Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Existing Spectrum Sensing Techniques for Cognitive Radio using Modulation Techniques

: Cognitive Radio plays an important role in wireless communication. With the ever increasing demand for wireless communications, the spectrum has become a scarce resource. It is becoming a major obstacle for the development of new wireless technologies and introduction of new applications and services. Cognitive Radio (CR) is one of the major technologies that is the solution to the spectrum scarcity problem. It allows the use of spectrum in an efficient manner. The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. Although a lot of research has been done on techniques, but sensing of modulated signals hasn’t been analysed. In this work, an analytical study and simulation result of spectrum sensing techniques for cognitive radio will be presented. Results of spectrum sensing techniques like, energy detection, cyclo-stationary feature detection and matched filter detection will be analyzed. All the analysis will be done for modulated signal communication, as modulated signals brings us to a more practical approach. Modulation Using BPSK has been done so other technique QPSK will be used.
Modeling of Power Electronic Controller Using Incremental Conductance Algorithm for Solar-Cell Based Distributed Generation

M.R. Thansekhar and N. Balaji (Eds.): ICIET’14 ABSTRACT— Nowadays power demand is high for utilizing more amount of power for various types of loads in the distribution system. This paper deals with implementation of solar power for standalone distributed generation system for commercial loads. The optimal placement of solar array is essential for standalone distributed generation (DG) system. The proposed system focuses on the design of the solar array with boost converter for achieving high power for standalone system. It uses suitable power electronic devices for improving the power quality, voltage regulation and reducing the ripple voltage and current. The electricity can be generated from solar energy and that can be supplied to the commercial loads. The power quality problems will be reduced with the help of Suitable control algorithm (Incremental conductance algorithm). The presented scheme portrays modeling of solar cell and experimental results have been demonstrated achieving high performance and reliability. The proposed system topology and its control strategy are designed and analyzed by using MATLAB/Simulink tool.
A KNOWLEDGE BASED APPROACH TO ENHANCE SOFTWARE ARCHITECTURE REVIEW PROCESS

: The software architecture [SA] has received increasing attention as an important subfield of software engineering. The Software Architecture is the foundation for building any software and it is deciding the quality of software. Evaluating Software Architecture is a key part in software development. This paper intends to increase the consistency of software architecture evaluation process to a step ahead. The use of knowledge base system and the intelligent agent helps to achieve the consistency in Software Architecture Evaluation.
Mathematical Modelling For EEG Source Localization Using Subspace Method

A subspace based source localization method is proposed to estimate the location of sources of electrical activity within brain from EEG signals recorded by an array of sensors. Main intention is to localize sources with a minimum of localization error. The proposed method here is the Multiple Signal Classification (MUSIC) algorithm that locates multiple dipole sources from EEG data. In this, multiple dipole locations are found by scanning potential locations using a simple one dipole model. This method provides high spatial resolution than conventional methods.
Performance Comparison Of PV Module Using INC MPPT With Boost & SEPIC Converter

Nowadays, at this stage of energy crisis, it has become essential to switch towards renewable energy sources from conventional sources of energy to obtain power. Among all other renewable sources, solar energy is getting more attention in a country like India due to its abundant availability. This paper presents a study on Photovoltaic Module. As we know, solar energy is intermittent in nature, i.e. it varies according to the environment conditions, so we need to have some sort mechanism that helps it to operate efficiently. In this paper, an Incremental Conductance MPPT has been used to track that maximum power for the different environmental conditions. After tracking the maximum power, we need to obtain that power by changing the voltage and current values. This is done by changing the duty cycle of the DC-DC converters which gets its pulses through the MPPT and firing circuits and accordingly changes the voltage and current values. So, in this paper a study on PV module using INC Conductance MPPT with Boost and SEPIC Converter is presented and their values are obtained and compared for different environmental conditions. The whole system is simulated on MATLAB/Simulink.
Portable Biometric Attendance System using Raspberry Pi

