Iterative Window Research Articles

Multi-line structured light stripes clustering based on a custom iterative window

ObjectiveThis paper proposes a multi-line structured light stripe clustering method based on a custom iterative window to address the issue of stripes confusion caused by stripe breakage and noise points during the multi-line laser stripe matching process in binocular stereo vision. MethodsFirstly, the paper segments the measured object from the carrier plane. Secondly, the extracted centerlines are clustered using the custom iterative window. Finally, the stripes are numbered one by one after clustering the left and right images using barycentric coordinates of the stripes, based on the property of sequential consistency constraint, to complete the stereo matching of the stripes with the same number. ResultsThe experimental results show that the method can effectively cluster the centerline stripes on the measured object and solve the problem of stripes mis-corresponding when stereo matching of multi-line structured light stripes.

Intelligent framework for degradation monitoring, defect identification and estimation of remaining useful life (RUL) of bearing

The proposed intelligent framework seamlessly integrates degradation monitoring, defect identification, and remaining useful life (RUL) estimation for a comprehensive and holistic solution to bearing health assessment. It leverages a novel directed divergence (DD) measure to compare probability distributions, generating a Health Indicator (HI) that captures the health status of the bearings. This HI serves as the cornerstone for the framework's cohesive approach. An alert system, utilizing the modified Pauta criterion and iterative window scanning, enhances anomaly detection, while a double-check mechanism minimizes false alarms. For defect identification, dynamic analysis-assisted wavelet filtering harnesses a customized wavelet filter generated from simulated interactions between defective components and rolling elements. In parallel, the framework employs a graph convolution network (GCN) with a 1D health indicator, derived from the proposed directed divergence measure between the reference data and running conditions, to estimate RUL. By unifying these components, proposed framework offers an efficient, integrated, and proactive solution for monitoring, defect identification, and RUL estimation in bearing health assessment, ultimately enhancing system reliability and maintenance strategies.

Model-free optimal tracking policies for Markov jump systems by solving non-zero-sum games

This paper develops model-free optimal tracking policies for Markov jump systems by solving non-zero-sum games (NZSGs). First, coupled action and mode-dependent value functions (CAMDVFs) are built for solving a two-player NZSG and getting Nash equilibrium solutions. Second, we propose a value iteration (VI) algorithm to parallelly update policies under each mode by collecting data on different operation modes within each iterative window. Moreover, the iterative increasing convergence of the CAMDVFs is proved by introducing auxiliary functions between two adjacent iterations. It is worth pointing out that an influence function is introduced to remove abnormal data to improve the learning capability of the VI algorithm effectively. Finally, the tracking policies' validity, self-adaptability and application potential are verified by a numerical example and a generalized economic model.

An improved particle filter indoor fusion positioning approach based on Wi-Fi/ PDR/ geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning (PDR) and geomagnetic technology have the problems of large initial position error, low sensor accuracy, and geomagnetic mismatch. In this study, a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed, where Wi-Fi, PDR, and geomagnetic signals are integrated to improve indoor positioning performances. One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm. During the positioning process, an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively. The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected, which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm. In addition, this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving, which satisfies the real-time requirement of our fusion positioning approach. Through experimental verification, the average positioning accuracy of the proposed approach reaches 1.59 m, which improves 33.2% compared with the existing particle filter fusion positioning algorithms.

