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Purpose The present study aimed to establish a test battery for the clinical assessment of retronasal odor thresholds and retronasal odor identification. Study design Prospective case–control series. Methods Sixty participants (36 women, 24 men, mean age 37.6 ± 19.4 years) were enrolled in this study; 36 were healthy controls and 24 were patients with olfactory dysfunction. Orthonasal olfactory function was assessed with the “Sniffin’ Sticks” test battery. Retronasal olfaction was assessed with oral odorant delivery using special containers for threshold function, and with oral tasteless powders for odor identification. Results Retronasal and orthonasal olfaction were significantly correlated for threshold scores, identification score, and the sum of threshold and identification score (TI score). Validity analyses showed that the retronasal TI score was able to discriminate between healthy controls and patients with olfactory dysfunction. Conclusions Normosmic participants can be distinctly differentiated from patients with olfactory dysfunction using a valid test battery comprised of retronasal threshold and identification tests. Based on the current findings, we advocate a TI score of 16 as a cut-off between patients and controls. Therefore, TI scores of 17 and above would indicate retronasal normosmia.

In this paper, we show several results about the strict submodularity of the influence spread in a social network with general threshold model if at each node, the threshold function is strictly increasing and strictly submodular.

Academic conferences are popular platforms for academicians to share their research with colleagues, get feedback, and stay up to date on recent academic studies. Conferences also provide opportunities for the participants to express themselves, expand their network, and become socialized. However, academicians are forced to choose a limited number of conferences to participate due to several different factors such as time required for preparing a research, traveling and lodging expenses, and conference fees. At this multi-criteria decision problem, relevant factors can be used to evaluate the alternatives (i.e., academic conferences to participate) and prioritization of these factors would be necessary in advance. To address this issue, this study suggests an improved fuzzy cognitive mapping (FCM) approach to analyze factors affecting the choice of academic conferences to participate. The classical FCM allows to observe the dynamic behavior of complex systems during time. While the approach is widely used in different areas, it has considerable drawbacks: (i) producing same steady state values under different initial conditions and (ii) yielding completely different steady state values when different threshold functions are used. The new approach provides a mathematical formulation that produces steady state values sensitive to initial conditions. Since the selection of the threshold function in classical FCM is a highly subjective choice, the proposed approach offers an alternative way to obtain comparable values.

Highlights • NIR overlay threshold function of VISIONSENSE® allows us to set a floor for the NIR signal to be included in the overlay. • We report the case of a patient who underwent indocyanine green fluorescence-guided parathyroidectomy using the threshold-adjustment function of VISIONSENSE®. • The threshold-adjustment function of VISIONSENSE® may be useful to readily identify the PG in parathyroid surgery., Introduction VISIONSENSE® is a new near-infrared (NIR) fluorescence laparoscope and has an NIR overlay threshold function that allows us to set a floor for the NIR signal to be included in the overlay. We report the case of a patient who underwent indocyanine green (ICG) fluorescence-guided parathyroidectomy for primary hyperparathyroidism due to parathyroid adenoma using the threshold-adjustment function of VISIONSENSE®. Presentation of case A 40-year-old man was referred to our department for examination and treatment of hypercalcemia. ICG fluorescence-guided parathyroidectomy using VISIONSENSE® was planned on diagnosis of primary hyperparathyroidism due to parathyroid tumor. In the operation, we were unable to readily recognize the parathyroid gland (PG). After intravenous injection of ICG, fluorescence from ICG appeared from the left thyroid lobe to the PG, but PG contours remained unclear. We therefore used the threshold-adjustment function of VISIONSENSE® to discard NIR signal values

Earlier, many PPDM algorithms have been proposed to conceal sensitive items in a database in order to disclose sensitive itemsets. All prior techniques, however, ignored a crucial problem in setting minimum support thresholds. Thus, a new concept of minimal support for solving this issue is proposed in this paper. In compliance with a given threshold function, the proposed approach would set a tighter threshold for an object containing several items. Experimental results are then evaluated to show the performance of the traditional Greedy PPDM approach, GA-based PPDM approaches, and the proposed PSO-based algorithm with the new flexible and minimal support function.

