American Journal of Mathematical and Management Sciences最新文献_第4页

Exact and Approximation Algorithms for Minimizing Energy in Wireless Sensor Data Gathering Network with Data Compression 基于数据压缩的无线传感器数据采集网络能量最小化的精确和近似算法

American Journal of Mathematical and Management Sciences Pub Date : 2021-08-27 DOI: 10.1080/01966324.2021.1960226

Chaofan Li, Wenchang Luo

{"title":"Exact and Approximation Algorithms for Minimizing Energy in Wireless Sensor Data Gathering Network with Data Compression","authors":"Chaofan Li, Wenchang Luo","doi":"10.1080/01966324.2021.1960226","DOIUrl":"https://doi.org/10.1080/01966324.2021.1960226","url":null,"abstract":"Abstract This article studies the problem of minimizing the total energy consumed in a heterogeneous wireless sensor data gathering network with data compression. In a wireless sensor data gathering network, a set of sensors is used to collect data and all the data are required to be transmitted to a single base station. Whether the base station is working in data receiving or idle mode, it consumes energy. To reduce the data transmission time, each sensor has the option to compress its collected data to decrease the original size before sending the data to the base station. However, compressing data takes some time delaying the data transmission starting time and also consuming energy. The task is to choose which sensors should compress their data and determine the data transmission order between the sensors and the base station with the goal of minimizing the total energy consumed. We prove that the studied problem is NP-hard, and propose a pseudo-polynomial dynamic programming exact algorithm. Furthermore, we present an approximation algorithm with the performance ratio that depends on the given energy consuming parameters for each unit time in different energy consuming activities.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44214786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Multi-Stage Estimation Methodologies for an Inverse Gaussian Mean with Known Coefficient of Variation 已知变异系数的高斯反均值多阶段估计方法

American Journal of Mathematical and Management Sciences Pub Date : 2021-08-26 DOI: 10.1080/01966324.2021.1966350

Neeraj Joshi, Sudeep R. Bapat, A. Shukla

引用次数: 1

Soft Matrix Game: A Hesitant Fuzzy MCDM Approach 软矩阵博弈:一种犹豫模糊MCDM方法

American Journal of Mathematical and Management Sciences Pub Date : 2021-08-23 DOI: 10.1080/01966324.2020.1730273

Jishu Jana, Sankar Kumar Roy

{"title":"Soft Matrix Game: A Hesitant Fuzzy MCDM Approach","authors":"Jishu Jana, Sankar Kumar Roy","doi":"10.1080/01966324.2020.1730273","DOIUrl":"https://doi.org/10.1080/01966324.2020.1730273","url":null,"abstract":"Abstract Soft set theory has emerged recently as a new mathematical tool to handle uncertainty. Sometimes decision makers are not sure about the decision-making criteria, where soft set theory provides an idea to deal with such uncertainties. Multi-criteria decision making (MCDM) involves choosing the best from several alternatives. MCDM methods such as TOPSIS and VIKOR depend on an aggregating function for presenting “closeness to the ideal” which arises due to the compromise solution. The VIKOR method of compromise ranking describes a compromise solution, providing a maximum for the “maximizing player” and minimum for the “opponent”, which is an effective approach in an MCDM game. TOPSIS method presents a solution with the shortest distance to the positive ideal solution (PIS) and largest distance from the negative ideal solution (NIS). Also, hesitant fuzzy soft set is an appropriate tool to tackle the imprecise parameters introduced in MCDM problems by the decision maker (DM). In this paper, we extend the VIKOR and TOPSIS methods for solving MCDM game problems with hesitant fuzzy soft payoffs to determine the optimal strategies. Finally, a numerical example is incorporated to verify the extended VIKOR approach and the results are compared with those for the TOPSIS method. The paper ends with conclusions and outlooks.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41432420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Demosaicing Method for Multispectral Images Using Derivative Operations 基于导数运算的多光谱图像去马赛克方法

American Journal of Mathematical and Management Sciences Pub Date : 2021-08-23 DOI: 10.1080/01966324.2021.1939206

