Clippling Noise Mitigation in Optical OFDM System

This letter portrays another non-straight calculation for cut-out clamor relief in force regulation/coordinate discovery dc one-sided optical symmetrical recurrence division multiplexing (DCO-OFDM) frameworks. Cut-out commotion is frequently the significant constraint in DCO-OFDM. In this letter, we demonstrate that additional data about the cut flag can be extricated utilizing a non-direct process and afterward used to alleviate the cut-out clamor. The adequacy of the new calculation is shown by recreation and in an optical remote trial. Decision errors, resulting in decision noise, limit the performance of the blind estimator even when estimation is based on very long signals. However, the pilot system can achieve more accurate estimations, and thus a better performance. Results are first presented for typical SEM waveforms for the case where the fundamental frequency of the SEM is known. The algorithms are then extended to include a frequency estimation step and the mitigation algorithm is shown also to be effective in this case

Malicious URL Website Detection using Selective Hyper Feature Link Stability based on Soft-Max Deep Featured Convolution Neural Network

The web resource contains many domains with different users' Uniform Resource Locators (URLs). Due to the increasing amount of information on the Internet resource, malicious activities are done by hackers by expecting malicious websites in URL sub-links. Increasing information theft leads data sources to be vested in huge mediums. So, to analyze the web features to find the malicious webpage based on the deep learning approach, we propose a Selective Hyper Feature Link stability rate (SHFLSR) based on Soft-max Deep featured convolution neural network (SmDFCNN) for identifying the malicious website detection depends on the actions performed and its feature responses. Initially, the URL Signature Frame rate (USFR) is estimated to verify the domain-specific hosting. Then the link stability was confirmed by post-response rate using HyperLink stability post-response state (LSPRS). Depending upon the Spectral successive Domain propagation rate (S2DPR), the features were selected and trained with a deep neural classifier with a logically defined Softmax- Logical activator (SmLA) using Deep featured Convolution neural network (DFCNN). The proposed system performs a high-performance rate by detecting the malicious URL based on the behavioral response of the domain. It increases the detection rate, prediction rate, and classifier performance

Traffic-aware adaptive server load balancing for software defined networks

Servers in data center networks handle heterogenous bulk loads. Load balancing, therefore, plays an important role in optimizing network bandwidth and minimizing response time. A complete knowledge of the current network status is needed to provide a stable load in the network. The process of network status catalog in a traditional network needs additional processing which increases complexity, whereas, in software defined networking, the control plane monitors the overall working of the network continuously. Hence it is decided to propose an efficient load balancing algorithm that adapts SDN. This paper proposes an efficient algorithm TA-ASLB-traffic-aware adaptive server load balancing to balance the flows to the servers in a data center network. It works based on two parameters, residual bandwidth, and server capacity. It detects the elephant flows and forwards them towards the optimal server where it can be processed quickly. It has been tested with the Mininet simulator and gave considerably better results compared to the existing server load balancing algorithms in the floodlight controller. After experimentation and analysis, it is understood that the method provides comparatively better results than the existing load balancing algorithms

Implementation Of ALU sing Low Power Full Adder

This paper is resolved to structure a quick Arithmetic Logic Unit. We as a whole understand that, ALU is a module which can perform math and method of reasoning exercises. The speed of ALU essentially depends on the speed of the Multiplier. This paper demonstrates a strategy called, "Vedic Mathematics" for organizing the multiplier that is fast when diverged from various multipliers reliant on logical strategies that have been for all intents and purposes for a long time. Here, a quick 32x32 piece multiplier is organized and inspected which relies upon the Vedic science instrument. The proposed methodology is capable and snappy, wherein the planning incorporates the vertical and crossed growth of perspective Vedic math. Within multiplier is implemented using Vedic-Wallace structure for quick utilization. The case of the last result is gotten by using Brent-Kung snake for fast figuring’s with less zone use. The foreseen Vedic multiplier is coded in a High-level Digital Language (VHDL) trailed by synthetization using an EDA mechanical assembly, XilinxISE14.5. The proposed ALU can perform three different math and eight particular lucid assignments at quick. The major focus of this paper is to grow the speed of the multiplier and to reduce the delay, and region of the hardware

Genetic Variability and Heritability for Growth and Yield in Cucumber (Cucumis sativus L.)

