Chebyshev Distance Research Articles

Sparse Locally Adaptive Regression Kernel For Face Verification

The paper presents several thresholds obtained by heuristic approach for face verification using Locally Adaptive Regression Kernel (LARK) descriptors for euclidean, cosine and chebyshev distance metrics. The absence of a threshold for several distance metrics possess several setbacks such as increased computational complexity and escalated runtime. The proposed method has significantly higher influence when the verification process is computationally intensive. The proposed approach requires minimal training and face verification is accomplished based on the threshold value obtained during training. The whole process is modest and nearly all of the time spent is solely on quantifying any of the distance metrics between the LARKs obtained from the two faces for verification. LARK descriptors compute a measure of resemblance on the basis of “signal-induced distance” between a pixel and its nearby pixels. We assess the interspace between the LARKs from these faces and analogize the resemblance from the threshold resulting in a binary decision.

Dynamic Summarization of Videos Based on Descriptors in Space-Time Video Volumes and Sparse Autoencoder

This paper addresses the problem of generating meaningful summaries from unedited user videos. A framework based on spatiotemporal and high-level features is proposed in this paper to detect the key-shots after segmenting the videos into shots based on motion magnitude. To encode the time-varying characteristics of a video, we explore the local phase quantization feature descriptor from three orthogonal planes (LPQ-TOP). The sparse autoencoder (SAE), an instance of deep learning strategy, is used for the extraction of high-level features from LPQ-TOP descriptors to represent the shots carrying key-contents of videos efficiently. The Chebyshev distance between the feature vectors of the consecutive shots are calculated and thresholded using the mean value of the distance score as the threshold value. The optimal subset of shots with distance score greater than the threshold value is used to generate a high-quality video summary. The method is evaluated using SumMe data set. The summaries thus generated are of better quality than those produced by the other state of the art techniques. The effectiveness of the method is further evaluated by comparing with the human-created summaries in the ground truth.

Quantitation of Malarial parasitemia in Giemsa stained thin blood smears using Six Sigma threshold as preprocessor

Malaria is a precarious disease and a serious illness that can become life threatening very fast and can be incurable if not treated on time. According to the World Health Organization, Malaria causes 150–250 million infections and an estimated more than a million deaths each year. Microscopic image capture followed by visual observation as per the manual is the gold standard method for diagnosis of malaria. However, due to subjectivity and the complexity of manual assessment, microscopic diagnosis of malaria is tiring, time consuming and subject to human error. In this study, we have developed a novel automatic high performance method with minimal human dependence for detection and quantitation of malaria infected Red Blood Cells (RBC). The parasites detection test to identify malaria was computed using digital image processing techniques. This automated quantitation procedure was done in three parts: Segmentation, Identification and Detection. The methods used for the quantitation were Six Sigma threshold for segmenting Region of Interest followed by modified Hough transform to identify and count RBCs, and Kapur’s threshold method to detect malaria parasite infected RBCs. The developed package using Microsoft® VB.NET 2008 framework is executed on various Giemsa stained thin blood smears digitally acquired using a charge coupled device attached to a microscope. Following analysis performed on seven sets of thin blood smear images, the values of Precision, Recall and F-measures obtained were 96%, 97% and 97% respectively. This is the first attempt to use a combination of Six Sigma threshold, Chess-Board distance, Hough transform and Kapur’s threshold to find the RBCs by deriving information from the image itself. Further, Kapur’s entropy measure was successfully applied to distinguish parasitized from un-infected cells.

Micro Thin-Layer Chromatography Fingerprints of Selected Basil Species and Their Chemometric Analysis

The dried aerial parts of 12 plants of Ocimum spp. were extracted with the Soxhlet apparatus using dichloromethane and methanol as solvents. A micro-TLC system with silica and a normal-phase solvent system (propan-2-ol-n-heptane-formic acid) was used for the chemometric analysis of 12 selected basil methanolic extracts. Some indices of similarity (Pearson's correlation coefficient, determination coefficient, congruence coefficient, and Euclidean, Manhattan (city-block), Chebyshev, and Hausdorff distances) were calculated on the basis of thin-layer chromatograms using ImageJ software. Principal component analysis was also performed. The method allowed the comparison of the analyzed extracts and confirmed the identities of two selected unknown plants.