This paper combines the image adaptive threshold denoising algorithm and performs double threshold mapping processing to the infrared image, which effectively reduces the influence of these phenomena to the infrared image and improves the quality of the image. In this paper, the infrared image denoising technology is studied, and an infrared image denoising method based on the wavelet coefficient threshold processing is proposed. This method is based on the noise distribution characteristics of infrared images, the multiplicative noise in the infrared image is transformed into an additive noise, and the wavelet transform coefficient of the transformed infrared image is processed to denoise the image. On this basis, the advantages and disadvantages of the soft and hard threshold functions are deeply analyzed, and an adaptive threshold function with adjustable parameter is constructed. At the same time, in order to suppress the Gibbs visual distortion caused by the absence of translation invariance of the orthogonal wavelet transform, the two-input wavelet transform with translation invariance is introduced, and a double threshold mapping infrared image processing method based on the adaptive threshold denoising algorithm based on the two-input wavelet transform is formed. Simulation results show that the method proposed in this paper has a better suppression of noise, maintains the integrity of image details, and improves the image quality to a certain extent.

Hyper-wavelet transforms, such as a non-subsampled shearlet transform (NSST), are one of the mainstream algorithms for removing random noise from ground-penetrating radar (GPR) images. Because GPR image noise is non-uniform, the use of a single fixed threshold for noisy coefficients in each sub-band of hyper-wavelet denoising algorithms is not appropriate. To overcome this problem, a novel NSST-based GPR image denoising grey wolf optimisation (GWO) algorithm is proposed. First, a time-varying threshold function based on the trend of noise changes in GPR images is proposed. Second, an edge area recognition and protection method based on the Canny algorithm is proposed. Finally, GWO is employed to select appropriate parameters for the time-varying threshold function and edge area protection method. The Natural Image Quality Evaluator is utilised as the optimisation index. The experiment results demonstrate that the proposed method provides excellent noise removal performance while protecting edge signals.

W artykule przedstawiono sposób wprowadzenia funkcji progowej do modelu Relacyjnej Rozmytej Mapy Kognitywnej, wykorzystującego arytmetykę liczb rozmytych. Funkcyjne przetwarzanie liczby rozmytej powoduje deformacje kształtu liczby i jej nośnika. Zaproponowano „geometryczne”podejście do tego problemu, pozwalające zachować niezmienność nośnika oraz utrzymać kształt przetwarzanej liczby rozmytej przy jednoczesnym odpowiednim przesunięciu centrum tej liczby. Metoda została przetestowana na liczbach rozmytych o różnych funkcjach przy

In this paper, we explore the process of emotional state transition. And the process is impacted by emotional state of interaction objects. First of all, the cognitive reasoning process and the micro-expressions recognition is the basis of affective computing adjustment process. Secondly, the threshold function and attenuation function are proposed to quantify the emotional changes. In the actual environment, the emotional state of the robot and external stimulus are also quantified as the transferring probability. Finally, the Gaussian cloud distribution is introduced to the Gross model to calculate the emotional transitional probabilities. The experimental results show that the model in human–computer interaction can effectively regulate the emotional states, and can significantly improve the humanoid and intelligent ability of the robot. This model is consistent with experimental and emulational significance of the psychology, and allows the robot to get rid of the mechanical emotional transfer process.

A signal denoising method using improved wavelet threshold function is presented for microchip electrophoresis based on capacitively coupled contactless conductivity detection (ME-C4D) device. The evaluation results of denoising effect for the ME-C4D simulation signal show that using Daubechies 5 (db5) wavelet at a decomposition level 4 can produce the best performance. Furthermore, the denoising effect is compared with, as well as proved to be superior to, the existing techniques, such as Savitzky–Golay, Fast Fourier Transform, and soft threshold method. This method has been successfully applied to the self-developed ME-C4D equipment. After executing this method, the noise is cleanly removed, and the signal peak shape and peak area are well maintained.

Despite that face detection has progressed significantly in recent years, it is still a challenging task to get a fast face detector with competitive performance, especially on CPU based devices. In this paper, we propose a novel loss function based on knowledge distillation to boost the performance of lightweight face detectors. More specifically, a student detector learns additional soft label from a teacher detector by mimicking its classification map. To make the knowledge transfer more efficient, a threshold function is designed to assign threshold values adaptively for different objectness scores such that only the informative samples are used for mimicking. Experiments on FDDB and WIDER FACE show that the proposed method improves the performance of face detectors consistently. With the help of the proposed training method, we get a CPU real-time face detector that runs at 20 FPS while being state-of-the-art on performance among CPU based detectors.

