Development of UVM based Reusabe Verification Environment for SHA-3 Cryptographic Core

In this work, an industry standard methodology for ASIC verification domain, SystemVerilog (SV) with Universal Verification Methodology (UVM) is introduced with its features and application to Keccak SHA-3 Cryptographic Core. The ASIC verification flow for SHA-3 core is followed with creation of UVM based verification environment. By application of UVM on the core, horizontal and vertical re-use can be achieved in standard projects. Proposed verification environment uses OOPs concepts from SV UVM to develop layered testbench. In this approach initial learning curve is slow, considering overhead to learn new verification methodology. But, once full fledge working environment is created, re-usability feature from SV UVM can be achieved with less amount of time. Also coverage results give effectiveness of the proposed verification environment. DOI: 10.17762/ijritcc2321-8169.15057

IMPERTINENT TRILATERATION: SECURE LOCALIZATION OF WIRELESS SENSOR NETWORK USING GREEDY TECHNIQUE

Wireless sensor network localization is an important area that attracts significant research interest. Current localization algorithms mainly focus to localize as many nodes as possible for a given static set of anchor nodes and distance measurement. In this paper, we discuss a new technique that aims to localize all the sensor nodes in the network using trilateration with greedy technique, and a security protocol is used for providing confidentiality and authentication between anchor nodes and sensor nodes

Access and Use of Consortium for e-Resources in Agriculture (CeRA) by the Research Scholars and Post Graduate Students of the KRC College of Horticulture, Arabhavi, UHS Bagalkot

In the electronic information age, consortiums are gaining more importance. The consortia movement has entered libraries and has received attention from the information-producing community. CeRA consortium is a tremendously great effort on the part of the Indian Council of Agricultural Research (ICAR) to propose single-window access to e-resources to State Agricultural Universities (SAU)/ICAR institutes all over India. CeRA subscribed to e-Resources and create/facilitate an e-environment and e-access culture for faculty, scientists, research scholars, and students in the National Agricultural Research and Education System. The current study attempts to understand the access and use of CeRA among the researchers and PG students of the KRC College of Horticulture, Arabhavi (UHS, Bagalkot). The study has exposed that all the respondents are aware of CeRA and know its importance

A comparative study of efficacy and safety of flupirtine versus piroxicamin patients with low back pain

Background: Low back pain is a common musculoskeletal symptom caused by a variety of disorders that affect the lumbar spine. The most frustrating aspect in the treatment of low back pain is that there is “no magic bullets”. The objective of the study was to compare the efficacy and safety of flupirtine versus piroxicam in patients with back pain.Methods: This was prospective, open labeled, randomized, comparative clinical study conducted by the Departments Orthopedics and Pharmacology, BMC&H, Chitradurga. Study was conducted on 60 patients of either sex, aged above 18 years with low back pain. Assessments were done for Finger-to-Floor Distance (FFD), lumbar pain, Lasegue’s sign, tenderness of vertebral muscles, pain & sensory disturbance in lower limbs and response to therapy for efficacy. Parametric data was analyzed by t-test and proportions were compared using Chi-square test.Results: 74 patients were randomized to 2 groups of 37 each. Group I patients received flupirtine maleate 100 mg twice daily and Group II patients received piroxicam 20 mg twice daily for 14 days. 30 patients in each group completed the study and were analysed. On intergroup comparison, there was no statistical difference (p>0.05) in the efficacy parameters of finger-to-floor distance (FFD), lumbar pain, Lasegue’s sign, tenderness of vertebral muscles, sensory disturbance in lower limbs, VAS scores & global assessment of response to therapy. 13.3% in flupirtine group and 16.6% in piroxicam group reported adverse events.  Conclusions: Both flupiritine and piroxicam were equally effective but flupirtine was better tolerated than piroxicam.

Prediction of Gate In Time of Scheduled Flights and Schedule Conformance using Machine Learning-based Algorithms

Prediction of Gate to Gate block time for scheduled flights is considered as one of the challenging tasks in Air Traffic Flow Management (ATFM)system. Establishing an effective and practically reliable model to manage the problem of block time variation is a significant work. The airlines do tend to pad or inflate block time to Actual Block time to calculate Schedule block times which is approved by aviation regulator. This will lead to flaws in air traffic flow strategic decision-making and in turn affect the efficiency, estimation and undesirable delays, which leads to traffic congestion and inefficient ground delay programs. This study evaluates the effectiveness of nonlinear and time varying regression models to predict block time with minimal attributes in order to solve the problem of difficulty in predicting the block time variation. The key research outcome of this paper is to trace the temporal variations of flying time for different aircraft types and to predict the variation of actual arrival time from the scheduled arrival time at the destination airport. Ultimately, a combination of M5P regression model and logistic regression model is proposed to predict early, delayed and on-time conformity with approved schedules. Analysis based on a realistic data set of a domestic airport pair (Mumbai International Airport and New Delhi International Airport) in India shows that the proposed model is able to predict in block time at the time of departure with an accuracy of minutes for of test instances. As a result of the scheduled arrival time performance (early, delayed and timely) has been classified accurately using Logistic regression Classifier of machine learning. The test results show that the proposed model uses a minimum number of attributes and less computational time to more accurately predict the actual arrival time and scheduled arrival performance without details on the weather

Predictability improvement of Scheduled Flights Departure Time Variation using Supervised Machine Learning

