MAC chastised Dynamism Efficient in Wireless Device Lattice Spending Mistralapproach

In Wireless sensor Network, several researchers have provided different routing protocol for sensor networks, particularly routing protocols depending on clusters protocols. Reliability of nodes is necessary parameter in effective sensor networks. We use MAC protocol for controlling the network packets. This is because the usage of cluster based routing has several merits like minimized control messages, re-usability of bandwidth and enhanced power control. Different cluster based routing protocol is proposed by many researchers for the purpose of reducing the consumption energy in wireless sensor networks. Those techniques reduces the energy consumption but with several disadvantages like lack of QoS, inefficient transmission, etc., To overcome those problems, modified QoS enhanced base station controlled in Mistrial Approach (flooding Technique) for wireless sensor networks is proposed in this work. Here we reduce the number of retransmission and detect the overlay packets in networks using proposed approach. Simulation results show the better energy consumption, Maximum Life time & Efficient Bandwidth is achieved by flooding management when compared to the conventional techniques

Grid Lifespan Enlargement for Assessment in Multihop Wireless Detector Facilities

In energy limited wireless sensor networks, both local quantization andmultihop transmission are essential to save transmission energy and thus prolong the network lifetime. The goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional.The network lifetime optimization problem includes three components: Optimizing source coding at each sensor node, optimizing source throughput at each sensor node.Optimizing multihop routing path. Source coding optimization can be decoupled from source throughput and multihop routing path optimization and is solved by introducing a concept of equivalent 1-bit Mean Square Error (MSE) function. Based on optimal source coding, multihop routing path optimization is formulated as a linear programming problem, which suggests a new notion of character based routing. It is also seen that optimal multihop routing improves the network lifetime bound significantly compared with single-hop routing for heterogeneous networks. Furthermore, the gain is more significant when the network is denser since there are more opportunities for multihop routing. Also the gain is more significant when the observation noise variances are more diverse.

Towards the effectiveness of communication in adopting virtual team for software development

: Advancement in technology particularly the development of smart application has caused a paradigm shift in software development. Teams for developing software do not need to be physically present at all times. Members of development teams may be at remote sites but still communicate with each other. Technology has enabled the creation of virtual teams. While technology put at the disposal of software development teams a range of devices for supporting their communication interaction, members still face many challenges in terms of time difference, language barriers and cultural diversification. Ineffective communication among team members lead to delays in software development and contribute much to make project failures. The primary focus of this research is to identify how communication in virtual teams may become efficient. A survey is carried to assess the factors which affect communication in virtual teams. Different team sizes are considered and their relevance and differences in communication interaction are studied. More in-depth data are extracted for this research by interviewing potential members of virtual teams who work and interact from remote sites. The factors which influence communication interaction is finally established which help in successfully managing virtual team projects

Implementation of Classification Algorithms in Neo4j using IPL data

The main objective of this paper is to implement the classifications algorithms in Neo4j graph database using cypher query language. For implementing the classification algorithm, we have used Indian Premier League (IPL) dataset to predict the winner of the matches using some different features. The IPL is the most popular T20 cricket league in the world. The prediction models are based on the city where the matches were played, winner of the toss and decision of the toss. In this paper we have implemented Naïve Bayes and K-Nearest Neighbors (KNN) classification algorithms using cypher query language. Different classifiers are used to predict the outcome of different games like football, volleyball, cricket etc, using python and R. In this paper we shall use cypher query language. We shall also compare and analysis the results which are given by Naïve Bayes and K-Nearest Neighbors algorithms to predict the winner of the matches.