With the development of communication technology and network technology, as well as the rising popularity of digital electronic products, an image has become an important carrier of access to outside information. However, images are vulnerable to noise interference during collection, transmission and storage, thereby decreasing image quality. Therefore, image noise reduction processing is necessary to obtain higher-quality images. For the characteristics of its multi-analysis, relativity removal, low entropy, and flexible bases, the wavelet transform has become a powerful tool in the field of image de-noising. The wavelet transform in application mathematics has a rapid development. De-noising methods based on wavelet transform is proposed and achieved with good results, but shortcomings still remain. Traditional threshold functions have some deficiencies in image de-noising. A hard threshold function is discontinuous, whereas a soft threshold function causes constant deviation. To address these shortcomings, a method for removing image noise is proposed in this paper. First, the method decomposes the noise image to determine the wavelet coefficients. Second, the wavelet coefficient is applied on the high-frequency part of the threshold processing by using the improved threshold function. Finally, the de-noised images are obtained to rebuild the images in accordance with the estimation in the wavelet-based conditions. Experiment results show that this method, discussed in this paper, is better than traditional hard threshold de-noising and soft threshold de-noising methods, in terms of objective effects and subjective visual effects.

Cell classifiers are decision-making synthetic circuits that allow in vivo cell-type classification. Their design is based on finding a relationship between differential expression of miRNAs and the cell condition. Such biological devices have shown potential to become a valuable tool in cancer treatment as a new type-specific cell targeting approach. So far, only single-circuit classifiers were designed in this context. However, reliable designs come with high complexity, making them difficult to assemble in the lab. Here, we apply so-called Distributed Classifiers (DC) consisting of simple single circuits, that decide collectively according to a threshold function. Such architecture potentially simplifies the assembly process and provides design flexibility. We present a genetic algorithm that allows the design and optimization of DCs. Breast cancer case studies show that DCs perform with high accuracy on real-world data. Optimized classifiers capture biologically relevant miRNAs that are cancer-type specific. The comparison to a single-circuit classifier design approach shows that DCs perform with significantly higher accuracy than individual circuits. The algorithm is implemented as an open source tool.

The contradiction between de-noising and preserving the local features of the original signal simultaneously cannot be solved with the traditional wavelet threshold function (TF), and thus an improved combination of soft and hard threshold functions (TFs) of power quality signal de-noising is proposed in this paper. The improved wavelet TF is utilized to overcome the problem of “Pseudo-Gibbs phenomenon”, the deviation of wavelet coefficients, caused by the traditional wavelet TF, which fills a gap for the deficiency of wavelet soft and hard threshold de-noising, and with stronger adaptability. Finally, the de-noising tests of three typical signals of power quality are simulated based on MATLAB R2016a. Compared with the traditional wavelet TFs, the results obtained based on the proposed improved wavelet TF can guarantee better effectiveness and feasibility.

Aiming at the shortcomings of traditional hard threshold method and soft threshold method, this paper puts forward a wavelet denoising method based on wavelet entropy and improved threshold function. First, the noisy partial discharge (PD) signals are processed by wavelet decomposition, then the wavelet coefficients are processed by a new improved threshold function using adaptive selection of threshold based on wavelet entropy, finally the denoised signal can be got by reconstruction. The denoising results of typical simulation signals and the field signals reveal that this present method can effectively remove white noise.

Optoelectronic neuronal element on the n-type c-negatron is researched in paper. the offered neuron executes the main functions of neuron, such as spatial and temporal integration of optical (current) signals and has a threshold function of activating. theoretical basis of neuronal elements construction on the c-negatrons and computer simulations results of the neuron on the n-type c-negatron are shown in paper. advantages of the offered neuron element are schematic and technological simplicity, high speed, possibility of signal voltage amplification, low power consumption.

Accuracy of the trigonometric measurement of elevations is affected by the systematic influence of a vertical refraction, which is caused by changes of meteorological parameters. Submitted paper deals with a modelling of the impact of the vertical refraction using selected meteorological parameters. At first, a concise derivation of a physical principle of the vertical refraction is given. Then, a multiple regression model and its extension into a form of two-regime model are given. Division into two regimes provides a threshold function, which expresses the dependence of the original explanatory variables. Different types of the threshold function are considered and finally a comparison of the quality of the proposed models and application of a chosen model on the results of repeated trigonometric measurements is given.

As of the technological growth, Wireless Sensor Networks (WSNs) becomes an emerging research area. Sensors are combined as group called clusters to collect the data from where it is deployed. Clusters are maintained by Cluster Head (CH) and it maintains the transmission process which needs high energy. LEACH Protocol is considered for this work and in which the CHs are elected based on threshold function. The proposed methodology enhances the threshold function of LEACH with distance and count of neighbor nodes metrics develops better energy efficient function. Power Amplification for the elected CH chosen high power in order to increase the performance of the network. The number of alive nodes and residual energy of a node based on distance metrics are checked with the existing model. The simulation result shows the proposed scheme achieves high impact than the existing methodologies for WSNs.

