizes and the call center operates as a M/ M/ N + M queue with an outsourcing option. Our objective is to find a joint policy for staffing and call outsourcing that minimizes the long‐run average cost of this two‐stage stochastic program when there is a linear staffing cost per unit time and linear costs associated with abandonments and outsourcing. We propose a policy that uses a square‐root safety staffing rule, and outsources calls in accordance with a threshold rule that characterizes when the system is “too crowded.” Analytically, we establish that our proposed policy is asymptotically optimal, as the mean arrival rate becomes large, when the level of uncertainty in the arrival rate is of the same order as the inherent system fluctuations in the number of waiting customers for a known arrival rate. Through an extensive numerical study, we establish that our policy is extremely robust. In particular, our policy performs remarkably well over a wide range of parameters, and far beyond where it is proved to be asymptotically optimal.

Estimation of a Cumulative Distribution Function Under Interval Censoring “case 1” Via Warped Wavelets

The estimation of an unknown cumulative distribution function in the interval censoring “case 1” model from dependent sequences is considered. We construct a new adaptive estimator based on a warped wavelet basis and a hard thresholding rule. Under mild assumptions on the parameters of the model, considering the risk and the weighted Besov balls, we prove that the estimator attains a sharp rate of convergence. We also investigate its practical performances thanks to simulation experiments.

Effects of inspections in small world social networks with different contagion rules

We study the way the structure of social links determines the effects of random inspections on a population formed by two types of individuals, e.g. tax-payers and tax-evaders (free riders). It is assumed that inspections occur in a larger scale than the population relaxation time and, therefore, a unique initial inspection is performed on a population that is completely formed by tax-evaders. Besides, the inspected tax-evaders become tax-payers forever. The social network is modeled as a Watts–Strogatz Small World whose topology can be tuned in terms of a parameter p∈[0,1] from regular (p=0) to random (p=1). Two local contagion rules are considered: (i) a continuous one that takes the proportion of neighbors to determine the next status of an individual (node) and (ii) a discontinuous (threshold rule) that assumes a minimum number of neighbors to modify the current state. In the former case, irrespective of the inspection intensity ν, the equilibrium population is always formed by tax-payers. In the mean field approach, we obtain the characteristic time of convergence as a function of ν and p. For the threshold contagion rule, we show that the response of the population to the intensity of inspections ν is a function of the structure of the social network p and the willingness of the individuals to change their state, r. It is shown that sharp transitions occur at critical values of ν that depends on p and r. We discuss these results within the context of tax evasion and fraud where the strategies of inspection could be of major relevance.

Wavelet methodology to improve single unit isolation in primary motor cortex cells

The proper isolation of action potentials recorded extracellularly from neural tissue is an active area of research in the fields of neuroscience and biomedical signal processing. This paper presents an isolation methodology for neural recordings using the wavelet transform (WT), a statistical thresholding scheme, and the principal component analysis (PCA) algorithm. The effectiveness of five different mother wavelets was investigated: biorthogonal, Daubachies, discrete Meyer, symmetric, and Coifman; along with three different wavelet coefficient thresholding schemes: fixed form threshold, Stein's unbiased estimate of risk, and minimax; and two different thresholding rules: soft and hard thresholding. The signal quality was evaluated using three different statistical measures: mean-squared error, root-mean squared, and signal to noise ratio. The clustering quality was evaluated using two different statistical measures: isolation distance, and L-ratio. This research shows that the selection of the mother wavelet has a strong influence on the clustering and isolation of single unit neural activity, with the Daubachies 4 wavelet and minimax thresholding scheme performing the best.

