bluuebunny/crossref_metadata_2025 huggingface dataset:
A hybrid pan-sharpening approach using maximum local extrema
Authors: Patil Prajakta , Rani C.M. Sheela , Arya Meenakshi
Published: April 2019

Mixing or combining different elements for getting enhanced version, is practiced across various areas in real life. Pan-sharpening is a similar technique used in the digital world; a process to combine two images into a fused image that comprises more detailed information. Images referred herein are Panchromatic (PAN) and Multispectral (MS) images. This paper presents a pansharpening algorithm which integrates multispectral and panchromatic images to generate an improved multispectral image. This technique merges the Discrete wavelet transform (WT) and Intensity-Hue-Saturation (IHS) through separate fusing criterion for choosing an approximate and detail sub-images. Whereas the maximal local extrema are used for merging detail sub-images and finally merged high-resolution image is reconstructed through inverse transform of wavelet and IHS. The proposed fusion approach enhances the superiority of the resultant fused image is demonstrated by quality measures like CORR, RMSE, PFE, SSIM, SNR and PSNR with the help of satellite Worldview-II images. The proposed algorithm is correlated with the other fusion techniques through empirical outcomes proves the superiority of the final merged image in terms of resolutions than the others.
Improvement of binarization performance using local otsu thresholding
Authors: Saddami Khairun , Munadi Khairul , Away Yuwaldi , Arnia Fitri
Published: April 2019

Ancient document usually contains multiple noises such as uneven-background, show-through, water-spilling, spots, and blur text. The noise will affect the binarization process. Binarization is an extremely important process in image processing, especially for character recognition. This paper presents an improvement to Nina binarization technique. Improvements were achieved by reducing processing steps and replacing median filtering by Wiener filtering. First, the document background was approximated by using Wiener filter, and then image subtraction was applied. Furthermore, the manuscript contrast was adjusted by mapping intensity of image value using intensity transformation method. Next, the local Otsu thresholding was applied. For removing spotting noise, we applied labeled connected component. The proposed method had been testing on H-DIBCO 2014 and degraded Jawi handwritten ancient documents. It performed better regarding recall and precision values, as compared to Otsu, Niblack, Sauvola, Lu, Su, and Nina, especially in the documents with show-through, water-spilling and combination noises.
Improved method for image security based on chaotic-shuffle and chaotic-diffusion algorithms
Authors: Sharma Sanjeev , Kumar Tarun , Dhaundiyal Ravi , Mishra Amit Kumar , Duklan Nitin , Maithani Ashish
Published: April 2019

In this paper, we propose to enhance the security performance of the color image encryption algorithm which depends on multi-chaotic systems. The current cryptosystem utilized a pixel-chaotic-shuffle system to encode images, in which the time of shuffling is autonomous to the plain-image. Thus, it neglects to the picked plaintext and known-plaintext attacks. Also, the statistical features of the cryptosystem are not up to the standard. Along these lines, the security changes are encircled to make the above attacks infeasible and upgrade the statistical features also. It is accomplished by altering the pixel-chaotic-shuffle component and including another pixel-chaotic-diffusion system to it. The keys for diffusion of pixels are extracted from the same chaotic arrangements created in the past stage. The renovation investigations and studies are performed to exhibit that the refreshed version of cryptosystem has better statistical features and invulnerable to the picked plaintext and known plaintext attacks than the current algorithm.
The role of speech technology in biometrics, forensics and man-machine interface
Authors: Singh Satyanand
Published: April 2019

Day by day Optimism is growing that in the near future our society will witness the Man-Machine Interface (MMI) using voice technology. Computer manufacturers are building voice recognition sub-systems in their new product lines. Although, speech technology based MMI technique is widely used before, needs to gather and apply the deep knowledge of spoken language and performance during the electronic machine-based interaction. Biometric recognition refers to a system that is able to identify individuals based on their own behavior and biological characteristics. Fingerprint success in forensic science and law enforcement applications with growing concerns relating to border control, banking access fraud, machine access control and IT security, there has been great interest in the use of fingerprints and other biological symptoms for the automatic recognition. It is not surprising to see that the application of biometric systems is playing an important role in all areas of our society. Biometric applications include access to smartphone security, mobile payment, the international border, national citizen register and reserve facilities. The use of MMI by speech technology, which includes automated speech/speaker recognition and natural language processing, has the significant impact on all existing businesses based on personal computer applications. With the help of powerful and affordable microprocessors and artificial intelligence algorithms, the human being can talk to the machine to drive and control all computer-based applications. Today's applications show a small preview of a rich future for MMI based on voice technology, which will ultimately replace the keyboard and mouse with the microphone for easy access and make the machine more intelligent.
Iterative improved learning algorithm for petrographic image classification accuracy enhancement
Authors: Marathe Ashutosh , Jain Priya , Vyas Vibha
Published: April 2019

