Filtering Tasks Research Articles

Robust Kalman filters based on the sub-Gaussian [formula omitted]-stable distribution

Motivated by filtering tasks under a linear system with non-Gaussian heavy-tailed noise, various robust Kalman filters (RKFs) based on different heavy-tailed distributions have been proposed. Although the sub-Gaussian α-stable (SGαS) distribution captures heavy tails well and is applicable in various scenarios, its potential has not yet been explored for RKFs. The main hindrance is that there is no closed-form expression of its mixing density. This paper proposes a novel RKF framework, RKF-SGαS, where the process noise is assumed to be Gaussian and the heavy-tailed measurement noise is modelled by the SGαS distribution. The corresponding joint posterior distribution of the state vector and auxiliary random variables is approximated by the Variational Bayesian approach. Also, four different minimum mean square error (MMSE) estimators of the scale function are presented. The first two methods are based on the Importance Sampling (IS) and Gauss–Laguerre quadrature (GLQ), respectively. In contrast, the last two estimators combine a proposed Gamma series (GS) based method with the IS and GLQ estimators and hence are called GSIS and GSGL. Besides, the RKF-SGαS is compared with the state-of-the-art RKFs under three kinds of heavy-tailed measurement noises, and the simulation results demonstrate its estimation accuracy and efficiency.

Social media distraction: How distracting are visual social media cues and what influences users' distractibility?

Social media cues can distract people from working on intended tasks. Accordingly, in the context of social media, this study examines what influences a person's distractibility, specifically whether social media cues are more distracting than neutral cues. Across three experiments (N = 246), we measured user distractibility with the filter task paradigm, a measure of the ability to filter out distractions. The results showed that social media cues are not more visually distracting than complex neutral cues, but they are more distracting than simple neutral cues. In addition, distractibility increases with an increase in the number of distractors. Moreover, the findings show that individual differences (i.e. self-control, impulsivity, FoMO, problematic social media use) did not impact distractibility (Experiment 1). Experiment 2 found that state cyberostracism influenced distractibility, whereby socially excluded participants were more distracted than those who felt included. State fear of missing out (FoMO) did not influence distractibility (Experiment 3). This study contributes to the understanding of the underlying mechanisms of social media distraction by finding that social media cues are not more distracting than other neutral, more complex cues. This might be due to the high task demand under which we measured distractibility in our experiments. Future work is needed to find out more about differences in social media distraction and influencing factors of user's distractibility.

Machine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade

The Global Event Processor (GEP) FPGA is an area-constrained, performance-critical element of the Large Hadron Collider's (LHC) ATLAS experiment. It needs to very quickly determine which small fraction of detected events should be retained for further processing, and which other events will be discarded. This system involves a large number of individual processing tasks, brought together within the overall Algorithm Processing Platform (APP), to make filtering decisions at an overall latency of no more than 8ms. Currently, such filtering tasks are hand-coded implementations of standard deterministic signal processing tasks.In this paper we present methods to automatically create machine learning based algorithms for use within the APP framework, and demonstrate several successful such deployments. We leverage existing machine learning to FPGA flows such as hls4ml and fwX to significantly reduce the complexity of algorithm design. These have resulted in implementations of various machine learning algorithms with latencies of 1.2 μs and less than 5% resource utilization on an Xilinx XCVU9P FPGA. Finally, we implement these algorithms into the GEP system and present their actual performance.Our work shows the potential of using machine learning in the GEP for high-energy physics applications. This can significantly improve the performance of the trigger system and enable the ATLAS experiment to collect more data and make more discoveries. The architecture and approach presented in this paper can also be applied to other applications that require real-time processing of large volumes of data.

