bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the impact of metal thickness and geometric parameters on the quality factor-Q in integrated spiral inductors by means of artificial bee colony technique
Authors: Bouyghf Hamid , Benhala Bachir , Raihani Abdelhadi
Published: October 2019

The goal of this present paper is to design, analysis the influence of the inductor geometrical parameters and the effect of the metal thickness on the quality factor-Q in integrated square spiral inductor using an efficient application of the artificial bee colony (ABC) algorithm. The inductors were optimized at 2.4 GHz to determinate their major geometrical dimensions (sp, w, din…) and their number of turns, for uses in radio-frequency integrated circuits (RFICs). The optimization results are validated by the simulation using an electromagnetic simulator (ADS-Momentum). Using matlab software, the study on the impact of the effect of geometrical parameters and the effect of metal thickness, on the factor of quality-Q of spiral inductors, is shown. We first reported that it is possible to improve Q-factors further by increasing the metal thickness, and in the design of inductor; a compromise must be reached between the value of w, n, sp and din to achieve the desired quality factor-Q and other electrical parameters.
Adaptive CSLBP compressed image hashing
Authors: Patil Varsha , Sarode Tanuja
Published: October 2019

Hashing is popular technique of image authentication to identify malicious attacks and it also allows appearance changes in an image in controlled way. Image hashing is quality summarization of images. Quality summarization implies extraction and representation of powerful low level features in compact form. Proposed adaptive CSLBP compressed hashing method uses modified CSLBP (Center Symmetric Local Binary Pattern) as a basic method for texture extraction and color weight factor derived from L*a*b* color space. Image hash is generated from image texture. Color weight factors are used adaptively in average and difference forms to enhance discrimination capability of hash. For smooth region, averaging of colours used while for non-smooth region, color differencing is used. Adaptive CSLBP histogram is a compressed form of CSLBP and its quality is improved by adaptive color weight factor. Experimental results are demonstrated with two benchmarks, normalized hamming distance and ROC characteristics. Proposed method successfully differentiate between content change and content persevering modifications for color images.
A multilabel classification approach for complex human activities using a combination of emerging patterns and fuzzy sets
Authors: Sakr Nehal A. , Abu-ElKheir Mervat , Atwan A. , Soliman H. H.
Published: October 2019

In our daily lives, humans perform different Activities of Daily Living (ADL), such as cooking, and studying. According to the nature of humans, they perform these activities in a sequential/simple or an overlapping/complex scenario. Many research attempts addressed simple activity recognition, but complex activity recognition is still a challenging issue. Recognition of complex activities is a multilabel classification problem, such that a test instance is assigned to a multiple overlapping activities. Existing data-driven techniques for complex activity recognition can recognize a maximum number of two overlapping activities and require a training dataset of complex (i.e. multilabel) activities. In this paper, we propose a multilabel classification approach for complex activity recognition using a combination of Emerging Patterns and Fuzzy Sets. In our approach, we require a training dataset of only simple (i.e. single-label) activities. First, we use a pattern mining technique to extract discriminative features called Strong Jumping Emerging Patterns (SJEPs) that exclusively represent each activity. Then, our scoring function takes SJEPs and fuzzy membership values of incoming sensor data and outputs the activity label(s). We validate our approach using two different dataset. Experimental results demonstrate the efficiency and superiority of our approach against other approaches.
Performance evaluation of two degree of freedom conventional controller adopting the smith principle for first order process with dead time
Authors: Bright Belinda Sharon , Swarnalatha R.
Published: October 2019

The Proportional Integral Derivative Controller is a typical controller implemented frequently in many services and integrating the Smith predictor is an extremely useful control system structure for processes with dead time. This paper has evaluated two control schemes with the modified structures of the Smith predictor incorporating dead time compensators and conventional controllers for first order process with dead time. The disturbance response and the set point response for both the control schemes were decoupled from each other. Therefore two degrees of freedom control design was formulated, and hence the responses could be designed separately. The two control schemes have mainly two variables to be adjusted that decide the robustness and closed-loop behaviour. This paper also contains the calculation of various parameters that were used in each scheme. A comparison of the two control schemes along with the general Smith predictor control scheme was made using Simulink/Matlab. The conclusion is the second control scheme gave better response overall for the processes with dead time having dead time uncertainty and for the processes with dead time without dead time uncertainty.
Rapidly IPv6 multimedia management schemes based LTE-A wireless networks
Authors: Khalil Abeer Twakol , Abdel-Fatah A. I. , Sakr Hesham Ali
Published: October 2019