Quantification of variability is the most essential pre-breeding tool in any crop improvement programme. The present investigation was carried out to assess variability existing in twenty four diverse cucumber genotypes. Results revealed high phenotypic and genotypic coefficient of variation for yield per plant, fruit flesh thickness, number of fruits per plant, number of nodes per plant, number of branches per plant, average fruit weight, internode length and vine length. High heritability, coupled with high genetic advance as per cent mean, was recorded for all the characters studied except days to first female-flower opening, days to 50% flowering and days to first-fruit harvest, indicating a scope for improvement through selection

Ensemble Machine Learning Model to Predict Benefaction of an Individual in the Health Sector

Ensemble methodis a machine learning technique that combines several base models in order to produce one optimal predictive model. This work aims to develop blood donor’s prediction model, for the management of the blood bank during emergency situations using ensemble method. The proposed model uses two supervised algorithms including multivariate regression and decision tree algorithms. An automated intelligent system is developed that learns from the data presented to the machine learning model to predict blood donors. The system is integrated with score allocation to the blood donors. A network of available ethical blood donors’ model has been developed which can be used in case of an emergency for an ailing person. Machine Learning techniques are used to find a perfect matched donor with respect to blood group, medical history and other demographics. A prioritized/ranked donor list based on their medical history, habits and other blood related metrics is generated to benefit the receivers. The ensemble methods used in this intelligent system helps in report generation facilitating medical experts and the society in decision making leading to increased number of donors

ESTIMATION OF RESPIRATORY RATE FROM ECG

Clinical investigation of some sleep disorders, stress testing, ambulatory monitoring requires simultaneous monitoring of heart rate and respiratory rates. [3] Numerous methods have been reported for deriving respiratory information from the electrocardiogram (ECG). [1] Initially ECG signal is sent to microcontroller AT89S52 through ADC0848. The digital samples are once again transmitted to personal computer via a cable. The digital data is read with the help of graphical user interface software – Visual C++ (serial port programming). The data is stored in an array and the QRS peaks per minute are detected & heart rate is calculated. As these QRS peaks consist of respiratory information, an algorithm will be applied onto the QRS data to find the number of slopes per minute, which gives the respiratory rate. Hence the Heart Rate & Respiratory Rate per minute will be calculated and displayed real time on PC

Implementation of a Regression-based Trust Model in a Wireless Ad hoc Testbed

Wireless ad hoc networks are resource constraint and vulnerable to various security attacks. Trust based security modelling go hand in hand with cryptographic services to offer good security services. We have implemented a vector auto regression (VAR) based trust model over ad hoc on demand distance vector protocol and optimised link state routing protocol. The novelty in this model lies in  capturing individual functional behaviours of a neighbour in an ad hoc network and modeling them as regression parameters. The experimental results show the feasibility of implementing trust models over real ad hoc network deployments. The simulations results show that the proposed VAR trust model offers better performance compared to the existing trust models.Defence Science Journal, 2012, 62(1), pp.167-173, DOI:http://dx.doi.org/10.14429/dsj.62.143

A Delphi Consensus Study to identify most valuable assessment tools and to assess anatomy competencies in CBME curriculum

\ua9 Journal of Krishna Institute of Medical Sciences University.Background: Over recent years, wide-ranging changes have occurred in undergraduate medical curricula with reduced hours allocated for teaching anatomy. Anatomy forms the foundation of clinical practice. However, the challenge of acquiring sufficient anatomical knowledge in undergraduate medical education for safe and competent clinical practice remains. Assessment is an essential component of the teaching and learning process and is more important than teaching methods. It is essential to assess anatomy competencies for competent clinical practice. A single tool is not sufficient to assess anatomy competencies, so multiple methods are employed to assess anatomy in Competency-based Medical Education (CBME). Aim and Objectives: The present study was undertaken to identify the most appropriate tools to assess anatomy competencies in CBME curriculum. Material and Methods: A modified Delphi technique with three rounds involving twenty renowned anatomists across the country was conducted. Anatomy assessment tools were generated from the opinions of this expert panel in the first round. The relevance of these tools was rated with a five-point Likert scale in the subsequent two rounds to generate consensus. Results: Response rates were 80% for the first round and 100% for the next two rounds. After three Delphi rounds, seven assessment tools were identified as the most valuable following iterations. Conclusion: The findings of this study provide anatomists with the current required essential tools for assessing anatomy competencies of higher-order cognitive domains and psychomotor domains for the CBME curriculum