An Aggregated Fuzzy Model for the Selection of a Managed Security Service Provider

In this study, by analyzing the related literature, the companies providing security services and, more importantly, the data provided by a group of experts, a novel set of 39 criteria is extracted which assists the Managed Security Service Provider (MSSP) selection process. The set is further categorized into eight general classes. The validity and weights of these criteria are measured by a group of experts in Iran. Due to the large number and often conflicting criteria, and the qualitative nature of the evaluations of the service providers, fuzzy multi-criteria decision-making methods (FMCDM) are adopted. In order to demonstrate the application of the proposed model, a numerical example is included, in which eight service providers are evaluated by four decision makers applying fuzzy TOPSIS, fuzzy VIKOR, fuzzy Group ELECTRE, and fuzzy SAW methods. Owing to the variations of the outputs of the applied MCDM methods, they are further analyzed by an aggregation method to propose a unique service provider. A comparison between the output of the aggregation method and the four applied Fuzzy MCDM methods is also made with the help of Euclidean, Hamming, Manhattan and Chebyshev distances. The comparison shows the minimum diversion between the outputs of the Fuzzy TOPSIS and the aggregation method, which indicates the appropriateness of the fuzzy TOPSIS method in this particular problem.

A novel optimized vertical handover framework for seamless networking integration in cyber-enabled systems

Seamless integration of wireless networks for secure mobile applications has increased interest in the research area of mobile cyber-enabled systems. Multi-objective handover needs to compensate the different quality of services provided by the wireless technologies. Notably, the recent works select only the performance perspective on the network to suffice user requirements. In mobile cyber-enabled systems, the security policies are essential while providing the ubiquitous connectivity. In this paper, a novel Optimized Vertical Handover (OVH) framework is proposed to optimize and secure the handover process and reduce the handover execution time significantly. This framework integrates IEEE 802.21 Media Independent Handover (MIH) and Software-Defined Networking (SDN) for seamless link establishment and agile network path reconfiguration. In the handover process, the network selection process is divided into two stages, such as pre-filtering and handover network selection. The pre-filtering eliminates the candidates with incompatible security capabilities using coarse and fine-grained methods. The network selection process utilizes the advantages of Manhattan and Chebyshev distance functions to feature the static and dynamic attributes during the handover. Among multiple factors, the OVH applies for compensation according to the network and mobile node states and improves the efficiency of the handover decision. Compared with the original VIKOR, the proposed OVH algorithm reduces the time complexity significantly through the periodic evaluation of dynamic attributes. Finally, the simulation results highlight that the OVH framework significantly reduces the handover delay and execution time.

Early Recognition of Alzheimer’s Disease using Brain MRI

Abstract—Alzheimer’s disease (AD) is a brain disease that causes a slow decline in memory, thinking and reasoning skills. It represents a major public health problem. Magnetic Resonance Imaging (MRI) have shown that the brains of people with (AD) shrink significantly as the disease progresses. This shrinkage appears in specific brain regions such as the hippocampus which is a small, curved formation in the brain that plays an important role in the limbic system also involved in the formation of new memories and is also associated with learning and emotions. Medical information on brain MRI is used in detecting the abnormalities in physiological structures. Structural MRI measurements can detect and follow the evolution of brain atrophy which is a marker of the disease progression; therefore, it allows diagnosis and prediction of AD. The research’s main target is the early recognition of Alzheimer’s disease automatically, which will thereby avoid deterioration of the case resulting in complete brain damage stage. Alzheimer’s disease yields visible changes in the brain structures. The aim is to recognize if the patient belongs to Alzheimer’s disease category or a normal healthy person at an early stage.
 Initially, image pre-processing and features extraction techniques are applied including data reduction using Discrete Cosine Transform (DCT) and Cropping, then traditional classification techniques like Euclidean Distance, Chebyshev Distance, Cosine Distance, City Block Distance, and Black pixel counter, were applied on the resulting vectors for classification. Image pre-processing includes noise reduction, Gray-scale conversion and binary scale conversion were applied for the MRI images. Feature extraction techniques follow including cropping and low spatial frequency components (DCT). This paper aims to automatically recognize and detect Alzheimer’s infected brain using MRI, without the need of clinical expert. This early recognition would be helpful to postpone the disease progression and maintain it at an almost steady stage. It was concluded after collecting a dataset of 50 MRI , 25 for normal MRI and 25 for AD MRI that Chebyshev Distance classifier yielded the highest success rate in the recognition of Alzheimer’s disease with accuracy 94% compared to other classification techniques used where, Euclidean Distance is 91.6%, Cosine Distance is 86.8%, City block Distance is 89.6%, Correlation Distance is 86.4% and Black pixels counter is 90%.

