Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies.Early diagnosis of many diseases will improve the patient life.The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things(IoT),Wireless Sensor Networks(WSN),Embedded systems,Deep learning approaches and Optimization and aggregation methods.The data generated through these technologies will demand the bandwidth,data rate,latency of the network.In this proposed work,efficient discrete grey wolf optimization(DGWO)based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code(ECEMAC)has been used to aggregate the parameters generated from the wearable sensor devices of the patient.The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO.Aggregation scheme will reduce the number of transmissions over the network.The aggregated data are preprocessed at edge node to remove the noise for better diagnosis.Edge node will reduce the overhead of cloud server.The aggregated data are forward to cloud server for central storage and diagnosis.This proposed smart diagnosis will reduce the transmission cost through aggrega-tion scheme which will reduce the energy of the system.Energy cost for proposed system for 300 nodes is 0.34μJ.Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme(SPPDA),concealed data aggregation scheme for multiple application(CDAMA)and secure aggregation scheme(ASAS)are 1.3μJ,0.81μJ and 0.51μJ respectively.The optimization approaches and encryption method will ensure the data privacy.

Design of similarity measure for discrete data and application to multi-dimension

Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.

Discrete Event Simulation-Based Evaluation of a Single-Lane Synchronized Dual-Traffic Light Intersections

This research involved an exploratory evaluation of the dynamics of vehicular traffic on a road network across two traffic light-controlled junctions. The study uses the case study of a one-kilometer road system modelled on Anylogic version 8.8.4. Anylogic is a multi-paradigm simulation tool that supports three main simulation methodologies: discrete event simulation, agent-based modeling, and system dynamics modeling. The system is used to evaluate the implication of stochastic time-based vehicle variables on the general efficiency of road use. Road use efficiency as reflected in this model is based on the percentage of entry vehicles to exit the model within a one-hour simulation period. The study deduced that for the model under review, an increase in entry point time delay has a domineering influence on the efficiency of road use far beyond any other consideration. This study therefore presents a novel approach that leverages Discrete Events Simulation to facilitate efficient road management with a focus on optimum road use efficiency. The study also determined that the inclusion of appropriate random parameters to reflect road use activities at critical event points in a simulation can help in the effective representation of authentic traffic models. The Anylogic simulation software leverages the Classic DEVS and Parallel DEVS formalisms to achieve these objectives.

A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform

Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most infor- mative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods: Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface- enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality re- duction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results: From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.

Comparative Study and Spatial-Temporal Distribution of Regolith and Rock Geochemical Data from Xingmeng-North China

1 Introduction Geochemical mapping at national and continental scales continues to present challenges worldwide due to variations in geologic and geotectonic units.Use of the proper sampling media can provide rich information on

Spatial-Temporal Event Detection Method with Multivariate Water Quality Data

With the rapid development of the society,water contamination events cause great loss if the accidents happen in the water supply system.A large number of sensor nodes of water quality are deployed in the water supply network to detect and warn the contamination events to prevent pollution from speading.If all of sensor nodes detect and transmit the water quality data when the contamination occurs,it results in the heavy communication overhead.To reduce the communication overhead,the Connected Dominated Set construction algorithm-Rule K,is adopted to select a part fo sensor nodes.Moreover,in order to improve the detection accuracy,a Spatial-Temporal Abnormal Event Detection Algorithm with Multivariate water quality data(M-STAEDA)was proposed.In M-STAEDA,first,Back Propagation neural network models are adopted to analyze the multiple water quality parameters and calculate the possible outliers.Then,M-STAEDA algorithm determines the potential contamination events through Bayesian sequential analysis to estimate the probability of a contamination event.Third,it can make decision based on the multiple event probabilities fusion.Finally,a spatial correlation model is applied to determine the spatial-temporal contamination event in the water supply networks.The experimental results indicate that the proposed M-STAEDA algorithm can obtain more accuracy with BP neural network model and improve the rate of detection and the false alarm rate,compared with the temporal event detection of Single Variate Temporal Abnormal Event Detection Algorithm(M-STAEDA).