Rock image classification using image processing has been practiced to assist trained geologists in decision making. However, the study of microstructures of rocks and their use in geological investigations offer challenges in the areas of Image processing and Pattern Classification due to the stochastic nature of the mineral textures that is revealed at the microscopic level. Locally relevant Igneous Rock Microstructure images were classified from Volcanic and Plutonic Rock subtypes. The imaging method used mineral grain size as the key physical feature of classification. Three algorithms, namely Radial Basis Function (RBF) Support Vector Machine classifier; Improved (RBF) Support Vector Machine classifier; and AdaBoost algorithm with Improved RBF Support Vector Machine algorithm as base classifier, were used as a base classifier in a novel ‘Iterative Improved Learning (IIL)’ approach. Implementing the IIL approach in the chosen algorithm resulted in accurately classified images that were added to the training set to enhance the ‘breadth and depth’ of the learning knowledge. The algorithm iterated through all available classifier approaches and compared the inter-classifier performance and knowledge of the misclassified images accumulated during the execution of all algorithms.
Strategic human resource management technology effect and implication for distance training and learning
Authors: Alsaadat Khalil
Published: April 2019

Strategic human resource management involves a future-oriented process of developing and implementing HR programs that address and solve business problems and directly contribute to major long-term business objectives. HR management has changed dramatically in recent decades. It was once largely an administrative function focused on day-to-day responsibilities such as employee recruiting and selection and managing employee benefits. Changing labor market conditions and new business thinking call for HR business strategies that include recruiting and retaining the right people, as well as providing ethical and cultural leadership[1].This paper discusses the following issues : The importance of human resource activity , The importance of strategic human resource planning, The changing role of human resource management and nature of work and non government organizations , Technology effect on human resource management and implications for distance training and learning .A field study was conducted to know the attitude of one hundred and five Saudi employees towards the effect of distance learning and training on human resource management and development
Enhanced signal detection slgorithm using trained neural network for cognitive radio receiver
Authors: Borde Sheetal Dhananjay , Joshi Kalyani Rajeev
Published: April 2019

Over the past few years, Cognitive Radio has become an important research area in the field of wireless communications. It can play an important role in dynamic spectrum management and interference identification. There are many spectrum sensing techniques proposed in literature for cognitive radio, but all those techniques detect only presence or absence of the primary user in the designated band and do not give any information about the used modulation scheme. In certain applications, in cognitive radio receiver, it is necessary to identify the modulation type of the signal so that the receiver parameters can be adjusted accordingly. Most of the modulated signals exhibit the property of Cyclostationarity that can be used for the purpose of correct detection of primary user and the modulation type. In this paper, we have proposed an enhanced signal detection algorithm for cognitive radio receiver which makes use of cyclostationarity property of the modulated signal to exactly detect, the modulation type of the received signal using a trained neural network. The algorithm gives better accuracy of signal detection even in low SNR conditions. The use of a trained neural network makes it more flexible and extendible for future applications
PVPF tool: an automatedWeb application for real-time photovoltaic power forecasting
Authors: Alomari Mohammad H. , Adeeb Jehad , Younis Ola
Published: April 2019

In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records’ time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.
Implementation of message authentication code using DNA-LCG key and a novel hash algorithm
Authors: Sodhi Gurpreet Kour , Gaba Gurjot Singh , Kansal Lavish , Bakkali Mohamed El , Tubbal Faisel Em
Published: April 2019