RCC: Enabling Receiver-Driven RDMA Congestion Control With Congestion Divide-and-Conquer in Datacenter Networks

The development of datacenter applications leads to the need for end-to-end communication with microsecond latency. As a result, RDMA is becoming prevalent in datacenter networks to mitigate the latency caused by the slow processing speed of the traditional software network stack. However, existing RDMA congestion control mechanisms are either far from optimal in simultaneously achieving high throughput and low latency or in need of additional in-network function support. In this paper, by leveraging the observation that most congestion occurs at the last hop in datacenter networks, we propose RCC, a receiver-driven rapid congestion control mechanism for RDMA networks that combines explicit assignment and iterative window adjustment. Firstly, we propose a network congestion distinguish method to classify congestions into two types, last-hop congestion and in-network congestion. Then, an Explicit Window Assignment mechanism is proposed to solve the last-hop congestion, which enables senders to converge to a proper sending rate in one-RTT. For in-network congestion, a PID-based iterative delay-based window adjustment scheme is proposed to achieve fast convergence and near-zero queuing latency. RCC does not need additional in-network support and is friendly to hardware implementation. In our evaluation, the overall average FCT (Flow Completion Time) of RCC is <inline-formula> <tex-math notation="LaTeX">$4{\sim}79\%$</tex-math> </inline-formula> better than Homa, ExpressPass, DCQCN, TIMELY, and HPCC.

Iterative design window search for polymer micromachined flapping-wing nano air vehicles using nonlinear dynamic analysis

Iterative design window search for polymer micromachined flapping-wing nano air vehicles using nonlinear dynamic analysis

Damage detection of large concrete structure via dual imaging system and contrastive embedding

Regular surface damage detection of large concrete structures is one of the important measures to ensure their stableness and reliability. Recent advancements in computer vision and deep learning have been increasingly applied to the high-precision detection of concrete surface damage. However, most damage monitoring and localization methods are based on high-resolution images taken from close range, and the images usually contain tiny areas of actual structures. This paper proposes a contrastive embedding model to detect and localize damage on a wide range of concrete images. To achieve high-definition imaging of damage to the surface of large structures, a multifocus, and high-resolution imaging system is designed and employed. Furthermore, a concrete surface damage dataset containing 1006 detection areas and 45,375 images with seven types is constructed by manual annotation based on the obtained multiscale and multiresolution images. In addition, a contrastive embedding model based on a deep neural network is then modified, trained, and tested using the constructed dataset. To the best of our knowledge, this paper is the first to jointly use contrastive embedding to deal with damage monitoring and localization. Moreover, an evaluation framework based on sliding window iteration and nonmaximum suppression is proposed to verify the robustness and accuracy of the proposed contrastive embedding model. Experiments show that the best model achieves the classification accuracy of up to 94.84% and localization of 97.57%.

An optimized joint medium access control and routing protocol with iterative contention window adjustment mechanism for a guaranteed lifetime in Internet of Things‐based wireless sensor networks

SummaryThe problem of extending the lifespan of wireless sensor networks (WSN) based on the Internet of Things (IoT) has been widely investigated over the last 20 years. This paper proposes an Optimized J‐RMAC (optimized joint routing and media access control protocol) to guarantee the network lifetime in IoT‐based WSN. Initially, all sensor nodes report their position and coverage information to the sink, which uses this information to pick a list of active nodes based on energy usage and active time. Then, the k‐covered network is formed to execute the routing task by selecting the active nodes with the largest sensing areas. A multi‐objective seagull optimization algorithm (MO‐SOA) represents routing paths between the source and destination by considering two objective functions: energy consumption cost and end‐to‐end delay of a routing path. After that, the contention window of the nodes in the routing path is adjusted using a new iterative adaptive adjustment process of the contention window with adjustment parameters (IAACW‐AP) to avoid message conflicts. The proposed protocol is simulated in the NS2 simulator. The performance of the proposed protocol will be compared with existing strategies in terms of network lifetime, packet delivery ratio, communication overhead, energy consumption, and delay.

