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Many past earthquakes with the magnitude close to 7.0 (Mw) or more have shown liquefaction phenomenon. Often liquefiable deposits occur in layers. Seismic waves travelling from bedrock are modified by the presence of layered soil, where seismic impedance contrast is seen as the reason. Recent studies have shown that the presence of a liquefiable soil layer can significantly reduce inertial load on the superstructure, i.e. liquefaction of soil layer prevents the transmission of seismic waves (mainly shear waves) acting as shield protecting the above layers (base isolation effect). However, many studies are limited to only two-layered soil, where a liquefiable soil layer is present above a non-liquefiable soil layer. However, in the field, there could be situations with multiple soil layers and a liquefiable layer could be present as a sandwiched layer between non-liquefiable layers. The seismic site response study considering the effect of layering in the soil deposits for liquefiable sites is least considered while carrying out foundation design. In the present study, two soil profiles (a sandwiched liquefiable layer and a sandwiched non-liquefiable layer) were studied by varying the thickness of each soil layer. The soil profiles were subjected to a typical ground motionfrom 1995 Kobe earthquake. The effect of layered liquefiable deposits on the seismic response of the ground is studied. The results, such as attenuation of acceleration, displacement and the spectral acceleration, were studied in detail and presented in this paper.

In recent years, big data applications with scheduling algorithms have evolved lot due to the advancement of new technologies and techniques. We are living in digital data world where the data size is in terms of Exabyte or Pico Byte. This large volume of data is referred as big data. In today’s business environment, the performance of applications largely depends on the efficient retrieval of relevant data on time; the data analysis and retrieval of relevant data need to be done at faster rate. The traditional scheduling algorithms will not be efficient to handle such huge volume of data, considering the above facts managing big data applications and scheduling of big data on distributed architecture has become a challenging research area in the last three–four years. To process such huge volume of data, efficient scheduling algorithms need to be adopted to achieve better performance. The existing MapReduce implementation on Hadoop framework on single node cluster limits themselves to implement all the jobs on single node cluster. In this paper, we will discuss different scheduling techniques and their performance effects on a multimode clusters. The parameters considered for performance evaluation are CPU time, physical memory, and virtual memory. The main aim is to provide survey of different scheduling algorithms that can be used across distributed architecture to achieve better performance in analysis of big data considering YouTube dataset. The results interpret that capacity-based scheduling algorithm is more efficient as compared to FIFO and FAIR in terms of CPU cycles, physical and virtual memory utilization.

Pile foundations are often preferred in high seismic zones; however, even having high factor of safety against bending strength many historic evidences showed that pile foundations are vulnerable during earthquakes, especially when liquefaction occurs. Major code specification treats pile as beam element subjected to lateral loading, and no definitive provisions are available on analysis method for pile foundation embedded in alternate liquefiable and non-liquefiable soil. The present study is conducted to model layered liquefiable soils for pile foundation response considering state-of-the-art understating along with available code recommendations. Both force-based and displacement-based approaches for typical layered soil profile were chosen in this study. The soil liquefaction depth of the middle layer was varied to understand the behavior during successive stages of liquefaction. The pile-soil interaction was modeled through p-y lateral springs, and suitable reduction was made for the p-y springs which represent liquefaction by various methods available in literature. The static nonlinear analysis was carried out for various models, and the response such as bending moment, shear force, and displacement have been studied. A great variability was seen among the results of pile response for any typical layered soil profile, when different analysis approaches have been adopted.

In today’s multimedia age, Content Based Video Retrieval (CBVR) is a trending area and lot of research is being carried out in Video Surveillance, Big Data analysis and multimedia applications. Usage of multimedia data is becoming very common in day today life, content based video retrieval provides an effective mechanism for maintaining, managing and retrieving large number of videos efficiently as per user’s interest. The advancement in the technology is evolving and multimedia applications are gaining more importance so the performance of the CBVR system need to be high and accurate to fulfill user demands. The proposed paper focuses on surveying the different techniques for feature extraction and similarity computation for retrieving relevant videos. Feature extraction can be done using different techniques such as shot boundary detection, based on histogram, PCA Shift, Gist and SURF (Speeded up Robust Features) and Quadratic Equation are used for feature extraction and similarity computation. In CBVR technique the videos are retrieved from the large databases based on the given Input Query as an image, on processing this query the features from videos and the query image are extracted mainly the color, texture and shape. Once these features are extracted then a similarity between query and the videos is computed, ranking will be done based on the similarity score. The results interpret that SURF technique provides better results as compared to other techniques and the system has a retrieval performance of more than 70%.