With the introduction of electronic form of data, the need for an automatic system of security to protect the integrity of data while being transferred from one place to another is required. This is especially the case for a network in which the systems are accessed over a public network or internet. Security mechanisms involve the use of more than one algorithm. They further require that the participants should possess a secret key, which raises issues about creation, distribution and proper usage of these keys. The most effective technique used in provisioning security is Message Authentication Code (MAC) which helps in preserving integrity. MAC involves the use of secret key along with a hash algorithm. In this paper, we present an implementation of MAC using a secret key created by Deoxyribonucleic Acid (DNA) and random output sequence of Linear Congruential Generator (LCG). The hash algorithm used is made more robust by adding complexity to the traditional SHA-160. The presented scheme RMAC (Robust Message Authentication Code) is tested on National Institute of Science and Technology (NIST) test suite for random numbers, avalanche criteria and resistance towards network attacks. The results reveal that the scheme is efficient and is applicable for a variety of security demanding environments.
MPR selection to the OLSR quality of service in MANET using minmax algorithm
Authors: Alamsyah Alamsyah , Purnama I Ketut Eddy , Setijadi Eko , Purnomo Mauridhi Hery
Published: April 2019

Optimized link state routing (OLSR) is a routing protocol that has a small delay, low traffic control, support the application of denser networks, and adopts the concept of multipoint relays (MPR). The problem of OLSR is routing table updating which continually causes excessive packet delivery, and energy consumption becomes increased. This article proposes the improvement of OLSR performance using the min-max algorithm based on the quality of service (QoS) with considering the density of the node. The Min-max algorithm works in selecting MPR nodes based on the largest signal range. The QoS parameters analyzed with a different number of nodes are packet delivery ratio (PDR), throughput, delay, energy consumption, and topology control (TC). Simulation result of network simulator version 2 (NS-2) shows that OLSR performance using the min-max algorithm can increase PDR of 91.17%, packet loss of 60.77% and reduce topology control packet of 8.07%, energy consumption of 16.82% compared with standard OLSR.
Combined heat and power - optimal power flow based on thermodynamic model with associated petroleum and wet gas utilization constraints
Authors: Pujihatma Priambudi , Hadi Sasongko Pramono , Sarjiya Sarjiya , Rohmat Tri Agung
Published: April 2019

Oil fields produce associated petroleum and wet gas, which can be mixed with commercial natural gas as fuel. Associated petroleum and wet gas are a low cost, low quality fuel, whereas commercial natural gas is the opposite. Two parameters are affected by this mixture: the fuel cost and the power – steam output of gas turbine – heat recovery steam generators. This research develops a Unit Commitment and Optimal Power Flow model based on Mixed Integer Nonlinear Programming to optimize combined heat and power cost by considering the optimal mixture between associated petroleum - wet gas and commercial natural gas. A thermodynamic model is used to represent the performance of gas turbine – heat recovery steam generators when subjected to different fuel mixtures. The results show that the proposed model can optimize cost by determining the most efficient power – steam dispatch and optimal fuel mixture. Furthermore, the optimization model can analyse the trade-off between power system losses, steam demand and associated - wet gas utilization. 
A secure image steganography based on burrows wheeler transform and dynamic bit embedding
Authors: Thahab Ahmed Toman
Published: April 2019

In modern public communication networks, digital data is massively transmitted through the internet with a high risk of data piracy. Steganography is a technique used to transmit data without arousing suspicion of secret data existence.  In this paper, a color image steganography technique is proposed in spatial domain. The cover image is segmented into non-overlapping blocks which are scattered among image size window using Burrows Wheeler transform before embedding. Secret data is embedded in each block according to its sequence in the Burrows Wheeler transform output. The hiding method is an operation of an exclusive-or between a virtual bit which is generated from the most significant bit and the least significant bits of the cover pixel. Results of the algorithm are analyzed according to its degradation of the output image and embedding capacity. The results are also compared with other existing methods.
Opinion mining framework using proposed RB-bayes model for text classication
Authors: Bhalla Rajni , Bagga Amandeep
Published: April 2019

Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.
Noise reduction in ECG Signals for Bio-telemetryb
Authors: Naveen V. Jagan , Krishna K. Murali , Rajeswari K. Raja
Published: April 2019