Heat Conduction Analytical Solutions to be Applied in Boundary Conditions Obtained from Discrete Data

Analytical solutions have varied uses. One is to provide solutions that can be used in verification of numerical methods. Another is to provide relatively simple forms of exact solutions that can be used in estimating parameters, thus, it is possible to reduce computation time in comparison with numerical methods. In this paper, an alternative procedure is presented. Here is used a hybrid solution based on Green＇s function and real characteristics （discrete data） of the boundary conditions.

Intrusion Detection System for PS-Poll DoS Attack in 802.11 Networks Using Real Time Discrete Event System

Wi-Fi devices have limited battery life because of which conserving battery life is imperative. The 802.11 Wi-Fi standard provides power management feature that allows stations(STAs) to enter into sleep state to preserve energy without any frame losses. After the STA wakes up, it sends a null data or PS-Poll frame to retrieve frame(s) buffered by the access point(AP), if any during its sleep period. An attacker can launch a power save denial of service(PS-DoS) attack on the sleeping STA(s) by transmitting a spoofed null data or PS-Poll frame(s) to retrieve the buffered frame(s) of the sleeping STA(s) from the AP causing frame losses for the targeted STA(s). Current approaches to prevent or detect the PS-DoS attack require encryption,change in protocol or installation of proprietary hardware. These solutions suffer from expensive setup, maintenance, scalability and deployment issues. The PS-DoS attack does not differ in semantics or statistics under normal and attack circumstances.So signature and anomaly based intrusion detection system(IDS) are unfit to detect the PS-DoS attack. In this paper we propose a timed IDS based on real time discrete event system(RTDES) for detecting PS-DoS attack. The proposed DES based IDS overcomes the drawbacks of existing systems and detects the PS-DoS attack with high accuracy and detection rate. The correctness of the RTDES based IDS is proved by experimenting all possible attack scenarios.

Discrete rough set analysis of two different soil-behavior-induced landslides in National Shei-Pa Park,Taiwan

The governing factors that influence landslide occurrences are complicated by the different soil conditions at various sites.To resolve the problem,this study focused on spatial information technology to collect data and information on geology.GIS,remote sensing and digital elevation model（DEM） were used in combination to extract the attribute values of the surface material in the vast study area of SheiPa National Park,Taiwan.The factors influencing landslides were collected and quantification values computed.The major soil component of loam and gravel in the Shei-Pa area resulted in different landslide problems.The major factors were successfully extracted from the influencing factors.Finally,the discrete rough set（DRS） classifier was used as a tool to find the threshold of each attribute contributing to landslide occurrence,based upon the knowledge database.This rule-based knowledge database provides an effective and urgent system to manage landslides.NDVI（Normalized Difference Vegetation Index）,VI（Vegetation Index）,elevation,and distance from the road are the four major influencing factors for landslide occurrence.The landslide hazard potential diagrams（landslide susceptibility maps） were drawn and a rational accuracy rate of landslide was calculated.This study thus offers a systematic solution to the investigation of landslide disasters.

Target threat estimation based on discrete dynamic Bayesian networks with small samples

The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target threat level.Unfortunately,the traditional discrete dynamic Bayesian network(DDBN)has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing.Considering the finiteness and discreteness of DDBN parameters,a fuzzy k-nearest neighbor(KNN)algorithm based on correlation of feature quantities(CF-FKNN)is proposed for DDBN parameter learning.Firstly,the correlation between feature quantities is calculated,and then the KNN algorithm with fuzzy weight is introduced to fill the missing data.On this basis,a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning.Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing,and improve the effect of DDBN parameter learning in the case of serious sample missing.With the proposed method,the final target threat assessment results are reasonable,which meets the needs of engineering applications.