- In the entire world every educational organization is concerned about the attendance of individuals because this has an adverse effect on their overall performances. In conventional method attendance of students is taken by calling out the names of students or signing on paper which is extremely time overwhelming. To eliminate this problem one of the solutions is a biometric-based attendance system that would be able to automatically capture the students’ attendance by recognizing their fingerprints. Fingerprint recognition is regarded as one of the most reliable, accurate and efficient biometric identification system. The module enrolls the student’s fingerprints. Enrolling is a one-time process. The fingerprints are stored in the fingerprint sensor. The attendance of each student is displayed on LCD. At the same time it is updated in a database. When the users log into the database, the data is sent through Wi-Fi to the server. Server maintains the records of the students. If the student’s attendance goes below certain percentage, a SMS will be sent to their registered mobile number. In many real time applications biometric authentication is used .
GENETIC TRANSMITTANCE OF TYPE 2 DIABETES MELLITUS WITH ANTHROPOMETRIC INDICES IN ADULTS OF MAJHA REGION OF PUNJAB

*Corresponding Author: Dr. Neenu Khanna, Department of Physiology, G. G. S. Medical College, BFUHS, Faridkot, India. 3926 POSITIVE CORRELATION OF GENETIC TRANSMITTANCE OF TYPE 2 DIABETES MELLITUS WITH ANTHROPOMETRIC INDICES IN ADULTS OF MAJHA REGION OF PUNJAB Neenu Khanna and Ram Sarup Sharma Department of Physiology, G. G. S. Medical College, BFUHS, Faridkot, Punjab, India Department of Physiology, Govt. Medical College, BFUHS, Amritsar, Punjab, India
Heterogeneous Computing Based K-Means Clustering Using Hadoop-MapReduce Framework

— K-means is a well-known clustering algorithm in the field of data mining. It is simple to implement and its speed allows it to run on large data sets. However, it also has a drawback. Advancement in many data collection techniques has been generating enormous amount of data, leaving scientists with the challenging task of processing them. Its performance will not be sufficient when it has to deal with large data sets. To solve this problem, a method is proposed in this paper by which k-means will be implemented using OpenCL heterogeneous computing platform with the help of Hadoop-MapReduce framework. MapReduce is a framework which is pioneered by Goggle for distributed programming. It includes user specified Map and Reduce functions which process inputs in the form of key/value pairs. Along with the MapReduce paradigm, Hadoop also implements HDFS which is known distributed file system. GPU Computing with many-core graphics processors is playing today an important role in the advancements of modern highly concurrent processors. Their ability to accelerate computation is being explored under several scientific fields. OpenCL is a heterogeneous computing platform and one of the widely used for GPU Computing. In the current paper we present the acceleration of a widely used data clustering algorithm, K-means, implemented using Hadoop & MapReduce framework, in the context of heterogeneous computing devices like CPUs and GPUs.
Patient Record data hiding in Medical Images using M-ary Modulation

Telemedicine is a general practice among remote areas. There is a chance of modifying in medical images intentionally or accidently if transmitted through open network. The medical practitioner has to verify region of interest (ROI), the diagnosis area of the received medical images before rendering any decision report. In this paper, a medical image watermarking scheme using DCT domain is discussed. The decision area (ROI) is to be separated using Fuzzy c-means and embed watermark in region of non-interest (RONI) using M-ary modulation. The effectiveness of the system is experimentally proved for different MRI medical images using different quality measures as PSNR, MSE, NRMSE, MAE and NCC and revealed that the proposed scheme produced a preferable quality of watermarked medical images.
A Technique of Human Action Recognition Using 3D Convolutional Neural Network

— This research involves the study of 3D Convolutional Neural Network (CNN) and its application as human action recognition from a video data base. Human action recognition is very important in real world environment. The current methods used for action recognition are highly problem dependent. Therefore the automated method known as 3D convolutional Neural Network is used for human action recognition in the field of image processing. Here a database is created which consists of features, trained, 3D CNN features were extracted using hardware layered architecture by performing 3D convolution and sub sampling separately on each channel . Thus multiple features are extracted. Number of actions limited was three. The main application of this method is in the field of video surveillances, to find the presence or absence of cluttered backgrounds, occlusions etc.
Screening of ten indian medicinal plant extracts for antioxidant activity