Performance of Biomarker-Based Subgroup Selection Rules in Adaptive Enrichment Designs

In the planning stage of a clinical trial investigating a potentially targeted therapy, there is commonly a high degree of uncertainty whether the treatment is more efficient (or efficient only) in a subgroup compared to the whole population. Recently developed adaptive designs enable to allow for an efficacy assessment both for the whole population and a subgroup and to select the target population mid-course based on interim results (see, e.g., Wang et al., Pharm Stat 6:227–244, 2007, Brannath et al., Stat Med 28:1445–1463, 2009, Wang et al., Biom J 51:358–374, 2009, Jenkins et al., Pharm Stat 10:347–356, 2011, Friede et al., Stat Med 31:4309–4120, 2012). Frequently, predictive biomarkers are used in these trials for identifying patients more likely to benefit from a drug. We consider the situation that the selection of the patient population is based on a biomarker and where the diagnostics that evaluates the biomarker may be perfect, i.e., with 100 % sensitivity and specificity, or not. The performance of the applied subset selection rule is crucial for the overall characteristics of the design. In the setting of an adaptive enrichment design, we evaluate the properties of subgroup selection rules in terms of type I error rate and power by taking into account decision rules with a fixed ad hoc threshold and optimal decision rules developed for the situation of uncertain assumptions. In a simulation study, we demonstrate that designs with optimal decision rules are under certain assumptions more powerful as compared to those with ad hoc decision rules. Throughout the results, a strong impact of sensitivity and specificity of the biomarker on both type I error rate and power is observed.

History-dependent risk attitude

We propose a model of history-dependent risk attitude, allowing a decision maker's risk attitude to be affected by his history of disappointments and elations. The decision maker recursively evaluates compound risks, classifying realizations as disappointing or elating using a threshold rule. We establish equivalence between the model and two cognitive biases: risk attitudes are reinforced by experiences (one is more risk averse after disappointment than after elation) and there is a primacy effect (early outcomes have the greatest impact on risk attitude). In a dynamic asset pricing problem, the model yields volatile, path-dependent prices.

Research on Flatness Signal De-noising Based on Wavelet Theory

On the basis of wavelet transform theory, using computer to simulate ∞atness signal with noise emerging in all kinds of environment, through the analysis of wavelet basis, decomposition scale and threshold rules, the actual ∞atness signal is extracted, which not only provides evidence for the improvement of ∞atness gauge accuracy, but precisely describes ∞atness ∞aw thus laid better foundation for the real-time control of ∞atness.

Improving model performance with the integrated wavelet denoising method

Abstract Intelligent pattern recognition imposes new challenges in high-frequency financial data mining due to its irregularities and roughness. Based on the wavelet transform for decomposing systematic patterns and noise, in this paper we propose a new integrated wavelet denoising method, named smoothness-oriented wavelet denoising algorithm (SOWDA), that optimally determines the wavelet function, maximal level of decomposition, and the threshold rule by using a smoothness score function that simultaneously detects the global and local extrema. We discuss the properties of our method and propose a new evaluation procedure to show its robustness. In addition, we apply this method both in simulation and empirical investigation. Both the simulation results based on three typical stylized features of financial data and the empirical results in analyzing high-frequency financial data from Frankfurt Stock Exchange confirm that SOWDA significantly (based on the RMSE comparison) improves the performance of classical econometric models after denoising the data with the discrete wavelet transform (DWT) and maximal overlap discrete wavelet transform (MODWT) methods.

Comparison of Threshold Rules for a Normal Approximation to a Binomial Distribution

A commonly used rule for determining if a Binomial(n, p) distribution may be reasonably approximated by a normal distribution is whether or not np and n(1−p) are at least some constant, such as 10. Two competing rules, one based on the binomial variance and the other based on the coefficient of variation, are considered when constructing confidence intervals and performing hypothesis testing, both using and not using a continuity correction. Under one criterion the rule based on the coefficient of variation is found to be the best in terms of coverage probabilities, and under another criterion the rule based on the binomial variance is found to be the best.

Generalized optimal wavelet decomposing algorithm for big financial data

Using big financial data for the price dynamics of U.S. equities, we investigate the impact that market microstructure noise has on modeling volatility of the returns. Based on wavelet transforms (DWT and MODWT) for decomposing the systematic pattern and noise, we propose a new wavelet-based methodology (named GOWDA, i.e., the generalized optimal wavelet decomposition algorithm) that allows us to deconstruct price series into the true efficient price and microstructure noise, particularly for the noise that induces the phase transition behaviors. This approach optimally determines the wavelet function, level of decomposition, and threshold rule by using a multivariate score function that minimizes the overall approximation error in data reconstruction. The data decomposition method enables us to estimate and forecast the volatility in a more efficient way than the traditional methods proposed in the literature. Through the proposed method we illustrate our simulation and empirical results of improving the estimation and forecasting performance.

