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Modeling of the strong ground motion of 25th April 2015 Nepal earthquake using modified semi-empirical technique

Lal S., Joshi A., Sandeep, Tomer M., Kumar P., Kuo C.-H., Lin C.-M., Wen K.-L., Sharma M. L.

Acta Geophysica

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2018

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Vol. 66, no. 4

461--477

EN

On 25th April, 2015 a hazardous earthquake of moment magnitude 7.9 occurred in Nepal. Accelerographs were used to record the Nepal earthquake which is installed in the Kumaon region in the Himalayan state of Uttrakhand. The distance of the recorded stations in the Kumaon region from the epicenter of the earthquake is about 420–515 km. Modified semiempirical technique of modeling finite faults has been used in this paper to simulate strong earthquake at these stations. Source parameters of the Nepal aftershock have been also calculated using the Brune model in the present study which are used in the modeling of the Nepal main shock. The obtained value of the seismic moment and stress drop is 8.26 9 1025 dyn cm and 10.48 bar, respectively, for the aftershock from the Brune model .The simulated earthquake time series were compared with the observed records of the earthquake. The comparison of full waveform and its response spectra has been made to finalize the rupture parameters and its location. The rupture of the earthquake was propagated in the NE–SW direction from the hypocenter with the rupture velocity 3.0 km/s from a distance of 80 km from Kathmandu in NW direction at a depth of 12 km as per compared results.

2

ECLogger: Cross-Project Catch-Block Logging Prediction Using Ensemble of Classifiers

Lal S., Sardana N., Sureka A.

e-Informatica Software Engineering Journal

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2017

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Vol. 11, nr 1

7--38

EN

Background: Software developers insert log statements in the source code to record program execution information. However, optimizing the number of log statements in the source code is challenging. Machine learning based within-project logging prediction tools, proposed in previous studies, may not be suitable for new or small software projects. For such software projects, we can use cross-project logging prediction. Aim: The aim of the study presented here is to investigate cross-project logging prediction methods and techniques. Method: The proposed method is ECLogger, which is a novel, ensemble-based, cross-project, catch-block logging prediction model. In the research We use 9 base classifiers were used and combined using ensemble techniques. The performance of ECLogger was evaluated on on three open-source Java projects: Tomcat, CloudStack and Hadoop. Results: ECLoggerBagging, ECLoggerAverageVote, and ECLoggerMajorityVote show a considerable improvement in the average Logged F-measure (LF) on 3, 5, and 4 source!target project pairs, respectively, compared to the baseline classifiers. ECLoggerAverageVote performs best and shows improvements of 3.12% (average LF) and 6.08% (average ACC – Accuracy). Conclusion: The classifier based on ensemble techniques, such as bagging, average vote, and majority vote outperforms the baseline classifier. Overall, the ECLoggerAverageVote model performs best. The results show that the CloudStack project is more generalizable than the other projects.

3

Fully automatic ROI extraction and edge-based segmentation of radius and ulna bones from hand radiographs

Simu S., Lal S., Nagarsekar P., Naik A.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 4

718--732

EN

Bone age is a reliable measure of person's growth and maturation of skeleton. The difference between chronological age and bone age indicates presence of endocrinological problems. The automated bone age assessment system (ABAA) based on Tanner and Whitehouse method (TW3) requires monitoring the growth of radius, ulna and short bones (phalanges) of left hand. In this paper, a detailed analysis of two bones in the bone age assessment system namely, radius and ulna is presented. We propose an automatic extraction method for the region of interest (ROI) of radius and ulna bones from a left hand radiograph (RUROI). We also propose an improved edge-based segmentation technique for those bones. Quantitative and qualitative results of the proposed segmentation technique are evaluated and compared with other state-of-the-art segmentation techniques. Medical experts have also validated the qualitative results of proposed segmentation technique. Experimental results reveal that these proposed techniques provide better segmentation accuracy as compared to the other state-of-the-art segmentation techniques.

4

Fatigue Detection Using Computer Vision

Patel M., Lal S., Kavanagh D., Rossiter P.

International Journal of Electronics and Telecommunications

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2010

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Vol. 56, No. 4

457-461

EN

Long duration driving is a significant cause of fatigue related accidents of cars, airplanes, trains and other means of transport. This paper presents a design of a detection system which can be used to detect fatigue in drivers. The system is based on computer vision with main focus on eye blink rate. We propose an algorithm for eye detection that is conducted through a process of extracting the face image from the video image followed by evaluating the eye region and then eventually detecting the iris of the eye using the binary image. The advantage of this system is that the algorithm works without any constraint of the background as the face is detected using a skin segmentation technique. The detection performance of this system was tested using video images which were recorded under laboratory conditions. The applicability of the system is discussed in light of fatigue detection for drivers.

5

Armstrong C., Kavanagh D., Lal S., Rossiter P.

International Journal of Electronics and Telecommunications

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2010

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Vol. 56, No. 1

87-97

EN

The need and interest in conducting biomedical research outside the traditional laboratory is increasing. In the field testing such as in the participant's home or work environment is a growing consideration when undertaking biomedical investigation. This type of research requires at a minimum semi-autonomous computer systems that collect such data and send it back to the laboratory for processing and dissemination with the smallest amount of attendance by the participant or even the experimenter. A key aspect of supporting this type of research is the selection of the appropriate software and hardware components. These supporting systems need to be reliable, allow considerable customizability and be readily accessible but also able to be locked down. In this paper we report a set of requirements for the hardware and software for such a system. We then utilise these requirements to evaluate the use of game consoles as a hardware platform in comparison to other hardware choices. We finish by outline one particular aspect of the supporting software used to support the chosen hardware platform based on the OSGi framework.