Threshold Selection Process Research Articles

Solar panel surface dirt detection and removal based on arduino color recognition

Color sensing is a technique for identifying physical changes in materials based on appearance assessment. Dirt deposition on solar panels can change their physical appearance and performance. Considering that dirt accumulation on solar panels needs monitoring to make efficient cleaning schedules, reduce unnecessary costs, and optimize solar panel output generation. Color sensing can achieve fast, accurate, and economical dirt detection, unlike the use of robotic cameras, mathematical formulae, and considering varying output current and voltage methods. Here, we introduce a method that detects and removes dirt on solar panels based on TCS3200 and Arduino Uno components. The approach targets (i.) Panel color measurement, calibration, threshold selection process, (ii.) comparison of color measurement values, and (iii.) align further calibration in response to discoloration of solar panels. This method aims to correct the dirt detection methods previously in use. Hence, a high-speed rolling brush arrangement is designed to improve the cleaning of the solar panel without using water. Further investigations of the panel's color may require some improvement in terms of increasing the sensitivity of the color sensor even with increased distance from the solar panel. Combining multiple color sensors may also be necessary.

An adaptive time–frequency approach based secured back-up distance protection scheme for system stress conditions

Critical operating conditions of power system may lead to distance relay security issues. Distance relay back-up protection is offered with long reach setting and due to this under sudden change in voltage and current parameters during non-fault situations also considered as a fault by the protection function in an integrated network. The conventional technique fails to discriminate system stress conditions such as power swing, voltage instability and load encroachment from an actual symmetrical fault in the back-up zone (third zone (Z3)) of the distance relay. To mitigate this, a Modified Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (MCEEMDAN) technique is used to decompose the instantaneous power and to develop a reliable index. The most significant intrinsic mode function (IMFs) is extracted to estimate the energy as used as detection index. An adaptive statistical threshold selection process is developed to detect and discriminate all system stress conditions from symmetrical fault in the back-up zone of the distance relay. The proposed algorithm is a supervisory scheme for the back-up zone setting (Z3) of the distance relay. Simulation results for different stress conditions, presence of noise, sudden switching of load and symmetrical fault are presented to highlight the strength of the method.

Balanced Mix Design and Benchmarking: A Case Study in Establishing Performance Test Thresholds

A balanced mix design (BMD) approach for asphalt mixtures incorporates laboratory performance tests into the mix design acceptance process. However, test selection and subsequent performance thresholds must be selected, which is no easy endeavor when considering the number of promising tests and the variation in asphalt mix designs and components between states or regions. While the Virginia Department of Transportation’s (VDOT) BMD specification has been circulated for multiple years now, this article presents the approach taken by VDOT to investigate the various tests, considerations taken in selecting tests, and the establishment of performance thresholds. Data and discussion of six different performance tests encompassing durability, rutting, and cracking for 11 common Virginia mixes are presented. The performance threshold selection process is outlined. Further considerations in the final test selection process are discussed. In conclusion, the initial draft specification for BMD is provided.

Design of an Effective Archive Management System with a Compression Approach for Network Information Technology

Due to the recent advances of Internet and information technologies, massive quantity of archive data gets generated and it becomes difficult to handle it using conventional techniques. Archive management is the field of management related to the maintenance and utilization of archives, once they have been sent from the client to the repository. The drastic increase in the size of archive data necessitates effective storage schemes, which can be accomplished by the use of data compression approaches. Generally, data compression techniques are used for reducing the count of data being saved from a system or network without compromising the data quality. With this motivation, this study designs an effective archive storage system with a compression approach for network management (EASS-CANM). The major intention of the EASS-CANM technique is to archive the textual and image data effectively in its compact form in order to reduce the storage area. In the context of archive management, the EASS-CANM technique might be considered a successful instrument. The proposed EASS-CANM technique involves a two-stage process: textual data compression and image compression. At the initial stage, neighborhood indexing sequence (NIS) with the Prediction by Partial Matching (PPM) technique was applied for textual data compression. Secondly, fruit fly optimization (FFO) with modified Haar wavelet (MHW) is used for effective image compression where the optimal threshold selection process takes place utilizing the FFO technique. We improved the Haar wavelet filtering process in order to preserve higher image quality and clarity (MHW). With the introduction of MHW, a new transformation is made possible, allowing for improved compression outcomes as well as improved PSNR and CR values. In order to demonstrate the improved outcomes of the EASS-CANM approach, a series of simulations are performed utilizing a benchmark dataset. The experimental results reported the supremacy of the EASS-CANM technique on existing approaches. The benchmark dataset is used to conduct a wide range of studies to see whether the EASS-CANM approach improves archival efficacy. According to the full comparative result analysis, the EASS-CANM strategy is more effective than existing approaches in terms of numerous evaluation criteria. Therefore, the EASS-CANM technique can be used effectively in the administration of archives.

