Showing papers in "Indian journal of science and technology in 2016"

Abstract: This paper focuses on a keen study of different clustering algorithms highlighting the characteristics of big data. Brief overview of various clustering algorithms which are grouped under partitioning, hierarchical, density, grid based and model based are discussed.

Abstract: This paper presents a novel One-shot pulse Generator Circuit, which is composed of only one differential voltage current conveyor (DVCC) as the active element. The application circuits utilizing the DVCC are introduced and implemented. Only one DVCC and two resistors and one capacitor are required to construct every circuit. Each circuit is able to provide a pulse-shaped response having changeable width via a positive-edge triggered signal. The first one is a general one-shot pulse generating circuit. The second design can reduce the recovery time after applying triggered signals. Is-Spice is the simulation software to simulate every model. To fabricate the models commercially available ICs (AD844AN) and passive elements are required. Program and experimental outputs satisfy theoretical results.

Abstract: Background/Objectives: This appraisal investigates the meta-heuristics resource allocation techniques for maximizing financial gains and minimizing the financial expenses of cloud users for IaaS in cloud computing environment. Methods/Statistical Analysis: Overall, a total of ninety-one studies from 1954 to 2015 have been reviewed in this paper. However, twenty-three studies are selected that focused on the meta-heuristic algorithms for their research. The selected papers are categorized into eight groups according to the optimization algorithms used. Findings: From the analytical study, we pointed out the various issues addressed (optimal and dynamically resource allocation, energy and QoS aware resource allocation, VM allocation and placement) through resource allocation meta-heuristics algorithms.Whereas, the improvement shows better performance concerns minimizing the execution and response time, energy consumption and cost while enhancing the efficiency and QoS in this environment. The comparison parameters (makespan 35%,execution time 13%, response time 26%, cost 22%, utilization21% and other 13% including energy, throughput etc) and also the experimental tools (CloudSim 43%, GridSim 5%, Simjava 9%, Matlab 9% and others 13%) used for evaluation of the various techniques for resource allocation in IaaS cloud computing. Applications/Improvements: The comprehensive review and systematic comparison of meta-heuristic resource allocation algorithms described in this appraisal will help researchers to analyze different techniques for future research directions.

Abstract: Background: The following study presents a colorimetric method for the assessment of serum catalase activity which yields precise, accurate, reproducible results and is simplified so that clinical pathology laboratories may achieve this determination without the need for special techniques. Methods: In this method, dichromate in acetic acid is reduced to chromic acetate when heated in the presence of undecomposed hydrogen peroxide (H2 O2 ), with the formation of perchromic acid as an unstable intermediate. Hydrogen peroxide concentration is directly proportional to the concentration of chromic acetate that produced from the reaction. The chromic acetate produced is measured calorimetrically at 570 nm. Findings: The imprecision of the method was calculated by measuring the coefficient of variation, which equals to 3.4% within run and 5.9% between run. The catalase assay performed using the kinetic method yielded a good correlation (r = 0.9771). Applications: The present method characterizes by adding a correction factor to eliminate the interference that arises from the presence of sugars, amino acids, proteins and vitamins in serum. Keywords: Catalase Activity, Clinical Pathology, New Method Serum, Spectrophotometry

Abstract: Background/Objectives: In this paper, Firefly Algorithm (FA) based multilevel thresholding is proposed to segment the gray scale image by maximizing the entropy value. Methods/Statistical analysis: Better segmentation method gives appropriate threshold values to enhance the region of interest in the digital image. The entropy based methods, such as Kapur’s and Tsallis functions are chosen in this paper to segment the image. This work is implemented using the gray scale images obtained from Berkeley segmentation dataset. The FA assisted segmentation with entropy function is confirmed using the universal image superiority measures existing in the literature.Findings: Results of this simulation work show that Tsallis function offers better performance measure values, whereas the Kapur’s approach offers earlier convergence with comparatively lower CPU time. Applications/Improvements: Proposed method can be tested using other recent heuristic methods existing in the literature.

Abstract: In the VLSI chip designing process, Floor-planning is one of the vital stages which in turn have Placement and Routing tasks. This paper concentrates on solving the problems which occur in VLSI floor planning and gives an overview of placement and routing problems in ICs. This approach depends on recursive optimization prototypes with redefined algorithm. The searching for best solutions is carried out by Genetic Algorithm (GA) on each iteration since these algorithms is already known and proven to solve similar type of problems. GA has been tested randomly and is simulated. By conducting experiments on GA we can realize optimized solutions for the above said problems.