Ensuring the best quality of smart multimedia services becomes an essential goal for modern enterprises so there is always a need for effective IP mobility smart management schemes in order to fulfill the following two main functions: (I) interconnecting the moving terminals around the extended indoor smart services. In addition, (II) providing session continuity for instant data transfer in real-time and multimedia applications with negligible latency, efficient bandwidth utilization, and improved reliability. In this context, it found out that the Generalized Multi-Protocol Label Switching (GMPLS) over LTE-A network that offers many advanced services for large numbers of users with higher bandwidths, better spectrum efficiency, and lower latency. In GMPLS, there is an elimination of the routing searches and choice of routing protocols on every core LTE-A router also it provides the architecture simplicity and increases the scalability. A comparative assessment of three types of IPv6 mobility management schemes over the LTE-A provided by using various types of multimedia. By using OPNET Simulator 17.5, In accordance with these schemes, it was proven that the IPv6-GMPLS scheme is the best choice for the system's operation, in comparison to the IPv6-MPLS and Mobile IPv6 for all multimedia offerings and on the overall network performance.
Resonance frequency analysis of laser optical fiber based on microcantilever
Authors: Aljanabi Mohanad
Published: October 2019

The normal frequency of smart beams was originated utilizing FEM [Ansys and Comsol] code for first five modes by varying the position of actuator from the fixed end of the structure, and it has a suitable arrangement with analytically found the standard frequency. This paper includes learning a resonance frequency analysis of laser optical fiber based on microcantilever of designing magnetic actuator using Ansys and Comsol simulation. The design of optical fiber includes Nickel cantilever, two magnets and one coil that apply to force on the cantilever. After the current flows in the coil domain, the shape of microcantilever will be deformed. It will move to z- direction that depends on the force direction. Two methods including, Comsol Multiphysics, Ansys and analytical equations have been utilized to calculate the resonance frequency, current and force values. The simulation results include calculating the current (magnetic current density) and effects of the magnetic field of the coil on the cantilever (force calculation). Utilizing this method is to limit faults(errors) of optical fiber laser between transmitter and receiver system (detection system) for any time of cutting coil when the signal of a laser passes through the coil. In conculsions, resonant frequency (f_n) tuning using cantilivier presented in the resrach have larger variable range by using simulations. However,the adjusting of the system and changing the deminsions.Resolutions to this problematic contain tuning the modes of resonant frequency to produce by cantilivier with  2-magnets and coil when the signal  pass from  laser source. Based on these simulations and characterization results, the proposed assembly can be a good applicant for evolving a low price, high material platform for many biological, laser optical fiber, communication, machine learning, biosensors and biomedical applications. 
Performance investigation of WOFDM for 5G wireless networks
Authors: Ghanim M. F.
Published: October 2019

Nowadays, emerging wireless networks scenarios such as the proposed systems for 5G is discussed widely with diverse requirements. Orthogonal Frequency Division Multiplexing (OFDM) is a conservative proposal which is used to build 5G WOFDM system (Wavelet OFDM system). The simulation of the system is initialized with BPSK then with QAM and 64-QAM the system is improved by increasing the number of levels of Discrete Wavelet Transform to five levels and finally compared with original system to prove that the it is convenient for 5G Wireless networks.
A regexcriteria API to complete the power of regular expressions engine
Authors: Hassan Boulchahoub , Amina Rachiq , Amine Labriji , Elhoussin Labriji , Azouazi Mohamed
Published: October 2019

Regular expressions are heavily used in the field of computer programming. They are known by their strength to search or replace parts of strings according to a given structure (mails, phone, numbers, etc.). Currently  regular expressions are only used to search for some patterns or to make some substitutions in strings. However, the need may be wider than that when it comes to order the results of a regular expression or to group them according to some criteria. Developers are always called to analyze the results of a regular expression by doing some restrictions such as (equal, not equal, between) or  some projections like (maximum, average, grouping by ..) or sorts. Unfortunately, to do these treatments, the developer must implement his own algorithms which cost him a remarkable effort and a waste of time. We propose in this paper an API called RegexCriteria inspired from Hibernate Criteria to support developer while analysing the results of a regular expression.
UCSY-SC1: A Myanmar speech corpus for automatic speech recognition
Authors: Mon Aye Nyein , Pa Win Pa , Thu Ye Kyaw
Published: October 2019

