An end-user platform for FPGA-based design and rapid prototyping of feedforward artificial neural networks with on-chip backpropagation learning

The hardware implementation of an artificial neural network (ANN) using field-programmable gate arrays (FPGAs) is a research field that has attracted much interest and attention. With the developments made, the programmer is now forced to face various challenges, such as the need to master various complex hardware-software development platforms, hardware description languages, and advanced ANN knowledge. Moreover, such an implementation is very time consuming. To address these challenges, this paper presents a novel neural design methodology using a holistic modeling approach. Based on the end-user programming concept, the presented solution empowers end users by means of abstracting the low-level hardware functionalities, streamlining the FPGA design process and supporting rapid ANN prototyping. A case study of an ANN as a pattern recognition module of an artificial olfaction system trained to identify four coffee brands is presented. The recognition rate versus training data features and data representation was analyzed extensively

Understanding and Personalising Smart City Services Using Machine Learning, the Internet-of-Things and Big Data

This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) to lever Internet of Things (IoT) and Big Data in the development of personalised services in Smart Cities. We do this by studying the performance of four well-known ML classification algorithms (Bayes Network (BN), Naïve Bayesian (NB), J48, and Nearest Neighbour (NN)) in correlating the effects of weather data (especially rainfall and temperature) on short journeys made by cyclists in London. The performance of the algorithms was assessed in terms of accuracy, trustworthy and speed. The data sets were provided by Transport for London (TfL) and the UK MetOffice. We employed a random sample of some 1,800,000 instances, comprising six individual datasets, which we analysed on the WEKA platform. The results revealed that there were a high degree of correlations between weather-based attributes and the Big Data being analysed. Notable observations were that, on average, the decision tree J48 algorithm performed best in terms of accuracy while the kNN IBK algorithm was the fastest to build models. Finally we suggest IoT Smart City applications that may benefit from our work

Holistic Blockchain Approach to Foster Trust, Privacy and Security in IoT Based Ambient Assisted Living Environment

The application of blockchains techniques in the Internet of Things (IoT) is gaining much attention with new solutions proposed in diverse areas of the IoT. Conventionally IoT systems are designed to follow the centralised paradigm where security and privacy control is vested on a 'trusted' third-party. This design leaves the user at the mercy of a sovereign broker and in addition, susceptible to several attacks. The implicit trust and the inferred reliability of centralised systems have been challenged recently following several privacy violations and personal data breaches. Consequently, there is a call for more secure decentralised systems that allows for finer control of user privacy while providing secure communication. Propitiously, the blockchain holds much promise and may provide the necessary framework for the design of a secure IoT system that guarantees fine-grained user privacy in a trustless manner. In this paper, we propose a holistic blockchain-based decentralised model for Ambient Assisted Living (AAL) environment. The nodes in our proposed model utilize smart contracts to define interaction rules while working collaboratively to contribute storage and computing resources. Based on the blockchain technique, our proposed model promotes trustless interaction and enhanced user's privacy through the blockchain-Interplanetary File System (IPFS) alliance. The proposed model also addresses the shortfall of storage constraints exhibited in many IoT systems

An Investigation on Assessment Strategies, Student Engagement, Retention for Large Cohorts Affected by COVID Learning Disruptions

This paper reports the results of an investigation of assessment strategies for student learning, engagement, and retention of an undergraduate year 1 module in the Computing Science discipline at the University of East Anglia, UK. In this study, three different assessment methods were considered, one for each year, over a three-year period (2020-21 to 2022-23). The study period coincided with the COVID pandemic where the cohorts had their secondary school learning disrupted one way or another, prior to embarking on their university career. The assessment methods investigated did not cover all of the learning objectives, however the learning objectives assessed were comparable with one another. The results show that the presentation and in-class test assessment methods achieved normal distributions of marks, while the marks for the practice-based portfolio assessment were negatively skewed, suggesting the nature of the assessment requires more balancing tasks. Further, student attendance and submission rate were found to have been influenced by the assessment type students had to undertake. Cohorts who undertook the practice-based portfolio assessment had better student engagement and submission rate, at 73% and 91.92% respectively. Finally, learning disruption caused by the COVID pandemic was found to be correlated with student retention, where cohorts whose grades were determined solely by their teacher prior to attending university had a 24% higher chance of withdrawing from the course or transferring to a different course compared to those whose grades were determined by exams

