A NoSQL-Based Framework for Managing Home Services

Individuals and companies have an increasing need for services by specialized suppliers in their homes or premises. These services can be quite different and can require different amounts of resources. Service suppliers have to specify the activities to be performed, plan those activities, allocate resources, follow up after their completion and must be able to react to any unexpected situation. Various proposals were formulated to model and implement these functions; however, there is no unified approach that can improve the efficiency of software solutions to enable economy of scale. In this paper, we propose a framework that a service supplier can use to manage geo-localized activities. The proposed framework is based on a NoSQL data model and implemented using the MongoDB system. We also discuss the advantages and drawbacks of a NoSQL approach

Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation

Predictive health monitoring systems help to detect human health threats in the early stage. Evolving deep learning techniques in medical image analysis results in efficient feedback in quick time. Fibrous dysplasia (FD) is a genetic disorder, triggered by the mutation in Guanine Nucleotide binding protein with alpha stimulatory activities in the human bone genesis. It slowly occupies the bone marrow and converts the bone cell into fibrous tissues. It weakens the bone structure and leads to permanent disability. This paper proposes the study of FD bone image analyzing techniques with deep networks. Also, the linear regression model is annotated for predicting the bone abnormality levels with observed coefficients. Modern image processing begins with various image filters. It describes the edges, shades, texture values of the receptive field. Different types of segmentation and edge detection mechanisms are applied to locate the tumor, lesion, and fibrous tissues in the bone image. Extract the fibrous region in the bone image using the region-based convolutional neural network algorithm. The segmented results are compared with their accuracy metrics. The segmentation loss is reduced by each iteration. The overall loss is 0.24% and the accuracy is 99%, segmenting the masked region produces 98% of accuracy, and building the bounding boxes is 99% of accuracy

A Multiagent Approach Towards Solving Complex Problems of Sociotechnical Systems

Complex resource allocation problems arise due to complex human societies and scarce resources to be distributed. Scarce resources could be food, water, and energy. Meanwhile, the size of the problem, the intersection of different areas, and possible global consequences all add to the complexity of the problems, which makes it difficult for humans to solve the problems by themselves. For all these reasons, humans need technical help to tackle complex problems. Since humans participating in the problems usually own part of the information about the problems, and no one may see the whole picture of the problems, it is natural to use distributed systems to simulate and analyze the problems. In a distributed system, humans represented by agents knowing only partial information interact with each other in order to achieve a common goal while maximizing their own interests. The resultant distributed system is called a multiagent system, because multiple agents are involved in the systems. In this dissertation, we study three cases of multiagent systems that help with distributing a certain kind of resource. First, we present an approach to assist individuals shop for groceries. The aim is to help a customer to find the most economical way of shopping. We show that a customer could save 22% or more most of the time with simulated price data and 6.7% with real price data. Robustness is also considered with deceptive stores and wrongly reported prices. Second, we simulate a healthcare system in which agents are used to assist a patient to find a physician. We investigate four different strategies for assisting a person in choosing a physician and three physician-waiting policies in three common social network models. The results show that the resultant sociotechnical system can decrease the number of annual sick days per person by 0.4-1.8 days compared with choosing a physician randomly. Third we investigate the influence of humans\u27 personalities on resource allocation in mixed human-agent societies. It is shown that humans treat other humans and agents differently and humans with different temperaments behave differently, but not with significantly difference, which means fair is more important than personality types while making decisions. The three cases investigate different aspects of a sociotechnical system. The grocery-shopping case involves agents that interact with each other indirectly through a central aggregator of local results. The physician choosing case involves agents that interact with each other directly in a social network that is a subset of the complete network of agents. The resource-allocation case investigates the relationships between the agents and the humans in a sociotechnical system

Application priority framework for fixed mobile converged communication networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The current prospects in wired and wireless access networks, it is becoming increasingly important to address potential convergence in order to offer integrated broadband services. These systems will need to offer higher data transmission capacities and long battery life, which is the catalyst for an everincreasing variety of air interface technologies targeting local area to wide area connectivity. Current integrated industrial networks do not offer application aware context delivery and enhanced services for optimised networks. Application aware services provide value-added functionality to business applications by capturing, integrating, and consolidating intelligence about users and their endpoint devices from various points in the network. This thesis mainly intends to resolve the issues related to ubiquitous application aware service, fair allocation of radio access, reduced energy consumption and improved capacity. A technique that measures and evaluates the data rate demand to reduce application response time and queuing delay for multi radio interfaces is proposed. The technique overcomes the challenges of network integration, requiring no user intervention, saving battery life and selecting the radio access connection for the application requested by the end user. This study is split in two parts. The first contribution identifies some constraints of the services towards the application layer in terms of e.g. data rate and signal strength. The objectives are achieved by application controlled handover (ACH) mechanism in order to maintain acceptable data rate for real-time application services. It also looks into the impact of the radio link on the application and identifies elements and parameters like wireless link quality and handover that will influence the application type. It also identifies some enhanced traditional mechanisms such as distance controlled multihop and mesh topology required in order to support energy efficient multimedia applications. The second contribution unfolds an intelligent application priority assignment mechanism (IAPAM) for medical applications using wireless sensor networks. IAPAM proposes and evaluates a technique based on prioritising multiple virtual queues for the critical nature of medical data to improve instant transmission. Various mobility patterns (directed, controlled and random waypoint) has been investigated and compared by simulating IAPAM enabled mobile BWSN. The following topics have been studied, modelled, simulated and discussed in this thesis: 1. Application Controlled Handover (ACH) for multi radios over fibre 2. Power Controlled Scheme for mesh multi radios over fibre using ACH 3. IAPAM for Biomedical Wireless Sensor Networks (BWSN) and impact of mobility over IAPAM enabled BWSN. Extensive simulation studies are performed to analyze and to evaluate the proposed techniques. Simulation results demonstrate significant improvements in multi radios over fibre performance in terms of application response delay and power consumption by upto 75% and 15 % respectively, reduction in traffic loss by upto 53% and reduction in delay for real time application by more than 25% in some cases