This paper introduces a speech corpus which is developed for Myanmar Automatic Speech Recognition (ASR) research. Automatic Speech Recognition (ASR) research has been conducted by the researchers around the world to improve their language technologies. Speech corpora are important in developing the ASR and the creation of the corpora is necessary especially for low-resourced languages. Myanmar language can be regarded as a low-resourced language because of lack of pre-created resources for speech processing research. In this work, a speech corpus named UCSY-SC1 (University of Computer Studies Yangon - Speech Corpus1) is created for Myanmar ASR research. The corpus consists of two types of domain: news and daily conversations. The total size of the speech corpus is over 42 hrs. There are 25 hrs of web news and 17 hrs of conversational recorded data.
The corpus was collected from 177 females and 84 males for the news data and 42 females and 4 males for conversational domain. This corpus was used as training data for developing Myanmar ASR. Three different types of acoustic models  such as Gaussian Mixture Model (GMM) - Hidden Markov Model (HMM), Deep Neural Network (DNN), and Convolutional Neural Network (CNN) models were built and compared their results. Experiments were conducted on different data  sizes and evaluation is done by two test sets: TestSet1, web news and TestSet2, recorded conversational data. It showed that the performance of Myanmar ASRs using this corpus gave satisfiable results on both test sets. The Myanmar ASR  using this corpus leading to word error rates of 15.61% on TestSet1 and 24.43% on TestSet2.
Ensemble learning for software fault prediction problem with imbalanced data
Authors: Khuat Thanh Tung , Le My Hanh
Published: October 2019

Fault prediction problem has a crucial role in the software development process because it contributes to reducing defects and assisting the testing process towards fault-free software components. Therefore, there are a lot of efforts aiming to address this type of issues, in which static code characteristics are usually adopted to construct fault classification models.  One of the challenging problems influencing the performance of predictive classifiers is the high imbalance among patterns belonging to different classes. This paper aims to integrate the sampling techniques and common classification techniques to form a useful ensemble model for the software defect prediction problem. The empirical results conducted on the benchmark datasets of software projects have shown the promising performance of our proposal in comparison with individual classifiers.
Facility maintenance management system based on GIS and indoor map
Authors: Puttinaovarat Supattra , Jutapruet Suwat , Saeliw Aekarat , Pruitikanee Siwipa , Kongcharoen Jinda , Jiamsawat Watchara , Limpasamanon Suchakree
Published: October 2019

This research presented a system development approach for facility maintenance management system based on GIS and indoor map in the form of web applications that can be used with all devices and no worries about time limitations. The capabilities of GIS, indoor map, and geospatial data visualization help speeding up facility maintenance management process and create benefits to all concerned parties, i.e., users can notify and follow the data of facility errors at the time; or officers in charge can operate quickly because they can access real-time data. Indoor map display makes it easier to access locations or places of damaged facilities. In addition, the data from the model system presented in this research can also be applied to planning and decision-making of executives.
A water-activated battery based on activated carbon
Authors: Nguyen Dang Trang , Taguchi Kozo
Published: October 2019

In this research, a novel property of activated carbon powder (AC powder) has been utilized to realize a disposable paper-based battery. AC powder was loaded on a 3D carbon paper substrate to make the anode. The cathode was integrated directly on the paper-based battery case by coating multiwalled carbon nanotube mixed with potassium ferricyanide on a side of a sheet of filter paper, the other side worked as a paper-based proton exchange membrane. This design provides a simple but practical disposable water-activated battery. The developed battery generated the maximum power density of 10.4 µW/cm2 at the AC powder concentration of 17 mg/cm2. Although, the output power of the battery is low, it is made of low-cost and abundant materials, and therefore being able to scale up. The battery is a disposable and on-demand micropower generation activated anytime, anywhere by water.
Securing vehicular cloud networks
Authors: Moussaoui Djilali , Feham Mohamed , Bensaber Boucif Amar , Kadri Benamar
Published: October 2019

Vehicular Cloud Networks (VCN) is the network that ensures mobility and availability of resources allowing new services and applications like Network as a Service (NaaS), STorage as a Service (STaaS), Computation as a Service (CompaaS) and Cooperation as a Service (CaaS). In this paper, we propose a solution to secure the Vehicular Cloud Network (VCN). Our challenge in this work is to adapt the PKI architecture, which is mainly used in wired networks to be used in VCN. To propose a security solution for Vehicular Cloud Networks (VCN), our work is based on three steps; the first one is to make network architecture study, where we tried to highlight the main network components. The second step is to propose the security solution architecture. Finally, the last step is to program a test and validate the solution using a simulation.
The improvement of node Mobility in RPL to increase transmission efficiency
Authors: Satanasaowapak Pak , Khunboa Chatchai
Published: October 2019