Human action detection and recognition is the most trending research topic in applications like surveillance of videos, analysis of sports videos and many applications which involve human computer interaction. Many researchers are working on different algorithms to improve the accuracy of human detection. Identifying the actions of human from the given video is a challenging task. In the proposed paper combination of two different techniques is applied i.e. SVM and SIFT techniques are used to identify and recognize the human actions in a given video or image. To extract local features of the given video SIFT based technique is used. In this techniques initially we extract features based on the interest points at a particular point or frames, Mainly SIFT techniques involves 4 basic steps Scale-space extreme detection, Key-point localization, Orientation assignment and Key-point descriptor. Once the key features are extracted they are further classified using SVM classifier. In the results and discussion we perform the comparative analysis of these two techniques on a standard KTH dataset with running and hand clapping actions. The experimental results determine the overall accuracy of 82% for the actions: running and hand clapping actions.

Currently in India and most of the other countries, if there is any leakage of water pipes in remote areas then it’s difficult to trace and repair the leakage within a short span of time, it takes some amount of time usually two to three days to locate the leakage and to repair it. During this time a lot of water is wasted. In this paper we discuss the techniques to detect the leakage and locate the leak point on the pipeline. In the proposed approach we discuss efficient techniques for monitoring, detecting leakage in pipes and checking the durability of the pipes using flow rate and vibration sensors. The objective is to build a prototype model for detecting leakage in water pipeline using flow rate sensors and also to predict the durability of the pipe using vibration and pressure sensors. Data from remote sensors are collected through wireless medium and are monitored in a server. If there is any leakage in the pipelines then the server sends the sms message to the water board authorities and notifies the leak point in the pipeline system. Further the durability of pipe can be predicted using pressure sensor so that the authorities can be intimated in prior about chances of breakage in pipelines. Thus the proposed system can save huge amount of water by intimating the authorities in time. Thus the experimental result validates the effectives and reliability of the proposed system.

Today in India and most other countries, accident is considered as an unexpected and unintended event. According to the survey road accidents lead to hospitalization, injuries and disabilities, considering these facts the safety of the riders has become a most crucial and important issue and concern in majority of the countries. The usage of two wheeler’s vehicles is increasing day by day for transport convenience we need to have proper safety and efficient measures to handle accidents. In the proposed paper we discuss smart helmet system using cloud and GPS technology for accident detection and location tracking, GSM is used to send a notification message of accident location to the concerned people to avoid major casualty/life of the person can be saved. MQ3 sensor is also embedded in the helmet to check whether the motorcyclist has consumed alcohol or not. The sensor data captured is stored in the cloud so that the accident information can be fetched anywhere and anytime at a faster rate. Considering the above facts, the prototype of smart helmet is designed and implemented to enhance the safety of the motorcyclist. This new idea will reduce the risk of motorcyclist life. The experimental results confined that the developed prototype model gives 84% accurate result of the accident detection and concerned persons will receive notification message.

The process in which two or more adjoining liquid drops contact each other and merge to form a single drop is referred as coalescence. Drop coalescence is seen in many applications including dropwise condensation of vapor on textured surfaces and in micro-fluidics to enhance scalar mixing with the host medium. Coalescence is initiated with bridge formation at the interface and is followed by large fluid velocities during which the participating liquid media are momentarily set into motion. The origin of coalescence is the internal pressure difference between the initial drops as well as the pressure difference relative to the negative bridge curvature which serves as location of low pressure. The conversion of surface energy to kinetic energy is accompanied by changes in gravitational energy and viscous dissipation. Dissipation here refers to the bulk as well as that occurring at the three-phase contact line over the surface. Contact line motion can be substantial, thus making the surface characteristics central to flow oscillations and decay. After equilibrium is achieved, the single coalesced drop will have a smaller curvature, indicating an irreversible loss of surface energy as dissipation of the coalescence process. The first part of the present chapter examines the literature on the subject and provides a state-of-the-art review. In the second part, an experiment involving two small water drops that are placed adjacent to each other on the hydrophobic surface is discussed. Sessile configuration is considered, and the resulting coalescence process is imaged using a high-speed camera. The three-phase contact line of the combined drop remains unpinned and moves in time, while the liquid bridge relaxes when flow takes place from a region of higher to lower pressure. The digital image sequence is analyzed to find the position of the instantaneous center of mass of the drop, whose movement yields the two velocity components. The possibility of distinct timescales during coalescence is explored from these experiments. The third part of this chapter examines an important application wherein vapor condenses on horizontal and inclined surfaces in the form of drops. Here, drops formed at selected nuclei over the surface grow with time by direct condensation, contact neighboring drops, and grow subsequently by coalescence. At certain instants, the drop volume may be large enough to make them gravitationally unstable, forcing them to leave the surface. While these condensation cycles may last for a few hundred seconds, each coalescence event itself will persist only for a few milliseconds. Coalescence should still be represented in the mathematical model of dropwise condensation, particularly from the viewpoint of local wall shear stresses.