MUSCULOSKELETAL DISORDERS RISK ASSESSMENT AMONG SMALL TOFU FACTORY WORKERS IN TANGERANG CITY: AN INDONESIAN CASE STUDY

Introduction: Conventional tofu production involves various manual handling tasks which increases the risk of musculoskeletal disorders (MSDs). A previous study found a very high risk of MSDs in tofu manufacturing, especially in the filtering task with a REBA score of 13. Aims: This study aims to evaluate the risk of MSDs in tofu factory workers. Methods: Workplace observations and unstructured interviews were conducted at two traditional tofu factories (X and Y) and a more modern silken tofu factory to identify the risk of MSDs. The Nordic Musculoskeletal Questionnaire and the Quick Exposure Check (QEC) method were used to determine the prevalence and risk of MSDs. A total of 52 workers voluntarily participated in this study. Results: The traditional and silken tofu manufacturing processes involved seven and fifteen tasks, respectively. This study found that workers in the traditional tofu factories had a higher prevalence of MSDs than those in the silken tofu factory. In the past 12 months, traditional tofu factory workers reported MSDs in their lower back (94.4%), right shoulder (77.8%), and neck (61.1%). On the other hand, silken tofu factory workers reported MSDs in their lower back (81.3%), right shoulder (62.5%), and upper back region (56.3%). According to the QEC method, grinding and filtering were identified as two tasks with a very high risk of MSDs in the traditional factories. In addition, transferring tofu crates from cold water to hot water tub in the silken factory was identified as a very high risk of MSDs. Conclusion: Intervention is necessary for tasks with a very high risk of MSDs in small tofu factories.

MEASUREMENTS AGAINST NOISE BACKGROUND AND WITH FLUCTUATIONS

When performing non-contact and remote measurements of various parameters, additional obstacles arise due to the influence of the intermediate medium and the measuring path between different links of the measuring system. These obstacles include:
 
 Noises arising in communication paths, intermediate media, complexes of measuring and auxiliary equipment. These noises are superimposed on the useful signal and distort it.
 Fluctuations of the general and selective energy absorption of the useful signal in the intermediate medium and measuring communication path. In all these cases, it is necessary to isolate the useful signal and restore its original true value and nature of non-stationarity.
 
 The task of filtering in its general form has not been completely solved until now. In order to obtain maximum information about the useful signal, such an operator L is selected that the function Y(τ)=L[X(τ)] slightly differs from the useful signal U(τ), where X(τ)= U(τ)+ V(τ ); V(τ) is noise (interference). To determine the values of the function Y(τ) at a certain time τ_0, all previous values of the function X(τ) from the interval -∞≤τ≤τ_0 should be used. To distinguish the functions Y(τ) and U(τ) the variance of the difference of these functions is used. The problem of Wiener filtering has been solved by various authors, in particular the Kalman-Busy filter equation for linear Gaussian systems. Optimal (suboptimal) Bayesian filters are constructed for a nonlinear stochastic system disturbed by "white" Gaussian noise with continuous time and discrete measurements that minimizes the root-mean-square error of estimation. The exact solution of this problem in the interval between the moments of measurement results is subject to the Fokker-Planck differential equation with private derivatives.
 Considered examples: measuring the temperature and pressure of the environment under investigation, distorted by various random factors in the form of "white" noise, as well as tracking the coordinates and velocities of an aircraft performing a maneuver in the horizontal plane - as test tasks in the construction of a sigma-point Kalman filter - as a multi-cube Kalman filter.

Further tests of sequence-sensitive models in a modified garner task using separable dimensions.

In the study of perceptual categorization, a key distinction is made between separable and integral dimensions. Separable dimensions are easy to attend in isolation, while integral dimensions are not. Little et al. (2016) showed that when trial-by-trial responses are analyzed, a consistent pattern of sequential effects was found in a modified Garner paradigm using integral-dimension stimuli. The present experiments investigated whether these pronounced sequential effects are also found with separable-dimension stimuli. Four experiments using two different types of separable dimensions were conducted. The results indicated that similar patterns of sequential effects were present for separable-dimension stimuli, but, unlike for integral dimensions, the effect of a change in the irrelevant dimension in the filtering task was not found. Further, for separable dimensions, the overall pattern of sequential effects did not vary between the Garner tasks (i.e., control, correlated, and filtering). To explain these results, we fit a sequence-sensitive exemplar model and compared the fits of this model to a novel sequence-sensitive feature model, in which only the relevant feature influences the categorization decision. We found that the full exemplar model provided a more compelling account of both our separable dimension data and the integral dimension data of Little et al. (2016). These findings provide a more complete understanding of perceptual categorization and add to the growing body of literature on the prevalence and critical implications of strong sequential effects in cognitive tasks. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Energy-Saving Task Scheduling Based on Hard Reliability Requirements: A Novel Approach with Low Energy Consumption and High Reliability

