Accurate Threshold Research Articles

A Goal Programming-Based Methodology for Machine Learning Model Selection Decisions: A Predictive Maintenance Application

The paper develops a goal programming-based multi-criteria methodology, for assessing different machine learning (ML) regression models under accuracy and time efficiency criteria. The developed methodology provides users with high flexibility in assessing the models as it allows for a fast and computationally efficient sensitivity analysis of accuracy and time significance weights as well as accuracy and time significance threshold values. Four regression models were assessed, namely the decision tree, random forest, support vector and the neural network. The developed methodology was employed to forecast the time to failures of NASA Turbofans. The results reveal that decision tree regression (DTR) seems to be preferred for low values of accuracy weights (up to 30%) and low accuracy and time efficiency threshold values. As the accuracy weights tend to increase and for higher accuracy and time efficiency threshold values, random forest regression (RFR) seems to be the best choice. The preference for the RFR model however, seems to change towards the adoption of the neural network for accuracy weights equal to and higher than 90%.

A Novel Island Detection Threshold Setting Using Phasor Measurement Unit Voltage Angle in a Distribution Network

Islanding detection needs are becoming a pivotal constituent of the power system, since the penetration of distributed generators in the utility power system is continually increasing. Accurate threshold setting is an integral part of the island detection scheme since an inappropriate threshold might cause a hazardous situation. This study looked at the islanding conditions as well as two transient faults, such as a single line to ground fault and a three-phase balance fault, to assess the event distinguishing ability of the proposed method. Therefore, the goal of this research was to determine the threshold of the island if the distributed generator (DG) capacity is greater than the connected feeder load, which is the over-frequency island condition, and if the DG capacity is less than the connected feeder load, which is the under-frequency island condition. The significance of this research work is to propose a new island detection threshold setting method using the slip angle and acceleration angle that comes from phasor measurement unit (PMU) voltage angle data. The proposed threshold setting method was simulated in the PowerWorld simulator on a modified IEEE 30 bus system equipped with DG. There are three different interconnection scenarios in the test system and the performance of the proposed method shows that getting the island threshold for all the scenarios requires a single time step or 20 mile seconds after incepting an island into the network. In addition, it can distinguish between the real islanding threshold and the transient faults threshold.

Spot Image Segmentation of Lifting Container Vibration Based on Improved Threshold Method and Mathematical Morphology

Because the mine is damp and dark, it is not easy to detect the rigid tank channel’s structural failure directly. Therefore, we judged the tank channel’s surface condition by detecting the magnitude of the vibration displacement of the lifting container. In our study, we used a laser vision system to measure the structural vibration displacement. In order to accurately segment the laser spot information from the vibration image, we proposed an approach that links the relationship between the gray value of the area adjacent to the threshold point and the background’s gray value to the target in the image. We used MCE to evaluate the segmentation effect of threshold segmentation and verified the improved algorithm’s accuracy by detecting the pixel centroid of laser spots. Results show that the improved algorithm in our study has the best threshold segmentation effect, the error classification can be close to 0.0003, and the minimum deviation of the obtained vibration displacement is close to 0.1 pixels, which can realize the accurate extraction of the vibration signal of the vertical shaft tank. The novelty of this method lies in the accurate threshold segmentation and noise reduction processing of the laser speck vibration image under various interference environments in the operation of the mine hoisting system and the accurate acquisition of vibration signals. The research work provides a basis for the accurate evaluation of mechanical faults of automation technology.

Surrogate-based sequential Bayesian experimental design using non-stationary Gaussian Processes

Inferring arbitrary quantities of interest (QoI) using limited computational or, in realistic scenarios, financial budgets, is a challenging problem that requires sophisticated strategies for the optimal allocation of the available resources. Bayesian optimal experimental design identifies the optimal set of design locations for the purpose of solving a parameter inference problem and the optimality criterion is typically associated with maximizing the worth of information in the experimental measurements. Sequential design strategies further identify the optimal design in a sequential manner, starting from a initial budget and iteratively selecting new optimal points until either an accuracy threshold is reached, or a cost limit is exceeded. In this paper, we present a generic sequential Bayesian experimental design framework that relies on maximizing an information theoretic design criterion, namely the Expected Information Gain, in order to infer QoIs formed as nonlinear operators acting on black-box functions. Our framework relies on modeling the underlying response function using non-stationary Gaussian Processes, thus enabling efficient sampling from the QoI in order to provide Monte Carlo estimators for the design criterion. We demonstrate the performance of our method on an engineering problem of steel wire manufacturing and compare it with two classic approaches: uncertainty sampling and expected improvement.