A distributed scatterers InSAR method based on adaptive window with statistically homogeneous pixel selection for mining subsidence monitoring

Statistically homogeneous pixels (SHP) selection is one of the primary steps in time-series InSAR technologies based on distributed scatterers. However, using a fixed window to extract SHP in mining areas may fail to effectively protect and restore the deformation phase in subsequent phase optimization. Therefore, we propose a method based on iterative adaptive window to extract SHP. This approach uses the variation coefficient to judge the intensity of fringes to realize the adaptive window adjustment in and outside the subsidence areas based on different ranges. Moreover, we use eigenvalue decomposition to optimize the phase, and use small baseline subset (SBAS) interferometry to perform time-series modelling and deformation calculations for the selected high-coherence points. The reliability of this method is verified from comparisons with leveling data. Compared with traditional time-series InSAR methods, the proposed method significantly improves the monitoring point density, deformation accuracy and has broad prospects in mining subsidence monitoring.

Development of shutdown dose rate calculation code based on cosRMC and application to benchmark analysis

Accurate estimation of shutdown dose rate (SDR) has important reference significance for the design, operation and maintenance of fusion devices. In order to obtain the decay gamma source distribution with higher spatial resolution, a shutdown dose rate calculation code was developed based on the Rigorous two-step (R2S) method. The Monte Carlo code cosRMC and the radioactivity inventory code ALARA were coupled to calculate SDR in complex geometry based on mesh-tally. The ray tracking method was used to obtain the voxel-averaged nuclide density, and a decay photon sampling module was developed to accurately describe the distribution of the activation source. In order to improve the computational efficiency in deep penetration problems, the global variance reduction technique based on iterative weight window was adopted. The developed code was firstly verified with rectangular parallelepiped model consists of steel and water, in which the effect of bremsstrahlung photon was also discussed. Then the code was applied to the calculation and analysis of the ITER port plug benchmark, by comparing with other SDR calculation codes. The influence of different activation libraries on the decay gamma spectra and the effect of mesh resolution on the calculation accuracy were investigated. The results showed that the SDR function in cosRMC code is able to calculate SDR accurately with high spatial resolution for deep penetration problems.

Thermal zoning and window optimization framework for high-rise buildings

Window sizing and configuration can have a significant influence on building energy consumption. Window selection often has a conflicting objective on heating, cooling, and lighting performance. The smaller window performs better on controlling heat loss in winter and solar heat gain in summer, while the larger window performs better on providing views, daylight, and solar heat gains in winter. Also, the energy consumption analyses of high-rise buildings have some fundamental limitations that include the changes in microclimate parameters with altitude, the treatment of building size, uncertainties associated with the existing convective heat transfer coefficients correlations (CHTC). This study provides a framework for simulation-based optimization of window configuration for a high-rise building to minimize its energy consumption. The technique involves CFD modeling to validate and develop new-CHTCs, a Building Energy Simulation used to assess the energy consumption using the newly developed CHTC, and a numerical optimizer for iterative optimal window configuration selection. The decision parameters are window size and room location. The thermal comfort temperature set points and daylight illuminance are taken as constraints. The proposed approach is implemented as a case study on a single by single room model basis positioned at different heights in an isolated 100 m tall building exposed to Boston, MA microclimate. For a room located on the 2nd, 15th, and 29th floor, an optimum window configuration of 30%, 48%, and 30%, window-to-wall ratio, respectively, are obtained.

Effects of Smoking on Heart Rate Variability Through Information Based Similarity Index

Smoking is a major public health problem. In this paper, we introduce a new approach regarding the analysis of the Heart Rate Variability (HRV) signals using Information based Similarity Index Method. From Electrocardiogram (ECG) signals first HRV signals extracted using Moving Window iteration technique. Finally study the changes of the R-R interval signals before and after smoking of twenty young male volunteers using 3 bit rank from Symbolic Dynamics which is extracted from Forced Dynamics of the original signals. Then we compare probability distributions between each pair of subjects both in normal and smoking states by using Q-Q plot. Our experimental provides satisfactory results in this context.

Heuristics for the Reversal and Transposition Distance Problem.