With the increasing complexity of application situations in multi-core processing systems, how to assure task execution reliability has become a focus of scheduling algorithm research in recent years. Most fault-tolerant algorithms achieve hard reliability requirements through task redundancy, which increases energy consumption and contradicts the concept of sustainable development. In this paper, we propose a new algorithm called HDFE (Heterogeneous-Dag-task-fault-tolerance-energy-efficiency algorithm) that combines DVFS technology and task replication technology to solve the scheduling problem of DAG applications concerning energy-saving and hard reliability requirements in heterogeneous multi-core processor systems. Our algorithm is divided into three phases: the priority calculation phase, the task replication phase, and the task assignment phase. The HDFE algorithm achieved energy savings while meeting hard reliability requirements for applications, which was based on the interrelationship between reliability and energy consumption in filtering task replicas. In the experimental part of this paper, we designed four comparison experiments between the EFSRG algorithm, the HRRM algorithm, and the HDFE algorithm. The experimental results showed that the energy consumption of task scheduling using the HDFE algorithm is lower than other algorithms under different scales, thus achieving energy savings and complying with the concept of sustainable development.

Split ‘n’ merge net: A dynamic masking network for multi-task attention

In this paper we propose a novel Multi-Task Learning (MTL) framework, Split ‘n’ Merge Net. We draw the inspiration from the multi-head attention formulation of Transformers and propose a novel, simple and interpretable pathway to process information captured and exploited by multiple tasks. In particular, we propose a novel splitting network design, which is empowered with multi-head attention, and generates dynamic masks to filter task specific information and task agnostic shared factors from the input. To drive this generation, and to avoid the oversharing of information between the tasks, we propose a novel formulation of the mutual information loss which encourages the generated split embeddings to be distinct as possible. A unique merging network is also introduced to fuse the task specific, and shared information and generate an augmented embedding for the individual downstream tasks in the MTL pipeline. We evaluate the proposed Split ‘n’ Merge Network on two distinct MTL tasks where we achieve state-of-the-art results for both. Our primary, ablation and interpretation evaluations indicate the robustness and flexibility of the propose approach and demonstrates its applicability to numerous, diverse real-world MTL applications.

Quality evaluation of metal surfaces treatment by wavelet analysis

Purpose. Analyze the effectiveness of using wavelet analysis to assess the quality of metal surfaces. Investigate the possibility of using wavelet analysis in ultrasonic flaw detection. Determine the optimal wavelet families and their criteria for assessing the quality of metal surface processing.
 Research methods. Orthogonal wavelets are considered: Daubechies wavelet, Simlet wavelet and Coiflet wavelet, which provide the possibility of performing a discrete wavelet transform procedure. The criteria influencing the effectiveness of ultrasonic signal filtering by methods using wavelet analysis are considered. Ultrasonic signals were filtered using wavelet functions.
 Results. It has been determined that for successful signal filtering, the selected wavelet method must provide a discrete wavelet transformation and have a similarity in the wavelet function shape in the local features of the ultrasonic signals flaw detector. During the work, a rigid threshold for limiting the detail coefficients of wavelet analysis was chosen, as it is the best for filtering tasks. The filtering efficiency is confirmed by the relatively high signal to noise ratio, as well as by the fact that the shape of the pulse extracted from the defect remained almost unchanged.
 Scientific novelty. When using the Daubechies and Coiflet wavelets as basic functions, as a result of wavelet filtering, it was possible to increase the signal to noise ratio by 20 dB and confidently isolate the useful signal against the background noise, which indicates the prospects of using this kind of transformations in filtering problems.
 Practical value. The obtained solutions can be used for implementation in signal filtering algorithms in digital processing units of automated non-destructive ultrasonic control systems.