Abstract: Performance analysis of Mustard Oil (MO) methyl ester as a potential alternative fuel is performed in CI engine at various injection timing. Performance analysis such as thermal efficiency, fuel consumption, Exhaust gas temperature and Pressurecrank angle were investigated and compared with diesel (D). Result signified that by advancing the fuel injection by 60 bTDC brake thermal efficiency was found to increase by 4.85% for MO comparing 3.63% increase in diesel. In addition, BSFC is reduced by 3.19% for MO while for diesel it was 12.86%. This experimental study obviously signifies that by advancing the fuel injection, performance aspects of CI engines is enhanced considerably.

Abstract: Yield prediction benefits the farmers in reducing their losses and to get best prices for their crops. The objective of this work is to analyze the environmental parameters like Area under Cultivation (AUC), Annual Rainfall (AR) and Food Price Index (FPI) that influences the yield of crop and to establish a relationship among these parameters. In this research, Regression Analysis (RA) is used to analyze the environmental factors and their infliction on crop yield. RA is a multivariate analysis technique which analyzes the factors groups them into explanatory and response variables and helps to obtain a decision. A sample of environmental factors like AR, AUC, FPI are considered for a period of 10 years from 1990-2000. Linear Regression (LR) is used to establish relationship between explanatory variables (AR, AUC, FPI) and the crop yield as response variable. R 2 value clearly shows that yield is mainly dependent on AR. AUC and FPI are the other two factors influencing the crop yield. This research can be extended by considering other factors like Minimum Support Price (MSP), Cost Price Index (CPI), Wholesale Price Index (WPI) etc. and their relationship with crop yield.

Abstract: Background/Objectives: In the meta-heuristic algorithms, randomization plays a very crucial role in both exploration and exploitation. So meta-heuristic algorithms are proposed to avoid these problems. Methods/Statistical Analysis: A novel bio-inspired optimization algorithm based on the special bubble-net hunting strategy used by humpback whales called the Whale Optimization Algorithm (WOA). In contrast to meta-heuristic, main feature is randomization having a relevant role in both exploration and exploitation in optimization problem. A novel randomization technique termed adaptive technique is integrated with WOA and exercised on ten unconstraint test benchmark function. Findings: WOA algorithm has quality feature that it uses logarithmic spiral function so it covers a broader area in exploration phase then addition with powerful randomization adaptive technique potent the adaptive whale optimization Algorithm (AWOA) to attain global optimal solution and faster convergence with less parameter dependency. Application/Improvements: Adaptive WOA (AWOA) solutions are evaluated and results shows its competitively better performance over standard WOA optimization algorithm.

Abstract: Background: Present day's car parking has become a major issue in urban areas with lack of parking facilities and increased amount of vehicles, due to this drivers who are searching for parking space they were roaming around the city in peak hours. This causes traffic, waste of time and money. Methods: To solve those problems, this prototype is developed using sensor circuit, RFID and IoT. RFID used here to detect the car details, IR sensor is used to find the presence of the car and all details are accessed from remotely through IoT. Findings: This system helps user to find parking space availability with the help of Internet of Things (IoT) technology by providing parking free space information. The IoT maintains the database of the parked vehicles through a shared server. So drivers can book the slots in advance and the parking information updated in server. In addition to the parking, theft management will be done i.e. a theft vehicle came for parking then the number plate is checked with theft list in the database, if it is in theft list then a message is sent to the police. Applications/ Improvements: This prototype developed for the parking system with less human interaction, increases flexibility and security. This system is employable in airports and multiplexes parking.