Medha Gupta, P. Goyal

{"title":"Demosaicing Method for Multispectral Images Using Derivative Operations","authors":"Medha Gupta, P. Goyal","doi":"10.1080/01966324.2021.1939206","DOIUrl":"https://doi.org/10.1080/01966324.2021.1939206","url":null,"abstract":"Abstract Multispectral images have been found useful for various applications such as remote sensing, medical imaging, military surveillance, vision inspection for food quality control, etc. but the high costs of multispectral cameras limit their usage. Low cost multispectral cameras can be developed using a single sensor multispectral filter array (MSFA) and a demosaicing method to reconstruct the complete image from under sampled multispectral image data acquired using a single sensor MSFA imaging system. In this paper, we present a new demosaicing method based on the derivative operations for the multi-spectral images. To design MSFA patterns, binary tree method is often used and the band sequence is chosen such that the middle band has a higher probability of appearance in MSFA pattern. In the proposed method, first the middle spectral band pixel values are estimated and then it is used to compute derivatives that help estimate other spectral band pixel values. Unlike many recently developed demosaicing methods that are applicable to only specific band size multispectral images, the proposed method is generic and can be applied to obtain multispectral images for any number of spectral bands. The TokyoTech dataset and CAVE dataset of multispectral images are used for the evaluation purpose, and the experimental results show that the proposed method outperforms currently best known generic multispectral demosaicing method, namely binary tree edge sensing (BTES) method on both datasets and for different band-size multispectral images.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01966324.2021.1939206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43575668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Computational Approach for One and Two Dimensional Fisher’s Equation Using Quadrature Technique 用求积技术求解一维和二维Fisher方程的一种计算方法

American Journal of Mathematical and Management Sciences Pub Date : 2021-08-23 DOI: 10.1080/01966324.2021.1933660

G. Arora, V. Joshi

引用次数: 4

Predicting Bitcoin Return Using Extreme Value Theory 利用极值理论预测比特币收益

American Journal of Mathematical and Management Sciences Pub Date : 2021-08-23 DOI: 10.1080/01966324.2021.1950086

Mohammad Tariquel Islam, K. Das

{"title":"Predicting Bitcoin Return Using Extreme Value Theory","authors":"Mohammad Tariquel Islam, K. Das","doi":"10.1080/01966324.2021.1950086","DOIUrl":"https://doi.org/10.1080/01966324.2021.1950086","url":null,"abstract":"Abstract The study investigates and develops the ability of the extreme value theory (EVT) to predict bitcoin return. EVT is used to deal with rare but extreme events, such as severe losses or excessive damages. It is being used as a powerful statistical tool in various disciplines, including finance, engineering, environmental science, and actuarial science. As the largest among all cryptocurrencies in existence, bitcoin’s behavior is primarily characterized by great volatility. Predicting bitcoin return is complex and important, primarily because of the extreme nature of its return. There is not enough substantial research involving EVT in bitcoin analysis. This study has three objectives. First, confirming the extreme nature of bitcoin return by various statistical tests; second, modeling the bitcoin return using two different EVT approaches (block maxima approach and peak over threshold approach); and third, assessing uncertainties by predicting bitcoin return levels for 5-, 10-, 20-, 50-, and 100-years with a 95% confidence interval using both of these methods. These results could certainly serve policymakers and investors, as these return levels can be useful in characterizing bearish and bullish trends and predicting the same. Moreover, these can serve as starting points for future studies regarding the stationary and non-stationary properties of bitcoin return.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01966324.2021.1950086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46606379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

K 1 K 2–Inflated Conway–Maxwell–Poisson Model: Bayesian Predictive Modeling with an Application in Soccer Matches k1k2 -膨胀康威-麦克斯韦-泊松模型:贝叶斯预测建模及其在足球比赛中的应用

American Journal of Mathematical and Management Sciences Pub Date : 2021-08-12 DOI: 10.1080/01966324.2021.1960225

A. Sadeghkhani

引用次数: 0

Some Inferential Results on a Two Parameter Generalized Half Normal Distribution 两参数广义半正态分布的一些推论结果

American Journal of Mathematical and Management Sciences Pub Date : 2021-08-09 DOI: 10.1080/01966324.2021.1959469

Matinee Sudsawat, N. Pal

引用次数: 1

Parametric Confidence Intervals of Spmk for Generalized Exponential Distribution 广义指数分布Spmk的参数置信区间

American Journal of Mathematical and Management Sciences Pub Date : 2021-08-04 DOI: 10.1080/01966324.2021.1949412

S. Dey, Mahendra Saha, Sumit Kumar

引用次数: 2

Reparameterized Flexible Weibull Distribution with Some Applications 重新参数化柔性威布尔分布及其应用

American Journal of Mathematical and Management Sciences Pub Date : 2021-08-04 DOI: 10.1080/01966324.2021.1957731

F. Prataviera

引用次数: 5