Optimization of wavelet filtering algorithms with two- and three-parameter threshold functions is considered. The optimization is performed in two ways: by selecting the best threshold function and estimating its optimal parameters. Recommendations for the use of certain threshold functions in practice are given based on analysis of the results of numerical studies.

An improved wavelet thresholding method is presented and successfully applied to CCD measuring image denoising. On the analysis of the current widely used soft threshold and hard threshold, combining characteristics of the CCD measuring image and use of local correlation of wavelet coefficients, an improved threshold function is proposed, and the denoising results were contrasted among different threshold functions. The simulation results show that adopting the improved threshold function can acquire better filtering effect than traditional soft threshold and hard threshold methods.

Purpose: A multispectral algorithm for detection and differentiation of defective (defects on apple skin) and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at 676 nm, 714 nm and 779 nm. The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing. Results: The rapid multispectral algorithm detected over 95% of defectiv e apples and 91% of normal apples investigated. Conclusions: The multispectral defect detection algorithm can potentially be used in commercial apple processing lines.

Based on the characteristics of fingerprint image as well as the traditional soft and hard threshold functions for the fingerprint image de-noising, this paper puts forward a new threshold function. The result of the experiment confirms that if the parameter of the new threshold function is appropriately defined, the de-noising performance is better than those methods [1-3]. In the simulation experiment, a fitting model about the noise intensity and the decomposition level is proposed on the basis of a large number of experiment data. The model can adaptively avoid the noise. Hence the new threshold function is supposed to help to improve the efficiency of the fingerprint image de-noising and to have certain application value.

For soft threshold function are likely to cause a constant deviation with the original signal, hard threshold function can not fully remove noise and the selection of semi threshold function parameters is complex, we presented a critical threshold function, and analyzed the parameter selection for the new threshold. The simulation experiments prove that the denoising of critical threshold method is much better, and it also can make up for the deficiencies of traditional threshold.

Using wavelet analysis for non-stationary signal de-noising of electro-mechanical system is considered to be the best approach, and wavelet threshold de-noising method is the most simple method that needs the minimum amount of calculation. But this method in selecting threshold functions needs to be improved. Based on different domestic and foreign methods of improving threshold function, propose an improved bivariate threshold function. According to the simulation of non-stationary signal de-noising, the results show that the optimal de-noising results of different signals exist by taking different parameters. Compared with all the de-noising effects, application of the bivariate threshold function considering signal-to-noise ratio and mean square error is superior to the traditional soft and hard threshold functions. At the same time, it can significantly improve the filtering precision, and reserve the main signal details while effectively removing the noise well.

An effective filtering method for MEMS gyro random drift is discussed. On the basis of the fundamentals and defect analysis of wavelet threshold de-noising, a new continuous threshold function at λ is presented, signal concussion and distortion in wavelet hard and soft threshold method is effectively overcome. The improved wavelet de-noising algorithm is applied to MEMS gyro signal filtering experiment and experimental results show that the proposed method can effectively reduce the high frequency noise, restrain the MEMS gyro random drift, increases the SNR and decrease the MSE.

To predict the aeroengine exhaust gas temperature (EGT) more precisely, a process neuron with time-varying threshold function is proposed in this paper, and then the time-varying threshold process neural network model comprised of the presented process neurons is used for EGT prediction. By introducing a group of appropriate orthogonal basis functions, the input functions, the weight functions and the threshold functions of the time-varying threshold process neural network can be expanded as linear combinations of the given orthogonal basis functions, thus to eliminate the integration operation, then to simplify the time aggregation operation. The corresponding learning algorithm is also presented, and the effectiveness of the time-varying threshold process neural network model is evaluated through the prediction of EGT series from practical aeroengine condition monitoring.

The relevant properties of the selected classical threshold function and selected threshold were compared and analyzed for the methods of noisy-image wavelet threshold de-noising. On the basis of this, a new wavelet threshold de-noising function was given in this paper. The new defined threshold function overcomes the shortcoming which the hard threshold function and the soft threshold function on higher derivatives are discontinuous by adding a variable. Theoretical analysis and experimental results show that, the constructed wavelet threshold de-noising function possess the better adaptability and de-noising effect. In the case of strong Gauss noise for the image, relatively soft threshold method, the PSNR of a new threshold de-noising method can be improved from 4dB to 6dB.

