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Optimal Proportional Integral Derivative (PID) Controller Design for Smart Irrigation Mobile Robot with Soil Moisture Sensor
Uncertainty on the condition of the weather always give a major headache to the agricultural industry as the cultivated plant that is grown on a large scale commercially rely on the condition of the weather. Therefore, to reduce the interdependency on the weather itself, a recommendation to develop a prototypic mobile robot for smart irrigation is submitted. Smart irrigation system is an essential tool from yield point of view and scarcity of the water. This smart irrigation system adopts a soil moisture sensor to measure the moisture content of the soil and automatically provide a signal to
Facility layout and supply chain management of food service industries: A case study
Although there is a massive number of food tech enabled startups all over the world only few of them can succeed globally, determining the main factors which affect success or failure are still a challenge. In this paper, industrial engineering fundamentals are used to develop the startup structure and workplace design in two main areas; layout design to improve the work environment as well as flow of workers and flow of materials, supply chain management to design push-pull hybrid system and demand forecasting using non-stationary exponential smoothing method. © 2021 IEEE.
Study of Approaches to Implement the Prism-Based Surface Plasmon Resonance Sensors
Surface plasmon resonance (SPR) sensors are increasingly in demand due to their high sensitivity, better accuracy, and improved detection limit. Such performance parameters make these sensors suitable for biological and medical field’s applications. During the last decade, prism coupling-based SPR sensors had been a preferred choice among the designer and developers across the globe. This article summarizes a review of prism coupling-based SPR photonic sensors. Important performance characteristics of such sensors have also been studied with respect to their detection accuracy, sensitivity
Sustainable Product Design through Non-dominated Sorting Cuckoo Search
Sustainability is an important consideration in product design. The sustainable design should fully consider the environmental, social, and economic factors of the product. However, the three factors are often conflicting with each other. This paper aims to strike a balance between these factors and achieve sustainable product design through multi-objective optimization. The three influencing factors of sustainability, namely, the environmental factor, social factor and economic factor, were respectively defined as environmental impact, labor time and labor cost. Then, the product to be
Study of optical power variations in multi-layer human skin model for monitoring the light dose
Monitoring light dose is essential in much clinical procedures like bio-stimulation, neuro-medicine and photodynamic therapy and in many biophotonics applications such as optogenetics and biosensing. However, monitoring the optical power dissipation as light travels in different layers of tissue is essential in determining the required optical dose. Each part in the human body is protected by different thickness of skin layer; therefore, studying the variations of the optical power when light propagates in different thicknesses of the human skin is essential for safe and accurate medical
FPGA Realizations of Chaotic Epidemic and Disease Models including Covid-19
The spread of epidemics and diseases is known to exhibit chaotic dynamics; a fact confirmed by many developed mathematical models. However, to the best of our knowledge, no attempt to realize any of these chaotic models in analog or digital electronic form has been reported in the literature. In this work, we report on the efficient FPGA implementations of three different virus spreading models and one disease progress model. In particular, the Ebola, Influenza, and COVID-19 virus spreading models in addition to a Cancer disease progress model are first numerically analyzed for parameter
Fractional-order bio-impedance modeling for interdisciplinary applications: A review
Bio-impedance circuit modeling is a popular and effective non-invasive technique used in medicine and biology to fit the measured spectral impedance data of living or non-living tissues. The variations in impedance magnitude and/or phase at different frequencies reflect implicit biophysical and biochemical changes. Bio-impedance is also used for sensing environmental changes and its use in the agriculture industry is rapidly increasing. In this paper, we review and compare among the fractional-order circuit models that best fit bio-impedance data and the different methods for identifying the
Sustainable Evaluation of Using Nano Zero-Valent Iron and Activated Carbon for Real Textile Effluent Remediation
In this study, the performance of using two different adsorbents, nano-zero-valent iron (nZVI) and activated carbon (AC), was examined for the treatment of real textile effluents. The porous structure and chemical composition of the synthesized nZVI were detected via X-ray diffraction, scanning electron microscopy and EDX analysis. Batch adsorption studies were conducted to investigate the optimal operating conditions including pH, adsorbent dose, contact time and stirring rate for the removal of COD, TSS and color from real textile wastewater. At same optimal operating conditions, pH 6, dose
Joint relay assignment and adaptive modulation for energy-efficient cellular networks
Energy efficient operation of cellular systems becomes a core design goal for economic and environment-friendly network operation. Several studies have shown that the energy consumed in base stations represents 60-80% of the energy consumption in cellular networks. In this paper, we develop an optimization framework that exploits several energy efficient techniques including switching power modes of base stations, Adaptive Modulation (AM), and the use of relays. Our main objective is to reduce both, transmitted and circuit power, subject to satisfying the quality of service constraints. To
Towards Intelligent Web Context-Based Content On-Demand Extraction Using Deep Learning
Information extraction and reasoning from massive high-dimensional data at dynamic contexts, is very demanding and yet is very hard to obtain in real-time basis. However, such process capability and efficiency might be affected and limited by the available computational resources and the consequent power consumption. Conventional search mechanisms are often incapable of real-time fetching a predefined content from data source, without concerning the increased number of connected devices that contribute to the same source. In this work, we propose and present a concept for an efficient approach
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