Cryptography from sublinear-time average-case hardness of time-bounded Kolmogorov complexity

Author(s):  
Yanyi Liu ◽  
Rafael Pass
Author(s):  
R. Irawan

Leap frog concept was created to address the loss of single joint rig agility and drive the cycle time average lower than ever. The idea is to move the preparation step into a background activity that includes moving the equipment, killing the well, dismantling the wellhead and installing the well control equipment/BOP before the rig came in. To realize the idea, a second set of equipment is provided along with the manpower. By moving the preparation step, the goal is to eliminate a 50% portion of the job from the critical path. The practice is currently performed in tubing pump wells on land operations. However, the work concept could be implemented for other type of wells, especially ESP wells. After implementation, the cycle time average went down from 18 hours to 11 hours per job, or down by ~40%. The toolpusher also reports more focused operations due to reduced scope and less crew to work with, making the leap frog operation safer and more reliable. Splitting the routine services into 2 parts not only shortened the process but it also reduces noise that usually appear in the preparation process. The team are rarely seen waiting on moving support problems that were usually seen in the conventional process. Having the new process implemented, the team had successfully not only lowered cycle time, but also eliminated several problems in one step. Other benefits from leap frog implementation is adding rig count virtually to the actual physical rig available on location, and also adding rig capacity and completing more jobs compared to the conventional rig. In other parts, leap frog faced some limitation and challenges, such as: limited equipment capability for leap frog remote team to work on stuck plunger, thus hindering its leap frog capability, and working in un-restricted/un-clustered area which disturb the moving process and operation safety.


Author(s):  
Rajesh Kumar Verma ◽  
Chhabi Rani Panigrahi ◽  
Bibudhendu Pati ◽  
Joy Lal Sarkar

Background & Objective: Multimedia aggregates various types of media such as audio, video, images, animations, etc., to form a rich media content which produces an everlasting effect in the minds of the people. Methods: In order to process multimedia applications using mobile devices, we encounter a big challenge as these devices have limited resources and power. To address these limitations, in this work, we have proposed an efficient approach named as mMedia, wherein multimedia applications will utilize the multi cloud environment using Mobile Cloud Computing (MCC), for faster processing. The proposed approach selects the best available network. The authors have also considered using the Lyapunov optimization technique for efficient transmission between the mobile device and the cloud. Results: The simulation results indicate that mMedia can be useful for various multimedia applications by considering the energy delay tradeoff decision. Conclusion: The results have been compared alongside the base algorithm SALSA on the basis of different parameters like time average queue backlog, delay and time average utility and indicate that the mMedia outperforms in all the aspects.


Author(s):  
Sunil Pathak

Background: The significant work has been present to identify suspects, gathering information and examining any videos from CCTV Footage. This exploration work expects to recognize suspicious exercises, i.e. object trade, passage of another individual, peeping into other's answer sheet and individual trade from the video caught by a reconnaissance camera amid examinations. This requires the procedure of face acknowledgment, hand acknowledgment and distinguishing the contact between the face and hands of a similar individual and that among various people. Methods: Segmented frames has given as input to obtain foreground image with the help of Gaussian filtering and background modeling method. Suh foreground images has given to Activity Recognition model to detect normal activity or suspicious activity. Results: Accuracy rate, Precision and Recall are calculate for activities detection, contact detection for Best Case, Average Case and Worst Case. Simulation results are compare with performance parameter such as Material Exchange, Position Exchange, and Introduction of a new person, Face and Hand Detection and Multi Person Scenario. Conclusion: In this paper, a framework is prepared for suspect detection. This framework will absolutely realize an unrest in the field of security observation in the training area.


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