Palmprint Recognition System Based on Multi-Block Local Line Directional Pattern and Feature Selection

In this paper, we deal with multimodal biometric systems based on palmprint recognition. In this regard, several palmprint-based approaches have been already proposed. Although these approaches show interesting results, they have some limitations in terms of recognition rate, running time and storage space. To fill this gap, we propose a novel multimodal biometric system combining left and right palmprints. For building this multimodal system, two compact local descriptors for feature extraction are proposed, fusion of left and right palmprints is performed at feature-level, and feature selection using evolutionary algorithms is introduced. To validate our proposal, we conduct intensive experiments related to performance and running time aspects. The obtained results show that our proposal shows significant improvements in terms of recognition rate, running time and storage space. Also, the comparison with other works shows that the proposed system outperforms some literature approaches and comparable with others.

Design and Implementation of Smart Classroom Based on Internet of Things and Cloud Computing

With the development of emerging technology innovations such as the internet of things, classroom management has also shown an informatization trend. Among them, smart classrooms are an important part of the current university information environment construction. The purpose of this article is to build a smart classroom into an intelligent teaching environment with many functions such as intelligent perception and identification, real-time monitoring based on the internet of things technology and cloud computing technology. A questionnaire survey was conducted among freshman students in some majors, and interviews were conducted with the instructors. It was found that 92.19% of the students were satisfied with the classroom learning in the smart classroom environment, and most teachers thought that the teaching effect had been improved. Experiments have proven that the operation of smart classrooms based on the internet of things and cloud computing realizes the intelligence of teaching management services and improves the level of education informationization in schools.

Information Dissemination Mechanism Based on Cloud Computing Cross-Media Public Opinion Network Environment

As an important expression of social public opinion, network public opinion develops rapidly with the popularization of the internet and then affects the real society. Therefore, the use of computer technology to study the network public opinion information transmission mechanism has strong practical significance. The purpose of this paper is to use cloud computing to realize the research of information dissemination mechanism in the context of cross-media public opinion network. Researched from three aspects of operator supervision, number of media, and user density, the hotspot propagation mechanism of Storm platform given in this paper can solve the efficiency problems of traditional algorithms while ensuring accuracy, improve efficiency, and lay the foundation for the research on the monitoring of Internet public opinion propagation.

Improvement of K-Means Algorithm for Accelerated Big Data Clustering

With the rapid development of the computer level, especially in recent years, “Internet +,” cloud platforms, etc. have been used in various industries, and various types of data have grown in large quantities. Behind these large amounts of data often contain very rich information, relying on traditional data retrieval and analysis methods, and data management models can no longer meet our needs for data acquisition and management. Therefore, data mining technology has become one of the solutions to how to quickly obtain useful information in today's society. Effectively processing large-scale data clustering is one of the important research directions in data mining. The k-means algorithm is the simplest and most basic method in processing large-scale data clustering. The k-means algorithm has the advantages of simple operation, fast speed, and good scalability in processing large data, but it also often exposes fatal defects in data processing. In view of some defects exposed by the traditional k-means algorithm, this paper mainly improves and analyzes from two aspects.

Application Research of Speech Signal Processing Technology Based on Cloud Computing Platform

In recent years, in the context of the rapid development of information technology, artificial intelligence has also developed. People have begun to train machines. Many machines have been able to gradually understand human languagesand perform a series of actions based on language instructions. On this basis, scientific researchers hope that the machine can be more intelligent and humane. In the noise estimation stage, a noise estimation algorithm based on speech detection is used to effectively estimate the noise. Secondly, according to the characteristics of the method of speech noise reduction processing, a method of processing speech noise is realized. Finally, simulation experiments are used to illustrate the effectiveness of the algorithm. Aiming at the shortcomings of traditional speech noise reduction algorithms, improvements were made in adaptive filter estimation. The model's speech noise reduction algorithm was obtained. The cepstrum estimation of speech signals was modified, and the effect of speech enhancement was significantly improved.