Abstract: Objective: This paper has been prepared as an effort to reassess the research studies on the relevance of machine learning techniques in the domain of agricultural crop production. Methods/Statistical Analysis: This method is a new approach for production of agricultural crop management. Accurate and timely forecasts of crop production are necessary for important policy decisions like import-export, pricing marketing distribution etc. which are issued by the directorate of economics and statistics. However one has understand that these prior estimates are not the objective estimates as these estimate requires lots of descriptive assessment based on many different qualitative factors. Hence there is a requirement to develop statistically sound objective prediction of crop production. That development in computing and information storage has provided large amount of data. Findings: The problem has been to intricate knowledge from this raw data , this has lead to the development of new approach and techniques such as machine learning that can be used to unite the knowledge of the data with crop yield evaluation. This research has been intended to evaluate these innovative techniques such that significant relationship can be found by their applications to the various variables present in the data base. Application / Improvement: The few techniques like artificial neural networks, Information Fuzzy Network, Decision Tree, Regression Analysis, Bayesian belief network. Time series analysis, Markov chain model, k-means clustering, k nearest neighbor, and support vector machine are applied in the domain of agriculture were presented.

Abstract: Background/Objectives: Research in recent years has probed integration amongst research field of Software Engineering & Semantic Web technology, addressing the advantages of applying Semantic techniques to the field of Software Engineering. Prolifically published studies have further substantiated the benefits of ontologies to the field of Software Engineering, which clearly motivate us to explore further opportunities available in this collaborated field. This paper is a survey expounding such opportunities while discussing the role of ontologies as a Software Life-Cycle support technology. Method/Statistical Analysis: Survey centred on providing an overview of the state-of-art of all the ontologies available for Software Engineering followed by their categorization based on software life cycle phases and their application scope. Findings: Characterization of ontologies as a Software Life-cycle support technology, instigated by the increasing need to investigate the interplay between Semantic Web & Software Engineering with the ultimate goal of enabling & improving Software Engineering capabilities. Application/Improvements: This paper discusses the practical and potential applications of ontologies in the field of Software Engineering followed by the issues and challenges that will keep this field dynamic and lively for years to come.

Abstract: Background/Objectives: The latched regenerative comparator is an essential block in all ADC architectures. It majorly suffers from the non-idealities such as kickback noise, thermal noise and offset voltage. Especially in an ADC implemented in Cardiac IMDs, the generated kickback noise in latched comparator can make a difference to the accuracy, resolution and settling time to an extent. The main objective of this work is to implement a technique for kickback noise reduction in latched comparators. Methods/Statistical Analysis: This work reviews the various architectures of latched comparators implemented in Cardiac IMDs and also make assessment of the available solutions to reduce the generated kickback noise in a latched comparator. The available kickback noise reduction techniques are implemented in SR latched dynamic comparators and resultant findings are compared. Findings: This brief proposes a new solution to cancel out the unwanted charge injections in the comparator and thus reduces the kickback noise effectively. The proposed solution is implemented in the latched comparator with SR latch and also compared with the already available solutions with regard to kickback noise and power dissipation. Application/Improvements: The proposed Kickback noise reduction technique reduces the noise to 40% more when compared with the other techniques and this technique is applicable to the dynamic Comparators used in Cardiac IMDs.

Abstract: Background/Objectives: Digital image processing is used various fields for analyzing different applications such as medical sciences, biological sciences. Various image types have been used to detect plant diseases. This work is analyzed and compared two types of images such as Grayscale, RGB images and the comparative result is given. Methods/Statistical Analysis: We examined and analyzed the Grayscale and RGB images using image techniques such as pre processing, segmentation, clustering for detecting leaves diseases. Results/Finding: In detecting the infected leaves, color becomes an important feature to identify the disease intensity. We have considered Grayscale and RGB images and used median filter for image enhancement and segmentation for extraction of the diseased portion which are used to identify the disease level. Conclusion: RGB image has given better clarity and noise free image which is suitable for infected leaf detection than Grayscale image.