Currently there has been an increased interest globally to identify antioxidant compound that are pharmacologically potent and have low or no side effects. As plants are source of natural antioxidants, much attention has been gain to plants. The quest for natural antioxidants for dietary, cosmetic and pharmaceutical uses has become a major industrial and scientific research challenges over the last two decades. A variety of free radical scavenging antioxidants exist within the body in which many of them are derived from dietary sources like fruits, vegetables and teas. In this study methanolic crude extract of 10 traditionally used Indian medicinal plants were screened for their free radical scavenging and superoxide anion scavenging properties using Rutin and Curcumin as standard antioxidants. The overall antioxidant activity of ocimum sanctum was the strongest, followed in descending order by pterospermum acerifolium, Achyranthes aspera, Delonix regia, Mentha spicata, Datura stramonium, Coccinia indica, Hygrophilla auriculata, Cassia auriculata and Coriandrum sativum. Five plant, namely ocimum sanctum, pterospermum acerifolium, Achyranthes aspera, Delonix regia and Mentha spicata Showed strong super oxide anion scavenging activity. Both activities expressed as IC 50 and strong correlation showed between total polyphenolic contents in plants and their antioxidant activity.
A Review on Cooperative Spectrum Sensing in Cognitive Radio Networks

– Spectrum sensing is a key function of cognitive radio to prevent the harmful interference with licensed users and identify the available spectrum for improving the spectrum’s utilization. However, detection performance in practice is often compromised with multipath fading, shadowing and receiver uncertainty issues. To mitigate the impact of these issues, cooperative spectrum sensing has been shown to be an effective method to improve the detection performance by exploiting spatial diversity. While cooperative gain such as improved detection performance and relaxed sensitivity requirement can be obtained, cooperative sensing can incur cooperation overhead. The overhead refers to any extra sensing time, delay, energy, and operations devoted to cooperative sensing and any performance degradation caused by cooperative sensing. In this paper, the state of-the-art survey of cooperative sensing is provided to address the issues of cooperation method, cooperative gain, and cooperation overhead. Specifically, the cooperation method is analysed by the fundamental components called the elements of cooperative sensing, including cooperation models, sensing techniques, hypothesis testing, data fusion, control channel and reporting, user selection, and knowledge base. Moreover, the impacting factors of achievable cooperative gain and incurred cooperation overhead are presented. The factors under consideration include sensing time and delay, channel impairments, energy efficiency, cooperation efficiency, mobility, security, and wideband sensing issues. The open research challenges related to each issue in cooperative sensing are also discussed.
https://www.ijew.io/paper/projecting-future-temperature-using-cmip5-gcms-over-transboundary-gomal-river-basin

Temperature is a key driving force in hydrological cycle, defining the extent of climate change. It causes the alteration in hydrological cycle process, limiting and intensifying the rainfall, increasing the rate of evapotranspiration and changes the crop pattern and duration over a region. Its future analysis is utmost to cope with negative effects of climate change over a specified region. This study also investigates, the future temperature pattern over Gomal River Basin (GRB). To undertake the study, downscaled daily temperature data of four General Circulation Models (GCMS) namely; bcc_csm1_1_m, mpi_esm_mr, ncar_ccsm4 and ncc_noresm1_m and their ensemble mean were first compared and validated with observed data for the period of 1980-2005. After that, temperature was projected for the mid-century (2020-2060) for the Representative Concentration Pathways (RCPs) 4.5. The analysis were carried out based on the four seasons; winter (December-February), spring (March-May), summer (June-August) and autumn (September-November). The results indicate that, the basin temperature was accurately predicted by the ensemble mean of the four GCMs with R value of 0.9. All the GCMs projected a warming in future in all seasons. Winter warming is more compared to other seasons. Proper adaptation strategies are needed to cope with the adverse impacts of global warming in the basin. Keywords— Temperature, Climate Change, CMIP5, GCMs, GRB
A RAPID AND SIMPLE METHOD FOR THE ISOLATION OF TOTAL DNA FROM Trichoderma spp