A Model-Based Framework for Assessing the Physiologic Structure of Electrodermal Activity.

Objective:We present a statistical model for extracting physiologic characteristics from electrodermal activity (EDA) data in observational settings.Methods:We based our model on the integrate-and-fire physiology of sweat gland bursts, which predicts inverse Gaussian (IG) inter-pulse interval structure. At the core of our model-based paradigm is a subject-specific amplitude threshold selection process for EDA pulses based on the statistical properties of four right-skewed models including the IG. By performing a sensitivity analysis across thresholds and fitting all four models, we selected for IG-like structure and verified the pulse selection with a goodness-of-fit analysis, maximizing capture of physiology at the time scale of EDA responses.Results:We tested the model-based paradigm on simulated EDA time series and data from two different experimental cohorts recorded during different experimental conditions, using different equipment. In both the simulated and experimental data, our model-based method robustly recovered pulses that captured the IG-like structure predicted by physiology, despite large differences in noise level. In contrast, established EDA analysis tools, which attempted to estimate neural activity from slower EDA responses, did not provide physiological validation and were susceptible to noise.Conclusion:We present a computationally efficient, statistically rigorous, and physiology-informed paradigm for pulse selection from EDA data that is robust across individuals and experimental conditions, yet adaptable to varying noise level.Significance:The robustness of the model-based paradigm and its physiological basis provide empirical support for the use of EDA as a clinical marker for sympathetic activity in conditions such as pain, anxiety, depression, and sleep states.

A generalized Spatio-Temporal Threshold Clustering method for identification of extreme event patterns

Abstract. Extreme weather and climate events such as floods, droughts, and heat waves can cause extensive societal damages. While various statistical and climate models have been developed for the purpose of simulating extremes, a consistent definition of extreme events is still lacking. Furthermore, to better assess the performance of the climate models, a variety of spatial forecast verification measures have been developed. However, in most cases, the spatial verification measures that are widely used to compare mean states do not have sufficient theoretical justification to benchmark extreme events. In order to alleviate inconsistencies when defining extreme events within different scientific communities, we propose a new generalized Spatio-Temporal Threshold Clustering method for the identification of extreme event episodes, which uses machine learning techniques to couple existing pattern recognition indices with high or low threshold choices. The method consists of five main steps: (1) construction of essential field quantities; (2) dimension reduction; (3) spatial domain mapping; (4) time series clustering; and (5) threshold selection. We develop and apply this method using a gridded daily precipitation dataset derived from rain gauge stations over the contiguous United States. We observe changes in the distribution of conditional frequency of extreme precipitation from large-scale well-connected spatial patterns to smaller-scale more isolated rainfall clusters, possibly leading to more localized droughts and heat waves, especially during the summer months. The proposed method automates the threshold selection process through a clustering algorithm and can be directly applicable in conjunction with modeling and spatial forecast verification of extremes. Additionally, it allows for the identification of synoptic-scale spatial patterns that can be directly traced to the individual extreme episodes, and it offers users the flexibility to select an extreme threshold that is linked to the desired geometrical properties. The approach can be applied to broad scientific disciplines.

MC4.5 decision tree algorithm: an improved use of continuous attributes

C4.5 is one of the top ten data mining algorithms; it is the most widely used decision trees construction techniques. Although effective, it suffers from the problem of complexity when it deals with continuous attributes. It also leads to a certain level of information loss. Therefore, minimising such loss and reducing the time complexity is one of the main goals in this paper. With the intention of alleviating these problems, this paper presents a novel algorithm namely MC4.5, which proposes the statistical mean as an alternative to the C4.5 threshold selection process. To demonstrate the effectiveness of the new algorithm, a complete evaluation was launched to prove that MC4.5 complies with the objectives previously mentioned. From the theoretical perspective, we develop an analysis of the complexity to compare algorithms. Empirically, we conduct an experimental study using 30 datasets to prove that, in most cases, the proposed algorithm leads to smaller decision trees with better accuracy comparing to the C4.5 algorithm.