Abstract: Background: Wireless sensor networks are application-based networks designed by large number of sensor nodes. Utilizing the energy in efficient way is the one of the main design issue in Wireless Sensor Network (WSN). Limited battery capacity of sensor nodes makes energy efficiency a major and challenging problem in wireless sensor networks. Methods : In order to improve Network lifetime, Energy efficiency and Load balance in Wireless Sensor Network, a Cluster Arrangement Energy Efficient Routing Protocol CAERP is proposed. It mainly includes efficient way of node clustering and distributed multi-hop routing. In the clustering part of CAERP we introduce an un-even clustering mechanism. Cluster head which are closer to the Base Station (BS) have smaller cluster size than those farther from BS, so in here they can preserve some energy in the time of inter-cluster data communication. Our protocol consists of cluster head selection algorithm, a cluster formation scheme and a routing algorithm for the data transmission between cluster heads and the base station. Findings: Each sensor node should effectively handle its energy in order to keep the WSN at its operation state. In each time duration Q-leach is consume more energy than the CAERP. CAERP eliminate the initial dead node problem. During the initial stage the message overhead between the Q-Leach and CAERP have somewhat similar, but after the uneven clustering formation the CAERP message overhead is reduced comparing with the Q-LEACH. In CAERP protocol it mainly focuses for utilizing the energy in efficient way. This improvement is accomplished because the nodes remain alive due to the efficient way of cluster arrangement. CAERP has mainly five cluster Head so each cycle the Cluster Head varying based on the CAERP CH selection algorithm. Due to efficient CH selection algorithm the CAERP have high network life time compared to Q-LEACH. The simulation result shows that CAERP significantly increasing the network lifetime and minimizes energy consumption of nodes compared with Q-leach protocol. Conclusion: The performance of the proposed protocol is compared with that of Q-LEACH using different parameters with the help network simulators. Our protocol CAERP has significantly improved in average energy consumption, survival rate and the extended the network life cycle which means the energy efficiency of the CAERP network is improved.

Abstract: Background/Objectives:The objective of this article is to clarify what is meant by employee engagement and why it is important (particularly with respect to its effect on employee retention and performance), as well as to identify factors that are critical to its effective implementation.Methods/Statistical Analysis: For this study, researchers have used review method. Under the process of review around thirty academic and popular research papers/ literature in the area of employee engagement, researchers have come up with different factors which are mostly commonly mentioned in these research papers. The review process aims at strengthening existing literature. After studying all the factors in each research paper, authors have taken the findings.Findings:In this research paper, various factors have been discussed of engagements which are at macro i.e. at organisational level and micro level i.e. at individual level. These variations in factors may arise due to differences in individual and job characteristics, gender diversity; ethnic diversity etc. Suggestions presented in this paper include different employee engagement approaches for new employees like strong induction programs, rigorous training and development programme, certification programme and giving them a realistic job preview. The findings of this study will be useful to any organisation, irrespective of the type of business, to construct strong employee engagement policy with mix of all these factors of employee engagement. Managers can redesign the work and policy on the basis of the factors presented in this paper would lead to happy workforce. This article will be ofvalue to anyone seeking better understanding in employee engagement to improve organisation performance.Applications/Improvements:Study results has scope offuture reference where by implementing various engagement factors and there by reduction in employee turnover and improved productivity

Abstract: Background/Objectives: The overall purpose of this study is to determine what influences hotel guests' intentions to stay at green hotels and engage in green programs while staying at the hotel. Methods/Statistical analysis: Data was collected from 168 respondents staying in selected Indian hotels using judgmental sampling by employing both qualitative (word association test) and quantitative (questionnaire survey) methods. Analysis was performed by descriptive statistics and multivariate analysis of variance in Statistical data analysis tool (SPSS, Version 20.0). Findings: The results reveal that green attributes such as energy efficient light bulbs in guest rooms, recycle bins in the room as well as hotel lobby, green certification were received favorably. However, some of them like towel reuse program, refillable shampoo dispensers and sheets changed upon request raises some doubt in the consumers mind, were not received favorably. Further a new possible definition of green hotel is developed to contribute in the present literature. Applications/Improvements: The results will provide hoteliers with indication about which attributes they could or should encourage in order to attract travelers that are concerned about the environment. As the movement of greening the hotels will continues to grow, the attributes need to be updated cordially

Abstract: Objectives: In our Country, even though it has super power and an economic development, but still there are many crimes against women. The atrocities against the women can be brought to an end with the help of our product "FEMME". This device is a security system, specially designed for women in distress. Method/Analysis: Using ARM controller for the hardware device is the most efficient and it consumes less power. We use radio frequency signal detector to detect hidden cameras. Findings: We analysed that there are no security device for our total safety. The user has to carry multiple devices. We found an ALL-IN-ONE security device which has all the features in one click. Applications/Improvements: In this paper we used ARM controller and android application in which both the device and the smart phone are synchronized using Bluetooth, hence both can be triggered independently. We can record audio for further investigation and can give an alert call and message to the pre-set contacts with the instant location every 2 minutes and can be tracked live using our application. Hidden camera detector is also a distinct feature using which we can ensure our privacy.