A new technique to analyse threshold-intensities based on time dependent change-points in the ratio of minute ventilation and end-tidal partial pressure of carbon-dioxide production

The aim of this study was to test the utility and effectiveness of an alternative computational approach to threshold-intensities based on time dependent change-points in minute ventilation divided by end-tidal partial pressure of CO2 (VE/PETCO2) to reveal whether respiratory compensation point (RCP) is a third ventilatory threshold, or not. Ten recreationally active young adults and ten well-trained athletes volunteered to take part in this study. Following incremental ramp tests, gas exchange threshold (GET) and respiratory compensation point (RCP) were respectively evaluated by the slopes of VCO2-VO2 and VE-VCO2 using the Innocor system automatically. Respiratory threshold (RT) was analysed based on time dependent change-points in the VE/PETCO2 using binary segmentation algorithm. Additionally, those intersections were analysed independently by two experienced investigators using a visual identification technique in a double-blind design. According to the results, in the recreationally active group, there were the first (GET1) and the second (GET2) gas exchange thresholds which were identical with the RT1 (139 W; 1.9 L⋅min−1 of VO2; 1.73 L⋅min−1 of VCO2; 49.9 L⋅min−1 of VE versus 139 W; 1.88 L⋅min−1; 1.7 L⋅min−1; 49 L⋅min−1, respectively) and RT2 (186 W; 2.39 L⋅min−1 of VO2; 2.44 L⋅min−1 of VCO2; 66 L⋅min−1 of VE versus 187 W; 2.41 L⋅min−1; 2.49 L⋅min−1; 65.7 L⋅min−1, respectively). However, there were three threshold intensities which were determined by GET1, GET2, and RCP in well-trained athletes. Additionally, RT1, RT2, and RT3 were determined as valid surrogates of the GET1 (194 W; 2.56 L⋅min−1 of VO2; 1.99 L⋅min−1 of VCO2; 57.5 L⋅min−1 of VE versus 192 W; 2.61 L⋅min−1; 1.99 Lmin−1; 57.7 L⋅min−1, respectively), GET2 (267 W; 3.6 L⋅min−1 of VO2; 3.29 L⋅min−1 of VCO2; 94.5 L⋅min−1 of VE versus 266 W; 3.58 L⋅min−1; 3.26 L⋅min−1; 93.4 L⋅min−1, respectively), and RCP (324 W; 4.05 L⋅min−1 of VO2; 4.13 L⋅min−1 of VCO2; 124 L⋅min−1 of VE versus 322 W; 4.02 L⋅min−1; 4.07 L⋅min−1; 122 L⋅min−1, respectively) in well-trained athletes. There were high levels of agreements between the power outputs determined by traditional techniques and newly proposed change-points in RT. All markers were strongly correlated (p < 0.001). It was shown that RT technique can provide an accurate threshold determination. Furthermore, the RCP was observed as a third threshold-intensity for well-trained athletes but not for recreationally active young adults.

Initial Maximum Overlap Method for Large Systems by the Quantum Mechanics/Extremely Localized Molecular Orbital Embedding Technique.