Improving Performance of Hardware Adaptive Filter

An adaptive filter (AF) is a digital filter that has a transfer function that changes based on changes in the surroundings. Adaptive filters can adjust their weights using cost functions similar to a neural network. Implementation of the adaptive filter in hardware allows it to have higher speed (Consumes lesser number of clock cycles) and hence also saving on power. A regular Digital Signal Processor (DSP) may also be employed to do the same but it will never come close to the performance of dedicated hardware. An improvement in this said hardware will directly boost the performance of all use-cases. Simulation of the existing design gives an idea of the current data flow and architecture. Exploring different potential improvements in design and then weighing the outcome gain vs effort to add the functionality is done. An improvement is chosen and implemented. Once it does the intended functionality, It is profiled to see the improvement in performance. A large Filter task is divided into multiple threads. These are executed sequentially. In the current design, a thread has 3 status registers to indicate whether it’s in progress, pending, next. A scenario in which a certain thread needs to be canceled, it can only be done if the thread is not already in progress, which will lead to wasted clock cycles. Hence the ability to stop a thread executing midway will save those clock cycles.

PLB: a resilient and adaptive task scheduling scheme based on multi-queues for cloud environment

This research paper proposes a novel approach named priority-based load balancing (PLB) for cloud computing environment. The PLB provides a resilient and adaptive task scheduling using multi-queues. Numerous strategies have already been proposed in the past researches to prioritize the tasks and mapping all the tasks to different resources available on the cloud. There is still a hindrance in the performance due to the negligible attention paid to the unused resources and tasks having low priority, eventually leading to starvation problem. To this end, the PLB algorithm has been partitioned into four sub-procedures, namely (i) Starvation-free task allocation, (ii) Inserting tasks into the dispatcher, (iii) Re-ordering tasks inside the queues and eventually, (iv) Mapping tasks onto the Virtual Machines (VMs) calculating the cost incurred for all the corresponding VMs. The sole motivation of this research work is to optimize the performance parameters by allocating all the jobs to all the available resources in the workflow model. It also consolidates the job categorization in the priority-based multi-queues, while filtering tasks from all the queues to overcome the deprivation of low priority tasks. In this paper, a test-bed setup has been deployed using CloudSim 3 and TCS WAN emulator for experimentation and results evaluation. The experimental setup imbibes different aspects such as performance measures, average response time, makespan time in order to ascertain efficiency, resource utilization ratio and bandwidth of the workflow model. The obtained results are further compared with five different approaches including- First Come First Serve, Round Robin, Min–Min, Max–Min and ACO and it was observed that the proposed strategy yielded more efficiency and accuracy in most of the cases. The experimental results have been further validated and demonstrated in order to justify the claims of the proposed approach, being able to tackle out different priority tasks and resource allocation in a stable and optimum manner.

Unsharp Mask Guided Filtering.

The goal of this paper is guided image filtering, which emphasizes the importance of structure transfer during filtering by means of an additional guidance image. Where classical guided filters transfer structures using hand-designed functions, recent guided filters have been considerably advanced through parametric learning of deep networks. The state-of-the-art leverages deep networks to estimate the two core coefficients of the guided filter. In this work, we posit that simultaneously estimating both coefficients is suboptimal, resulting in halo artifacts and structure inconsistencies. Inspired by unsharp masking, a classical technique for edge enhancement that requires only a single coefficient, we propose a new and simplified formulation of the guided filter. Our formulation enjoys a filtering prior from a low-pass filter and enables explicit structure transfer by estimating a single coefficient. Based on our proposed formulation, we introduce a successive guided filtering network, which provides multiple filtering results from a single network, allowing for a trade-off between accuracy and efficiency. Extensive ablations, comparisons and analysis show the effectiveness and efficiency of our formulation and network, resulting in state-of-the-art results across filtering tasks like upsampling, denoising, and cross-modality filtering. Code is available at https://github.com/shizenglin/Unsharp-Mask-Guided-Filtering.

