Design and simulation of UWB microstrip patch antenna for Ku/K bands applications

In this paper, a compact Rectangular Microstrip Patch Antenna (RMPA) fed by microstrip line has been designed to operate for Ku/K bands applications. The proposed antenna is slotted and optimized to reach a large bandwidth and cover various applications such as 5G communication (in frequency range 24.25-27.5 GHz), fixed and mobile satellite, radionavigation, space research, radiolocation etc. In this design, the substrate used is FR4 with a relative permittivity of 4.4 and a thickness of 1.6 mm. The slotted RMPA has been simulated using Advanced Design System (ADS) software and the obtained results are presented and discussed. The proposed antenna achieves a return loss less than -10 dB and VSWR (voltage standing wave ratio) < 2 in frequency range from 16.58 to 25.29 GHz. The percentage bandwidth provided by the proposed microstrip antenna is 41.61%

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

IR-UWB Cognitive Radio System Based on the M-OAM Modulations

Communication systems plays decisive role in the evolution of the transport field, and during the last years, requests to have the safest and most efficient transport modes has been expressed by users. The use of the ultra-wideband (UWB) technique can satisfy such a need. UWB offers numerous advantages such as: the large offered bandwidth, the signal flexibility, the quality of service, the transmission power and the limited cost. In this paper, a new system dedicated to the domain of transport, based on UWB technology is presented. The implementation of the new modulation M-OAM (Orthogonal Amplitude Modulation) which is based on the use of original mathematical tools called Modified Gegenbauer functions (MGF), derived from orthogonal polynomials, increases the data rate flow and enhances the robustness ensured by UWB communication for multimedia and transport applications. With the purpose of improving the functioning of the communication system and ensuring the very high data rate, this work consists in combination of UWB and cognitive radio technologies in order to develop an adapted and efficient universal receiver. This receiver is able to detect the signal arrival and identify the coding parameters used in the transmission so as to be adapted to them automatically. The receiver requires intelligent capacities of observation, learning and decision, therefore, our conception is based on the concept of the cognitive radio which is characterized with the ability to detect the presence of the signal. In this paper, we present the principle of the modulation M-OAM, the waveforms used in our system, and the method which allows the receiver to identify the type of the used modulation

System-independent ASR error detection and classification using Recurrent Neural Network

This paper addresses errors in continuous Automatic Speech Recognition (ASR) in two stages: error detection and error type classification. Unlike the majority of research in this field, we propose to handle the recognition errors independently from the ASR decoder. We first establish an effective set of generic features derived exclusively from the recognizer output to compensate for the absence of ASR decoder information. Then, we apply a variant Recurrent Neural Network (V-RNN) based models for error detection and error type classification. Such model learn additional information to the recognized word classification using label dependency. As a result, experiments on Multi-Genre Broadcast Media corpus have shown that the proposed generic features setup leads to achieve competitive performances, compared to state of the art systems in both tasks. Furthermore, we have shown that V-RNN trained on the proposed feature set appear to be an effective classifier for the ASR error detection with an Accuracy of 85.43%

Design of an Analog RFID-Based UHF Band Tag Antenna with Opened Circuited L-shaped Stubs for the Applications in Localization

This paper presents a new analog design of a radio-frequency identification (RFID) tag antenna with a long-read range oriented to localization applications. The actual work focuses on the analog input characterization of antenna impedance by studying the capacitive effect, created by the gaps, and the effect of introduced opened circuited L-shaped stubs, on the RFID tag characteristics. Numerical and measured results confirm that proposed tag antenna performances are significantly improved by introducing gaps and stub structures and after optimizing their dimensions such as length and width. Introduced stubs with optimal dimensions lead to a well level of impedance matching, lower return loss values. Furthermore, two operating frequency bands have been created when the antenna is excited by a 50 Ω port: a low-frequency band around 837 MHz and a higher one around 927 MHz These results have been validated by measured ones. The proposed RFID antenna is mainly composed by three split rectangular resonators (SRR) where introduced structures concern only the larger SRR. The optimized antenna has an area of 76 × 24.6 mm2 and is printed on the Taconic RF-60A substrate with a dielectric constant of 6.12, the thickness of 1.6 mm, and a loss tangent of 0.025. Simulation results show interesting communication performances, of the proposed tag antenna, with a return loss of -22.3 dB around 916 MHz and a long read range up to 25m when it is fed by an industrial Mping M730 chip having a power sensitivity of -24dB and an output impedance Zchip16-j194 (Ω) at 916 MHz