The Internet of Thing has gained interested to use for daily devices to industrial applications. Mission-critical applications such as connected car and healthcare services require real-time communications and mobility support. The 6LoWPAN protocol and IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) have become the standard for the IoT. However, the RPL protocol is unable to support the application requirement causing from the high network overhead, long message latency and high packet loss rate due to mobility. Thus, in this paper, we propose a new cost metric combining the number of hops, RSSI values, and the summation of delay to enhance RPL mobility. In addition, we define the movement notification for the mobile node to activate mobile detection and parent selection processes. Finally, we presented a comparative study of the improved RPL protocols in terms of packet delivery ratio, end-to-end delay and the number of control messages. The result shows that improved RPL protocol with the new cost metrics provides a high packet delivery ratio and offers a low message latency.
Interactive on smart classroom system using beacon technology
Authors: Dong Hu Jia , Abdulla Raed , Selvaperumal Sathish Kumar , Duraikannan Shankar , Lakshmanan Ravi , Abbas Maythem K.
Published: October 2019

The emergence of many internet industries ushers in IOT era, and about to bring us to the point of universal connectivity. In the field of education, the IOT technology has a broad applicable prospect for a more interactive and intelligent way by improving the quality of teaching and management. The proposed class affair management system is mean to enrich the interaction between lecturers and students which in an efficient and smart way. Based on the existing model, a layered architecture is proposed to build the beacon based campus management system. Backend device and protocols compose the physical layer to collect the raw data from physical objects. Data link layer and control layer are responsible for forming required package and sending to corresponding layer. Beacon technology used for proposed design applies Bluetooth low energy 4.0 standard which allowing devices exchange data through Bluetooth at an extremely low power consumption-using a single coin cell battery can last for several years. Saved up to 97 percentage energy compared with similar system. The entire proposed platform allows participants to bring personally owned devices to access campus management system. Through location information, teaching activities and personalized information notification can be automatically accomplished, which will inspire the innovation and development of classroom teaching mode. Beacon technology has a great potential that can be completely transplanted into other scenario such as the hypermarket and library.
Web based fingerprint roll call attendance management system
Authors: A. Ademola , E. Somefun T. , A. Oluwabusola
Published: October 2019

Managing people and keeping adequate record of the attendance for most of the tertiary institutions are difficult task. Moreover, the manual taking of attendance adds to the difficulty of this task because it is strenuous, time intensive and can easily be falsified. Meanwhile, With the advent of new technology, a new world of authentication and security has been created via biometrics. This work aims at improving Covenant Universities paper-based attendance by using biometrics, specifically fingerprint technology. It is a web application that works with a fingerprint reader from which the features of the student’s fingerprint are extracted during enrolment. This is also needed during the authentication phase which occurs during the roll call attendance in the halls of residence. It is implemented using PHP and MySQL.
Combining convolutional neural networks and slantlet transform for an effective image retrieval scheme
Authors: Hamoud Al-Tamimi Mohammed Sabbih
Published: October 2019

In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this paper, the author focuses on recommendation of a fresh method for retrieving image. For multi presentation of image in Convolutional Neural Network (CNN), Convolutional Neural Network - Slanlet Transform (CNN-SLT) model uses Slanlet Transform (SLT). The CBIR system was therefore inspected and the outcomes benchmarked. The results clearly illustrate that generally, the recommended technique outdid the rest with accuracy of 89 percent out of the three datasets that were applied in our experiments. This remarkable performance clearly illustrated that the CNN-SLT method worked well for all three datasets, where the previous phase (CNN) and the successive phase (CNN-SLT) harmoniously worked together.
Optimized memory model for hadoop map reduce framework
Authors: Bhaskar Archana , Ranjan Rajeev
Published: October 2019

Map Reduce is the preferred computing framework used in large data analysis and processing applications. Hadoop is a widely used Map Reduce framework across different community due to its open source nature. Cloud service provider such as Microsoft azure HDInsight offers resources to its customer and only pays for their use. However, the critical challenges of cloud service provider is to meet user task Service level agreement (SLA) requirement (task deadline). Currently, the onus is on client to compute the amount of resource required to run a job on cloud. This work present a novel memory optimization model for Hadoop Map Reduce framework namely MOHMR (Optimized Hadoop Map Reduce) to process data in real-time and utilize system resource efficiently. The MOHMR present accurate model to compute job memory optimization and also present a model to provision the amount of cloud resource required to meet task deadline. The MOHMR first build a profile for each job and computes memory optimization time of job using greedy approach. Experiment are conducted on Microsoft Azure HDInsight cloud platform considering different application such as text computing and bioinformatics application to evaluate performance of MOHMR of over existing model shows significant performance improvement in terms of computation time. Experiment are conducted on Microsoft Azure HDInsight cloud. Overall, good correlation is reported between practical memory optimization values and theoretical memory optimization values.
Identifying learning style through eye tracking technology in adaptive learning systems
Authors: Guabassi Inssaf El , Bousalem Zakaria , Al Achhab Mohammed , Jellouli Ismail , EL Mohajir Badr Eddine
Published: October 2019