DNA isolation from filamentous fungi is often tedious, time consuming and difficult because they have cell walls that are relatively unsusceptible to lysis. Here described a rapid, inexpensive and reproducible protocol for the isolation of DNA from the filamentous fungus, Trichoderma spp. The present protocol is based on the sodium dodecyl sulfate method without using βmercaptoethanol and devoid of liquid nitrogen for the maceration. The precipitation of DNA was done using isopropanol and ethanol. The A260/280 absorbance ratio of isolated DNA was ≈1.9 indicating that DNA fraction was pure and could be used for further analysis. The result of DNA electrophoresis in 0.8% agarose gel showed sharp clear bands of DNA, indicated the good quality of DNA. Since this protocol yielded the genetic DNA in sufficient quality and quantity this can be used for the successful isolation of DNA from Trichoderma spp. KeywordsTrichoderma, βmercaptoethanol, DNA isolation, SDS, Tris – HCl, lysis
AN EFFICIENT SENTIMENT CLASSIFICATION BASED ON TWITTER DATASET

— Focus on using Twitter, the most popular social blogging platform, for the task of sentiment analysis. To automatically collect a corpus for sentiment analysis and opinion mining purposes are shown. The POS tagger that is able to determine the positive and negative sentiment words. Finally NaiveBayes Classification introduces a computationally trivial approach for classifying a given Tweet’s sentiment. If a Tweet contains an emoticon corresponding to a particular emotional sentiment, then it is simply assumed that the rational author actually felt and/or sought to convey the stated sentiment. Here, use classifier for sentiment analysis and efficient pre-processing are used. This proposed system discusses an approach where a publicized stream of tweets from the Twitter micro blogging site are pre-processed and classified based on their subjectivity word and semantic phrase content as positive, negative and irrelevant. Analyses the performance of various classifying algorithms based on their precision and recall in such cases. In this system, focus on using Twitter, the most popular micro-blogging platform, for the task of sentiment analysis. Show how to automatically collect a corpus for sentiment analysis and opinion mining purposes. Using the corpus, we build a sentiment classifier that is able to determine positive, negative and neutral sentiments for a twitter document. Experimental evaluations show that proposed techniques are efficient and perform better than previously proposed methods
Artificial Neural Networks in Data Mining

: There are plenty of technologies available to data mining practitioners, including Artificial Neural Networks, Regression, and Decision Trees. Data mining tools can forecast the future trends and activities to support the decision of Officials. Some data mining tools can also solve some traditional problems which consumed enough time, this is because that they can rapidly browse the entire database and find some useful information experts undetected. Neural network is a parallel processing network which made with simulating the intuitive thinking of human, the neural network in data mining was not optimistic, and the key reasons are that the nerve organs network has the problems of complex structure, poor interpretability and long training time. Nevertheless advantages such as high affordability to the noise data and low error rate, the consistently advancing and optimization of various network training algorithms, especially the continually advancing and improvement of varied network pruning algorithms and rules extracting algorithm, associated with application of the neural network in the data mining increasingly preferred by the overwhelming vast majority of users. This paper introduces the Comprehensive view of artificial neural networks and their advantages by data mining practitioners.
High Prevalence of Zinc Deficiency in Rural Nigerian Preschool Children: A Community-Based Cross-Sectional Study

Abstract Background and Aims: In Nigeria, community-based epidemiological data related to the prevalence of zinc deficiency in preschool children are scarce. We assessed the prevalence of zinc deficiency and the associated socio-demographic variables in children aged between 6 and 60 months, living in a Nigerian rural community. Materials and Methods: In this cross-sectional study, the serum zinc concentrations of 252 children aged between 6 and 60 months in a rural community in Nigeria were assessed, using atomic absorption spectrophotometry. The study population was selected by multistage random sampling and at least two children were selected from every household which had children in the study age group. The socio-demographic characteristics of the subjects were obtained, using an interviewer-administered questionnaire. Results: A total of 252 children were studied, 134 (53.2%) males and 118 (46.8%) females. The mean age was 32.7±17.0 months, similar for both sexes. Overall, 220 (87.3%) had low serum zinc concentrations (less than 7.65µmol/L). According to age, the highest mean serum zinc concentration was 5.43±3. 52µmol/L in children aged between 6 and 12 months. Correlation between serum zinc concentration and family size or socio-economic status (SES) showed that the smaller the family size and the higher the SES, the higher the serum zinc concentration. Conclusions: The high prevalence of low serum zinc concentration indicates that zinc deficiency is a public health problem in our rural communities, requiring public health intervention.
Dose response of CPAP on nasal symptoms, obstruction and inflammation in vivo and in vitro