The departure time uncertainty exacerbates the inaccuracy of arrival time estimation and demand for arrival slots, particularly for movements to capacity constrained airports. The Estimated Take-Off Time (ETOT) or Estimated Departure Time(ETD) for each individual flight is currently derived from Air Traffic Flow Management System (ATFMS), which are solely determined based on individual flight plan Estimated Off Block Time(EOBT) or subsequent delays updated by Airline. Even if normal weather conditions prevail, aircraft departure times will differ from ETOTs determined by the ATFMS due to a number of factors such as congestion, early/delayed inbound flight (linked flights), reactionary delays and air traffic flow management slot changes. This paper presents a model that predicts departure time variance based on the previous leg departure time using a combination of exponential moving average and machine learning methods. The model correctly classifies the departure time (Early, On Time, Delay) based on the previous leg departure state, allowing the ATFM system to measure the arrival time of a capacity constrained airport with greater accuracy and better assess demand requirements. The results show that the proposed model with M5P Regression tree provides the best results, with Mean Absolute Error and Root Mean Square Error (RMSE) of 3.43 and 4.83, respectively, indicating a 50% improvement over previous research findings. Whereas, with logistic regression, the classification of departure time (Early, On Time, Delay) is achieved a better accuracy of 91 %, which is higher than previous works

Emergency Detection and Monitoring Daily Routine of a Cattle using IOT

The main aim of this system is to smarten the infrastructure of cattle farming and to track the biological and physiological activities of cattle by implementing a noninvasive wearable by using IOT. In these, we come into picture the lightning sensor depend upon the climate it will turn on and off, temperature sensor will depend upon room temperature of cattle farm; methane sensor is used to check methane level in the farm; fire sensor depend upon any fire emergency and is responsible for smart lighting and also smart ventilation, it is also responsible for sprinkler actuation to make the infrastructure safer and smarter. We are also implementing automatic food releasing mechanism. We can identify the single cattle count with the help of the IR sensor

Centralized Cloud Service Providers in Improving Resource Allocation and Data Integrity by 4G IoT Paradigm

Due to the expansion of Internet of Things (IoT), the extensive wireless, and 4G networks, the rising demands for computing calls and data communication for the emergent EC (EC) model. By stirring the functions and services positioned in the cloud to the user proximity, EC could offer robust transmission, networking, storage, and transmission capability. The resource scheduling in EC, which is crucial to the accomplishment of EC system, has gained considerable attention. This manuscript introduces a new lighting attachment algorithm based resource scheduling scheme and data integrity (LAARSS-DI) for 4G IoT environment. In this work, we introduce the LAARSS-DI technique to proficiently handle and allot resources in the 4G IoT environment. In addition, the LAARSS-DI technique mainly relies on the standard LAA where the lightning can be caused using the overall amount of charges saved in the cloud that leads to a rise in electrical intensity. Followed by, the LAARSS-DI technique designs an objective function for the reduction of cost involved in the scheduling process, particularly for 4G IoT environment. A series of experimentation analyses is made and the outcomes are inspected under several aspects. The comparison study shown the improved performance of the LAARSS-DI technology to existing approaches

Edge Computing in Centralized Data Server Deployment for Network Qos and Latency Improvement for Virtualization Environment

With the advancement of Internet of Things (IoT), the network devices seem to be raising, and the cloud data centre load also raises; certain delay-sensitive services are not responded to promptly which leads to a reduced quality of service (QoS). The technique of resource estimation could offer the appropriate source for users through analyses of load of resource itself. Thus, the prediction of resource QoS was important to user fulfillment and task allotment in edge computing. This study develops a new manta ray foraging optimization with backpropagation neural network (MRFO-BPNN) model for resource estimation using quality of service (QoS) in the edge computing platform. Primarily, the MRFO-BPNN model makes use of BPNN algorithm for the estimation of resources in edge computing. Besides, the parameters relevant to the BPNN model are adjusted effectually by the use of MRFO algorithm. Moreover, an objective function is derived for the MRFO algorithm for the investigation of load state changes and choosing proper ones. To facilitate the enhanced performance of the MRFO-BPNN model, a widespread experimental analysis is made. The comprehensive comparison study highlighted the excellency of the MRFO-BPNN model

Silencing Early Viral Replication in Macrophages and Dendritic Cells Effectively Suppresses Flavivirus Encephalitis

West Nile (WN) and St. Louis encephalitis (SLE) viruses can cause fatal neurological infection and currently there is neither a specific treatment nor an approved vaccine for these infections. In our earlier studies, we have reported that siRNAs can be developed as broad-spectrum antivirals for the treatment of infection caused by related viruses and that a small peptide called RVG-9R can deliver siRNA to neuronal cells as well as macrophages. To increase the repertoire of broad-spectrum antiflaviviral siRNAs, we screened 25 siRNAs targeting conserved regions in the viral genome. Five siRNAs were found to inhibit both WNV and SLE replication in vitro reflecting broad-spectrum antiviral activity and one of these was also validated in vivo. In addition, we also show that RVG-9R delivers siRNA to macrophages and dendritic cells, resulting in effective suppression of virus replication. Mice were challenged intraperitoneally (i.p.) with West Nile virus (WNV) and treated i.v. with siRNA/peptide complex. The peritoneal macrophages isolated on day 3 post infection were isolated and transferred to new hosts. Mice receiving macrophages from the anti-viral siRNA treated mice failed to develop any disease while the control mice transferred with irrelevant siRNA treated mice all died of encephalitis. These studies suggest that early suppression of viral replication in macrophages and dendritic cells by RVG-9R-mediated siRNA delivery is key to preventing the development of a fatal neurological disease