Fuzzy Decoupling Energy Efficiency Optimization Algorithm in Cloud Computing Environment

With the vigorous development of information technology, cloud computing, as a distributed computing technology, has become a research hotspot in the industry. The cloud computing system has a huge resource pool. In order to meet user-specific quality of service requests, it needs to perform reasonable scheduling of various tasks. Under the premise of ensuring high computing performance and better service quality in the cloud computing environment, system energy efficiency optimization has become a key issue to be promoted in the promotion of cloud computing. The research purpose of this paper is to study the fuzzy decoupling energy efficiency optimization algorithm in cloud computing environment. This paper designs a fuzzy decoupling energy efficiency optimization scheme.

Application of Desktop Computing Technology Based on Cloud Computing

With the development of internet technology and computer technology, in order to solve the big data problem in the new era, cloud computing has emerged at the historic moment. Desktop virtualization, as an important application of cloud computing, has experienced unprecedented development. Although traditional virtual desktop solutions can solve the problem of PCs, they cannot be used for centralized distributed cluster deployment, and they mostly rely on the underlying virtualization technology. The dynamic management of virtual machines is mainly to take into account the goals of QoS, resource utilization balance, and power under the premise of ensuring the user experience, the maximum guarantee of the balance of host resource utilization, and the reduction of power consumption of the entire system. The results show that by deploying a test desktop virtualization system, this article reduces the power consumption of host resources, reduces the SLA violation rate (improves the user experience), and improves the balance of host resource utilization.

E-Commerce Model Oriented to Cloud Computing and Internet of Things Technology

In recent years, the number of e-commerce netizen users in China has grown dramatically, and e-commerce users who shop online have also grown rapidly. In this competitive environment, the rapid development of e-commerce has also risen rapidly. Therefore, it is very necessary to actively carry out research on e-commerce models for cloud computing and internet of things technology. The purpose of this article is mainly to explore the research on e-commerce model for cloud computing and internet of things technology and establish a model for cloud computing and internet of things. It has data mining and distribution functions to test multiple indicators such as cloud computing facilities and enterprise performance. The research results show that the number of complaints on the e-commerce model based on cloud computing and internet of things technology is 2,368; the number of complaints on the B2B e-commerce model is 19,955; the number of complaints on the B2C e-commerce model is 5,016; the number of complaints in C2C e-commerce was 51,854.

Implementation of a Service Management Office Into a World Food Company in Latin America

Research on service management office (SMO) related with its implementation, challenges, relevance, and outcomes is scarce. The purpose of this case study research is to contribute with the lessons learned from the implementation of an SMO into a big sized company. The lessons include the experience of the company designing and implementing an SMO. A general roadmap and main challenges for the creation of an SMO are presented through the lessons learned of three perspectives: people, processes, and technology. The results in the SMO implementation reveal that it could become a strategic complement for IT services in order to ensure quality, efficiency, and continuous improvement for information technology service management (ITSM). The main changes derived from the SMO implementation were migrate from a function model to a service model, go from management of cost by function to management by service, from an order taker to a focus on business transformation initiatives, from a portfolio management based on applications to a process based on flexible governance.

A CSP-Based Approach for Managing the Dynamic Reconfiguration of Software Architecture

Software applications are composed of a set of interconnected software components running on different machines. Most of these applications have a dynamic nature and need to reconfigure structure and behavior at run-time. The dynamic reconfiguration of software is a problem that must be dealt with. Reconfiguring this kind of applications is a complicated task and risks to take software at an undesirable situation. In this paper, the authors present a solution whose objective is to provide a complete support for reconfiguring and formally verifying consistency of dynamic updates of software before performing them. The aim is to provide highly available systems with the ability to safely modify their structure and behavior at run-time. The proposed approach is based mainly on the use of the meta-model concept for reconfiguration structural checking, and the CSP language, refinement technique, and the FDR model checking tool for the verification of reconfiguration behavioral consistency. The authors have also developed a tool prototype that validates and implements their proposals.