Quantum computing (QC) has played an important role as a modern research topic because the quantum mechanics phenomena (entanglement, superposition, projective measurement) can be used for different purposes such as data storage, communications and data processing, increasing security, and processing power.
Most of these methods are interconnected and used simultaneously during data processing, which increases system utilization tremendously (see Fig. 1).
A change in either laboratory procedure or data processing can increase real signal or just increase the noise.
Unfortunately, even though this rich information can be chronicled and archived in a common repository accessible to all stakeholders or even integrated into a database management system for higher-level data processing, the increasing amount of information and the increasing complexity of its structure make searching and exploring information in the project archive challenging and time-consuming.
In this scenario, the demand for automatic or semi-automatic algorithms for 3D data processing is increasing.
The goals of this paper are to create a 3D model robust enough to capture the lake bed heterogeneity, conduct sensitivity data processing to increase confidence in created 3D profile, and provide engineering applications of this model to aid in the management of the lake.
Another challenge is to further improve efficiency in data processing with increased data sizes.
Moreover, replicates are typically merged during data processing to increase confidence of the results, but thereby making them unavailable for subsequent significance analysis.
The trends concerning application of multicore processors are diverse; however, these trends are generalized as consolidation of functionality on embedded systems and increased data processing capabilities enabling increased workload and analysis.
Real-time multimedia data access plays an important role in electronic systems; as time goes by, with decrease in data processing speed and increase in communication time, storage time, and retrieval time, the overall response time increases for real-time applications.
