Computers and Systems, Faculty of Engineering

Mobile IP has been developed to provide the continuous information network access to mobile users. The performance of Mobile IP mobility management scheme is dependent on traffic characteristics and user mobility. Consequently, it is important to assess this performance in-depth through these factors. This paper introduces a novel analytical model of handoff management in mobile IP networks. The proposed model focuses on the effect the traffic types and their frame error rates on the handoff latency. It is derived based on general distribution of both successful transmission attempts and the residence time to be applicable in all cases of traffic characteristics and user mobility. The impact of the behavior of wireless connection, cell residence time, probability distribution of transmission time and the handoff time is investigated. Numerical results are obtained and presented for both TCP and UDP traffics. As expected, the reliability of TCP leads to higher handoff latency than UDP traffic. It is shown that, higher values of FER increase the probability of erroneous packet transfer across the link layer. A short retransmission time leads to end the connection most likely in the existing FA; however a long retransmission time leads to a large delivery time. The proposed model is robust in the sense that it covers the impact of all the effective parameters and can be easily extended to any distribution.

In mobile IPv6, packet transfer is interrupted due to the handoff of a mobile node, which results in burst packet loss. This transient behavior decreases the required quality of services for time-critical streaming media applications. This paper introduces an analytical model of streamed video over mobile IP networks considering the effects of handoff. The proposed model focuses on the effect of the frame error rates on the handoff latency of multimedia traffic. It is derived based on general distribution for the residence time to be applicable in all cases of user mobility. The impact of the behavior of wireless connection, cell residence time and the handoff time is investigated. Numerical results are obtained for video traffic presented by Gamma distribution for transmission behavior. It is shown that, a higher value of frame error rate increases the probability of erroneous packet transfer. A shorter video time leads to end this connection most likely in first foreign agents while a longer one leads to larger delivery time.

This paper outlines an algorithm for solving the maximum mixture likelihood clustering problem using an integer-coded genetic algorithm (IGA-ML) where a fixed length chromosome encodes the object-to-cluster assignment. The main advantage of the outlined algorithm (IGA-ML) compared with other known algorithms, such as the k-means technique, is that it can successfully discover the correct number of clusters, in addition to carrying out the partitioning process. The algorithm implements a post-fixing sorting mechanism that drastically reduces the searched solution space by eliminating duplicate solutions that appear after applying the genetic operations. Simulation results show the effectiveness of the algorithm especially with the case of overlapping clusters.

This paper outlines an algorithm for solving the maximum mixture likelihood clustering problem using an integer-coded genetic algorithm (IGA-ML) where a fixed length chromosome encodes the object-to-cluster assignment. The main advantage of the outlined algorithm (IGA-ML) compared with other known algorithms, such as the k-means technique, is that it can successfully discover the correct number of clusters, in addition to carrying out the partitioning process. The algorithm implements a post-fixing sorting mechanism that drastically reduces the searched solution space by eliminating duplicate solutions that appear after applying the genetic operations. Simulation results show the effectiveness of the algorithm especially with the case of overlapping clusters.

Multiagent computing on a cluster of workstations is widely envisioned to be a powerful paradigm for building useful distributed applications. The agents of the system span across all the machines of a cluster. Just like the case of traditional distributed systems, load balancing becomes an area of concern. With different characteristics between ordinary processes and agents, it is both interesting and useful to investigate whether conventional load-balancing strategies are also applicable and sufficient to cope with the newly emerging needs, such as coping with temporally continuous agents, devising a performance metric for multiagent systems, and taking into account the vast amount of communication and interaction among agent. This paper discusses the above issues with reference to agent properties and load balancing techniques and outlines the space of load-balancing design choices in the arena of multiagent computing. In view of the special agent characteristics, a novel communication-based load-balancing algorithm is proposed, implemented, and evaluated. The proposed algorithm works by associating a credit value with each agent. The credit of an agent depends on its affinity to a machine, its current workload, its communication behavior, and mobility, etc. When a load imbalance occurs, the credits of all agents are examined and an agent with a lower credit value is migrated to relatively lightly loaded machine in the system. Quasi-simulated experiments of this algorithm show load-balancing improvement compared with conventional workloadoriented load-balancing schemes.

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Research Article

Evaluation of Different Ansatze Designs for Quantum Neural Network Binary Classifiers


Maha A. Metawei, Hazem Said, Mohamed Taher, Hesham ElDeeb, Salwa M. Nassar


This is a preprint; it has not been peer reviewed by a journal.

https://doi.org/10.21203/rs.3.rs-1919180/v1

This work is licensed under a CC BY 4.0 License


Status:
Under Review

Quantum Machine Intelligence

Version 1
posted 17 Oct, 2022

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Abstract
For classification problems, ansatz circuits are an efficient and promising quantum-classical machine learning technique. However, a difficult design challenge arises: which quantum circuit should be used for model training? Low depth ansatz circuits have been used in a variety of applications in recent years. The earlier works defined several quantum circuit descriptors to evaluate an ansatz design such as the circuit expressibility, the efficiency with which a quantum circuit may exploit the Hilbert Space, and the entanglement capability, a circuit's capability of detecting data correlation. Additionally in this work, we investigate, measure, and analyze the effect of the number of ansatz repetition layers L, and the expressibility power of the data encoding module. The proposed quantum neural network classifiers were trained on four different classical datasets using 19 hardware-efficient parametrized circuits. The heuristic experimental results show that ansatz designs with high to moderate expressibiliy values outperformed ones with low expressibility values. By analyzing the circuit cost, we see a tendency toward maximizing the number of parameters which helps in the QNN model's convergence. On simulated hardware, we observe that the proposed quantum neural network achieves up to 97.5% prediction accuracy using the angle data encoding module.

In the era of Large Language Models, there is still potential for improvement in current Natural Language Processing (NLP) methods in terms of verifiability and consistency. NLP classical approaches are computationally expensive due to their high-power consumption, computing power, and storage requirements. Another computationally efficient approach to NLP is categorical quantum mechanics, which combines grammatical structure and individual word meaning to deduce the sentence meaning. As both quantum theory and natural language use vector space to describe states which are more efficient on quantum hardware, QNLP models can achieve up to quadratic speedup over classical direct calculation methods. In recent years, there is significant progress in utilizing quantum features such as superposition and entanglement to represent linguistic meaning on quantum hardware. Earlier research work has already demonstrated QNLP’s potential quantum advantage in terms of speeding up search, enhanci...