Abstract: Background: Magnetic Resonance Images (MRI) is an important medical diagnosis tool for the detection of tumours in brain as it provides the detailed information associated to the anatomical structures of the brain. MRI images help the radiologist to find the presence of abnormal cell growths or tumours. MRI image analysis plays a vital role in diagnosis of brain tumours in the earlier stages and treatment of diseases. Methods: Therefore, this paper introduces an efficient MRI brain image analysis method, where, the MRI brain images are classified into normal, non cancerous (benign) brain tumour and cancerous (malignant) brain tumour. This proposed method follows four steps, 1. Pre-processing, 2. Segmentation, 3. Textural and shape feature extraction and 4. Classification. In this proposed MRI image analysis using the region based Active Contour Method (ACM) used for segmentation and Artificial Neural Network (ANN) based Levenberg-Marquardt (LM) algorithm used for classification process, which used to efficiently classify the MRI image as normal and Tumourous. Findings: The results revealed that the proposed MRI brain image tumour diagnosis process is accurate, fast and robust. The classifier based MRI brain image processing approach produced the best MRI brain image classification with use of feature extraction and segmentation results, in terms of accuracy. Best overall classification accuracy results were obtained using the given DioCom Images; The performance results proven that there is not sufficient result given to the classification process when it perform separately. With the use of ACM segmentation and feature extraction approaches, the proposed LM classification approach provides better classification accuracy than the existing approach. Application: The proposed MRI image based brain tumour analysis would efficiently deal with segmentation and classification process for brain tumour analysis with use of feature extraction methods, so this method can yield the better result of brain tumour diagnosis in advance where this method using in medical fields.

Abstract: Background/Objectives: Educational Data mining is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. The objective of this work is to identify relevant attributes from socio-demographic, academic and institutional data from undergraduate students at the university located in India and develop an improved decision tree algorithm based on ID3 which can able to predict whether the students continue or drop their studies. Methods/Statistical Analysis: The traditional ID3 algorithm is improved by using Renyi entropy, Information gain and Association Function and the model generated by improved decision tree algorithm may be beneficial for university administrators to create guidelines and policies related to raise the enrollment rate in university and to take precautionary and advisory measures and thereby reduce student dropout. It can also used to find the reasons and relevant factors that affect the dropout students. Findings: Experimental results proved that improved decision tree algorithm provides better prediction accuracy in educational data than that of traditional classification algorithms in the literature. Improvements/Applications: Improved decision algorithm was proposed that enhances the ability to form decision trees and thereby to prove that the classification accuracy of improved decision algorithm on educational dataset is greater.

Abstract: Background/Objectives: The primary objective of this paper is to classify the clinical dataset of cervical cancer to identify the stage of cancer which helps in proper treatment of patient suffering from cancer. Methods/Statistical Analysis: This research work basically moves toward the detection of cervical cancer using Pap smear images. Analysis of Pap smear of cervical region is an efficient technique to study any abnormality in cervical cells. The proposed system firstly segment the pap image using Edge Detection to separate the cell nuclei from cytoplasm and background and then extract various features of cervical pap images like area, perimeter, elongation and then these features are normalized using min-max method. After normalization KNN method is used to classify cancer according to its abnormality. Findings: The classification accuracy with 84.3% of maximum performance with no validation and classification accuracy with 82.9% of maximum performance with 5 Fold cross validation is achieved.

Abstract: Background/Objectives: Air pollution due to vehicular and industrial emission has become menace to the living beings Due to this menace both indoor and outdoor air quality monitoring in real time has become mandatory Methods/ Statistical Analysis: The evolution of Internet of Things (IoT) and Single Board Computers (SBC) has made real time remote monitoring as a ubiquitous process Remote monitoring was facilitiated using classical motes in the past, which has some pitfalls like limited memory, processsing speed and complex programming strategies This paper portrays the usage of SBC for integration of IoT with WSN for Air Quality Monitoring System (AQMS), where SBC are capable of performing even complex task with enhanced speed and reduced complexity The integration of cloud services with SBC makes alerting process smart and realtime Findings: With the review and realizing of immense literature in the field of WSN for air quality management, the design of sensor web node becomes essential The evolution of SBC adds merit towards monitoring and measuring of the critical factors in centralized Air Quality Monitoring System (AQMS) in any plant Sensor web node is proposed with commercial gas sensors for detecting the gases like CO, CO 2 , NH 3 and NOx to monitor both indoor and outdoor air quality The observed results are properly evaluated using ThingSpeak open source IoT platform The integration of open source cloud services for SBC in this proposed prototype model confirms low cost, comfort, convenience and rapid prototyping for flexible AQMS Applications/Improvements: This prototype can be easily adapted to any monitoring systems with minor changes and can be made scalable for tomorrow

