Coastline, defined as the instantaneous divide between water and land regions, often provides crucial information on complex dynamics of the sea level, tide/wave energy and coastal erosion. Coastline detection and monitoring can serve important applications including but not restricted to: coastal resource management, coastal environmental protection and sustainable coastal development and planning. The proposed solution is an innovation in the combination of morphological operators, an interest point detector, a water index, distance transform, and geometric transform for coastline change detection. It is also completely autonomous that requires no intervention from the user, efficient with real-time performance in most stages, flexible as the end-user can fine tune parameters, and platform-independent.
Over the past few years, hybrid organic-inorganic perovskite solar cells have emerged as an attractive technology within the photovoltaic (PV) community. Perovskite is considered a promising material for high efficiency solar cells due to its ease of fabrication, low cost of production with excellent electronic and optical properties. In this paper, we investigate perovskite planar heterojunction solar cells using 2D Physics Based TCAD simulation. The perovskite cell is modeled as an in-organic material with physics based parameters. A planar structure consisting of TiO2 as electron transport material (ETM), CH3NH3PbI3-xClx as the absorber layer and Spiro-OmeTAD as the hole transport material (HTM) is simulated. The simulated results match published experimental results indicating the accuracy of the physics based model.
Majority of the current energy sources in the world are based on fossil fuels, which will someday be depleted. In addition, greenhouse emissions are exponentially increased with the augmented energy demand from the high carbon fossil fuel. The current research interest is to determine the sustainable alternative biofuels that would provide environmental benefits. In this study, the effects of the implementation of alternative fuels on the operation of an industrial gas turbine are investigated using performance models. Considering the case of Abu Dhabi, the impacts of the atmospheric conditions, such as ambient temperature, pressure and relative humidity on the engine's performance have been analyzed. In a hot climate, the performance of a gas turbine deteriorates drastically during the high temperature hours.
In this paper, we explore the opportunities in aluminum smelter to reduce emissions of greenhouse gases (GHG) and save energy and in ways that reduce cost and confer substantial competitive advantage to companies that embrace them. This belief is inhibiting the eagerness of companies to join the Chicago Climate Exchange (CCX) and the European Climate Exchange (ECX), which is one of the easier ways to initiate a program to capture these opportunities. The opportunities for Greenhouse Gas Reduction provide evidence to refute this limiting belief, demonstrating that deep cuts in GHG reductions can be achieved with profit. Clearly, improvements in aluminum industry can offer a significant energy savings as well as reduced emissions. The present study will present the achievements in the thermodynamics exergy analysis and its capability in determining the energy and hence gases savings and emission reduction by preheating metallic scrap before melting process in plant.
in this work, pine-based gasification integrated with iron-based chemical looping combustion process is proposed to investigate hydrogen and electricity cogeneration from a thermodynamics perspective. The whole process is modeled using Aspen Plus. The effects of key parameters on the thermodynamics performance, such as the hydrogen yield and energy efficiency of the process, are separately evaluated. The variables assessed herein include steam to biomass mass ratio(S/B) for the gasifier, oxygen biomass mass ratio (O/B), iron to carbon mole ratio in the fuel reactor (Fe/C) and iron to steam mole ratio in the steam reactor (Fe/S). The results show that at optimum operating parameters, namely, S/B=0.07, O/B=0.25, Fe/C=0.5 and Fe/S=0.40, the hydrogen yield is 85.41%, the CO2 capture efficiency is 99.9%, and the energy efficiency is 63.28% which is at least 10% higher of the one of the most efficient conventional technologies.
In this study, the effect of using blends of diesel and biofuels on harmful pollutant emissions, which result from incomplete combustion of fossil fuels like diesel, is explored. The smoke point experiments for the blends of diesel with biodiesel is conducted using ASTM D1322 technique to observe the sooting tendencies of blended fuels. The smoke point is found to increase with increasing percentage of biodiesel in the blend, which indicates a reduction in the sooting tendency of diesel upon the addition of biodiesel. To study the physical and chemical characteristics of the emitted soot particles during blended fuel combustion, soot particles are collected using a vacuum-filter system, and are analyzed using several techniques such as thermogravimetric analysis, high resolution transmission electron microscopy, and Xray diffraction. Their reactivity and nonstructural properties that define their toxicity are reported.
A neuromorphic computing architecture combining the emerging memristive synapses with neural learning algorithms promises an efficient bio-inspired computing paradigm. Software and hardware based approaches were carried by researchers in an attempt to model the human brain cortex. Software simulations were found to consume high power and occupy a large area. Moreover, previous CMOS based architectures were hindered by the lack of a component accurately mimicking the synaptic plasticity and learning behaviours. The invention of Memristor by Leon Chua and its physical implementation by Hewlett-Packard has opened the doors widely for researchers to investigate its power in bio-inspired applications. Its analog operation, low power consumption, high switching speeds and CMOS compatibility makes it a good option as a synapse device. Hence, in this paper different memristive-based structures of artificial neural networks implemented to learn and recognize real world patterns are described.
In this paper, we derive a closed form expression for the symbol error rate (SER) for 4- ary ASK semi-coherent detection receiver. Then, optimum amplitudes for the adopted detection scheme were found. Simulation result shows that the system performance using the optimum amplitudes is better than the system performance using equally spaced amplitudes.
The paper presents two different algorithms to change the pitch of a speech signal in the time domain. The first algorithm is pitch modification without preserving the vocal tract while the second algorithm is with preserving the vocal. The aim is to preserve the humanity nature on the voice and to implement them on the real time. The two algorithms have been implemented and tested on different audio files and then implemented on a DSP board in the real time with the aid of Simulink and Code Composer.
The emerging intra-body communication (IBC) and networking system is a prospective component in advancing health care delivery and empowering the development of new applications and services. Using the human body as a transmission medium unfastened the research perspective towards Human Body Communication which has been introduced by the IEEE as a third physical layer. In this paper, the concept of galvanic coupling is first discussed as an approach for wireless transmission inside the human body. Then, the channel characteristics of the HBC based on the IEEE 802.15.6 standards are addressed where we focus on both the frequency response and the noise characterization. The results obtained are necessary for developing a realistic humanbody channel model capable of estimating the performances of wearable systems using HBC technology.