Quantum chemistry offers a large variety of methods to treat excited states. Many of them are based on a multireference wave function ansatz and are therefore characterized by an intrinsic complexity and high computational costs. To overcome these drawbacks and also some limitations of simpler single-reference approaches (e.g., configuration interaction singles and time-dependent density functional theory), the single-determinant Δself-consistent field-initial maximum overlap method (ΔSCF-IMOM) has been proposed. This strategy substitutes the aufbau principle with a criterion that occupies molecular orbitals at successive SCF iterations on the basis of their maximum overlap with a proper set of guess orbitals for the target excited state. In this way, it prevents the SCF process to collapse to the ground state wave function and provides excited state single Slater determinant solutions to the SCF equations. Here, we propose to extend the applicability of the IMOM to the treatment of localized excited states of large systems. To accomplish this task, we coupled it with the QM/ELMO (quantum mechanics/extremely localized molecular orbitals) strategy, a quantum mechanical embedding method in which the most chemically relevant part of the system is treated with traditional quantum chemical approaches, while the rest is described by extremely localized molecular orbitals transferred from recently constructed libraries or proper model molecules. After presenting the theoretical foundations of the new IMOM/ELMO technique, in this paper, we will show and discuss the results of preliminary test calculations carried out on both model systems (i.e., decanoic acid, decene, decapentaene, and solvated acrolein) and a system of biological interest (flavin mononucleotide in the flavodoxin protein). We observed that, for localized excited states, the new IMOM/ELMO method provides reliable results, and it reproduces the outcomes of fully IMOM calculations within the chemical accuracy threshold (i.e., 0.043 eV) by including only a limited number of atoms in the QM region. Furthermore, the first application of our embedding technique to a larger biological system gave completely plausible results in line with those obtained through more traditional quantum mechanical methods, thus opening the possibility of using the new approach in future investigations of photobiology problems.

Validation of Sentinel-3 SLSTR Land Surface Temperature Retrieved by the Operational Product and Comparison with Explicitly Emissivity-Dependent Algorithms

Land surface temperature (LST) is an essential climate variable (ECV) for monitoring the Earth climate system. To ensure accurate retrieval from satellite data, it is important to validate satellite derived LSTs and ensure that they are within the required accuracy and precision thresholds. An emissivity-dependent split-window algorithm with viewing angle dependence and two dual-angle algorithms are proposed for the Sentinel-3 SLSTR sensor. Furthermore, these algorithms are validated together with the Sentinel-3 SLSTR operational LST product as well as several emissivity-dependent split-window algorithms with in-situ data from a rice paddy site. The LST retrieval algorithms were validated over three different land covers: flooded soil, bare soil, and full vegetation cover. Ground measurements were performed with a wide band thermal infrared radiometer at a permanent station. The coefficients of the proposed split-window algorithm were estimated using the Cloudless Land Atmosphere Radiosounding (CLAR) database: for the three surface types an overall systematic uncertainty (median) of −0.4 K and a precision (robust standard deviation) 1.1 K were obtained. For the Sentinel-3A SLSTR operational LST product, a systematic uncertainty of 1.3 K and a precision of 1.3 K were obtained. A first evaluation of the Sentinel-3B SLSTR operational LST product was also performed: systematic uncertainty was 1.5 K and precision 1.2 K. The results obtained over the three land covers found at the rice paddy site show that the emissivity-dependent split-window algorithms, i.e., the ones proposed here as well as previously proposed algorithms without angular dependence, provide more accurate and precise LSTs than the current version of the operational SLSTR product.

Minimal clinically important difference in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrosing lung disease with an estimated median survival of 2–5 years and a significant impact on quality of life (QoL). Current approved medications, pirfenidone and nintedanib, have shown a reduction in annual decline of forced vital capacity but no impact on QoL. The minimal clinically important difference (MCID) is a threshold value for a change in a parameter that is considered meaningful by the patient rather than solely relying on statistically significant change in the parameter. This review provides a brief overview of the MCID methodology along with detailed discussion of reported MCID values for commonly used physiological measures and patient-reported outcome measures in IPF. While there is no gold standard methodology for determining MCID, there are certain limitations in the MCID literature in IPF, mainly the choice of death, hospitalisation and pulmonary function tests as sole anchors, and pervasive use of distribution-based methods which do not take into account the patient's input. There is a critical need to identify accurate thresholds of outcome measures that reflect patient's QoL over time in order to more precisely design and evaluate future clinical trials and to develop algorithms for patient-oriented management of IPF in outpatient clinics.Educational aimsTo understand the concept of MCID and the methods used to determine these values.To understand the indications and limitations of MCID values in IPF.