Learner learning style represents a key principle and core value of the adaptive learning systems (ALS). Moreover, understanding individual learner learning styles is a very good condition for having the best services of resource adaptation. However, the majority of the ALS, which consider learning styles, use questionnaires in order to detect it, whereas this method has a various disadvantages, For example, it is unsuitable for some kinds of respondents, time-consuming to complete, it may be misunderstood by respondent, etc. In the present paper, we propose an approach for automatically detecting learning styles in ALS based on eye tracking technology, because it represents one of the most informative characteristics of gaze behavior. The experimental results showed a high relationship among the Felder-Silverman Learning Style and the eye movements recorded whilst learning.
Modeling of digital converter for GSM signals with MATLAB
Authors: Traykov Metodi , Mavrevski Radoslav , Trenchev Ivan
Published: October 2019

In this study will simulate steady state of Digital Down Convertor (DDC) for GSM signal with a narrow frequency range. The MATLAB model that is described in this article simulates the work of the TIGC4016 Quad Digital Down Converter. This converter is used for digital mixing (down conversion) of signals, narrow band low-pass filtering and decimation. To implementation of the model, we use high sample-rate (69,333 MSPS) bandpass signal. The result contains low sample-rate (270.83 KSPS) baseband signal, thus facilitating the demodulation process.
Suggesting new words to extract keywords from title and abstract
Authors: Gheni Hadeel Qasem , Hussein Ahmed Mohammed , Oleiwi Wed Kadhim
Published: October 2019

When talking about the fundamentals of writing research papers, we find that keywords are still present in most research papers, but that does not mean that they exist in all of them, we can find papers that do not contain keywords. Keywords are those words or phrases that accurately reflect the content of the research paper. Keywords are an exact abbreviation of what the research carries in its content. The right keywords may increase the chance of finding the article or research paper and chances of reaching more people who should reach them. The importance of keywords and the essence of the research and address is mainly to attract these highly specialized and highly influential writers in their fields and who specialize in reading what holds the appropriate characteristics but they do not read and cannot read everything. In this paper, we extract new keywords by suggesting a set of words, these words were suggested according to the many mentioned in the researches with multiple disciplines in the field of computer. In our system, we take a number of words (as many as specified in the program) that come before the proposed words and consider it as new keywords. This system proved to be effective in finding keywords that correspond to some extent with the keywords developed by the author in his research.
Handwritten digits recognition with decision tree classification: a machine learning approach
Authors: Assegie Tsehay Admassu , Nair Pramod Sekharan
Published: October 2019

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.
Elliptic curve and pseudo-inverse matrix based cryptosystem for wireless sensor networks
Authors: Deb Shomen , Haque Md. Mokammel
Published: October 2019

Applying asymmetric key security to wireless sensor network (WSN) has been challenging task for the researcher of this field. One common trade-off is that asymmetric key architecture does provide good enough security than symmetric key but on the other hand, sensor network has some resource limitations to implement asymmetric key approach. Elliptic curve cryptography (ECC) has significant advantages than other asymmetric key system like RSA, D-H etc. The most important feature of ECC is that it has much less bit requirement and at the same time, ensures better security compared to others. Hence, ECC can be a better option for implementing asymmetric key approach for sensor network. We propose a new cryptosystem which is based on Pseudo-inverse matrix and Elliptic Curve Cryptography. We establish a relationship between these two different concepts and evaluate our proposed system on the basis of the results of similar works as well as our own simulation done in TinyOS environment.
A significant energy management control strategy for a hybrid source EV
Authors: Geetha A , Subramani C
Published: October 2019

The conceptualization of Electric vehicles has been significant in solving the pollution due to the transportation problem. However, its design and working has not yet been perfected and can be optimized. An effective model of the electric vehicle is the integration of batteries and supercapacitors. In this paper, the optimal sharing of energy between the battery and the supercapacitor is provided by the Particle Swarm Optimization (PSO). The results reveal that the proposed algorithm can better share the energy between the hybrid sources. The simulations are carried out in MATLAB Simulink platform and the results obtained for ECE-15 driving cycle is presented.
Practical electrical energy production to solve the shortage in electricity in Palestine and Pay Back period
Authors: Badawi Ahmed S A , Hasbullah Nurul Fadzlin , Yusoff Siti , Hashim Aisha , Elamassie Mohammed
Published: October 2019