Abstract: Biometrics trait using finger-vein has attracted numerous attention from researchers all over the world since the last decade. Various approaches have been proposed in regard to improving the accuracy of identification result. This paper discusses on the approaches taken from other researches on preprocessing, feature extraction and classification stage specifically for recognizing individual identity. The strengths and weaknesses of these approaches are critically reviewed. The classification approach using machine learning method is highlighted to determine the future direction and to fill the research gap in this field.

Abstract: Objectives: To numerous individuals, archery is not generally seen as a sport demanding high levels of fitness in spites of long hours it takes during training and competition. Nonetheless, archers need to achieve a specific level in various aspects of fitness to permit them to perform the activity of shooting with exactness and have the capacity to repeat the activities without exhaustion. Determining the essential fitness attributes could help archers to reduce their long training hours by paying attention to the most mattered components. This study aims to identify the most essential physical fitness components needed for successful performance of archery. Methods/Statistical Analysis: A total of 17 youth archers with mean age ± 16.9 were repeatedly measured on basic physical fitness components, their scores were recorded, and Principle Components Analysis (PCA) was utilised to ascertain the most essential variables. Findings: The initial PCA identifies two components with higher Eigen value (>1). Moreover, PCA after varimax rotation indicates two components containing four and two varifacators (VF). The First VF revealing age (-0.69), BM1 (-0.71), 1 Minute sit-ups (0.75) and Predicted VO2max (0.78) recognising the need for endurance while, the second VF discloses V-sit and reach (0.75) and Maximum push-ups (0.76) identifying the requirement for upper muscular strength in the game. Application/ Improvement: The current study has successfully identified the most needed physical fitness variables in the productive performance of archery game.

Abstract: Background/Objectives: Image segmentation, a crucial and an essential step in image processing, determines the success of higher level of image processing. In this paper, a detailed study about different evaluation techniques based on subjective and objective methods have been discussed. Methods/Statistical analysis: An application specific characteristic of image segmentation paves a way for development of numerous algorithms. Traditionally subjective method of evaluation is used to determine the segmentation performance accuracy. As this evaluation method is quantitative and biased, a qualitative method of evaluation is demanded. This is done using the objective method of evaluation where discrepancy and goodness methods are used.Discrepancy method is used in widespread for predefined benchmark images where it has corresponding ground truth image for comparison. Goodness method is used for real time images where no ground truth image is available for comparison. These methods of objective evaluation are highly needed to validate the segmentation methods which are increasing rapidly in recent years. Findings: A detailed study of different evaluation methods are discussed and experimented over different segmentation methods. Boundary based methods like sobel, canny, susan, region based methods like region growing, thresholding and a hybrid method, combining boundary based and region based method are used for the purpose of experimentation.Experimental result shows that hybrid method performs better than other existing ones and also highlights the importance ofimage quality assessment method to identify a better segmentation technique for all type of images.

Abstract: Objectives: The objective of this paper is to analyze the different image quality metrics by testing and comparing with different distorted set of satellite images. Methods/Statistical Analysis: In this paper, we propose the methods for analyzing the quality of real time images that are corrupted due to different distortions. The several quality metrics are applied and ultimately the best metrics are derived based on the type of degradation. Different metrics such as metric based on single image and metric based on two images have been tested with different real time satellite images from NASA data sets. Findings: This framework will help to identify the metrics in order to prove the proposed filtering schemes that are applied to the corrupted images. Based on the results, we have concluded the characteristics of different quality metrics and further we successfully identified the quality metric appropriate to various distortions. Application/Improvements: The proposed quality metric analysis is used to estimate the performance of any filtering schemes which are used to enhance the quality of any real time images such as remote sensing field.