Accelerating Seismic Redatuming Using Tile Low-Rank Approximations on NEC SX-Aurora TSUBASA

With the aim of imaging subsurface discontinuities, seismic data recorded at the surface of the Earth must be numerically re-positioned inside the subsurface where reflections have originated, a process referred to as redatuming. The recently developed Marchenko method is able to handle full-wavefield data including multiple arrivals. A downside of this approach is that a multi-dimensional convolution operator must be repeatedly evaluated to solve an expensive inverse problem. As such an operator applies multiple dense matrix-vector multiplications (MVM), we identify and leverage the data sparsity structure for each frequency matrix and propose to accelerate the MVM step using tile low-rank (TLR) matrix approximations. We study the TLR impact on time-to-solution for the MVM using different accuracy thresholds whilst at the same time assessing the quality of the resulting subsurface seismic wavefields and show that TLR leads to a minimal degradation in terms of signal-to-noise ratio on a 3D synthetic dataset. We mitigate the load imbalance overhead and provide performance evaluation on two distributed-memory systems. Our MPI+OpenMP TLR-MVM implementation reaches up to 3X performance speedup against the dense MVM counterpart from NEC scientific library on 128 NEC SX-Aurora TSUBASA cards. Thanks to the second generation of high bandwidth memory technology, it further attains up to 67X performance speedup compared to the dense MVM from Intel MKL when running on 128 dual-socket 20-core Intel Cascade Lake nodes with DDR4 memory. This corresponds to 110 TB/s of aggregated sustained bandwidth for our TLR-MVM implementation, without suffering deterioration in the quality of the reconstructed seismic wavefields.

Multilevel thresholding for image segmentation with exchange market algorithm

Image segmentation is the prime factor to elicit the detailed investigation of an image. The desired information from an image can be easily obtained through the intuitive technique called thresholding. In this technique, detailed analysis of different classes of an image is realised through Multilevel Thresholding (MLT). Accurate and ideal threshold values achieved by non-parametric objective functions such as Tsallis and Renyi are briefed in this paper. The non-additive property of Tsallis and entropic threshold selection property of Renyi drive to search the global threshold value precisely. Higher the segmentation level more is the computational time for exploration of optimal threshold with Tsallis and Renyi. This challenge is countered by MLT based Tsallis and Renyi, aided with Exchange Market Algorithm (EMA). Several research on nature- inspired algorithms such as Bacterial Foraging (BF), Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) are carried out. For the first time, this paper proposes a powerful metaheuristic EMA technique for image segmentation, which implements the strategies of shareholders in stable and unstable mode to earn profit. The cognizance of the shareholders is extracted to attain the desired goal to reach the global threshold avoiding premature convergence. Empirical outcome of the results indicate that outstanding tuning search is achieved by EMA compared to extensive search techniques such as PSO, BF and GA. Exploration and exploitation assessment by metrics such as stability, computational efficiency, Peak Signal to Noise Ratio (PSNR), uniformity measure and Wilcoxon rank sum test affirm the Tsallis and Renyi based EMA surpass the existing techniques to analyse the real-world images.

Subthalamic deep brain stimulation affects heading perception in Parkinson's disease.

Parkinson's disease (PD) presents with visuospatial impairment and falls. It is critical to understand how subthalamic deep brain stimulation (STN DBS) modulates visuospatial perception. We hypothesized that DBS has different effects on visual and vestibular perception of linear motion (heading), a critical aspect of visuospatial navigation; and such effects are specific to modulated STN location. Two-alternative forced-choice experiments were performed in 14 PD patients with bilateral STN DBS and 19 age-matched healthy controls (HC) during passive en bloc linear motion and 3D optic-flow in immersive virtual reality measured vestibular and visual heading. Objective measure of perception with Weibull psychometric function revealed that PD has significantly lower accuracy [L: 60.71 (17.86)%, R: 74.82 (17.44)%] and higher thresholds [L: 16.68 (12.83), R: 10.09 (7.35)] during vestibular task in both directions compared to HC (p < 0.05). DBS significantly improved vestibular discrimination accuracy [81.40 (14.36)%] and threshold [4.12 (5.87), p < 0.05] in the rightward direction. There were no DBS effects on the slopes of vestibular psychometric curves. Visual heading perception was better than vestibular and it was comparable to HC. There was no significant effect of DBS on visual heading response accuracy or discrimination threshold (p > 0.05). Patient-specific DBS models revealed an association between change in vestibular heading perception and the modulation of the dorsal STN. In summary, DBS may have different effects on vestibular and visual heading perception in PD. These effects may manifest via dorsal STN putatively by its effects on the cerebellum.