H_∞ Filter Design for Discrete-time Systems with Missing Measurements

For packet-based transmission of data over a network, or temporary sensor failure, etc., data samples may be missing in the measured signals. This paper deals with the problem of H∞ filter design for linear discrete-time systems with missing measurements. The missing measurements will happen at any sample time, and the probability of the occurrence of missing data is assumed to be known. The main purpose is to obtain both full-and reduced-order filters such that the filter error systems are exponentially mean-square stable and guarantee a prescribed H∞ performance in terms of linear matrix inequality (LMI). A numerical example is provided to demonstrate the validity of the proposed design approach.

SPATIAL－TEMPORAL DISCRETE COORDINATION OF FEM AND DIRECT INTEGRAL METHOD FOR TRANSIENT DYNAMIC PROBLEMS

In this paper, the coordination of spatial-temporal discrets of FEM and direct integral method is investigates.By analyzing the numespatial-temporal discrete,the principle of balancing the principle of balancing the energy error induced by spatialdiscrete and the energy error induced by temporal discrete is presented, and the prioriprocess and adaptive method for the coordination of spatial discerand temporal discrete is obtained.

Authentication and Secret Message Transmission Technique Using Discrete Fourier Transformation

In this paper a novel technique, Authentication and Secret Message Transmission using Discrete Fourier Transformation (ASMTDFT) has been proposed to authenticate an image and also some secret message or image can be transmitted over the network. Instead of direct embedding a message or image within the source image, choosing a window of size 2 x 2 of the source image in sliding window manner and then con-vert it from spatial domain to frequency domain using Discrete Fourier Transform (DFT). The bits of the authenticating message or image are then embedded at LSB within the real part of the transformed image. Inverse DFT is performed for the transformation from frequency domain to spatial domain as final step of encoding. Decoding is done through the reverse procedure. The experimental results have been discussed and compared with the existing steganography algorithm S-Tools. Histogram analysis and Chi-Square test of source image with embedded image shows the better results in comparison with the S-Tools.

A Discrete Model of TB Dynamics in Khyber Pakhtunkhwa-Pakistan

The present paper investigates the theoretical analysis of the tuberculosis(TB)model in the discrete-time case.The model is parameterized by the TB infection cases in the Pakistani province of Khyber Pakhtunkhwa between 2002 and 2017.The model is parameterized and the basic reproduction number is obtained and it is found R_(0)=1:5853.The stability analysis for the model is presented and it is shown that the discrete-time tuberculosis model is stable at the disease-free equilibrium whenever R_(0)<1 and further we establish the results for the endemic equilibria and prove that the model is globally asymptotically stable if R_(0)>1.A discrete fractional model in the sense of Caputo derivative is presented.The numerical results of the model with various parameters and their effect on the model are presented.A comparison of discrete-time method with continuous-time model is presented graphically.A discrete fractional approach is compared with the existing method in literature and some reasonable results are achieved.Finally,a summary of results and conclusion are presented.

A Robust Digital Watermarking Algorithm Based on Finite-Set Discrete Radon Transform Tight Frame

Digital watermarking technology plays a powerful role in the effective protection of digital media copyright, image authentication, image sharing, image information transmission and other fields. Driven by strong demand, digital image watermarking technology has aroused widespread research interest and has gradually developed into one of the most active research directions in information science. In this paper, we present a novel robust digital watermarking algorithm based on discrete radon transform tight frame in finite-set (FDRT). FDRT of the zero mean image is a tight frame, the frame boundary A = B = 1, the dual of the frame is itself. The decomposition and reconstruction of the FDRT tight frame will not cause the phenomenon of image distortion. The embedding of hidden watermark is to add a weak signal to the strong background of the original image. Watermark extraction is to effectively identify the embedded weak signal. The feasibility of the watermarking algorithm is analyzed from two aspects of information hiding and robustness. We select the independent Gaussian random vector as the watermark series, and the peak signal-to-noise ratio (PSNR) as the visual degradation criterion of the watermark image. Basing the FDRT compact stand dual operator, we derived the relationship among the strength parameter, square sum of watermark series, the PSNR. Using Checkmark system, the simulation results show that the algorithm is robust enough to some very important image processing attacks such as lossy compression, MAP, filtering, segmentation, edge enhancement, jitter, quadratic modulation and general geometric attack (scaling, rotation, shearing), etc.

