Performance of symmetric and asymmetric links in wireless networks

Wireless networks are designed to provide the enabling infrastructure for emerging technological advancements. The main characteristics of wireless networks are: Mobility, power constraints, high packet loss, and lower bandwidth. Nodes’ mobility is a crucial consideration for wireless networks, as nodes are moving all the time, and this may result in loss of connectivity in the network. The goal of this work is to explore the effect of replacing the generally held assumption of symmetric radii for wireless networks with asymmetric radii. This replacement may have a direct impact on the connectivity, throughput, and collision avoidance mechanism of mobile networks. The proposed replacement may also impact other mobile protocol’s functionality. In this work, we are mainly concerned with building and maintaining fully connected wireless network with the asymmetric assumption. For this extent, we propose to study the effect of the asymmetric links assumption on the network performance using extensive simulation experiments. Extensive simulation experiments were performed to measure the impact of these parameters. Finally, a resource allocation scheme for wireless networks is proposed for the dual rate scenario. The performance of the proposed framework is evaluated using simulation

Resource Allocation using Genetic Algorithm in Multimedia Wireless Networks

Resource allocations in wireless networks is a very challenging task, at one hand wireless networks have scarce resources and suffers from many limitations. At the other hand, typical resource allocation problems requires extensive amount of computations and are usually NP-hard problems. Hence, there is dire need for effective and feasible solutions. Resource allocation problems are concerned in distributing the available network’s resources to all active users in a fair way. Although fairness is hard to define, this work considers the fairness aspects for both, the users and the network operator (service provider). Bio-inspired algorithm are used in many context to provide simple and effective solution tochallenging problems. This works employs Genetic Algorithm to provide effective solution to resource allocation problem for multimedia allocation in wireless networks. The performance of the proposed solution is evaluated using simulation. The obtained simulation results show that the proposed solutionachieved better performance

License Plate Detection using Deep Learning and Font Evaluation

License plate detection (LPD) in context is a challenging problem due to its sensitivity to environmental factors. Moreover, the chosen font type in the license plate (LP) plays a vital role in the recognition phase in computer-based studies. This work is two folded. On one hand, we propose to employ Deep Learning technique (namely, You Only Look Once (YOLO)) in the LPD. On the other hand, we propose to evaluate font characteristics in the LP context. This work uses 2 different datasets: UFPR-ALPR, and the newly created CENPARMI datasets. We propose a YOLO-based adaptive algorithm with tuned parameters to enhance its performance. In addition to report the recall ratio results, this work will conduct a detailed error analysis to provide some insights into the type of false positives. The proposed model achieved competitive recall ratio of 98.38% with a single YOLO network. Some fonts are challenging for humans to read; however, other fonts are challenging for computer systems to recognize. Here, we present 2 sets of results for font evaluation: font anatomy results, and commercial products recognition results. For anatomy results, 2 fonts are considered: Mandatory, and Driver Gothic. Moreover, we evaluate the effect of the used fonts in context for the two datasets using 2 commercial products: OpenALPR and Plate Recognizer. The font anatomy results revealed some important confusion cases and some quality features of both fonts. The obtained results show that the Driver font has no severe confusion cases in contrast to the Mandatory font

Peripheral Blood Smear Analyses Using Deep Learning

Peripheral Blood Smear (PBS) analysis is a vital routine test carried out by hematologists to assess some aspects of humans’ health status. PBS analysis is prone to human errors and utilizing computer-based analysis can greatly enhance this process in terms of accuracy and cost. Recent approaches in learning algorithms, such as deep learning, are data hungry, but due to the scarcity of labeled medical images, researchers had to find viable alternative solutions to increase the size of available datasets. Synthetic datasets provide a promising solution to data scarcity, however, the complexity of blood smears’ natural structure adds an extra layer of challenge to its synthesizing process. In this thesis, we propose a method- ology that utilizes Locality Sensitive Hashing (LSH) to create a novel balanced dataset of synthetic blood smears. This dataset, which was automatically annotated during the gener- ation phase, covers 17 essential categories of blood cells. The dataset also got the approval of 5 experienced hematologists to meet the general standards of making thin blood smears. Moreover, a platelet classifier and a WBC classifier were trained on the synthetic dataset. For classifying platelets, a hybrid approach of deep learning and image processing tech- niques is proposed. This approach improved the platelet classification accuracy and macro- average precision from 82.6% to 98.6% and 76.6% to 97.6% respectively. Moreover, for white blood cell classification, a novel scheme for training deep networks is proposed, namely, Enhanced Incremental Training, that automatically recognises and handles classes that confuse and negatively affect neural network predictions. To handle the confusable classes, we also propose a procedure called "training revert". Application of the proposed method has improved the classification accuracy and macro-average precision from 61.5% to 95% and 76.6% to 94.27% respectively. In addition, the feasibility of using animal reticulocyte cells as a viable solution to com- pensate for the deficiency of human data is investigated. The integration of animal cells is implemented by employing multiple deep classifiers that utilize transfer learning in differ- ent experimental setups in a procedure that mimics the protocol followed in experimental medical labs. Moreover, three measures are defined, namely, the pretraining boost, the dataset similarity boost, and the dataset size boost measures to compare the effectiveness of the utilized experimental setups. All the experiments of this work were conducted on a novel public human reticulocyte dataset and the best performing model achieved 98.9%, 98.9%, 98.6% average accuracy, average macro precision, and average macro F-score re- spectively. Finally, this work provides a comprehensive framework for analysing two main blood smears that are still being conducted manually in labs. To automate the analysis process, a novel method for constructing synthetic whole-slide blood smear datasets is proposed. Moreover, to conduct the blood cell classification, which includes eighteen blood cell types and abnormalities, two novel techniques are proposed, namely: enhanced incremental train- ing and animal to human cells transfer learning. The outcomes of this work were published in six reputable international conferences and journals such as the computers in biology and medicine and IEEE access journals

The Smart in Smart Cities: A Framework for Image Classification Using Deep Learning

The need for a smart city is more pressing today due to the recent pandemic, lockouts, climate changes, population growth, and limitations on availability/access to natural resources. However, these challenges can be better faced with the utilization of new technologies. The zoning design of smart cities can mitigate these challenges. It identifies the main components of a new smart city and then proposes a general framework for designing a smart city that tackles these elements. Then, we propose a technology-driven model to support this framework. A mapping between the proposed general framework and the proposed technology model is then introduced. To highlight the importance and usefulness of the proposed framework, we designed and implemented a smart image handling system targeted at non-technical personnel. The high cost, security, and inconvenience issues may limit the cities’ abilities to adopt such solutions. Therefore, this work also proposes to design and implement a generalized image processing model using deep learning. The proposed model accepts images from users, then performs self-tuning operations to select the best deep network, and finally produces the required insights without any human intervention. This helps in automating the decision-making process without the need for a specialized data scientist