Obstructive sleep apnoea (OSA) is a common condition associated with cardiovascular risk. Continuous positive airway pressure (CPAP) is an effective treatment but associated with nasal side effects, which hinder compliance and may result from inflammation. Published on March 9, 2012 as doi: 10.1183/09031936.00199911 ERJ Express Copyright 2012 by the European Respiratory Society. 2 We investigated whether CPAP was pro-inflammatory to human subjects in vivo, and to cultured bronchial epithelial cells in vitro. In vivo, we further investigated whether induction of nasal inflammation was associated with the development of systemic inflammation, nasal symptoms, and changes in nasal mucociliary clearance. In vitro, CPAP resulted in cytokine release from cultured BEAS-2B cells in a timeand pressure (dose)-dependent manner. In vivo, CPAP resulted in dose-dependent upregulation of nasal inflammatory markers associated with the development of nasal symptoms, and reduced mucociliary clearance. CPAP also up-regulated selected markers of systemic inflammation. CPAP results in dose-dependent release of inflammatory cytokines from human epithelial cells in vitro and in vivo. In vivo responses were associated with systemic inflammation, reductions in nasal mucociliary function and the development of nasal symptoms. This emphasises the need for novel strategies to reduce nasal inflammation and therefore aid compliance.
In vitro Antiplasmodial Activity and Cytotoxicity of Extracts and Fractions of Bidens pilosa

Malaria is one of the most important health problems in Cameroon. The aim of this study was to evaluate the in vitro activity of the extracts and fractions of Bidens pilosa on Plasmodium falciparum strains. Plasmodium falciparum were cultivated by the method of Trager and Jensen (1976) and maintained in fresh O+ human erythrocytes at 4 % haematocrit in complete medium (RPMI 1640 with Albumax II). The ring stage synchronized P. falciparum strains Pf3D7 (Chloroquine sensitive) and Pf INDO (Chloroquine resistant), 1% parasitemia, 2% hematocrit} were incubated in a 96-well microplate for 48 h with different concentrations of plant extracts and fractions of the most active extract ranging from 1.56 to 100 µg/ml. RPMI and 0.4% DMSO were used as negative controls; while Chloroquine (1 µM) was used as positive control. The results were obtained by the microtiter plate using SYBR Green I fluorescence assay. Cytotoxicity was determined against HEPG2 and L929 cells using MTT assay. The most potent extract was chromatographed on reverse phase HPLC towards antiplasmodial activity guided purification of metabolites. Of the 3 plant extracts tested, the highest antiplasmodial (IC50 of 8.18 ±2.15 µg/ml) activity was observed with Ethyl Acetate extract of the leaves of Bidens pilosa. This extract was then selected for antiplasmodial activity guided fractionation and isolation of active fractions. Neighbouring fractions were combined judiciously to ensure maximum purity to obtain 15 pools. Among the 15 pools evaluated, the most potent was number 12 with an IC50 of 0.73 ± 0.07 µg/ml (Pf 3D7) and 3.53 ± 1.85 µg/ml (Pf INDO). Pool 12 exhibited a CC50 of 88.34 ± 1.56 µg/ml (HEPG2 cells) and 81.31 ± 2.42 µg/ml (L929 cells). Therefore, pool 12 can be considered as highly active and non-toxic. Our findings, therefore, corroborate the use of B. pilosa as antimalarial in Cameroonian folk medicine.