A context sensitive energy thresholding based 3D Otsu function for image segmentation using human learning optimization

In this paper, a novel context-based 3D Otsu algorithm using human learning optimization (HLO) is proposed for multilevel color image segmentation. The performance of 3D Otsu algorithm is reported to be poor while dealing with between-class variances through the aid of three-dimensional histogram. To overcome this problem, the concept of context thresholding has been exploited to derive pixel intensity values and spatial information. The nature of spatial context and histogram of an image is very similar. The use of energy curve for 3D Otsu gives satisfactory results but it is more time-consuming during the process of threshold selection. HLO is a recently developed meta-heuristic optimization algorithm that involves the use of learning operators developed by mimicking human learning mechanisms. In this paper, in order to avoid an exhaustive search to obtain optimal thresholds, HLO is used. Experimental studies reported in this paper demonstrate that the proposed method is better than the histogram-based 1D Otsu, 2D Otsu, and 3D Otsu methods. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy.

Energy Efficient Canny Edge Detector for Advanced Mobile Vision Applications

In this paper, we present an energy-efficient architecture of the Canny edge detector for advanced mobile vision applications. Three key techniques for reducing computational complexity of the Canny edge detector are presented. First, by exploiting the rank characteristic of the convolution kernel of Gaussian smoothing and Sobel gradient filters, common computations are identified and shared in the image filter design to reduce the number of additions and multiplications. For the gradient magnitude/direction computation, only three directions of neighboring pixels are considered to reduce computation energy with minor degradation on conformance performance (CP). For the adaptive threshold selections, an interesting observation is that the mean values of gradient magnitudes show small variations depending on the classified block types. Thus, the threshold selection process can be simplified as multiplying the mean value of the local block with predecided constants. The proposed low complexity Canny edge detector has been implemented using both field-programmable gate arrays (FPGAs) and a 65-nm standard-cell library. The FPGA implementation with Xilinx Virtex-V (XC5VSX240T) shows that our edge detector achieves 48% of area and 73% of execution time savings over the conventional architecture without seriously sacrificing the detection performance. The proposed edge detector implemented with 65-nm standard-cell library can easily support real-time ultrahigh definition video data processing (50 frames/s) with the power consumption of 5.48 mW (108.84 $\mu \text{J}$ /frame).

Redundant Reader Elimination in Large-Scale Distributed RFID Networks

Radio frequency identification (RFID) is a key enabling technology for large-scale Internet of Things (IoT). Its deployment and management impact significantly on the operational effectiveness and scalability of IoT. Redundant reader elimination is of great importance to reduce the system’s overhead and prolong the lifetime of RFID networks. It helps to reduce unnecessary reader-tag interactions and the cost of collision avoidance algorithm in distributed data collection of RFID network. In large-scale distributed RFID networks, one of the most challenging tasks for redundant reader elimination is to improve the performance of distributed algorithms. This paper proposes a novel distributed redundant reader elimination algorithm based on the threshold selection process, named threshold selection algorithm (TSA), for RFID networks. TSA algorithm selects effective reader iteratively based on the threshold sequence determined by expected tag coverage. This paper also introduces an optimization mechanism into TSA based on detected movement, named TSA with movement detection algorithm. By preliminary simulation, we determine the suggested parameter of linear multiplier for TSA. Our experiments show that TSA algorithm can provide 30%–60% better performance than other major distributed algorithms and also multiphase schemes in both dense and sparse environments. The overhead of TSA algorithm is 30%–50% lower than other selected algorithms, especially in tag-write operations.

An adaptive overload threshold selection process using Markov decision processes of virtual machine in cloud data center

In Cloud Computing (CC), the cost for computation and energy is less by current cloud data centers because it exploits virtualization for an effective resource management. The Virtual Machine (VM) migration authorizes virtualization because it mitigates the difficulties of dynamic workload by repositioning VMs within cloud data centers. Through VM migration many goals of resource management are attained like load balancing, power management, fault tolerance, and system maintenance. The overload threshold is one of the key criterions to determine whether a host is overloaded or not. Achieving desired balance in guaranteeing quality of service, improving resource utilization and degrading energy consumption in data centers is the expected results of any overload threshold selection strategies. But, it is difficult due to the stochastic resource demands of VMs. In this paper, to address this problem, the overload threshold selection is modelled as a Markov decision process. With the solution of the improved Bellman optimality equation by the value iteration method, the optimization model is resolved, and the optimum overload threshold is adaptively selected. The hybrid processes are summarized as the Markov decision processes based adaptive overload threshold selection algorithm. Validations and comparisons are performed to illustrate its effectiveness and efficiency.