In Biotelemetry, Biomedical signal such as ECG is extremely important in the diagnosis of patients in remote location and is recorded commonly with noise. Considered attention is required for analysis of ECG signal to find the patho-physiology and status of patient. In this paper, LMS and RLS algorithm are implemented on adaptive FIR filter for reducing power line interference (50Hz) and (AWGN) noise on ECG signals .The ECG signals are randomly chosen from MIT_BIH data base and de-noising using algorithms. The peaks and heart rate of the ECG signal are estimated. The measurements are taken in terms of Signal Power, Noise Power and   Mean Square Error.
Energy harvesting earliest deadline first scheduling algorithm for increasing lifetime of real time systems
Authors: Kumar Arvind , Alam Bashir
Published: April 2019

In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life.  Energy is a parameter by which the lifetime of system can be enhanced.  To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches.  
Voltage sensitivity analysis to determine the optimal integration of distributed generation in distribution systems
Authors: Cabana Katherine , Candelo John , Castillo Rafael , Franco Emiro De la Hoz
Published: April 2019

This paper presents a voltage sensitivity analysis with respect to the real power injected with renewable energies to determine the optimal integration of distributed generation (DG) in distribution systems (DS). The best nodes where the power injections improve voltages magnitudes complying with the constraints are determined. As it is a combinatorial problem, particle swarm optimization (PSO) and simulated annealing (SA) were used to change injections from 10% to 60% of the total power load using solar and wind generators and find the candidate nodes for installing power sources. The method was tested using the 33-node, 69-node and 118-node radial distribution networks. The results showed that the best nodes for injecting real power with renewable energies were selected for the distribution network by using the voltage sensitivity analysis. Algorithms found the best nodes for the three radial distribution networks with similar values in the maximum injection of real power, suggesting that this value maintains for all the power system cases. The injections applied to the different nodes showed that voltage magnitudes increase significantly, especially when exceeding the maximum penetration of DG. The test showed that some nodes support injections up to the limits, but the voltages increase considerably on all nodes.
A hybrid algorithm to reduce energy consumption management in cloud data centers
Authors: Tarahomi Mehran , Izadi Mohammad
Published: April 2019

There are several physical data centers in cloud environment with hundreds or thousands of computers. Virtualization is the key technology to make cloud computing feasible. It separates virtual machines in a way that each of these so-called virtualized machines can be configured on a number of hosts according to the type of user application. It is also possible to dynamically alter the allocated resources of a virtual machine. Different methods of energy saving in data centers can be divided into three general categories: 1) methods based on load balancing of resources; 2) using hardware facilities for scheduling; 3) considering thermal characteristics of the environment. This paper focuses on load balancing methods as they act dynamically because of their dependence on the current behavior of system. By taking a detailed look on previous methods, we provide a hybrid method which enables us to save energy through finding a suitable configuration for virtual machines placement and considering special features of virtual environments for scheduling and balancing dynamic loads by live migration method.
Augmented reality objects design in augmented story book mobile application for better engagement
Authors: Izzaty Sherina , Tolle Herman , Dermawi Rizdania , Permana Frihandhika
Published: April 2019

The folktale is a kind of story that has some good points, such as developing a sense of story and shaping up positive attitudes towards children. However, people seem to abandon those good points and often give the children smartphones instead of folktale through the conventional book. Recently, smartphones’ capabilities have rapidly improved, so that augmented reality (AR) story books which combine conventional book and smartphone can be developed. This research is conducted to examine which one from three provided models that work best for engagement in a folktale augmented book. Those designs will be discussed in both subjective and objective approach, correspondingly. The result from subjective approach is the animated video AR design becomes the most pleasing design. The picture and narrator’s voice AR design, by contrast, becomes the most efficient design based on objective approach.
Chicken feed optimization using evolution strategies and firefly algorithm
Authors: Sihananto Andreas Nugroho , Burhan M. Shochibul , Mahmudy Wayan Firdaus
Published: April 2019

Mixing broiler chicken and layer hens feed using various feed ingredients is a difficult task. The feed must fulfill the minimum nutrient requirement and must break the constraint. Some classic approach like Pearson’s Square has been already introduced to solve this problem. However, the approaches cannot guarantee to fulfill nutrient requirements and desirable price. The two metaheuristic algorithms Evolution Strategies (ES) and Firefly Algorithms (FA) are being proposed in this paper to know how well they performed this problems. Result show that ES is perform much better compared to classic Pearson’s Square, but ES itself is outperform by FA on both cases.
Business intelligence analytics using sentiment analysis-a survey
Authors: Rokade Prakash P. , D Aruna Kumari
Published: April 2019

