Intelligent performance assessment in a virtual electronic laboratory

Laboratory work, in the undergraduate engineering course, is aimed at enhancing students’ understanding of taught concepts and integrating theory and practice. This demands that laboratory work is synchronised with lectures in order to maximise its derivable learning outcomes, measurable through assessment. The typical high costs of raditional engineering laboratory, which often militates against its increased use and the synchronisation of laboratory and lectures, have, in addition to other factors, catalysed the increased adoption of virtual laboratories as a complement to the traditional engineering laboratory. In extreme cases, virtual laboratories could serve as alternative means of providing, albeit simulated, meaningful practical experiences. A Virtual Electronic Laboratory (VEL), which can be used to undertake a range of undergraduate electronic engineering curriculum-based laboratory activities, in a realistic manner, has been implemented as part of the work presented in this thesis. The VEL incorporates a Bayesian Network (BN)-based model for the performance assessment of students’ laboratory work in the VEL. Detailed descriptions of the VEL and the assessment model are given. The evaluation of the entire system is in two phases: evaluation of the VEL as a tool for facilitating students’ deeper understanding of fundamental engineering concepts taught in lectures; and evaluation of the assessment model within the context of the VEL environment. The VEL is evaluated at two different engineering faculties, in two separate universities. Results from the evaluation of the VEL show the effectiveness of the VEL to enhance students’ learning, in the light of appropriate learning scenarios, and provide evidence and support for the use of virtual laboratories in the engineering educational context. Performance data, extracted from students’ behaviour logs (captured and recorded during the evaluation of the VEL) are used to evaluate the assessment model. Results of the evaluation demonstrate the effectiveness of the model as an assessment tool, and the practicability of the performance assessment of students’ laboratory work from their observed behaviour in a virtual learning environment.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

An interference management system for a shared spectrum access network

Internet access, in developing and underdeveloped countries, remains a huge challenge despite advancements in technology. Shared resources, amongst telecommunication systems, offer an affordability context to this problem. A shared spectrum interference management system is implemented by designing a geolocation database, for a television white space network, for a location in Nigeria. This is achieved using the Dynamic Spectrum Alliance framework (a rarely used methodology) and robust terrain-based propagation models. The designed spectrum coexistence manager (geolocation database) was created, presented, and evaluated, based on its channel availability, predictions, and ability to protect very weak TV signals. The result showed a 15% channel utilization of Analogue and Digital Terrestrial Television channels within the study area. Finally, key components of the framework, that can be adopted for further studies, were identified

Leveraging Fog Computing for Scalable IoT Datacenter Using Spine-Leaf Network Topology

With the Internet of Everything (IoE) paradigm that gathers almost every object online, huge traffic workload, bandwidth, security, and latency issues remain a concern for IoT users in today’s world. Besides, the scalability requirements found in the current IoT data processing (in the cloud) can hardly be used for applications such as assisted living systems, Big Data analytic solutions, and smart embedded applications. This paper proposes an extended cloud IoT model that optimizes bandwidth while allowing edge devices (Internet-connected objects/devices) to smartly process data without relying on a cloud network. Its integration with a massively scaled spine-leaf (SL) network topology is highlighted. This is contrasted with a legacy multitier layered architecture housing network services and routing policies. The perspective offered in this paper explains how low-latency and bandwidth intensive applications can transfer data to the cloud (and then back to the edge application) without impacting QoS performance. Consequently, a spine-leaf Fog computing network (SL-FCN) is presented for reducing latency and network congestion issues in a highly distributed and multilayer virtualized IoT datacenter environment. This approach is cost-effective as it maximizes bandwidth while maintaining redundancy and resiliency against failures in mission critical applications

Sensor Data Acquisition and Processing Parameters for Human Activity Classification

It is known that parameter selection for data sampling frequency and segmentation techniques (including different methods and window sizes) has an impact on the classification accuracy. For Ambient Assisted Living (AAL), no clear information to select these parameters exists, hence a wide variety and inconsistency across today’s literature is observed. This paper presents the empirical investigation of different data sampling rates, segmentation techniques and segmentation window sizes and their effect on the accuracy of Activity of Daily Living (ADL) event classification and computational load for two different accelerometer sensor datasets. The study is conducted using an ANalysis Of VAriance (ANOVA) based on 32 different window sizes, three different segmentation algorithm (with and without overlap, totaling in six different parameters) and six sampling frequencies for nine common classification algorithms. The classification accuracy is based on a feature vector consisting of Root Mean Square (RMS), Mean, Signal Magnitude Area (SMA), Signal Vector Magnitude (here SMV), Energy, Entropy, FFTPeak, Standard Deviation (STD). The results are presented alongside recommendations for the parameter selection on the basis of the best performing parameter combinations that are identified by means of the corresponding Pareto curve