AmIE: An Ambient Intelligent Environment for Assisted Living

In the modern world of technology Internet-of-things (IoT) systems strives to provide an extensive interconnected and automated solutions for almost every life aspect. This paper proposes an IoT context-aware system to present an Ambient Intelligence (AmI) environment; such as an apartment, house, or a building; to assist blind, visually-impaired, and elderly people. The proposed system aims at providing an easy-to-utilize voice-controlled system to locate, navigate and assist users indoors. The main purpose of the system is to provide indoor positioning, assisted navigation, outside weather information, room temperature, people availability, phone calls and emergency evacuation when needed. The system enhances the user's awareness of the surrounding environment by feeding them with relevant information through a wearable device to assist them. In addition, the system is voice-controlled in both English and Arabic languages and the information are displayed as audio messages in both languages. The system design, implementation, and evaluation consider the constraints in common types of premises in Kuwait and in challenges, such as the training needed by the users. This paper presents cost-effective implementation options by the adoption of a Raspberry Pi microcomputer, Bluetooth Low Energy devices and an Android smart watch.Comment: 6 pages, 8 figures, 1 tabl

Thermographic non-destructive evaluation for natural fiber-reinforced composite laminates

Natural fibers, including mineral and plant fibers, are increasingly used for polymer composite materials due to their low environmental impact. In this paper, thermographic non-destructive inspection techniques were used to evaluate and characterize basalt, jute/hemp and bagasse fibers composite panels. Different defects were analyzed in terms of impact damage, delaminations and resin abnormalities. Of particular interest, homogeneous particleboards of sugarcane bagasse, a new plant fiber material, were studied. Pulsed phase thermography and principal component thermography were used as the post-processing methods. In addition, ultrasonic C-scan and continuous wave terahertz imaging were also carried out on the mineral fiber laminates for comparative purposes. Finally, an analytical comparison of different methods was give

Improving Deep Learning Image Recognition Performance Using Region of Interest Localization Networks

Deep Learning has been gaining momentum and achieving the state-of-the-art results on many visual recognition problems. The roots of this field can be traced back to the 1940s of the 20th century. The field has recently started delivering some interesting results on many image understanding problems. This is mainly due to the availability of powerful hardware that can accelerate the training process. In addition, the growth of the Internet and imaging devices such as mobile phones and cameras has contributed to the increase in the amount of data that can be used to train neural networks. All of these factors have contributed to the success of deep learning on large scale image understanding tasks. Many image understanding problems do not have large training data. This is especially true in many special purpose datasets such as medical images, astronomical images, and environmental images. These application do not have large training datasets because unlike natural images, users do not typically take these images and upload them to the web. In addition, some of these applications, such as medical imaging, have many restrictions on sharing the data in order to protect the privacy of the patients. Finally, the labeling process needed for training natural images can be done by any person, unlike special purpose datasets. For example, in medical imaging, the images must be labeled by medical or clinical experts in the field. This results in datasets that are normally much smaller than natural images datasets as these experts have limited time to invest in the creation of the training sets. Luckily, in many of these applications, the most discriminative features may be present in a small region of interest. In this work, we present a method of training deep learning models on problems with low number of training images. We will do that by localizing a region of interest in these images, which will help reduce the problem of overfitting. In this thesis, two localization architectures are introduced, namely: the naive localization network and the wide localization network (wide net). The latter has several advantages which we explain thoroughly. The first problem we will introduce is the Right whale recognition problem. The problem involves recognizing whales from aerial images by analyzing the callosities pattern on their heads. We will study how localizing the region of interest can be used to make deep learning work on such a small dataset. The second problem we will study is the estimation of the ejection fraction and left ventricle volume by analyzing cardiac MRI images. Automatically estimating the ejection fraction and volume of the heart can help in identifying and diagnosing several cardiac health issues. Similarly, this dataset contains only a small number of training subjects

From Nuclear to Renewables: The role utility-led voluntary contribution, community renewable, and grid modernization initiatives can play in allowing for a transition to nuclear-free electricity production in Ontario