In this paper power energy had been estimated based on actual wind speed records in a coastal city in Palestine Ashdod. The main aims of this study to determine the feasibility of wind turbine and to estimate payback period. Therefore, to encourage investment in renewable energy in Palestine. The daily average wind speed data had been analyzed and fitted to the Weibull probability distribution function. The parameters of Weibull had been calculated by author using Graphical method the applied example wind turbine is 5kw wind turbine generator this is suitable turbine for small scale based on wind speed records on the coastal plain of Palestine. This study calculated the energy that can produce from wind turbine to estimate the revenue of any possible project in wind energy conversion system based on unit area. Energy has been calculated wind energy using two different method based on Weibull data and measured data. The total amount of energy for 2010 is 10749.8 kw.hr/m2 based on measured wind speed. Payback period for the project in wind energy turbines is around 3 years which make the generation electricity possible for small scale but not commercial. This study will lead to assess the wind energy production in Palestine to encourage investment in renewable energy sectors.
Backstepping nonlinear control to maximize energy capture in a variable speed wind turbine
Authors: Maguiri Ouadia El , Moussaif Driss , Smma El alami , Abdelmajid Farchi , Akhiate Aziz
Published: October 2019

We are considering the problem of maximum power point tracking MPPT in wind energy conversion system (WECS). The paper proposes a new control strategy to maximize the wind aerodynamic energy captured in variable speed wind turbine with a separately excited DC-Generator and transformed to the battery through a controlled DC-DC converter. The proposed strategy controls the stip speed ratio via the rotor angular speed to an optimum point at wich the power coefficient is maximal. The controller is designed using the backstepping technique. A formal analysis based on lyapunov stability is developed to describe the control system performances. In addition to closed-loop global asymptotic stability, it is proved that the controller actually meets the MPPT requirement. The above results are confirmed by simulations. 
Real time implementation of embedded devices as a security system in intelligent vehicles connected via Vanets
Authors: Palaniapan Senthilnathan , Kollathodi Mohammed Ahsan
Published: October 2019

The fast boom of  technology has made our lives easier. The number of computer based functions embedded in cars have multiplied extensively over the past two decades. These days, many embedded sensors allowing localization and verbal exchange are being advanced to enhance reliability, protection and define new exploitation modes in intelligent guided transports. An in-car embedded electronic architecture is a complex setup machine, the improvement of that particular system is related to unique manufacturers and providers. There are several factors required in an efficient and secure system along with protection features, real time monitoring, reliability, robustness, and many other integrated features[1-2]. The appearance of modern era has also expanded the use of vehicles and  its associated dangers. Dangers and the road accidents take place often which causes loss of lives and assets due to the bad emergency centres, lack of safety features and limitations within devices embedded within a vehicle.  A rpm-speed calculating device can be used in a vehicle such that risku situations while driving can be detected. A system with Ultra sonic sensor can be used as a crash detector of the automobile in the course of the event and also after a crash. With indicators from the device,  extreme coincidences also can be recognized. .As the amount of urban automobile grows automobile theft has become a shared difficulty for all citizens. As a solution an antitheft system can be implemented using PIR motion sensors where the system can be attached to the peripheral surface of the vehicle. When these sensors are interfaced with  Arduino microcontroller an efficient and reliable security system can be developed[3].
Image multiplexing using residue number system coding over MIMO-OFDM communication system
Authors: Youssif Mohamed Ibrahim , Emam Amr ElSayed , ElGhany Mohamed Abd
Published: October 2019

Image transmission over Orthogonal Frequency-Division Multiplexing (OFDM) communication system is prone to distortion and noise due to the encountered High-Peak-to-Average-Power-Ratio (PAPR) generated from the OFDM block. This paper studies the utilization of Residue Number System (RNS) as a coding scheme for digital image transmission over Multiple-Input-Multiple-Output (MIMO) – OFDM transceiver communication system. The use of the independent parallel feature of RNS, as well as the reduced signal amplitude to convert the input signal to parallel smaller residue signals, enable to reduce the signal PAPR, decreasing the signal distortion and the Bit Error Rate (BER). Consequently, improving the received Signal-to-Noise Ratio (SNR) and enhancing the received image quality. The performance analyzed though BER, and PAPR. Moreover, image quality measurement is achieved through evaluating the Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), and the correlation values between the initial and retrieved images. Simulation results had shown the performance of transmission/reception model with and without RNS coding implementation.