A novel active contour model based on modified symmetric cross entropy for remote sensing river image segmentation

The traditional active contour models cannot segment the remote sensing river images accurately. To solve this problem, a novel active contour model based on modified symmetric cross entropy is proposed. In the proposed model, the external energy constraint terms are defined by the symmetric cross entropy and the region fitting centers are represented by the medians of pixel grayscale values inside the object and background regions. Moreover, the penalty energy term is incorporated into the energy functional to avoid the re-initialization. In order to improve the segmentation efficiency of the proposed model, the Chebyshev distance between the pixel grayscale values inside the region and its region fitting center is chosen as its region energy weight, which can be adaptively adjusted, instead of the constant region energy weight. The extensive experiments are performed on a large number of remote sensing river images and the results demonstrate that, compared with the CV model, the GAC model, the CEACM model, the RSF model, the LIF model, and the LGIF model, the proposed model can segment the images more accurately and rapidly, which has the clear advantages in both segmentation performance and segmentation efficiency.

Designing Sequence With Minimum PSL Using Chebyshev Distance and its Application for Chaotic MIMO Radar Waveform Design

Controlling peak side-lobe level (PSL) is of great importance in high-resolution applications of multiple-input multiple-output (MIMO) radars. In this paper, designing sequences with good autocorrelation properties are studied. The PSL of the autocorrelation is regarded as the main merit and is optimized through newly introduced cyclic algorithms, namely; PSL Minimization Quadratic Approach (PMQA), PSL Minimization Algorithm, the smallest Rectangular (PMAR), and PSL Optimization Cyclic Algorithm (POCA). It is revealed that minimizing PSL results in better sequences in terms of autocorrelation side-lobes when compared with traditional integrated side-lobe level (ISL) minimization. In order to improve the performance of these algorithms, fast-randomized Singular Value Decomposition (SVD) is utilized. To achieve waveform design for MIMO radars, this algorithm is applied to the waveform generated from a modified Bernoulli chaotic system. The numerical experiments confirm the superiority of the newly developed algorithms compared to high-performance algorithms in mono-static and MIMO radars.

How to compare separation selectivity of high-performance liquid chromatographic columns properly?

Comparison and selection of chromatographic columns is an important part of development as well as validation of analytical methods. Presently there is abundant number of methods for selection of the most similar and orthogonal columns, based on the application of limited number of test compounds as well as quantitative structure retention relationship models (QSRR), from among Snyder’s hydrophobic-subtraction model (HSM) have been most extensively used.Chromatographic data of 67 compounds were evaluated using principal component analysis (PCA), hierarchical cluster analysis (HCA), non-parametric ranking methods as sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM), both applied as a consensus driven comparison, and complemented by the comparison with one variable at a time (COVAT) approach. The aim was to compare the ability of the HSM approach and the approach based on primary retention data of test solutes (logk values) to differentiate among ten highly similar C18 columns.The ranking (clustering) pattern of chromatographic columns based on primary retention data and HSM parameters gave different results in all instances. Patterns based on retention coefficients were in accordance with expectations based on columns’ physicochemical parameters, while HSM parameters provided a different clustering.Similarity indices calculated from the following dissimilarity measures: SRD, GPCM Fisher’s conditional exact probability weighted (CEPW) scores; Euclidian, Manhattan, Chebyshev, and cosine distances; Pearson’s, Spearman’s, and Kendall’s, correlation coefficients have been ranked by the consensus based SRD. Analysis of variance confirmed that the HSM model produced statistically significant increases of SRD values for the majority of similarity indices, i.e. HS transformation of original retention data yields significant loss of information, and finally results in lower performance of HSM methodology. The best similarity measures were obtained using primary retention data, and derived from Kendal’s and Spearman’s correlation coefficients, as well as GPCM and SRD score values. Selectivity function, Fs, originally proposed by Snyder, demonstrated moderate performance.

A modified firefly algorithm for general inverse p-median location problems under different distance norms

This paper concerns a general inverse p-median location problem on the k-dimensional real space where the aim is to change (increase or reduce) the weights and coordinates of the existing customer points at the minimum total cost subject to given modification bounds so that a set of p predetermined facility points becomes an optimal p-median location under the new weights and coordinates. A modified firefly algorithm is proposed to solve the problem under the rectilinear, Chebyshev and Euclidean distance norms. By computational experiments, the high efficiency of the algorithm is illustrated.