A Web Based Approach to Virtual Appliance Creation, Programming and Management

The Internet and Web technology is advancing at a frantic pace, expanding into almost every aspect of our everyday life. One of the latest scientific activities for the Internet and the Web is the so-called pervasive or ubiquitous computing where networking plays a vital role in its core computational framework. In this, people are able to use the Internet and Web to manage the operation of embedded network devices, services and to coordinate their services in ways that create applications such as smart-homes, smart-offices, smart-cars etc, collectively referred to as intelligent environments. For ordinary people (non technologists) to be able to use this technology, it is required that the interaction between the users and the environment must be as transparent and simple as possible, employing intuitive and user-friendly interfaces wherever possible. A popular approach to empowering users to customise the functionality of their environments is via end-user programming. In this work-in-progress paper we describe an approach based on using a web based GUI to augment earlier work of ours concerning an end user programming paradigm known as Pervasive interactive Programming (PiP), in a way that makes it more flexible and easy to use. By doing this, we present a conceptual model and discuss the issues in developing and using this model. © 2010 IEEE

Personalising the iCampus; an End-User Programming approach

Abstract-This paper explores the possibility of facilitating better end user engagement with the iCampus by providing a platform (Pervasive-interactive-Programming) to program the functionality of iCampus intelligent environments. We first introduce Pervasive-interactive-Programming (PiP), explaining the principles and presenting some recent results. By way of an example, we discuss how end-user programming could be used to configure the functionality of a student campus dormitory. We then consider how these techniques might be expanded to cater for other iCampus' areas. Finally, we comment on the future direction of our research

Smart-object based reasoning system for indoor acoustic profiling of elderly inhabitants

Many countries are facing significant challenges in relation to providing adequate care for their elderly citizens. The roots of these issues are manifold, but include changing demographics, changing behaviours, and a shortage of resources. As has been witnessed in the health sector and many others in society, technology has much to offer in terms of supporting people’s needs. This paper explores the potential for ambient intelligence to address this challenge by creating a system that is able to passively monitor the home environment, detecting abnormal situations which may indicate that the inhabitant needs help. There are many ways that this might be achieved, but in this paper, we will describe our investigation into an approach involving unobtrusively ’listening’ to sound patterns within the home, which classifies these as either normal daily activities, or abnormal situations. The experimental system we built was composed of an innovative combination of acoustic sensing, artificial intelligence (AI), and the Internet-of-Things (IoT), which we argue in the paper that it provides a cost-effective approach to alerting care providers when an elderly person in their charge needs help. The majority of the innovation in our work concerns the AI in which we employ Machine Learning to classify the sound profiles, analyse the data for abnormal events, and to make decisions for raising alerts with carers. A Neural Network classifier was used to train and identify the sound profiles associated with normal daily routines within a given person’s home, signalling departures from the daily routines that were then used as templates to measure deviations from normality, which were used to make weighted decisions regarding calling for assistance. A practical experimental system was then designed and deployed to evaluate the methods advocated by this research. The methodology involved gathering pre-design and post-design data from both a professionally run residential home and a domestic home. The pre-design data gathered the views on the system design from 11 members of the residential home, using survey questionnaires and focus groups. These data were used to inform the design of the experimental system, which was then deployed in a domestic home setting to gather post-design experimental data. The experimental results revealed that the system was able to detect 84% of abnormal events, and advocated several refinements which would improve the performance of the system. Thus, the research concludes that the system represents an important advancement to the state-of-the-art and, when taken together with the refinements, represents a line of research which has the potential to deliver significant improvements to care provision for the elderly

New Measure of Insulin Sensitivity Predicts Cardiovascular Disease Better than HOMA Estimated Insulin Resistance

10.1371/journal.pone.0074410PLoS ONE89-POLN

Intelligent image-based colourimetric tests using machine learning framework for lateral flow assays

This paper aims to deliberately examine the scope of an intelligent colourimetric test that fulfils ASSURED criteria (Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free, and Deliverable) and demonstrate the claim as well. This paper presents an investigation into an intelligent image-based system to perform automatic paper-based colourimetric tests in real-time to provide a proof-of-concept for a dry-chemical based or microfluidic, stable and semi-quantitative assay using a larger dataset with diverse conditions. The universal pH indicator papers were utilised as a case study. Unlike the works done in the literature, this work performs multiclass colourimetric tests using histogram based image processing and machine learning algorithm without any user intervention. The proposed image processing framework is based on colour channel separation, global thresholding, morphological operation and object detection. We have also deployed a server based convolutional neural network framework for image classification using inductive transfer learning on a mobile platform. The results obtained by both traditional machine learning and pre-trained model-based deep learning were critically analysed with the set evaluation criteria (ASSURED criteria). The features were optimised using univariate analysis and exploratory data analysis to improve the performance. The image processing algorithm showed >98% accuracy while the classification accuracy by Least Squares Support Vector Machine (LS- SVM) was 100%. On the other hand, the deep learning technique provided >86% accuracy, which could be further improved with a large amount of data. The k-fold cross validated LS- SVM based final system, examined on different datasets, confirmed the robustness and reliability of the presented approach, which was further validated using statistical analysis. The understaffed and resource limited healthcare system can benefit from such an easy-to-use technology to support remote aid workers, assist in elderly care and promote personalised healthcare by eliminating the subjectivity of interpretation