This paper presents a new ECG demising algorithm based on the self-optimizing method. This paper discusses the optimum threshold concept and the optimum threshold is decided by signal and threshold function. Based on the concept, this paper provides the ECG optimal method concrete steps. Through the dead value at high frequency phenomenon that is observed by experiment can identify the terminal value. Experiments show that the proposed method improves the signal-to-noise ratios. Moreover, the de-noising signals have a smooth and visual appearance.

In general, images of outdoor scenes often contain degradation due to dust, water drop, haze, fog, smoke and so on, as a result they cause the contrast reduction and color fading. Haze removal is not easier problem due to the inherent ambiguity between the haze and the underlying scene. So, we propose a novel method to solve single scene dehazing problem using the region segmentation based on graph algorithm that has used a gradient value as a cost function. We segment the scene into different regions according to depth-related information and then estimate the global atmospheric light. The medium transmission can be directly estimated by the threshold function of graph-based segmentation algorithm. After estimating the medium transmission, we can restore the haze-free scene. We evaluated the degree of the visibility restoration between the proposed method and the existing methods by calculating the gradient of the edge between the restored scene and the original scene. Results on a variety of outdoor haze scene demonstrated the powerful haze removal and enhanced image quality of the proposed method.

Automated negotiation is of great interest in artificial intelligence. An effective automated negotiation strategy can facilitate human in reaching better negotiation outcomes benefiting from the adoption of advanced computational methods. This paper deals with multi-lateral multi-issue negotiation where opponents’ preferences and strategies are unknown. A novel negotiation strategy called Phoenix is proposed following the negotiation setting adopted in The Sixth International Automated Negotiating Agents Competition (ANAC 2015) [13]. In attempt to maximize individual utility and social welfare, we propose two highlighted methods – Gaussian Process Regression (GPR) and Distance-based Pareto Frontier Approximation (DPFA). Integrating the idea of these methods into a single function called threshold function, we show that Phoenix is a fully adaptive, cooperative and rationally designed strategy.

Image edge detection algorithm is applied on images to remove Gaussian noise that is present in the image during capturing or transmission using a method which combines Prewitt operator and threshold function technique to do edge detection on the image. This method is better than a method which combines Prewitt operator and mean filtering. In this paper, firstly use mean filtering to remove initially Gaussian noise, then use Prewitt operator to do edge detection on the image, and finally applied a threshold function technique with Prewitt operator.

When collecting blasting signal, the blasting signal is inevitably mixed with a certain noise signal, and the noise signal will affect the analysis of the blasting seismic wave, and it is not conducive to formulate corresponding measures. The blasting signals are periodically and changeable, the previous denoising methods are not totally fit to blasting signals, sometimes the original signals are regarded as noise signal and are being removed. In this paper, a new algorithm combine the soft and hard threshold function is introduced and the Simulation experiments and results analysis show that it can not only effectively remove the noise signals, but also save the details of the blasting signals.

In this paper, we propose a 3-level heterogeneous network model for WSNs to enhance the network lifetime, which is characterized by a single parameter. Depending upon the value of the model parameter, it can describe 1-level, 2-level, and 3-level heterogeneity. Our heterogeneous network model also helps to select cluster heads and their respective cluster members by using weighted election probability and threshold function. We compute the network lifetime by implementing DEEC protocol for our network model. The DEEC implementation for the existing 1-level, 2-level, and 3-level heterogeneous network models are denoted as DEEC-1, DEEC-2, and DEEC-3, respectively, and for our proposed 3-level heterogeneous network model, the DEEC implementations are denoted as hetDEEC-1, hetDEEC-2, and hetDEEC-3, respectively. The network lifetime in DEEC-3 and hetDEEC-3 increases by 154.17% and 182.67%, respectively by increasing the total network energy 100% with respect to the original DEEC.

In this work, the feasibility of a system for the detection of multi-modal RFID sensing is presented. A reader is designed for detecting multiple resonances of frequency-shifting RFID sensors, each resonance corresponding to a different sensed variable. The reader exploits information of the actual return loss between the tag antenna and the RFID IC, rather than the tag power-on threshold that has been typically used in the prior art, overcoming limitations associated with the power-on threshold function. The estimator for extracting the return loss of a tag wirelessly is derived and experimentally tested with a low-cost software-defined radio platform. The developed platform can serve both the purposes of a tag performance testing reader and a central processing station for next-generation multi-sensor RFID tags.