Construction cost prediction on the basis of multiple parameters using case-based reasoning method

Nowadays, case-based reasoning (CBR) can be viewed as an effective and efficient method for cost prediction for building construction. Even if users have not very much experienced all the cases of knowledge base or database, this method makes it possible to retrieve the information of similar or exact cases, which are known to them, for new experiences. However, there are challenges related to the rapid retrieval process from the database library. One of the most important retrieval processes is the computation of similarity measures. With these issues, this research work attempts to build CBR in terms of similarity measuring method based on the concept of Euclidean distance and Chebyshev distance. The novelty of this paper is enhancing the accuracy of cost prediction as well as checking the effectiveness of the CBR model and both are relevant to academia and industry. In order to obtain the result, I have used indigenous tool.

The future of invasive African grasses in South America under climate change

Climate change will promote substantial effects on the distribution of invasive species. Here, I used an ensemble of bioclimatic envelope models (Gower Distance, Chebyshev Distance, and Mahalanobis Distance) to forecast climatically suitable areas of South America for 13 invasive African grass species under future climate conditions (year 2050). Under current climatic conditions, the areas with the potential for the highest invasive species richness are located mostly in the tropical climates of South America, except for the Amazon region. In the year 2050, the overall pattern of invasive species richness will not change considerably, and increases in northeastern Amazon and portions of the temperate regions of South America are predicted.

Computational Analysis of Distance Operators for the Iterative Closest Point Algorithm.

The Iterative Closest Point (ICP) algorithm is currently one of the most popular methods for rigid registration so that it has become the standard in the Robotics and Computer Vision communities. Many applications take advantage of it to align 2D/3D surfaces due to its popularity and simplicity. Nevertheless, some of its phases present a high computational cost thus rendering impossible some of its applications. In this work, it is proposed an efficient approach for the matching phase of the Iterative Closest Point algorithm. This stage is the main bottleneck of that method so that any efficiency improvement has a great positive impact on the performance of the algorithm. The proposal consists in using low computational cost point-to-point distance metrics instead of classic Euclidean one. The candidates analysed are the Chebyshev and Manhattan distance metrics due to their simpler formulation. The experiments carried out have validated the performance, robustness and quality of the proposal. Different experimental cases and configurations have been set up including a heterogeneous set of 3D figures, several scenarios with partial data and random noise. The results prove that an average speed up of 14% can be obtained while preserving the convergence properties of the algorithm and the quality of the final results.

An approach to determine unsupported non-dominated solutions in bicriteria integer linear programs

ABSTRACTIn this paper, we introduce a method for finding both supported and unsupported non-dominated solutions of a bicriteria integer linear program (BCILP). One-phase and two-phase implementations of the method are described, and their interactive versions are outlined. The one-phase method and the second phase of the other are based on the minimization of weighted Chebyshev distances to well-chosen reference points. The dynamic change of reference point proposed here makes this method particularly suitable for interactive approaches. Computational experiments on random instances of three classes of BCILP are reported and discussed. The implementation of the proposed method as a method to approximate the set of non-dominated solutions is described and evaluated in computational terms.

Proton exchange membrane fuel cell failure mode early diagnosis with wavelet analysis of electrochemical noise

A diagnostic method for the performance degradation of low temperature proton exchange membrane fuel cells is proposed. The method is based on the analysis of the cell electrochemical noise. Experimental noise data were collected for a range of air relative humidities and stoichiometries including conditions leading to water flooding, membrane dehydration and air starvation failure modes. Data were converted with a Fourier transform (frequency window averaging of the amplitude) and a wavelet transform (coefficients standard deviation). Data were compared to impedance spectroscopy results. The method based on the wavelet transform was more sensitive. Cell states labeled by their air relative humidity and stoichiometry were correctly identified using a brute force algorithm by minimizing the Chebyshev distance between the actual and the calculated states. Independent and uniformly distributed random variations were added to experimental wavelet coefficients' standard deviations to define the calculated states.

Chebyshev Distance

Summary In [21], Marco Riccardi formalized that ℝN-basis n is a basis (in the algebraic sense defined in [26]) of ℰ T n ${\cal E}_T^n $ and in [20] he has formalized that ℰ T n ${\cal E}_T^n $ is second-countable, we build (in the topological sense defined in [23]) a denumerable base of ℰ T n ${\cal E}_T^n $ . Then we introduce the n-dimensional intervals (interval in n-dimensional Euclidean space, pavé (borné) de ℝ n [16], semi-intervalle (borné) de ℝ n [22]). We conclude with the definition of Chebyshev distance [11].