Sentiment analysis (SA) is the study and analysis of sentiments, appraisals and impressions by people about entities, person, happening, topics and services. SA uses text analysis techniques and natural language processing methods to locate and extract information from big data. As most of the people are networked themselves through social websites, they use to express their sentiments through these websites.These sentiments are proved fruitful to an individual, business, government for making decisions. The impressions posted on different available sources are being used by organization to know the market mood about the services they are providing. Analyzing huge moods expressed with different features, style have raised challenge for users. This paper focuses on understanding the fundamentals of sentiment analysis, the techniques used for sentiment extraction and analysis. These techniques are then compared for accuracy, advantages and limitations. Based on the accuracy for expexted approach, we may use the suitable technique.
Blended intelligence of FCA with FLC for knowledge representation from clustered data in medical analysis
Authors: Neelima Ch. , Sarma S. S.V.N.
Published: April 2019

Formal concept analysis is the process of data analysis mechanism with emergent attractiveness across various fields such as data mining, robotics, medical, big data and so on. FCA is helpful to generate the new learning ontology based techniques. In medical field, some growing kids are facing the problem of representing their knowledge from their gathered prior data which is in the form of unordered and insufficient clustered data which is not supporting them to take the right decision on right time for solving the uncertainty based questionnaires. In the approach of decision theory, many mathematical replicas such as probability-allocation, crisp set, and fuzzy based set theory were designed to deals with knowledge representation based difficulties along with their characteristic. This paper is proposing new ideological blended approach of FCA with FLC and described with major objectives: primarily the FCA analyzes the data based on relationships between the set of objects of prior-attributes and the set of attributes based prior-data, which the data is framed with data-units implicated composition which are formal statements of idea of human thinking with conversion of significant intelligible explanation. Suitable rules are generated to explore the relationship among the attributes and used the formal concept analysis from these suitable rules to explore better knowledge and most important factors affecting the decision making. Secondly how the FLC derive the fuzzification, rule-construction and defuzzification methods implicated for representing the accurate knowledge for uncertainty based questionnaires. Here the FCA is projected to expand the FCA based conception with help of the objective based item set notions considered as the target which is implicated with the expanded cardinalities along with its weights which is associated through the fuzzy based inference decision rules. This approach is more helpful for medical experts for knowing the range of patient’s memory deficiency also for people whose are facing knowledge explorer deficiency.
Automatic detection of rust disease of Lentil by machine learning system using microscopic images
Authors: Singh Kuldeep , Kumar Satish , Kaur Pawan
Published: April 2019

Accurate and early detection of plant diseases will facilitate mitigate the worldwide losses experienced by the agriculture area. MATLAB image processing provides quick and non-destructive means of rust disease detection. In this paper, microscopic image data of rust disease of Lentil was combined with image processing with depth information and developed a machine learning system to detect rust disease at early stage infected with fungus Uromyces fabae (Pers) de Bary. A novel feature set was extracted from the image data using local binary pattern (LBP) and HBBP (Brightness Bi-Histogram Equalization) for image enhancement. It was observed that by combining these, the accuracy of detection of the diseased plants at microscopic level was significantly improved. In addition, we showed that our novel feature set was capable of identifying rust disease at haustorium stage without spreading of disease. 
Fault detection in power transformers using random neural networks
Authors: Kaur Amrinder , Brar Yadwinder Singh , G. Leena
Published: April 2019

This paper discuss the application of artificial neural network-based algorithms to identify different types of faults in a power transformer, particularly using DGA (Dissolved Gas Analysis) test. The analysis of Random Neural Network (RNN) using Levenberg-Marquardt (LM) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms has been done using the data of dissolved gases of power transformers collected from Punjab State Transmission Corporation Ltd.(PSTCL), Ludhiana, India. Sorting of the preprocessed data have been done using dimensionality reduction technique, i.e., principal component analysis. The sorted data is used as inputs to the Random Neural Networks (RNN) classifier. It has been seen from the results obtained  that BFGS has better performance for the diagnosis of fault in transformer as compared to LM.
Towards automatic setup of non intrusive appliance load monitoring – feature extraction and clustering
Authors: Chahine Khaled
Published: April 2019