Multilevel term analysis for adaptive document filtering1

Humans tend to organize information in documents in a logical and intentional way. This organization, which we call textual structure, is commonly in terms of sections, chapters, paragraphs, or sentences. This structure facilitates the understanding of the content that we want to transmit to the readers. However, such structure, in which we usually encode the semantic content of information, is not usually exploited by the filtering methods for the construction of a user profile. In this work, we propose the use of term relations considering different context levels for enhancing document filtering. We propose methods for obtaining the representation, considering the existence of imbalance between the documents that satisfy the information needs of users, as well as the Cold Start problem (having scarce information) during the initial construction of the user profile. The experiments carried out allowed to assess the impact, in terms of T11SU measure, on the filtering task of the proposed representation.

Mathematical method of graphic data spline-processing

When you need to process experimental data, there is a problem of adequate reflection of physical processes arises. In engineering practice, situations are quite common when, based on a limited amount of numerical data (experimental or calculated), it is necessary to determine the nature of the functional dependence that they represent and calculate the values of this dependence for an arbitrary argument. In this case, it becomes necessary to replace a complex dependence with a simpler one, which, however, would convey the character of a complex one with an accuracy acceptable for practical purposes. Also, in the process of filtering or compressing any information, data is usually represented as a sequence of values. The data sequence obtained in this way is used further to construct a function of a certain class that approximates the input signal in the sense of the selected criterion. Further, when carrying out various transformations, a constructed function is used instead of a signal, which approximates it. To process such data, it is proposed to use spline transformations, which are one of the most progressive methods of data processing. The theory of interpolation and smoothing computational schemes is currently widely developed. This approach, called the numerical-analytical approach, is being applied increasingly in modern signal processing theory, which is explained by computational considerations. In this case, the degree of adequacy of the numerical-analytical model constructed in this way to the real investigated signal, the error in the approximation of its individual characteristics is also important. The article is devoted to the development of a method for constructing a linear spline with an adapted mesh of gluing nodes of this spline to improve the approximation properties of the spline function. For this, an iterative method of constructing splines is used. The development of a method for constructing a linear spline with an adapted mesh of the gluing nodes of this spline makes it possible to reduce the standard deviations of the spline from the function being approximated. That is, it improves the approximation properties of the spline function, which can be used to process various digital data. In particular, we use them in the tasks of filtering and compressing graphic information, processing satellite signals.

Exploration of Collaboration Filtering Techniques for Product Recommendation

Today, recommendation system has been globally adopted as the most effective and reliable search engine for knowledge extraction in the field of education, economics and scientific research. Collaborative filtering is a proven techniques used in recommender system to make predictions or recommendations of the unknown preferences for users based on the known user preferences. In this paper, collaborative filtering task and their challenges are explored, study the different recommendation techniques and evaluate their performance using different metrics.

Optical refractive index sensor with two plasmonic double-square resonators for simultaneous sensing of human blood groups

In this study, we investigated a compact refractive index (RI) sensor containing two concentric double-square resonators with plasmonic metal/insulator/metal (MIM) structures to facilitate the sensing of human blood groups. The sensing surveys were conducted using the finite difference time domain method. Intensive coupling occurred due to momentum matching between the equivalent modes of the square resonators. The results demonstrated that the modes of resonance had high contraction ratios in this device and increasing the material RI in the resonators led to remarkable red shifts of the dip wavelengths. The sensing performance was enhanced by using two multiple concentric resonators to effectively increase the strength of the light–analyte interaction, which is useful for sensing applications. Our proposed nano-sensor obtained sensitivity, sensing resolution, and figure of merit values of 1380 nm/RIU, 7.24 × 10−4 RIU, and 104 RIU‒1, respectively. The effects of the geometrical variables on the output spectra were studied in detail. Furthermore, this MIM-based sensor may have potential applications in highly integrated optical circuits for monitoring and filtering tasks.