We present three heuristics-Sliding Window, Look Ahead, and Iterative Sliding Window-to improve solutions for the Sorting Signed Permutations by Reversals and Transpositions Problem. We investigate the classical version of the problem as well as versions restricted to prefix and prefix or suffix operations. To assess the heuristics based on its improvement, we implemented algorithms described in the literature to provide initial solutions. Although we have a limited number of problems, these heuristics can be applied to many others within the area of genome rearrangement. When time is a crucial factor, Sliding Window is a better choice because it runs in linear time. If the quality of the solution is a priority, Look Ahead should be preferred. Iterative Sliding Window is the most flexible heuristic and allows us to find a trade-off for specific scenarios where running time and solution quality are relevant.

Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus

Depth from focus (DFF) is a technique for estimating the depth and three-dimensional (3D) shape of an object from a multi-focus image sequence. At present, focus evaluation algorithms based on DFF technology will always cause inaccuracies in deep map recovery from image focus. There are two main reasons behind this issue. The first is that the window size of the focus evaluation operator has been fixed. Therefore, for some pixels, enough neighbor information cannot be covered in a fixed window and is easily disturbed by noise, which results in distortion of the model. For other pixels, the fixed window is too large, which increases the computational burden. The second is the level of difficulty to get the full focus pixels, even though the focus evaluation calculation in the actual calculation process has been completed. In order to overcome these problems, an adaptive window iteration algorithm is proposed to enhance image focus for accurate depth estimation. This algorithm will automatically adjust the window size based on gray differences in a window that aims to solve the fixed window problem. Besides that, it will also iterate evaluation values to enhance the focus evaluation of each pixel. Comparative analysis of the evaluation indicators and model quality has shown the effectiveness of the proposed adaptive window iteration algorithm.

Simulation of gamma ray deep penetration through iron based on grid weight window iteration

To improve the efficiency of gamma ray deep penetration shielding calculation, this paper analyzed the influence of gamma ray energies, shield thickness, shielding materials and number of simulated particles on the transport calculation of gamma ray deep penetration, and proposed a Monte Carlo simulation method for gamma ray deep penetration shielding by iterative calculation based on the combination of grid weight window and material density gradually approaching true density. The simulation results show that, by using the iterative calculation method based on grid weight window in MCNP program, it can be realized that gradual transition from non deep penetration shielding simulation to deep penetration shielding simulation. The method can effectively solve the problem of gamma ray deep penetration calculation, and it can make the statistical error of the transmission coefficient of gamma ray penetrating the iron shield with a thickness of 60 times the mean free path of gamma ray less than 5%, which can meet the demand of gamma ray deep penetration simulation.

A sensor-based wrist pulse signal processing and lung cancer recognition

Pulse diagnosis is an efficient method in traditional Chinese medicine for detecting the health status of a person in a non-invasive and convenient way. Jin’s pulse diagnosis (JPD) is a very efficient recent development that is gradually recognized and well validated by the medical community in recent years. However, no acceptable results have been achieved for lung cancer recognition in the field of biomedical signal processing using JPD. More so, there is no standard JPD pulse feature defined with respect to pulse signals. Our work is designed mainly for care giving service conveniently at home to the people having lung cancer by proposing a novel wrist pulse signal processing method, having an insight from JPD. We developed an iterative slide window (ISW) algorithm to segment the de-noised signal into single periods. We analyzed the characteristics of the segmented pulse waveform and for the first time summarized 26 features to classify the pulse waveforms of healthy individuals and lung cancer patients using a cubic support vector machine (CSVM). The result achieved by the proposed method is found to be 78.13% accurate.