A coupled models Hydrodynamics - Multi headed Deep convolutional neural network for rapid forecasting large-scale flood inundation

Modeling large-scale flood inundation requires weeks of calculations using complex fluid software. The state-of-the-art in operational hydraulic modeling does not currently allow flood real-time forecasting fields. Data driven models have small computational costs and fast computation times and may be useful to overcome this problem. In this paper, we propose a new modeling approach based on a coupled of Hydrodynamics finite element model and Multi-headed Deep convolutional neural network (MH-CNN) with rain precipitations as input to forecast rapidly the water depth reached in large floodplain with few hours-ahead. For this purpose, one first builds a database containing different simulations of the physical model according to several rain precipitation scenarios (historic and synthetic). The multi-headed convolutional neural network is then trained using the constructed database to predict water depths. The pre-trained model is applied successfully to simulate the real July 2014 flood inundation in an 870 km2 area of La Nive watershed in the south west of France. Because rain precipitation forecast data is more accessible than discharge one, this approach offers great potential for real-time flood modelling for ungauged large-scale territories, which represent a large part of floodplain in the world.

Video-Based Rope Skipping Repetition Counting with ResNet Model

Video Repetition Counting is one of the important research areas in computer vision. It focuses on estimating the number of repeating actions. In this paper, we propose a method for video-based rope skipping repetition counting that combines the ResNet Model and a counting algorithm. Each frame in the given video is first classified into two categories: upward and downward, describing its current motion status. Then the classification sequence of the video is processed by a statistical counting algorithm to obtain the final repetition number. The experiments on real-world videos show the efficiency of our model.

Kronecker Product of matrices and their applications to self-adjoint two-point boundary value problems associated with first order matrix differential systems

In this paper, we shall be concerned with Kronecker product or Tensor product of matrices and develop their properties in a systematic way. The properties of the Kronecker product of matrices is used as a tool to establish existence and uniqueness of solutions to two-point boundary value problems associated with system of first order differential systems. A new approach is described to solve the Kronecker product linear systems and establish best least square solutions to the problem. Several interesting examples are given to highlight the importance of Kronecker product of matrices. We present adjoint boundary value problems and deduce a set of necessary and sufficient conditions for the Kronecker product boundary value problem to be self-adjoint.

ECDSA: The Virtual Signature Set of Rules of a Higher Internet

Computerized Currency is an electronic kind of cash. These days, everything is developing into digitization measure. This contains all properties like actual cash and furthermore permits prompt trades which will be reliably executed across the world while partner with upheld contraptions and organizations. In this paper we presented the SHA3-512 bit hashing algorithm and ECDSA algorithm for generation of digital signature. The Elliptic curve cryptography (ECC) is one of the greater promising technology on this area. ECC-enabled TLS is quicker and greater scalable on our servers and presents the equal or higher protection than the default cryptography in use at the web. one of the elliptic curve algorithm, the elliptic curve virtual signature algorithm (ECDSA), may be used to enhance overall performance at the Internet. CloudFlare now helps custom ECDSA certificate for our clients and that’s true for all people the use of the Internet.

Image Classification Using Transfer Learning and Deep Learning

Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started since 2010. Classification of images has further augmented in the field of computer vision with the dawn of transfer learning. To train a model on huge dataset demands huge computational resources and add a lot of cost to learning. Transfer learning allows to reduce on cost of learning and also help avoid reinventing the wheel. There are several pretrained models like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which are widely used. This paper demonstrates image classification using pretrained deep neural network model VGG16 which is trained on images from ImageNet dataset. After obtaining the convolutional base model, a new deep neural network model is built on top of it for image classification based on fully connected network. This classifier will use features extracted from the convolutional base model.

Research On human motion recognition algorithm based on WLD

With the rapid development of intelligent technology,People’s lives have gradually entered the era of information and intelligentce,Wearable devices are becoming more and more popular,it is easier to use sensors to obtain data,even physiological data,from human body.When large amounts of data are collected by sensors,we can analyze and model them.the values of each characteristic are used to judge the user’s state,then according to the state we can provide users with more accurate and convenient services. In this paper,the data collected by different sensors are used to establish a prediction model and analyze the comparative effect of different recognition algorithms on the test data. The results of the experiment shows that the Bayesian method based on WLD identities the state of the human body better.