PCNN Model is widely used because it simulates the working of visual cortex in cats, but parameter setting in PCNN is hefty affair because of manual adjusting of many initial parameters. Through this paper, we present an efficient optimization approach to reducing the parameter setting in original PCNN model by changing the threshold function to modified Heaviside function. Experimental result reveals that the proposed algorithm is efficient in detecting the segments in medical images.

The surface electromyography signal is often submerged by the noise background while being gathered and recorded. To some extent, the useful signal and noise signal can be separated by applying the wavelet de-noising method effectively to eliminate the noisy signal. However, there are discontinuous points and constant deviations in the traditional wavelet threshold methods. Taking these problems into consideration, the improved threshold method is proposed and based on soft threshold function to use a transition of the nonlinear function to deal with the threshold function and de-noise the SEMG. Experimental results show that the improved threshold method has better de-noising effect and higher signal-noise ratio than the traditional threshold method.

Reducing the impact of a vertical refraction by a two-regime model This paper deals with the creation of a mathematical model to eliminate or reduce the impact of a vertical refraction from the results of repeated trigonometric measurements of elevations. A multiple regression model with air temperature and pressure as the explanatory variables is proposed and subsequently modified into the form of a two-regime model. The threshold parameter is given by the threshold function, which represents an approximation of the relationship between the atmospheric pressure and temperature. An investigation of the quality of the proposed models and the application of the selected model on the experimental measurement results is also performed.

A new method for image denoising based on the free distributed hypothesis test threshold (FDR) and the non-sub-sampled contourlet transform(NSCT) is proposed in this paper. This method firstly acquires the free distributed false discovery rate hypotheses test in statistics to set the threshold in the NSCT domain, and then removes the noise through soft threshold function, which doesn’t depend on the length of signal. The experimental results show that the proposed method can more effectively reduce Gaussian noise and improve the peak value signal-to-noise ratio in the remote sensing image; Meanwhile, this method utilizes the shift invariant of NSCT transform to inhibit the pseudo Gibbs distortion effect, and integrally preserves the texture and edge etc.. details’ information of the image, thus obviously ameliorate the visual effect of the image.

The EEG signal is one of mini-voltage. In order to improve the performance of EEG and increase the measure efficiency, we must eliminate the noise in EEG. Wavelet threshold de-nosing techniques provide a new way to reduce noise in EEG. This paper proposes a wavelet new threshold de-noising function based on the study of the disadvantages of the hard threshold function and the soft threshold function. The simulation results show that the wavelet new threshold function is able to eliminate the noise effectively which can not only save the smooth effect of the soft threshold function but save the numerical value advantage of the hard threshold function.

We propose a generic model for the “weighted voting” aggregation step performed by several methods in supervised classification. Further, we construct an algorithm to enumerate the number of distinct aggregate classifiers that arise in this model. When there are only two classes in the classification problem, we show that a class of functions that arises from aggregate classifiers coincides with the class of self-dual positive threshold Boolean functions.

Stakeholder Relationship Management Systems (SRMS) describe the general behavior of stakeholder relations at organizations. Knowing the interactions between the drivers of the SRMS helps to improve the efficiency of the whole system. This paper investigates the applicability of Fuzzy Cognitive Maps (FCM) to simulate the system dynamics and the causal connections within it. Previous investigations showed that FCM is a proper tool to analyze these relations and with the help of that the business management process and decision making in projects can be supported. The aim of this paper is to present the results of current simulations made with the help of FCM at Lithuanian and Hungarian organizations and to explain the causes of the identified differences. Beside that the paper investigates the influences of the modification of the threshold function's parameter on the final factor states.

Although the time-domain method, frequency-domain method and wavelet-domain method can used into signal denoising or filtering, the denoising of blood glucose photoacoustic signals are limited due to some advantages. In this paper, an improved wavelet threshold denoising method is used to remove the noise of the blood glucose photoacoustic signals. In order to overcome some drawbacks of classical wavelet threshold denoising, an improved wavelet threshold function was proposed. In the simulation experiments, the different denoising results are compared between this improved wavelet threshold function and other functions. And the experiments of this improved wavelet threshold function into the denoising of the time-resolved photoacoustic signals of glucose solution are performed. The experimental result verifies that the improved wavelet threshold function denoising is available. The improved wavelet threshold function has better flexibility than others due to the usage of two threshold values and two factors. So, the improved wavelet threshold function has the potential value in the denoising field of blood glucose photoacoustic signals.