Generalizing cell segmentation and quantification

BackgroundIn recent years, the microscopy technology for imaging cells has developed greatly and rapidly. The accompanying requirements for automatic segmentation and quantification of the imaged cells are becoming more and more. After studied widely in both scientific research and industrial applications for many decades, cell segmentation has achieved great progress, especially in segmenting some specific types of cells, e.g. muscle cells. However, it lacks a framework to address the cell segmentation problems generally. On the contrary, different segmentation methods were proposed to address the different types of cells, which makes the research work divergent. In addition, most of the popular segmentation and quantification tools usually require a great part of manual work.ResultsTo make the cell segmentation work more convergent, we propose a framework that is able to segment different kinds of cells automatically and robustly in this paper. This framework evolves the previously proposed method in segmenting the muscle cells and generalizes it to be suitable for segmenting and quantifying a variety of cell images by adding more union cases. Compared to the previous methods, the segmentation and quantification accuracy of the proposed framework is also improved by three novel procedures: (1) a simplified calibration method is proposed and added for the threshold selection process; (2) a noise blob filter is proposed to get rid of the noise blobs. (3) a boundary smoothing filter is proposed to reduce the false seeds produced by the iterative erosion. As it turned out, the quantification accuracy of the proposed framework increases from 93.4 to 96.8% compared to the previous method. In addition, the accuracy of the proposed framework is also better in quantifying the muscle cells than two available state-of-the-art methods.ConclusionsThe proposed framework is able to automatically segment and quantify more types of cells than state-of-the-art methods.

A fast automatic optimal threshold selection technique for image segmentation

In this article, a fast context-sensitive threshold selection technique is presented to solve the image segmentation problems. In lieu of histogram, the proposed technique employs recently defined energy curve of the image. First, the initial thresholds are selected in the middle of two consecutive peaks on the energy curve. Then based on the cluster validity measure, the optimal number of potential thresholds and the bounds where the optimal value of each potential threshold may exist are determined. Finally, genetic algorithm (GA) is employed to detect the optimal value of each potential threshold from their respective defined bounds. The proposed technique incorporates spatial contextual information of the image in threshold selection process without loosing the benefits of histogram-based techniques. Computationally it is very efficient. Moreover, it is able to determine the optimal number of segments in the input image. To assess the effectiveness of the proposed technique, the results obtained are compared with four state-of-the-art methods cited in the literature. Experimental results on large number of images confirmed the effectiveness of the proposed technique.

Image thresholding based on local activity feature matrix

Image thresholding is the simple and widely used image segmentation method in automatic image analysis. In this paper, a novel image thresholding technique based on the local image features is proposed. The core idea is to generate a local activity feature matrix assuming given image a random field. The histogram of the local activity feature matrix, which essentially incorporate the local features of the image, is used for threshold selection process. The performance of the proposed technique is validated by extensive experiments on different real images, where the definite advantages are demonstrated subjectively and objectively. Further, the comparisons with existing techniques confirmed the effectiveness of the proposed technique.

A novel context sensitive multilevel thresholding for image segmentation

Most of the traditional histogram-based thresholding techniques are effective for bi-level thresholding and unable to consider spatial contextual information of the image for selecting optimal threshold. In this article a novel thresholding technique is presented by proposing an energy function to generate the energy curve of an image by taking into an account the spatial contextual information of the image. The behavior of this energy curve is very much similar to the histogram of the image. To incorporate spatial contextual information of the image for threshold selection process, this energy curve is used as an input of our technique instead of histogram. Moreover, to mitigate multilevel thresholding problem the properties of genetic algorithm are exploited. The proposed algorithm is evaluated on the number of different types of images using a validity measure. The results of the proposed technique are compared with those obtained by using histogram of the image and also with an existing genetic algorithm based context sensitive technique. The comparisons confirmed the effectiveness of the proposed technique.