Given climate change concerns and incessantly increasing energy demands of the present time, improving energy efficiency becomes of significant environmental and economic impact. Monitoring household electrical consumption through a non-intrusive appliance load monitoring (NIALM) system achieves significant efficiency improvement by providing appliance-level energy consumption and relaying this information back to the user. This paper focuses on feature extraction and clustering, which constitute two of the four modules of the proposed automatic-setup NIALM system, the other two being labeling and classification. The feature extraction module applies the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), a well-known parametric estimation technique, to the drawn electric current. The result is a compact representation of the signal in terms of complex numbers referred to as poles and residues. These complex numbers are then used to determine a feature vector consisting of the contribution of the fundamental, the third and the fifth harmonic currents to the maximum of the total load current. Once a signature is extracted, the clustering module applies distance-based rules inferred off-line from various databases and decides either to create a new class out of the new signature or to discard it and increase the count of an existing signature. As a result, the feature space is clustered without the a priori knowledge of the number of appliances into singleton clusters. Results obtained from a set of appliances indicate that these two modules succeed in creating an unlabeled database of signatures.
Recognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier
Authors: George Fabian Parsia , Shaikat Istiaque Mannafee , Ferdawoos Hossain Prommy Sultana , Parvez Mohammad Zavid , Uddin Jia
Published: April 2019

The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.
Stochastic control for optimal power flow in islanded microgrid
Authors: Diep-Thanh Thang , Nguyen-Phung Quang , Nguyen-Duc Huy
Published: April 2019

The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
Robust pole placement using firefly algorithm
Authors: Sababha Moath , Zohdy Mohamed , Kafafy Maged
Published: April 2019

In this paper, the new automatic tool that is based on the firefly algorithm whose purpose is optimization of pole location in the control of state feedback has been presented. The aim is satisfying specifications of performance like settling and rise time, steady state as well as overshoot error. Utilization of Firefly algorithm has demonstrated the benefits of controllers based on this kind of time domain over controllers based on the frequency domain like Proportional-Integral Derivative (PID). The presented method is more particular for the multi-input multi-output (MIMO) systems that have substantial state numbers. The simulation results indicated that the proposed method had superior performance in providing solution to the problems that involved stabilization of helicopter under the Rationalized Model of helicopter/ Moreover, it demonstrates the Firefly algorithm effectiveness with regards to, the state observer design and feedback controller and auto-tuning.
Multi parametric model predictive control based on laguerre model for permanent magnet linear synchronous motors
Authors: Quang Nguyen Hong , Quang Nguyen Phung , Nam Dao Phuong , Binh Nguyen Thanh
Published: April 2019

The permanent magnet linear motors are widely used in various industrial applications due to its advantages in comparisons with rotary motors such as mechanical durability and directly creating linear motions without gears or belts. The main difficulties of its control design are that the control performances include the tracking of position and velocity as well as guarantee limitations of the voltage control and its variation. In this work, a cascade control strategy including an inner and an outer loop is applied to synchronous linear motor. Particularly, an offline MPC controller based on MPP method and Laguerre model was proposed for inner loop and the outer controller was designed with the aid of nonlinear damping method. The numerical simulation was implemented to validate performance of the proposed controller under voltage input constraints.
Using real interpolation method for adaptive identification of nonlinear inverted pendulum system
Authors: Tin Phu Tran , Minh Tran Hoang Quang , Trang Tran Thanh , Dung Nguyen Quang
Published: April 2019

In this paper, we investigate the inverted pendulum system by using real interpolation method (RIM) algorithm. In the first stage, the mathematical model of the inverted pendulum system and the RIM algorithm are presented. After that, the identification of the inverted pendulum system by using the RIM algorithm is proposed. Finally, the comparison of the linear analytical model, RIM model, and nonlinear model is carried out. From the results, it is found that the inverted pendulum system by using RIM algorithm has simplicity, low computer source requirement, high accuracy and adaptiveness in the advantages.
New robust bounded control for uncertain nonlinear system using mixed backstepping and lyapunov redesign
Authors: Kamarudin Muhammad Nizam , Rozali Sahazati Md. , Sutikno T. , Husain Abdul Rashid
Published: April 2019