Refractive index sensor based on concentric triple racetrack resonators side-coupled to metal–insulator–metal waveguide for glucose sensing

A refractive index sensor using two concentric triple racetrack resonators with plasmonic metal–insulator–metal (MIM) waveguides is suggested to provide more freedom for sensing applications. Due to momentum matching, intensive coupling happens between the equivalent modes of each section of the resonators. The sensing properties are numerically discussed in terms of the finite difference time domain method. According to the outcomes, when increasing the refractive index of the material in the outer sections of the racetrack resonators, the dip wavelengths show a notable red shift. The sensing performance can be enhanced by using two multiple concentric resonators that effectively increase the strength of the light–analyte interaction, which is useful for sensing applications. Our proposed nanosensor offers a high sensitivity value, sensing resolution, and figure of merit of 1618 nm/RIU, 6.18×10−4RIU, and 89RIU−1, respectively. Also, the racetrack resonators coupled to MIM waveguides can be simply integrated into chip circuits with other optical devices to perform monitoring and filtering tasks.

Frontal Alpha Asymmetry and Inhibitory Control among Individuals with Cannabis Use Disorders.

To better understand the biopsychosocial mechanisms associated with development and maintenance of cannabis use disorder (CUD), we examined frontal alpha asymmetry (FAA) as a measure of approach bias and inhibitory control in cannabis users versus healthy nonusers. We investigated: (1) whether FAA could distinguish cannabis users from healthy controls; (2) whether there are cue-specific FAA effects in cannabis users versus controls; and (3) the time course of cue-specific approach motivation and inhibitory control processes. EEG data were analyzed from forty participants (CUD (n = 20) and controls (n = 20)) who completed a modified visual attention task. Results showed controls exhibited greater relative right hemisphere activation (indicating avoidance/withdrawal motivation) when exposed to cannabis cues during the filtering task. By contrast, cannabis users exhibited greater relative left activation (approach) to all cues (cannabis, positive, negative, and neutral), reflecting a generalized approach motivational tendency, particularly during later stages of inhibitory control processes. The difference between cannabis users and controls in FAA was largest during mid- to late processing stages of all cues, indicating greater approach motivation during later stages of information processing among cannabis users. Findings suggest FAA may distinguish cannabis users from healthy controls and shows promise as a measure of inhibitory control processes in cannabis users.

Utilitarian Attention by Children with Autism Spectrum Disorder on a Filtering Task.

The findings are evidence that persons with ASD benefit more than typically developing (TD) persons from spatial framing cues in focusing their attention on a visual target. Participants were administered a forced-choice task to assess visual filtering. A target stimulus was presented on a screen and flanker stimuli were presented simultaneously with or after the target, with varying stimuli onset asynchronies (SOAs). Regardless of SOA, TD children showed the expected distracting effects with slower reaction times (RTs) when flankers were at closer distances from the target. However, children with ASD displayed shorter RTs in the conditions in which the stimuli were presented simultaneously or with a short SOA. These findings are interpreted as reflecting utilitarian attention among children with ASD.

Semiparametric statistical analysis of the blade tip timing data for detection of turbine rotor speed instabilities

AbstractIn this paper, we propose an extension of an existing standard approach to the blade tip timing data when analyzing turbine rotor vibrations. Instabilities related to the non‐constant trigonometric coefficients at prominent frequencies might not be noticed in the traditional analyses. Our methodology is based on time‐varying coefficient statistical models, and hence it allows a full formalization of the estimation and other inferential tasks (uncertainty assessments, hypothesis tests etc.). First, we formulate a univariate generalized additive model that is useful for investigation of vibration behavior of individual blades. It can extract trajectories of the trigonometric coefficients. Using the trajectories, one can investigate time changes of power at a given frequency. In the second approach, we use a multivariate model for simultaneous assessment of all of the rotor blades. The model acknowledges similarity of the vibration behavior of closely located blades. It is formulated as a state‐space model, and hence it allows for a wide range of prediction, smoothing, and filtering tasks. We illustrate the performance and practical usefulness of our models on real blade tip timing turbine monitoring data obtained from the Czech nuclear power plant Temelin.