Target Tracking Algorithm for Table Tennis Using Machine Vision.

The current table tennis robot system has two common problems. One is the table tennis ball speed, which moves fast, and it is difficult for the robot to react in a short time. The second is that the robot cannot recognize the type of the ball's movement, i.e., rotation, top rotation, no rotation, wait, etc. It is impossible to judge whether the ball is rotating and the direction of rotation, resulting in a single return strategy of the robot with poor adaptability. In this paper, these problems are solved by proposing a target trajectory tracking algorithm for table tennis using machine vision combined with Scaled Conjugate Gradient (SCG). Real human-machine game's data are obtained in the proposed algorithm by extracting ten continuous position information and speed information frames for feature selection. These features are used as input data for the deep neural network and then are normalized to create a deep neural network algorithm model. The model is trained by the position information of the successive 20 frames. During the initial sets of experiments, we found the shortcomings of the original SCG algorithm. By setting the accuracy threshold and offline learning of historical data and saving the hidden layer weight matrix, the SCG algorithm was improved. Finally, experiments verify the improved algorithm's feasibility and applicability and show that the proposed algorithm is more suitable for table tennis robots.

Computational modeling and damage threshold prediction of continuous-wave and multiple-pulse porcine skin laser exposures at 1070 nm

Computational models are capable of simulating the expected thermal response of biological tissue to laser irradiation. A typical laser tissue model accounts for optical energy deposition, heat transfer, and damage assessment, with the latter often represented by calculation of the Arrhenius integral. Previous studies have successfully employed these methods to predict skin damage thresholds at laser wavelengths with high absorption in water, and usually for single continuous-wave exposures. However, there remains a need for a robust and accurate predictive model in low-absorption, high-scattering cases, such as for exposures in the near-infrared region near 1000 nm. This study presents a framework for modeling laser irradiation of skin tissue at 1070 nm for both continuous-wave and pulsed exposures with durations ranging from 10−2 to 101 s. The authors derive an optical absorption coefficient for the epidermis that agrees with expected chromophore distribution and report the modeled skin thermal responses alongside surface thermography data from in vivo porcine exposures as validation of simulation accuracy. Comparisons of modeled damage thresholds calculated by the Arrhenius integral with documented experimentally determined minimum visible lesion ED50 data exhibit a high degree of agreement. The authors also provide new Arrhenius rate process coefficients of A = 2.74 × 1094 s−1 and Ea = 5.90 × 105 J/mol, determined from experimental thermal profiles with a unique method, that demonstrate more accurate threshold predictions than those used in previous modeling studies. The techniques outlined by this study provide a useful tool in assessing potentially hazardous near-infrared laser exposure scenarios.

A Framework for Multi-Threshold Image Segmentation of Low Contrast Medical Images

Accurate medical images segmentation plays a vital role in contouring during diagnosis and treatment planning. To improve the segmentation accuracy in low contrast images, we propose a method by combining Hill entropy and fuzzy c-partition. Here, using membership function, an image is first transformed into fuzzy domain. Subsequently, the fuzzy Hill entropies are defined for foreground (object) and background. Next, the total fuzzy Hill entropy is maximized to compute the accurate threshold; this process is employed to calculate a proper parameter combination of membership function. This Hill entropy is then optimized to acquire an image threshold by Differential Evolution “DE” optimization algorithm. The key benefit of the presented approach is that it considers the information of background and object as well as interactions between them in threshold selection mechanism. The results and performance evaluations show the better accuracy of our technique over other existing approaches.

Facial scanning accuracy depending on the alignment algorithm and digitized surface area location: An in vitro study