Ontario’s present electricity supply is one that relies heavily on nuclear generation to provide energy. Though it does not release greenhouse gases during operation, nuclear houses several other ecological risks. This paper looks at voluntary contribution initiatives, community renewable energy generation, and grid modernization, as three areas where initiatives and are being undertaken by utilities that will contribute to a greater portion of Ontario’s electricity demand being met by renewables as opposed to nuclear. Ultimately this paper seeks to determine if initiatives in any of these areas could ultimately lead to an electricity system transition in Ontario away from nuclear towards renewables. Grid modernization appears to have the highest potential to significantly increase the contribution of renewably-sourced electricity to Ontario’s supply. However, utilizing voluntary contribution strategies, and supporting the development of community renewable projects, while unlikely to eventually prompt a large electricity-system change, can meaningfully contribute to goal of increasing the supply of renewably-sourced electricity in Ontario

STUDY OF MAXIMUM POWER POINT TRACKING ALGORITHMS FOR EFFICIENCY GROWTH OF PHOTOVOLTAIC CELLS

Subject of Research.The paper considers simulation model of the electro generating installation based on photovoltaic converters. It is known that photovoltaic cells have rather low conversion efficiency of energy therefore performance improving of the designed energy system can be partially reached by means of controlled intermediate converters. The main goal of this paper is model implementation of a solar power system and also comparative analysis of the different maximum power point tracking algorithms which are used to control energy system with the purpose to increase power efficiency of all system. Method. All algorithms considered in the paper are based on the search for an extremum on the volt-power characteristic of a photovoltaic converter. Implementation of the most popular methods of maximum power point tracking is considered: "Perturbation and observation" and "Increasing conductivity". An algorithm based on the theory of fuzzy logic is proposed for application aimed at the growth of photovoltaic cells efficiency as an alternative method for traditional algorithms. Main Results. The model of solar panel control system is implemented in MATLAB/Simulink. Three methods for maximum power point tracking within this photovoltaic system are considered and implemented. Comparative analysis of operation of different control algorithms is carried out for different levels of solar radiation intensity. Practical Relevance. The algorithms can be implemented in real power systems for improvement of their overall performance

Digital Filter Design Using Improved Teaching-Learning-Based Optimization

Digital filters are an important part of digital signal processing systems. Digital filters are divided into finite impulse response (FIR) digital filters and infinite impulse response (IIR) digital filters according to the length of their impulse responses. An FIR digital filter is easier to implement than an IIR digital filter because of its linear phase and stability properties. In terms of the stability of an IIR digital filter, the poles generated in the denominator are subject to stability constraints. In addition, a digital filter can be categorized as one-dimensional or multi-dimensional digital filters according to the dimensions of the signal to be processed. However, for the design of IIR digital filters, traditional design methods have the disadvantages of easy to fall into a local optimum and slow convergence. The Teaching-Learning-Based optimization (TLBO) algorithm has been proven beneficial in a wide range of engineering applications. To this end, this dissertation focusses on using TLBO and its improved algorithms to design five types of digital filters, which include linear phase FIR digital filters, multiobjective general FIR digital filters, multiobjective IIR digital filters, two-dimensional (2-D) linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters. Among them, linear phase FIR digital filters, 2-D linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters use single-objective type of TLBO algorithms to optimize; multiobjective general FIR digital filters use multiobjective non-dominated TLBO (MOTLBO) algorithm to optimize; and multiobjective IIR digital filters use MOTLBO with Euclidean distance to optimize. The design results of the five types of filter designs are compared to those obtained by other state-of-the-art design methods. In this dissertation, two major improvements are proposed to enhance the performance of the standard TLBO algorithm. The first improvement is to apply a gradient-based learning to replace the TLBO learner phase to reduce approximation error(s) and CPU time without sacrificing design accuracy for linear phase FIR digital filter design. The second improvement is to incorporate Manhattan distance to simplify the procedure of the multiobjective non-dominated TLBO (MOTLBO) algorithm for general FIR digital filter design. The design results obtained by the two improvements have demonstrated their efficiency and effectiveness