This paper presents a new robust bounded control law to stabilize uncertain nonlinear system with time varying disturbance. The design idea comes from the advantages of backstepping with Lyapunov redesign, which avoid the needs of fast switching of discontinuous control law offered by its counterpart - a variable structure control. We reduce the conservatism in the design process where the control law can be flexibly chosen from Lyapunov function, hence avoiding the use of convex optimization via linear matrix inequality (LMI) in which the feasibility is rather hard to be obtained. For this work, we design two type control algorithms namely normal control and bounded control. As such, our contribution is the introduction of a new bounded control law that can avoid excessive control energy, high magnitude chattering in control signal and small oscillation in stabilized states. Computation of total energy for both control laws confirmed that the bounded control law can stabilize with less enegry consumption. We also use Euler's approximation to compute average power for both control laws. The robustness of the proposed controller is achieved via saturation-like function in Lyapunov redesign, and hence guaranting asymptotic stability of the closed-loop system.
Proposed T-Model to cover 4S quality metrics based on empirical study of root cause of software failures
Authors: Chhillar Dheeraj , Sharma Kalpana
Published: April 2019

There are various root causes of software failures. Few years ago, software used to fail mainly due to functionality related bugs. That used to happen due to requirement misunderstanding, code issues and lack of functional testing. A lot of work has been done in past on this and software engineering has matured over time, due to which software’s hardly fail due to functionality related bugs. To understand the most recent failures, we had to understand the recent software development methodologies and technologies. In this paper we have discussed background of technologies and testing progression over time. A survey of more than 50 senior IT professionals was done to understand root cause of their software project failures. It was found that most of the softwares fail due to lack of testing of non-functional parameters these days. A lot of research was also done to find most recent and most severe software failures. Our study reveals that main reason of software failures these days is lack of testing of non-functional requirements. Security and Performance parameters mainly constitute non-functional requirements of software. It has become more challenging these days due to lots of development in the field of new technologies like Internet of things (IoT), Cloud of things (CoT), Artificial Intelligence, Machine learning, robotics and excessive use of mobile and technology in everything by masses. Finally, we proposed a software development model called as T-model to ensure breadth and depth of software is considered while designing and testing of software. 
Opportunistic mobile social networks: architecture, privacy, security issues and future directions
Authors: Vidhya Lakshmi Vimitha R. , T. Gireesh Kumar
Published: April 2019

Mobile Social Networks and its related applications have made a very great impact in the society. Many new technologies related to mobile social networking are booming rapidly now-a-days and yet to boom. One such upcoming technology is Opportunistic Mobile Social Networking. This technology allows mobile users to communicate and exchange data with each other without the use of Internet. This paper is about Opportunistic Mobile Social Networks, its architecture, issues and some future research directions. The architecture and issues of Opportunistic Mobile Social Networks are compared with that of traditional Mobile Social Networks. The main contribution of this paper is regarding privacy and security issues in Opportunistic Mobile Social Networks. Finally, some future research directions in Opportunistic Mobile Social Networks have been elaborated regarding the data's privacy and security.
Random access improvement for M2M communication in LTE-A using femtocell
Authors: Sackey Eric , Paulus Rajeev
Published: April 2019

When an area is highly populated with Machine-to-Machine devices and all these devices attempt to access the Random Access Network Simultaneously, congestion is created on the network which degrades the performance of the network to other users. In this paper, the researchers are seeking to improve network accessibility by deploying more Femtocell into the network. They engaged the use of Extended Access Barring to restrict the M2M devices from accessing the network via macrocell eNB when a minimum load threshold is attained, thereby preventing the macrocell eNB from being congested. Deploying these Femtocells underneath the macrocell eNB comes with the issue of Inter-Cell Interference which nullifies any gains made by this deployment. The researchers employed Fractional Frequency Reuse and Complete Frequency Reuse schemes to mitigate the negative effects of ICI to augment the throughput of the network, improve the system capacity and enhanced the user experience within the network.
Spectrally efficient multicarrier modulation system for visible light communication
Authors: Deepa T. , Mathur Harshita , Sunitha K. A.
Published: April 2019

