Study of Speech Signal Recognition and its Applications in Signal Image Processing

Speech recognition is the inter disciplinary sub-field of computational etymology that creates systems and innovations that empowers the recognition and interpretation of talked language into content by computers. Intelligent frameworks have the ability to display and take care of numerous issues of practical significance [5]. The most ideal approach to comprehend these frameworks is do plan and grow such frameworks which uncovered their different merits and detriments. This part exhibits the essential investigation procedure of speech signals that would additionally help us in utilizing speech as a mode of creating intelligent systems

Mobile cloud computing for computation offloading: Issues and challenges

International audienceDespite the evolution and enhancements that mobile devices have experienced, they are still considered as limited computing devices. Today, users become more demanding and expect to execute computational intensive applications on their smartphone devices. Therefore, Mobile Cloud Computing (MCC) integrates mobile computing and Cloud Computing (CC) in order to extend capabilities of mobile devices using offloading techniques. Computation offloading tackles limitations of Smart Mobile Devices (SMDs) such as limited battery lifetime, limited processing capabilities , and limited storage capacity by offloading the execution and workload to other rich systems with better performance and resources. This paper presents the current offloading frameworks, computation offloading techniques, and analyzes them along with their main critical issues. In addition , it explores different important parameters based on which the frameworks are implemented such as offloading method and level of partitioning. Finally, it summarizes the issues in offloading frameworks in the MCC domain that requires further research

Image registration via stochastic gradient markov chain monte carlo

We develop a fully Bayesian framework for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large images along with calibrated uncertainty estimates is difficult for both computational and modelling reasons. To address the computational issues, we explore connections between the Markov chain Monte Carlo by backprop and the variational inference by backprop frameworks in order to efficiently draw thousands of samples from the posterior distribution. Regarding the modelling issues, we carefully design a Bayesian model for registration to overcome the existing barriers when using a dense, high-dimensional, and diffeomorphic parameterisation of the transformation. This results in improved calibration of uncertainty estimates

Development of step-compliant system for turn-mill operations

Today with the latest technology the information beyond tool movement and switching instruction such as tooling, manufacturing features and process sequences are needed to support global adaptability for manufacturing with a specific focus on CNC-based manufacture. This research focuses on the use of the new standard; ISO 14649 (STEP-NC), to address the process planning and machining of discrete turn/mill components and proposes a STEP Compliant NC structure for generation of ISO 14646 code which can be used for turning component manufacture. The novel application of this work is STEP-NC compliant process control where the research will utilise and extend the standard for in process measurement at the machine and also explore the application and integration of the STEP-NC standards. The major contribution of this research is the review of a computational environment for a STEPNC compliant system for turn/mill operations by focusing the outlines of the three major CAD/CAM frameworks to support the implementation of the standard with various levels of STEP compliant architecture. Issues related to the implementation of these frameworks and their use with STEP compliant NC controllers provide a major change in the current day use of CAD, CAPP, CAM and CNC systems. This change will bring new challenges to industrial users and software vendors to identify the new boundaries and define intelligent CNC manufacture in the 21st century

A Comparative Review on Computational Modeling Paradigms. A Study on Case-Based Modeling and Political Terrorism

We review the advances in Case-Based Computational Modeling on Political Analysis issues. Starting in early „70s, the research on political terrorism has been challenged by the latest advances of terrorism computational modeling research. Nowadays Political Analysis community has a wider perspective over the terrorism research aims, methodology and instruments. Widening up this perspective is not a matter of political analysis and research only, it is as well a long-term effect of an interdisciplinary style which has been adopted within the area by acknowledging the scientific advances and support of the Computational Modeling and Simulation as a specific scientific research method. Computational Modeling includes several research frameworks. The Case-Based Modeling is analysed and evaluated on a comparative basis with Agent-Based Modeling in a study on political terrorism phenomena