A Secured Data Processing Technique for Effective Utilization of Cloud Computing

Digital humanities require IT Infrastructure and sophisticated analytical tools, including datavisualization, data mining, statistics, text mining and information retrieval. Regarding funding, tobuild a local data center will necessitate substantial investments. Fortunately, there is another optionthat will help researchers take advantage of these IT services to access, use and share informationeasily. Cloud services ideally offer on-demand software and resources over the Internet to read andanalyze ancient documents. More interestingly, billing system is completely flexible and based onresource usage and Quality of Service (QoS) level. In spite of its multiple advantages, outsourcingcomputations to an external provider arises several challenges. Specifically, security is the majorfactor hindering the widespread acceptance of this new concept. As a case study, we review the use ofcloud computing to process digital images safely. Recently, various solutions have been suggested tosecure data processing in cloud environement. Though, ensuring privacy and high performance needsmore improvements to protect the organization's most sensitive data. To this end, we propose aframework based on segmentation and watermarking techniques to ensure data privacy. In this respect,segementation algorithm is used to to protect client's data against untauhorized access, whilewatermarking method determines and maintains ownership. Consequentely, this framework willincrease the speed of development on ready-to-use digital humanities tools

Business Intelligence Model to analyze Social Media through Big Data analytics

Business Intelligence (BI) is a collection of tools, technologies, and practices that include the entire process of collecting, processing, and analyzing qualitative information, to help entrepreneurs better understand their business and marketplace. Every day, social networks expand at a faster rate and pace, which sees them as a source of Big Data. Therefore, BI is developed in the same way on VoC (Voice of Customer) expressed in social media as qualitative data for company decision-makers, who desire to have a clear perception of customers’ behaviour. In this article, we present a comparative study between traditional BI and social BI, then examine an approach to social business intelligence. Next, we are going to demonstrate the power of Big Data that can be integrated into BI so that we can finally describe in detail how Big Data technologies, like Apache Flume, help to collect unstructured data from various sources such as social media networks and store it in Hadoop storage

A Secured Data Processing Technique for Effective Utilization of Cloud Computing

Digital humanities require IT Infrastructure and sophisticated analytical tools, including datavisualization, data mining, statistics, text mining and information retrieval. Regarding funding, tobuild a local data center will necessitate substantial investments. Fortunately, there is another optionthat will help researchers take advantage of these IT services to access, use and share informationeasily. Cloud services ideally offer on-demand software and resources over the Internet to read andanalyze ancient documents. More interestingly, billing system is completely flexible and based onresource usage and Quality of Service (QoS) level. In spite of its multiple advantages, outsourcingcomputations to an external provider arises several challenges. Specifically, security is the majorfactor hindering the widespread acceptance of this new concept. As a case study, we review the use ofcloud computing to process digital images safely. Recently, various solutions have been suggested tosecure data processing in cloud environement. Though, ensuring privacy and high performance needsmore improvements to protect the organization's most sensitive data. To this end, we propose aframework based on segmentation and watermarking techniques to ensure data privacy. In this respect,segementation algorithm is used to to protect client's data against untauhorized access, whilewatermarking method determines and maintains ownership. Consequentely, this framework willincrease the speed of development on ready-to-use digital humanities tools

A novel approach based on segmentation for securing medical image processing over cloud

International audienceHealthcare professionals require advanced image processing software to enhance the quality of clinical decisions. However, any investment in sophisticated local applications would dramatically increase healthcare costs. To address this issue, medical providers are interested in adopting cloud technology. In spite of its multiple advantages, outsourcing computations to an external provider arises several challenges. In fact, security is the major factor hindering the widespread acceptance of this new concept. Recently, various solutions have been suggested to fulfill healthcare demands. Though, ensuring privacy and high performance needs more improvements to meet the healthcare sector requirements. To this end, we propose a framework based on segmentation approach to secure cloud-based medical image processing in the healthcare system

A novel approach based on segmentation for securing medical image processing over cloud

Healthcare professionals require advanced image processing software to enhance the quality of clinical decisions. However, any investment in sophisticated local applications would dramatically increase healthcare costs. To address this issue, medical providers are interested in adopting cloud technology. In spite of its multiple advantages, outsourcing computations to an external provider arises several challenges. In fact, security is the major factor hindering the widespread acceptance of this new concept. Recently, various solutions have been suggested to fulfill healthcare demands. Though, ensuring privacy and high performance needs more improvements to meet the healthcare sector requirements. To this end, we propose a framework based on segmentation approach to secure cloud-based medical image processing in the healthcare system