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sarbishegi--sasan
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Entanglement Distillation for Weakly Entangled States
, M.Sc. Thesis Sharif University of Technology ; Mirkamali, Maryam Sadat (Supervisor)
Abstract
Quantum entanglement is an essential resource in quantum information processing, such as quantum communication and quantum computing. Although entanglement is critical for practical implementations in these fields, creating and transmitting entanglement without altering the states is challenging. External noise may destroy or weaken the entanglement. Consequently, there is a need for methods to improve entanglement. Entanglement distillation is an effort to address this issue. It is a process where, probabilistically, a smaller number of strongly entangled states are created from a fixed number of noisy entangled states. This process is carried out using only local operations and classical...
Improving Payload Attribution Techniques
, M.Sc. Thesis Sharif University of Technology ; Kharrazi, Mehdi (Supervisor)
Abstract
One of the most important steps in the process of network forensics is attacker attribution and tracing the victims of the attack. In some situations, there is no other information to track the attacker except the payload of packet. Network security professionals have introduced payload attribution techniques to attribute this type of attacks. In payload attribution techniques, a history of network traffic is stored so that after the attack, it can be queried to trace the source and destination of excerpts. Due to the high volume of traffic in today's networks, payload attribution techniques should be able to store traffic in compressed format so that querying on this data be done easily at...
Mitigating the performance and quality of parallelized compressive sensing reconstruction using image stitching
, Article 29th Great Lakes Symposium on VLSI, GLSVLSI 2019, 9 May 2019 through 11 May 2019 ; 2019 , Pages 219-224 ; 9781450362528 (ISBN) ; Mohammadi Makrani, H ; Tian, Z ; Rafatirad, S ; Akbari, M. H ; Sasan, A ; Homayoun, H ; ACM Special Interest Group on Design Automation (SIGDA) ; Sharif University of Technology
Association for Computing Machinery
2019
Abstract
Orthogonal Matching Pursuit is an iterative greedy algorithm used to find a sparse approximation for high-dimensional signals. The algorithm is most popularly used in Compressive Sensing, which allows for the reconstruction of sparse signals at rates lower than the Shannon-Nyquist frequency, which has traditionally been used in a number of applications such as MRI and computer vision and is increasingly finding its way into Big Data and data center analytics. OMP traditionally suffers from being computationally intensive and time-consuming, this is particularly a problem in the area of Big Data where the demand for computational resources continues to grow. In this paper, the data-level...