ObjectivesTo measure the accuracy (trueness and precision) of a facial scanner depending on the alignment method and the digitized surface area location. MethodsFourteen markers were adhered on a head mannequin and digitized using an industrial scanner (GOM Atos Q 3D 12 M; Carl Zeiss Industrielle Messtechnik GmbH). A control mesh was acquired. Subsequently, the mannequin was digitized using a facial scanner (Arc4; Bellus3D) (n = 30). The control mesh was delineated into 10 areas. Based on the alignment procedures, two groups were created: reference best fit (RBF group) and landmark-based best fit (LA group). The root mean square was used to calculate the discrepancy between the control mesh and each facial scan. A 2-way ANOVA and Tukey pairwise comparison tests were used to compare trueness and precision between the 2 groups across 10 areas (α = .05). ResultsBoth alignment algorithms (P = .007) and digitized area (P < .001) were significant predictors of trueness with a significant interaction between the two predictors (F (9, 580) =25.13, P < .001). Tukey pairwise comparison showed that there was a significant difference between mean trueness values of RBF (mean=0.53 mm) and LA (mean=0.55 mm) groups. Moreover, a significant difference was detected among the trueness values across surface areas. The A9-area (left tragus area) had the highest and A5-area (right cheek area) had the lowest mean trueness. Both alignment algorithm (P < .001) and digitized surface area (P < .001) were significant predictors of precision with a significant interaction between the two predictors (F (9, 580) =14.34, P < .001). Tukey pairwise comparison showed that there was a significant difference between mean precision values of RBF (mean=0.38 mm) and LA (mean=0.35 mm) groups. Moreover, a significant difference was detected among the precision values across surface areas. Comparing the surface areas, A9-area had the highest and A10-area (forehead area) had the lowest mean precision. ConclusionsAlignment procedures influenced on the scanning trueness and precision mean values, but the facial scanner accuracy values obtained were within the clinically acceptable accuracy threshold of less or equal than 2 mm. Furthermore, the scanning accuracy (for both trueness and precision) depended on the location of the scanned surface area, being more accurate on the middle of the face than on the sides of the face.

Arbitrary Reduced Precision for Fine-grained Accuracy and Energy Trade-offs

Full-precision Floating-Point Units (FPUs) can be a source of extensive hardware overhead (power consumption, area, memory footprint, etc.). As several modern applications feature an inherent tolerance to precision loss, a new computing paradigm has emerged: Transprecision Computing (TC). TC proposes several tools and techniques that trade precision for energy efficiency. However, most of these tools require developers to rewrite part or all of their existing software stacks, which is often infeasible, complex, or requires extensive development efforts. In addition to their intrusiveness, TC tools can only simulate the impact of precision loss, and they do not provide corresponding hardware designs that take advantage of the simulations.This work proposes a non-intrusive hardware-oriented approach, requiring no modification of source code that applies approximations at the low-level in assembly. The approach can be used to approximate virtually all types of executable binaries (bare-metal applications, single−/multi-threaded user applications, OS/RTOS, etc.). We introduce AxQEMU: a software based on the well known QEMU dynamic binary translator. We demonstrate how our approach can determine the effects of FP approximations on application-level Quality of Result (QoR), and how it interfaces with other tools from the literature. A hardware-level case study on a 28-nm FD-SOI implementation is presented, demonstrating how fine-grained energy/accuracy trade-offs can be made thanks to floating-point arbitrary reduced precision (ARP). For instance, considering the well-known arclength FP application, FPU computation energy savings of up to 19.4% were achieved with an accuracy threshold of 10 significant digits, and up to 60.7% with a 4-digit accuracy when using ARP. These savings compared favorably to the limited 7.7% saving afforded by using standard variable type optimization tools.

Statistical approaches to identifying lapses in psychometric response data.

Psychometric curve fits relate physical stimuli to an observer's performance. In experiments an observer may "lapse" and respond with a random guess, which may negatively impact (e.g., bias) the psychometric fit parameters. A lapse-rate model has been popularized by Wichmann and Hill, which reduces the impact of lapses on other estimated parameters by adding a parameter to model the lapse rate. Since lapses are discrete events, we developed a discrete lapse theory and tested a "lapse identification" algorithm to identify individual outlier trials (i.e., potential lapses) based upon an approximate statistical criterion and discard these trials. Specifically, we focused on stimuli sampled using an adaptive staircase for a one-interval, direction-recognition task (i.e., psychometric function ranging from 0 to 1 and the spread of the curve corresponds to the threshold, which is often a parameter of interest for many fitted psychometric functions). Through simulations, we found that as the lapse rate increased the threshold became substantially overestimated, consistent with earlier analyses. While the lapse-rate model reduced the overestimation of threshold with many lapses, with lower lapse rates it yielded substantial threshold underestimation, though less so when fitting many (e.g., 1,000) trials. In comparison, the lapse-identification algorithm yielded accurate threshold estimates across a wide range of lapse rates (from 0 to 5%), which is critical since the lapse rate is seldom known. We further demonstrate the performance of the lapse-identification algorithm to be suitable for a variety of experimental conditions and conclude with some considerations of its use. In particular, we suggest using the lapse-identification algorithm unless the experiment has many trials (e.g., >500) or if somehow the lapse rate is known to be high (e.g., ≥5%), for which the lapse-rate model approaches remain preferred.