Visual positioning method of printed circuit boards based on spatial moments

With the increasing necessities for reliable printed circuit board (PCB) product, there has been a considerable demand for high-speed and high-precision vision positioning systems. To locate a PCB board with high accuracy and reliability with the positioning mark images, a new visual positioning method is introduced. Considering the limitations of Lyvers subpixel edge detection algorithm, such as the lower positioning accuracy, the larger errors at the neighborhood of the edge intersections, and the more computing time and the more cumbersome process of threshold selection, we propose an improved algorithm, in which coarse and accurate edge detection methods are adopted, and a new criterion is put forward to detect the edge points with Otsu method. Furthermore, a formula is developed to determine the edge intersections, whose subpixel coordinates are calculated with bilinear interpolation and conjugate gradient method. Additionally, the causes of principle errors are explored, and the error compensation formulas are derived at any angle edges of the three-level edge model. The last experimental results show that the improved algorithm has good versatility, and compared with Lyvers and Ghosal algorithm, the process of threshold selection is easier, the detected edges are thinner, and the positioning accuracy for all edge points is much higher.

Behavior recognition in rehabilitation training based on modified naive Bayes classifier

This paper proposed a modified behavior recognition method to improve the recognition rate in rehabilitation training. First, it adopted Kinect sensor to detect human skeleton locations, defined the motion feature in rehabilitation training and designed the Bayes classifier. Second, the threshold selection process was improved to increase the recognition rate. The comparative experimental results with the unmodified one show that the modified naive Bayes classifier is simple and rapid, and it gains better identification effects in rehabilitation training.

Video Object Detection in Dynamic Scene using Inter-Frame Correlation based Histogram Approach

this paper the problem of video object detection under dynamic scene is considered. The dynamisms of the scene are assumed to be due to illumination variation and swaying of the trees and leaf. Many algorithms have been proposed to cope to this situation. But the major drawback in most of them is misclassified object and background area. Stochastic approaches including MRF based algorithms exist in literature but the practical implementation of such complex models remains still a challenge for the VLSI architecture designers. Thereby real-time object recognition and tracking process largely depends on simple and deterministic approaches and their accuracy. But many a times due to failure of the detection algorithms the efficacy of the hardware remains poor. In previous version of this work a supervised approach to improvised the correct classification of the object and background regions has been proposed. Although the results obtained were as per expectation but the model parameters estimation; such as the threshold selection process was manually done. In order to make it adaptive to the scene, in this paper a classification algorithm has been used which takes the histogram of correlation matrix into account and classify the object. The segmentation of the correlation plane is done by a threshold. This threshold selection is made adaptive to the video sequence considered. This segmented plane along with the moving edge image is then taken into consideration to improvise the correct classification of the moving object in the video. It is observed that the proposed algorithm yields quite manageable results in terms of correct classification.

Evaluation of energy detection for spectrum sensing based on the dynamic selection of detection-threshold

Cognitive radios (CRs) have been proposed as a possible solution to improve spectrum utilization by enabling opportunistic spectrum sharing. The main requirement for allowing CRs to use licensed spectrum on a secondary basis is not causing interference to primary users. Spectrum sensing allows cognitive users to autonomously identify unused portions of the radio spectrum, and thus avoid interference to primary users. In this work, energy detection technique is considered for spectrum sensing, and the performance evaluation of an energy detector is presented. The process of threshold selection for energy detection is addressed by the Constant False Alarm Rate (CFAR) method and selection is carried out considering present conditions of noise levels. Our results show that if we dynamically adjust detection-threshold based to the noise level present during detection process, the detection probability will be higher than the one obtained when a fixed threshold value is considered.

Selection of Statistical Thresholds in Graphical Models

Reconstruction of gene regulatory networks based on experimental data usually relies on statistical evidence, necessitating the choice of a statistical threshold which defines a significant biological effect. Approaches to this problem found in the literature range from rigorous multiple testing procedures to ad hoc P-value cut-off points. However, when the data implies graphical structure, it should be possible to exploit this feature in the threshold selection process. In this article we propose a procedure based on this principle. Using coding theory we devise a measure of graphical structure, for example, highly connected nodes or chain structure. The measure for a particular graph can be compared to that of a random graph and structure inferred on that basis. By varying the statistical threshold the maximum deviation from random structure can be estimated, and the threshold is then chosen on that basis. A global test for graph structure follows naturally.