Optimal Crowdfunding Design

This paper investigates the optimal design of crowdfunding where crowdfunders are potential consumers with standard motivations and entrepreneurs are profit-maximizing agents. We characterize the typical crowdfunding mechanism where the entrepreneur commits to produce only if aggregate funding exceeds a defined threshold. We study how the entrepreneur uses this threshold, in conjunction with a minimal price, for rent extraction. Compared to a standard posted-price mechanism, total welfare may rise because the entrepreneur can adapt the production decision to demand conditions, but may fall because rent-seeking can worsen. Crowdfunding platforms can raise threshold credibility. So we also compare outcomes when the entrepreneur commits to a threshold against those where the entrepreneur simply decides on production after observing crowdfunder bids. Finally, we contrast crowdfunding with the optimal mechanism where production is contingent on a general function of all bids, rather than the simple sum of bids obliged by the aggregate threshold rule. Crowdfunding is very different, for instance, never committing to produce the good when aggregate bids fall short of the fixed cost (even absent credit constraints).

Optimal Crowdfunding Design

This paper investigates the optimal design of crowdfunding where crowdfunders are potential consumers with standard motivations and entrepreneurs are profit maximizing agents. We characterize the typical crowdfunding mechanism where the entrepreneur commits to produce only if aggregate funding exceeds a defined threshold. We study how the entrepreneur uses this threshold, in conjunction with a minimal price, for rent, extraction. Compared to a standard posted price mechanism, total welfare may rise because the entrepreneur can adapt the production decision to demand conditions, but may fall because rent-seeking can worsen. Crowdfunding platforms can raise threshold credibility. So we also compare outcomes when the entrepreneur commits to a threshold against those where the entrepreneur simply decides on production after observing crowdfunder bids. Finally, we contrast crowdfunding with the optimal mechanism where production is contingent on a general function of all bids, rather than the simple sum of bids obliged by the aggregate threshold rule. Crowdfunding is very different, for instance, never committing to produce the good when aggregate bids fall short of the fixed cost (even absent credit constraints).

Bayesian Quickest Change-Point Detection With Sampling Right Constraints

In this paper, Bayesian quickest change detection problems with sampling right constraints are considered. Specifically, there is a sequence of random vari ables whose probability density function will change at an unknown time. The goal is to detect such a change in a way such that a linear combination of the average detection delay and the false alarm probability is minimized. Two types of sampling right constrains are discussed. The first one is a li mited sampling right constraint, in which the observer can take at most N observations from this random sequence. Under this setup, we show that the cost function can be written as a set of iterative fun ctions, which can be solved by Markov optimal stopping theory. The optimal stopping rule is shown to be a threshold rule. An asymptotic upper bound of the average detection delay is developed as the false alarm probability goes to zero. This upper bound indicates that the performance of the limited sampling right problem is close to that of the classic Bayesian quickest detection for several scenar ios of practical interest. The second constraint discussed in this paper is a stochastic sampling right const raint, in which sampling rights are consumed by taking observations and are replenished randomly. The observer cannot take observations if there are no sampling rights left. We characterize the optimal solution, which has a very complex structure. For practical applications, we propose a low complexity algorithm, in which the sampling rule is to take observations as long as the observer has sampling rights left and the detection scheme is a threshold rule. We show that this low complexity scheme is first order as ymptotically optimal as the false alarm probability goes to zero.

Functional Wavelet-Based Modelling of Dependence Between Lupus and Stress

The power of functional linear regression to estimate a set of curves from others involved is studied in this work in the context of life sciences. The objective is to determine the relationship between the degree of lupus and the level of stress for patients suffering this autoimmune disease. Daily stress and lupus curves have a strong local behavior with missing data those days that a patient does not answer the corresponding test. Because of this, wavelet smoothing with an appropriate thresholding rule is considered. Then, functional principal component analysis of the response and predictor variables is used to reduce the dimension and solve the multicollinearity problem that affects the estimation of the functional linear regression model with functional response. Model selection is solved by using a criterion that selects those pairs of response/predictor components that explain the highest proportions of response variability. The performance of the proposed functional model is tested on simulated and real data.