Reverse Masking in Clinical Audiology

Masking is a concept that is used in audiology to yield the accurate threshold of the test ear. The ear's selection to be masked is based on the Weber test, which can be misleading in certain conditions. There is a need to explain the concept, which can help audiologists give an accurate diagnosis. Reverse masking is one such technique where the nonlateralized ear is masked to get the lateralized ear's precise threshold. This presentation attempts to explain this concept in clinical audiological practice and avoid the limitations of conventional masking procedures.

ON THE EXCITATION OF LOCAL ELECTRIC CURRENT IN THE BIOLOGICAL ENVIRONMENT

The subject of the study in the article is to study the method of excitation of human body tissues using an electric current. The purpose of the work is to develop a method for exciting local current in a human body affecting the microcirculation of blood and excitability of local areas of muscle tissue during the treatment process. The article solves the following tasks: the creation of a model pattern of fabric, the rationale for the generation of electric current inside the sample, the development of the design of the current generation system and measuring the electrical response of the model sample of the tissue on the occurrence of electric current, determining the size and spatial current distribution in the model sample of the fabric, comparison The obtained current values with known and admissible in medical practice its values, determination of the advantages of the proposed method of excitation of current compared to the traditional used in medicine. The following methods were used: analysis of scientific publications for the subject of the study, the calculation of the expected current parameters in the model sample, the method of designing the nodes of the current generation and measurement system of the electrical response, the experimental method of excitation of the current and measuring the sample response to it. The following results were obtained: a new acoustic-magnetic method of exciting electric current in local areas of muscle tissue is justified, which allows determining for them the optimal values of the therapeutic current and the value of its threshold value. model samples of muscle tissue are created, a magnetohydrodynamic method of generating electric current inside the patient's body is justified, design of a system for generating current and measuring the electrical response of a model fabric sample to the occurrence of electric current in it; determining the magnitude and spatial distribution of the current in the model fabric sample; comparison of obtained current values with known and permissible values in medical practice and proved their safety for a person. Calculated ratios are obtained, which connect value of excited local current with parameters of ultrasonic radiation, external permanent magnetic field and biological medium. The materials have been found that the current density excited in the local area of the biological medium is independent of the ultrasound frequency and is determined mainly by the intensity of the ultrasound and the constant magnetic field. The advantages of the current excitation method according to the present invention over the conventional galvanic method of passing current through the patient's skin are the ability to generate current in any desired local area of the patient's tissue and its complete safety. Conclusions: The scientific foundations of the new method of excitation of local current inside the human body have been developed and experimentally tested on model samples. Using this method can significantly increase the effectiveness of the treatment process based on the effect of current on blood microcirculation in predetermined areas of muscle tissue and for the first time will allow distinguishing and determining with high accuracy thresholds of their excitability by electric current.

Centralized spectrum sensing based on covariance matrix decomposition and particle swarm clustering

As the essential technology of cognitive radio networks, numerous spectrum sensing approaches have been established to date. However, some spectrum sensing methods seem to fail in providing an effective signal feature and an accurate decision threshold, which affects the detection accuracy of the spectrum holes. In this article, to further ameliorate the recognition rate of the spectrum holes, a novel centralized spectrum sensing method based on covariance matrix decomposition and particle swarm clustering is presented. A novel signal feature vector is extracted by the IQ decomposition method and the Cholesky decomposition technique. Moreover, particle swarm clustering algorithm is trained to obtain a decision classifier, which can be used to identify whether the licensed spectrum is available or not. Additionally, two different application scenarios are considered here. Indeed, simulations examples are provided to demonstrate that the proposed algorithm can considerably ameliorate the sensing performance for centralized spectrum sensing.