Adaptive PIV algorithm based on seeding density and velocity information

An adaptive particle image velocimetry (PIV) processing algorithm to increase local spatial resolution and obtain accurate results is presented. Adaptive sampling criterion is based on seeding density and the analysis of velocity information. The proposed methodology places more measurement points in the region where high seeding density exists or flow parameters vary drastically. This methodology is effective in PIV image processing, especially in non-optimal PIV experimental conditions. The working principle of the adaptive processing method in this paper is to generate spring force on the bias of the deviation between the ideal and actual distances of measurement points. Under artificial force, the sampling points are moved to desirable places to satisfy sampling density function. Sampling density function is important in processing and ingeniously conceived based on the combination of vorticity and velocity gradients. The viability of the method is elaborated through synthetic and experimental tests. The synthetic test verifies the effectiveness of the adaptive processing on the basis of flow information. The bias and random error can be reduced by 7.3% and 4.0%, respectively. The result of the experimental test shows that the sampling points and the interrogation window size can be arranged according to the spatial distribution of seeding density and the actual flow features. Compared with iterative window deformation method, spatial resolution is locally enhanced, and the robustness and reliability of the result are increased, particularly in poor seeding regions.

First-Order Algorithms for Convex Optimization with Nonseparable Objective and Coupled Constraints

In this paper, we consider a block-structured convex optimization model, where in the objective the block variables are nonseparable and they are further linearly coupled in the constraint. For the 2-block case, we propose a number of first-order algorithms to solve this model. First, the alternating direction method of multipliers (ADMM) is extended, assuming that it is easy to optimize the augmented Lagrangian function with one block of variables at each time while fixing the other block. We prove that Open image in new window iteration complexity bound holds under suitable conditions, where t is the number of iterations. If the subroutines of the ADMM cannot be implemented, then we propose new alternative algorithms to be called alternating proximal gradient method of multipliers, alternating gradient projection method of multipliers, and the hybrids thereof. Under suitable conditions, the Open image in new window iteration complexity bound is shown to hold for all the newly proposed algorithms. Finally, we extend the analysis for the ADMM to the general multi-block case.

Multipart Vehicle Detection Using Symmetry-Derived Analysis and Active Learning

On-road vehicle detection is a critical operation in automotive active safety systems such as collision avoidance, merge assist, lane change assistance, etc. In this paper, we present VeDAS—Vehicle Detection using Active learning and Symmetry. VeDAS is a multipart-based vehicle detection algorithm that employs Haar-like features and Adaboost classifiers for the detection of fully and partially visible rear views of vehicles. In order to train the classifiers, a modified active learning framework is proposed that selects positive and negative samples of multiple parts in an automated manner. Furthermore, the detected parts from the classifiers are associated by using a novel iterative window search algorithm and a symmetry-based regression model to extract fully visible vehicles. The proposed method is evaluated on seven different datasets that capture varying road, traffic, and weather conditions. Detailed evaluations show that the proposed method gives high true positive rates of over 95% and performs better than existing state-of-the-art rear-view-based vehicle detection methods. Additionally, VeDAS also detects partially visible rear views of vehicles using the residues left behind after detecting the fully visible vehicles. VeDAS is able to detect partial rear views with a detection rate of 87% on a new partially visible rear-view vehicle dataset that we release as part of this paper.

MULTI-LEVEL SAMPLING APPROACH FOR CONTINOUS LOSS DETECTION USING ITERATIVE WINDOW AND STATISTICAL MODEL

This paper proposes a Multi-Level Sampling (MLS) approach for continuous Loss of Self-Similarity (LoSS) detection using iterative window. The method defines LoSS based on Second Order Self-Similarity (SOSS) statistical model. The Optimization Method (OM) is used to estimate self-similarity parameter since it is fast and more accurate in comparison with other estimation methods known in the literature. Probability of LoSS detection is introduced to measure continuous LoSS detection performance. The proposed method has been tested with real Internet traffic simulation dataset. The results demonstrate that normal traces have probability of LoSS detection below the threshold at all sampling levels. Meanwhile, false positive detection can occur where abnormal traces have probability of LoSS that imitates normal behavior at sampling levels below 100ms. However, the LoSS probability exceeds the threshold at sampling levels larger than 100ms. Our results show the possibility of detecting anomaly traffic behavior based on obtaining continuous LoSS detection monitoring