Abstract: Objectives: The main objective of this work is to explore and investigate the significance of power dissipation and kickback noise in the design of dynamic comparators used in cardiac pacemakers. Methods/Statistics: In this paper a power and noise efficient dynamic comparators are designed for cardiac pacemakers based on conventional dynamic comparators. Results/Findings: It is shown that the dual rail dynamic comparator suffers from low kickback noise but has more power dissipation and vice versa in case of balanced dynamic comparator. From the results it can be noted that either power or kickback noise can be minimized at a time. Application: Pacemakers are cardiac implantable medical devices used to boost up the heart rate. For such devices low power consumption is very vital. Dynamic comparators are power hungry and decision making devices in pacemakers.

Abstract: Background/Objectives : Sodium hydroxide (NaOH) pretreatmentwas used to determine the effect of pretreatment on chemical composition of pretreated rice straw. The experiment was designed to measure the effects in terms of NaOH concentration and pretreatment time mainly on the total carbohydrate content (TOC) and lignin content of pretreated rice straw. Methods/Statistical Analysis :Compositional characterization was performed based on the National Renewable Energy Laboratory (NREL) laboratory analysis protocols. Rice straw obtained from Sekinchan, Selangor Malaysia was dried to reduce the moisture content (<15%) and ground to 2 mm particle size. Rice straw was pretreated with different concentration of NaOH (2%w/v, 6%w/v and 12%w/v) and pretreatment time of 1 and 3 hours, while temperature was kept constant at 55°C. Findings: Rice straw sample pretreated with 12%w/v NaOH for 1 hour gave the highest glucan content, an increase of 85.6% from the native untreated rice straw. This condition also yielded the best delignification effect which reduced the lignin composition up to 79.6%, while sample pretreated with 2%w/v for 3 hours gave the highest composition on total carbohydrate content of 79.16% for which included glucan, xylan and arabinan.Hence, the pretreatment condition of 2%w/v NaOH concentration for 3 hours was the best condition in order to obtain high total carbohydrate content while severe pretreatment condition of 12%w/v NaOH concentration for 1 hours was best to give the delignification effect to the rice straw. Application/Improvements: The results from this work can be used for further evaluation of pretreated rice straw using NAOH particularly for enzymatic hydrolysis. Is useful in determining the carbohydrate conversion during the hydrolysis.

Abstract: The social web data has increased tremendously in the recent years in form of comments, blogs, reviews and tweets The nature of this data is highly un-structured and high- dimensional, making text classification a tedious task Sentiment analysis, which is a text classification technique is applied on this data to gauge user opinion on several pertinent issues As a natural language processing task, sentiment analysis automatically mines attitudes or views of users on specific issues It is a multi-step process where selecting and extracting features is a vital step that controls performance of sentiment classifier The statistical techniques of feature selection like document frequency thresholding produce sub optimal feature subset due to the Non Polynomial (NP) hard nature of the problem Swarm intelligence algorithms are extensively used in optimization problems Optimization techniques could be applied to feature selection problem to produce Optimum feature set Swarm Intelligence algorithms are used in feature subset selection for reducing feature subset dimensionality and computational complexity thereby increasing the classification accuracy In this paper we study the state-of-art of the various swarm intelligence algorithms which are presently used for feature subset selection within the sentiment analysis framework The study shows that swarm optimization brings significant accuracy gains There are only few swarm algorithms which have been applied in this area and there are many other algorithms which can be explored, this study provides an insight into the various algorithms which can be expounded for improved sentiment analysis

Abstract: Objectives: The big-data analysis with IoT is decisively helpful to provide diagnosis and treatment for patients and it becomes useful in healthcare industry. Methods/Statistical Analysis: Implementation of IoT healthcare is divided into five key characteristics: Stability, continuity, confidentiality, reliability and efficiency must be applied to the smart healthcare system to reliable the features of the IoT. Findings: Within this concept, we introduce iotHEALTHCARE to provide improved patients monitoring and diagnosis for shifting toward prevention and early detection of disease and those who want intensive monitoring for health conditions. Improvements/Applications: In this paper, we present the design features of iotHEALTHCARE and its logical architecture along with methodology of implementation.