Visible Light Communication (VLC) has become an accolade to its radio frequency counterpart. In VLC system, orthogonal frequency division multiplexing (OFDM) has drawn much attention, because of simple equalization, high spectral efficiency, high data rate and robustness to intersymbol interference (ISI). Besides, there are emerging applications that ought to be gotten with low latency and high reliability. To diminish power requirements with no transmission capacity extension, Trellis coded modulation (TCM) is utilized as a part of the framework in which the free distance of trellis diagram is equivalent to the minimum distance between the points of constellation focuses in partitioned subsets, which augments the coding gain up i.e. the performance parameter viably. TCM together with VLC-OFDM enhances the transmission execution in reasonable frameworks. In this paper, we propose OFDM which is based on TCM and is planned and exeuted for digitized OFDM frameworks by presenting delta sigma modulation (DSM) considering VLC channel. Simulation results show that the proposed TCM based VLC-OFDM offers incredible robustness against noises and nonlinear degradation.
Design and implementation of heterogeneous surface gateway for underwater acoustic sensor network
Authors: Jo Youngjun , Lim Sangsoon
Published: April 2019

Underwater Acoustic Sensor Networks (UASNs) are used for diverse purposes such as pollution monitoring, disaster prevention and industrial sensing in the oceans. Especially, UASNs are mainly focusing on monitoring various underwater environmental data and delivering the data to a monitoring center where nearby or far from the deployed area. To reliably deliver the data, a surface gateway should convert acoustic signal to RF (Radio Frequency) signal. In this paper, we devise a multiple interfaces-based surface gateway that can connect both a cellular network and a Zigbee network. Depends on the service requirement, the surface gateway can easily adopt each wireless interface and relay the data to a low power ZigBee network or a long range CDMA network.
A new efficient way based on special stabilizer multiplier permutations to attack the hardness of the minimum weight search problem for large BCH codes
Authors: Joundan Issam Abderrahman , Nouh Said , Azouazi Mohamed , Namir Abdelwahed
Published: April 2019

BCH codes represent an important class of cyclic error-correcting codes; their minimum distances are known only for some cases and remains an open NP-Hard problem in coding theory especially for large lengths. This paper presents an efficient scheme ZSSMP (Zimmermann Special Stabilizer Multiplier Permutation) to find the true value of the minimum distance for many large BCH codes. The proposed method consists in searching a codeword having the minimum weight by Zimmermann algorithm in the sub codes fixed by special stabilizer multiplier permutations. These few sub codes had very small dimensions compared to the dimension of the considered code itself and therefore the search of a codeword of global minimum weight is simplified in terms of run time complexity.  ZSSMP is validated on all BCH codes of length 255 for which it gives the exact value of the minimum distance. For BCH codes of length 511, the proposed technique passes considerably the famous known powerful scheme of Canteaut and Chabaud used to attack the public-key cryptosystems based on codes. ZSSMP is very rapid and allows catching the smallest weight codewords in few seconds. By exploiting the efficiency and the quickness of ZSSMP, the true minimum distances and consequently the error correcting capability of all the set of 165 BCH codes of length up to 1023 are determined except the two cases of the BCH(511,148) and BCH(511,259) codes. The comparison of ZSSMP with other powerful methods proves its quality for attacking the hardness of minimum weight search problem at least for the codes studied in this paper.
Mixed integer nonlinear programming (MINLP)-based bandwidth utility function on internet pricing scheme with monitoring and marginal cost
Authors: Sitepu Robinson , Puspita Fitri Maya , Kurniadi Elika , Yunita Yunita , Apriliyani Shintya
Published: April 2019

The development of the internet in this era of globalization has increased fast. The need for internet becomes unlimited. Utility functions as one of measurements in internet usage, were usually associated with a level of satisfaction of users for the use of information services used. There are three internet pricing schemes used, that are flat fee, usage based and two-part tariff schemes by using one of the utility function which is Bandwidth Diminished with Increasing Bandwidth with monitoring cost and marginal cost. Internet pricing scheme will be solved by LINGO 13.0 in form of non-linear optimization problems to get optimal solution. The optimal solution is obtained using the either usage-based pricing scheme model or two-part tariff pricing scheme model for each services offered, if the comparison is with flat-fee pricing scheme. It is the best way for provider to offer network based on usage based scheme. The results show that by applying two part tariff scheme, the providers can maximize its revenue either for homogeneous or heterogeneous consumers.