 
RB-Bayes algorithm for the prediction of diabetic in Pima Indian dataset
Authors: Rajni Rajni , Amandeep Amandeep
Published: October 2019

Diabetes is a major concern all over the world. It is increasing at a fast pace. People can avoid diabetes at an early stage without any test. The goal of this paper is to predict the probability of whether the person has a risk of diabetes or not at an early stage. This would lead to having a great impact on their quality of human life. The datasets are Pima Indians diabetes and Cleveland coronary illness and consist of 768 records. Though there are a number of solutions available for information extraction from a huge datasets and to predict the possibility of having diabetes, but the accuracy of their mining process is far from accurate. For achieving highest accuracy, the issue of zero probability which is generally faced by naïve bayes analysis needs to be addressed suitably. The proposed framework RB-Bayes aims to extract the required information with high accuracy that could survive the problem of zero probability and also configure accuracy with other methods like Support Vector Machine, Naive Bayes, and K Nearest Neighbor. We calculated mean to handle missing data and calculated probability for yes (positive) and no (negative). The highest value between yes and no decide the value for the tuple. It is mostly used in text classification. The outcomes on Pima Indian diabetes dataset demonstrate that the proposed methodology enhances the precision as a contrast with other regulated procedures. The accuracy of the proposed methodology large dataset is 72.9%.
An adaptive distributed Intrusion detection system architecture using multi agents
Authors: A. M. Riyad , Ahmed M. S. Irfan , Khan R. L. Raheemaa
Published: October 2019

Intrusion detection systems are used for monitoring the network data, analyze them and find the intrusions if any. The major issues with these systems are the time taken for analysis, transfer of bulk data from one part of the network to another, high false positives and adaptability to the future threats. These issues are addressed here by devising a framework for intrusion detection. Here, various types of co-operating agents are distributed in the network for monitoring, analyzing, detecting and reporting. Analysis and detection agents are the mobile agents which are the primary detection modules for detecting intrusions. Their mobility eliminates the transfer of bulk data for processing. An algorithm named territory is proposed to avoid interference of one analysis agent with another one. A communication layout of the analysis and detection module with other modules is depicted. The inter-agent communication reduces the false positives significantly. It also facilitates the identification of distributed types of attacks. The co-ordinator agents log various events and summarize the activities in its network. It also communicates with co-ordinator agents of other networks. The system is highly scalable by increasing the number of various agents if needed. Centralized processing is avoided here to evade single point of failure. We created a prototype and the experiments done gave very promising results showing the effectiveness of the system.
On the classification of arrhythmia using supplementary features from Tetrolet transforms
Authors: Jayagopi G. , Pushpa S.
Published: October 2019

Heart diseases had been molded as potential threats to human lives, especially to elderly people in recent days due to the dynamically varying food habits among the people. However, these diseases could be easily caught by proper analysis of Electrocardiogram (ECG) signals acquired from individuals. This paper proposes a better method to detect and classify the arrhythmia using 15 features which include 4 R-R interval features, 3 statistical and 6 chaotic features estimated from ECG signals. Additionally, Entropy and Energy features had been gained after converting one dimensional ECG signals to two dimensional data and applied Tetrolet transforms on that.  Total numbers of 15 features had been utilized to classify the heart beats from the benchmark MIT-Arrhythmia database using Support Vector Machines (SVM). The classification performance was analyzed under various kernel functions and different Tetrolet decomposition levels. It is found that Radial Basis Function (RBF) kernel could perform better than linear and polynomial kernels. This research attempt yielded an accuracy of 99.35 % against the existing works. Moreover, addition of two more features had introduced a negligible overhead of time. Hence, this method is better suitable to detect and classify the Arrhythmia in both online and offline.
An energy-aware system-on-chip architecture for intra prediction in HEVC standard
Authors: El Ansari Abdessamad , Mansouri Anas , Ahaitouf Ali
Published: October 2019

High resolution 4K and 8K are becoming the more used in video applications. Those resolutions are well supported in the new HEVC standard. Thus, embedded solutions such as development of dedicated ystems-On-Chips (SOC) to accelerate video processing on one chip instead of only software solutions are commendable. This paper proposes a novel parallel and high efficient hardware accelerator for the intra prediction block. This accelerator achieves a high-speed treatment due to pipelined processing units and parallel shaped architecture. The complexity of memory access is also reduced thanks to the proposed design with less increased power consumption. The implementation was performed on the 7 Series FPGA 28 nm technology resources on Zynq-7000 and results show, that the proposed architecture takes 16520 LUTs and can reach 143.65 MHz as a maximum frequency and it is able to support the throughput of 3840×2160 sequence at 90 frames per second.
Smart monitoring information system based on RF 433 Mhz (SMIS)
Authors: Hicham Ouldzira , Mouhsen Ahmed , Lagraini Hajar , Tabyaoui Abdelmoumen , Chhiba Mostafa
Published: October 2019