SU‐C‐BRB‐04: Delineation Uncertainty Maps: Proof of Concept Study for Recurrent Gynaecological Cancers

Purpose:To create a novel infrastructure which allows clinicians to record their GTV delineation uncertainty; to generate delineation uncertainty maps from this data and compare them to clinical GTVs.Methods:A contouring concept was developed in which clinicians draw up to two GTV boundaries per CT slice corresponding to the inner and outermost possible boundaries the GTV may have.A new uncertainty contouring tool was implemented in our research treatment planning system (TPS, DynaPlan) to accommodate these contour pairs and the derivation of delineation probability maps. 3D probability maps were generated from the contour pairs based on the minimum Chebyshev distance, at each pixel within the contours, from each boundary. The probability associated with each pixel was defined as the ratio of its distance from the inner boundary to the sum of its distances from each boundary.These tools were used to create probability maps using contours produced by a clinician experienced in treating recurrent gynaecological cancers. The corresponding 50% probability volumes (GTVP50) were compared against the observers corresponding clinical GTV (GTVC) contour, created in accordance with local clinical protocols, using the Jaccard Index.Results:This approach was successfully implemented within the research TPS. Comparisons between GTVC and GTVP50 were made for 10 recurrences from 9 patients. GTVC was larger than GTVP50 (p<0.01), with mean±s.d. volumes of 69.6±55.4cm3 and 61.6±53.7cm3 respectively, and a corresponding Jaccard Index of 0.83±0.11.Conclusion:Individual clinician's delineation uncertainty can be recorded and a corresponding uncertainty map derived. This is a crucial step in improving the handling of delineation uncertainty and moving away from population based margins, using techniques such as probabilistic treatment planning. Contours produced for recurrent gynaecological GTVs show the clinical GTV to be consistently larger than volume corresponding to 50% probability. Future work is to investigate non‐linear methods for generating these probability maps.This report is independent research arising from an NIHR/HEE Healthcare Science Doctoral Research Fellowship, David Bernstein, HCS‐DRF‐2014‐05‐005, supported by National Institute for Health Research (NIHR). The views expressed in this publication are those of the author(s); not necessarily those of the NHS, NIHR, Health Education England or Department of Health.

Distance Metric Based Oversampling Method for Bioinformatics and Performance Evaluation.

An imbalanced classification means that a dataset has an unequal class distribution among its population. For any given dataset, regardless of any balancing issue, the predictions made by most classification methods are highly accurate for the majority class but significantly less accurate for the minority class. To overcome this problem, this study took several imbalanced datasets from the famed UCI datasets and designed and implemented an efficient algorithm which couples Top-N Reverse k-Nearest Neighbor (TRkNN) with the Synthetic Minority Oversampling TEchnique (SMOTE). The proposed algorithm was investigated by applying it to classification methods such as logistic regression (LR), C4.5, Support Vector Machine (SVM), and Back Propagation Neural Network (BPNN). This research also adopted different distance metrics to classify the same UCI datasets. The empirical results illustrate that the Euclidean and Manhattan distances are not only more accurate, but also show greater computational efficiency when compared to the Chebyshev and Cosine distances. Therefore, the proposed algorithm based on TRkNN and SMOTE can be widely used to handle imbalanced datasets. Our recommendations on choosing suitable distance metrics can also serve as a reference for future studies.

TLC Profiles of Selected Cirsium Species with Chemometrics in Construction of Their Fingerprints.

The dried aerial parts of 12 plants of Cirsium species were extracted with the Soxhlet apparatus using dichloromethane and methanol as solvents. Next, the extracts were separated by TLC methods to obtain the fingerprint chromatograms. The analysis was performed on silica gel or RP-18 layers as stationary phases using the following eluents: ethyl acetate/formic acid/acetic acid/water (12/1.5/1.5/4; v/v) for silica gel, and 5% (v/v) aqueous solution of formic acid/methanol (70/30; v/v) for the first development and the same system in the proportion of 50/50 (v/v) for the second development for RP-18. The double development was applied in the case of RP-18 plates. The analysis was performed for all Cirsium methanolic extracts and five selected standards (naringin, apigenin, rutin, caffeic acid and chlorogenic acid). The results were analyzed using chemometrics. The comparison of individual Cirsium species and the identification of unknown species were performed using the similarity indices (Pearson's correlation coefficient, determination coefficient and congruence coefficient), distance indices (Euclidean distance, Manhattan distance and Chebyshev's distance) and Multi-Scale Structural SIMilarity. Based on chemometric analysis, the first extract of the widely grown species is identified as Cirsium arvense and the second one as Cirsium rivulare.