Securing Healthcare Data in IoMT Network Using Enhanced Chaos Based Substitution and Diffusion

Patient privacy and data protection have been crucial concerns in Ehealthcare systems for many years.In modern-day applications,patient data usually holds clinical imagery,records,and other medical details.Lately,the Internet of Medical Things(IoMT),equipped with cloud computing,has come out to be a beneficial paradigm in the healthcare field.However,the openness of networks and systems leads to security threats and illegal access.Therefore,reliable,fast,and robust security methods need to be developed to ensure the safe exchange of healthcare data generated from various image sensing and other IoMT-driven devices in the IoMT network.This paper presents an image protection scheme for healthcare applications to protect patients’medical image data exchanged in IoMT networks.The proposed security scheme depends on an enhanced 2D discrete chaotic map and allows dynamic substitution based on an optimized highly-nonlinear S-box and diffusion to gain an excellent security performance.The optimized S-box has an excellent nonlinearity score of 112.The new image protection scheme is efficient enough to exhibit correlation values less than 0.0022,entropy values higher than 7.999,and NPCR values around 99.6%.To reveal the efficacy of the scheme,several comparison studies are presented.These comparison studies reveal that the novel protection scheme is robust,efficient,and capable of securing healthcare imagery in IoMT systems.

基于离散数据流分割算法的预测方法

离散数据流具有不确定性高、演变趋势多变以及多极值性等特征.针对这些特征带来的预测难以兼顾准确性和实时性的问题,提出了基于离散数据流分割算法的预测方法.该方法融合了短期趋势提取与分割点在线自适应检测:预测前基于非参数回归短期预测模型和改进的趋势提取算法获取短期(预测日)趋势,提前挖掘、分析预测日的短期趋势规律,以用于在线预测;预测时主要针对在线的数据流,基于假设检验自适应地检测分割点,以解决分割点难以确定的问题,并基于短期趋势修正分割点处的预测模型,减少了预测模型对缓冲数据的依赖.数值实验结果验证了本研究所提预测方法的有效性和可行性.

环境执法自动化裁量的法治挑战与制度因应

碳中和、碳达峰旨在实现国家和全球的生态环境可持续治理,也为我国环境保护工作的高质量推进指明了方向。为提升执法效能,行政主体已逐步部署算法装置,用于环境违法问题的非现场治理。面对环境执法从街头官僚向算法官僚的转变过程,社会一般主体对技术裁量正当性的疑问成为了行政法治关注的焦点。况且,自动化裁量质效的提升通常基于对海量数据的训练,鉴于政府自身数据记录的局限性,引入并调度私人部门的数据存量用以形成更全面、准确的裁量模型,也是推进自动化裁量高水平演进的题中之义。此外,环境执法部门应正视裁量基准的正向价值,通过完善裁量话语体系的方式,达致生态友好型行政执法的法理情相统一。

基于功率控制环扰动的DC-DC变换器能量信息一体化研究

针对实现DC-DC变换器之间的信息交互较为复杂的问题,提出了一种基于功率控制环扰动实现其能量信息一体化的方法。通过在DC-DC变换器的传统功率控制环中叠加二进制频移键控(binary frequency shift keying,2FSK)信号作为扰动,将数据信息叠加到传统脉冲宽度调制(pulse-width modulation,PWM)信号中,使得该PWM信号同时包含数据信息,实现能量信息一体化。首先,给出基于2FSK的功率/数据双载波调制以及实时的滑动离散傅里叶变换(sliding discrete Fourier transform,SDFT)解调方案。然后,通过对Buck变换器的能量信息一体化模型进行小信号建模与分析可知,降低输入阻抗可提升通信的信噪比。最后搭建一个由两个Buck变换器并联的实验装置进行验证。实验结果表明在稳态和负载突变的工况下均可实现3 kb/s的稳定通信,验证了所提方法的可行性。该方法使得DC-DC变换器能同时进行功率变换和数据传输,提升数字化和智能化水平。