Many research works are devoted to the design of remote control systems of objects. Remote monitoring is among the technologies of data acquisition on a remote object and its characteristics without physical contact with it. These data can be collected by acoustic waves, the distribution of forces and electromagnetic energy to process them to locate the object and its characteristics. This paper presents a smart monitoring information system based on RF 433MHz, Arduinonano and arduinouno named SMIS.
Transformer based NPC multilevel inverter using reduced number of components
Authors: Palanisamy R. , Selvakumar K. , Vijayakumar K. , Karthikeyan D. , Vidyasagar S. , Kalyanasundaram V.
Published: October 2019

This paper revolves around the reduction of a number of switches and the sources for a multilevel inverter, for this, we have proposed a transformer-based topology which has helped us in reducing the number of switches from twenty-four to sixteen and also in the reduction of sources from eight to one. The circuit consists of two H-Bridges which are coupled by a single-phase transformer, the topology gives us a liberty of changing the number of levels in accordance to the number of turns in the secondary side of the transformer for example if our ratio is 1:1 the number of levels will be five subsequently if it is changed to 1:2 the number of levels will be changed to seven. As the number of switches is reduced the size and complexity of the circuit is also decreased. In order to improve on the part of switching efficiency, we have used space vector pulse width modulation which is a better method as compared to its counterpart switching methods such as sinusoidal pulse width modulation and multiple pulse width modulation techniques.
Improvisation of classification performance based on feature optimization for differentiation of Parkinson’s disease from other neurological diseases using gait characteristics
Authors: Aich Satyabrata , Joo Moon-il , Kim Hee-Cheol , Park Jinse
Published: October 2019

Most neurological disorders that include Parkinson’s disease (PD) as well as other neurological diseases such as Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD) have some common abnormalities regarding the movement, vocal, and cognitive behaviors of sufferers. Variations in the manifestation of these types of abnormality help distinguish one disorder from another. In this study, differentiation was performed based on the gait characteristics of patients afflicted by different neurological disorders. In the recent past, many researchers have applied different machine learning and feature selection techniques to the classification of different groups of patients based on common abnormalities. However, in an era of modernization where the focus is on timely low-cost automatization and pattern recognition, such techniques require improvisation to provide high performance. We attempted to improve the performance of such techniques using different feature optimization methods, such as a genetic algorithm (GA) and principal component analysis (PCA), and applying different classification approaches, i.e., linear, nonlinear, and probabilistic classifiers. In this study, gait dynamics data of patients suffering with PD, ALS, and HD were collated from a public database, and a binary classification approach was used by taking PD as one group and adopting ALS+HD as another group. Performance comparison was achieved using different classification techniques that incorporated optimized feature sets obtained from GA and PCA. In comparison with other classifiers using different feature sets, the highest accuracy (97.87%) was obtained using random forest combined with GA-based feature sets. The results provide evidence that could assist medical practitioners in differentiating PD from other neurological diseases using gait characteristics.
Novelty circular neighboring technique using reactive fault tolerance method
Authors: Noor Ahmad Shukri Mohd , Zian Nur Farhah Mat , Rahim Noor Hafhizah Abd , Mamat Rabiei , Azman Wan Nur Amira Wan
Published: October 2019

The availability of the data in a distributed system can be increase by implementing fault tolerance mechanism in the system. Reactive method in fault tolerance mechanism deals with restarting the failed services, placing redundant copies of data in multiple nodes across network, in other words data replication and migrating the data for recovery. Even if the idea of data replication is solid, the challenge is to choose the right replication technique that able to provide better data availability as well as consistency that involves read and write operations on the redundant copies. Circular Neighboring Replication (CNR) technique exploits neighboring policy in replicating the data items in the system performs well with regards to lower copies needed to maintain the system availability at the highest. In a performance analysis with existing techniques, results show that CNR improves system availability by average 37% by offering only two replicas needed to maintain data availability and consistency. The study demonstrates the possibility of the proposed technique and the potential of deploying in larger and complex environment.
Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification
Authors: Albkosh Fthi M. , Hitam Muhammad Suzuri , Yussof Wan Nural Jawahir Hj Wan , Hamid Abdul Aziz K Abdul , Ali Rozniza
Published: October 2019

Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform.  The multi-layered perceptron neural network is employed as an image texture classifier.  The proposed method tested on a high-resolution database of UMD texture. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for discrete wavelet transform features that lead to the best classification accuracy performance.