A multipart distributed modelling approach for an automated protection system in open-air cloth drying

There are different methods of drying wet clothes, but drying with direct sunlight is considered the best suited for the preservation of the quality and usability of the cloths. However, sudden rainfall during the drying period constitutes a major drawback of the method. This returns the cloths to a drenched state as it is practically impossible to watch the clothes dry off after washing. This paper has proposed a model for an automated system for controlled open-air fabric drying by detecting the rain and moisture status of the cloths in real-time, and capable of shielding them to safety from the rainfall and excess sun. The modelled part considers the sensing model, drying model, control model, and their validation. The implementation and evaluation stage relates the result of the validated results to the developed prototypes. The simulated results in the sensing unit indicate above 87.5% agreements with the analytical results, and the controller simulated result provides a relatively small overshoot and faster dynamic response. Manufacturers of hanger systems for cloth drying have a basis for the design and implementation of their products in the paper

Improving the performance of free space optical systems: a space-time orthogonal frequency division modulation approach

Free space optical (FSO) communication systems are known for high capacity and information security. The overall system performances of FSO systems are however significantly affected by atmospheric turbulence induced fading. This paper, therefore, proposes a technique to mitigate this effect through the introduction of an additional degree of error correction capacity by exploiting the spectral dimension in the coding space. A space-time trellis coded orthogonal frequency division modulation (OFDM) scheme was developed, simulated and evaluated for optical communication through a Gamma-Gamma channel. The evaluation of the coding gain obtained from the simulation results, the mathematical analysis and the truncation error analysis shows that the proposed technique is a promising and viable technique for improving the error correction performance of space-time codes for free space optical communication links

Optimetric analysis of 1x4 array of circular microwave patch antennas for mammographic applications using adaptive gradient descent algorithm

Interest in the use of microwave equipment for breast imagery is on the increase owing to its safety, ease of use and friendlier cost. However, some of the pertinent blights of the design and optimization of microwave antenna include intensive consumption of computing resources, high price of software acquisition and very large optimization time. This paper therefore attempts to address these concerns by devising a rapid means of designing and optimizing the performance of a 1×4 array of circular microwave patch antenna for breast imagery applications by deploying the adaptive gradient descent algorithm (AGDA) for a circumspectly designed artificial neural network. In order to cross validate the findings of this work, the results obtained using the adaptive gradient descent algorithm was compared with those obtained with the deployment of the much reported Levenberg-Marquardt algorithm for the same dataset over same frequency range and training constraints. Analysis of the performance of the AGDA neural network shows that the approach is a viable and accurate technique for rapid design and analysis of arrays of circular microwave patch antenna for breast imaging

Control Design and Management of a Distributed Energy Resources System

Energy generation, distribution, and transmission are crucial to the development and advancement of humans and their environment. Therefore, the need for a sustainable environment is essential. This study focuses on designing, building, testing, and commissioning an intelligent grid solar-powered distributed energy resource system to serve as an alternative to powering loads with conventional energy sources, creating a pollution-free and self-dependent system that can be built to the capacity of the required load. The solar panels, voltage regulator, microprocessor, solar charge controller, and batteries are all interconnected to automatically switch between the three solar substations. The simulation of the DER network system was executed with MATLAB, Simulink, and Simscape Electrical. The management system was created using the Visual Studio 2019 and ASP.NET MVC software. The management system was designed to keep tabs on the daily sales of the DER components to various clients. The results are achieved by subjecting a load (21W rated headlight bulb and a 5W rated fluorescent bulb) at specified time intervals (10, 20, 30 minutes). The results showed us a particular set threshold voltage for the sub-station switch. This project gives an insight into how good and reliable the distributed energy resource system can be as it provides a constant power supply to the equipment

Optimetric analysis of 1x4 array of circular microwave patch antennas for mammographic applications using adaptive gradient descent algorithm

Interest in the use of microwave equipment for breast imagery is on the increase owing to its safety, ease of use and friendlier cost. However, some of the pertinent blights of the design and optimization of microwave antenna include intensive consumption of computing resources, high price of software acquisition and very large optimization time. This paper therefore attempts to address these concerns by devising a rapid means of designing and optimizing the performance of a 1×4 array of circular microwave patch antenna for breast imagery applications by deploying the adaptive gradient descent algorithm (AGDA) for a circumspectly designed artificial neural network. In order to cross validate the findings of this work, the results obtained using the adaptive gradient descent algorithm was compared with those obtained with the deployment of the much reported Levenberg-Marquardt algorithm for the same dataset over same frequency range and training constraints. Analysis of the performance of the AGDA neural network shows that the approach is a viable and accurate technique for rapid design and analysis of arrays of circular microwave patch antenna for breast imaging

A Mathematical Modeling Approach for Optimal Trade-offs in a Wireless Sensor Network for a Granary Monitoring System

Wireless sensor networks can be deployed in the monitoring of granary systems and greenhouses. In ensuring the efficiency and reliability of such systems, optimal trade-offs should be guaranteed between the various considered constraints. This work has the important aim of translating the monitoring of the environmental factors that may influence the quality of stored agricultural grains into a mathematical model, in which optimal trade-offs are achieved between coverage efficiency, reduced costs and real-time monitoring. The intention is to mathematically model and optimize a developed distributed wireless sensor network system for quality bulk grains storability. The proposed model shows promise, as it attained optimal levels, with a coverage efficiency of 89% with minimum number of nodes

DEVELOPMENT OF AN ARDUINO-BASED OBSTACLE AVOIDANCE ROBOTIC SYSTEM FOR AN UNMANNED VEHICLE

The use of autonomous systems in the world to perform relevant and delicate task is fast growing. However, its application in various fields cannot be over emphasized. This paper presents an obstacle detection and avoidance system for an unmanned Lawnmower. The system consists of two (Infrared and Ultrasonic) sensors, an Arduino microcontroller and a gear DC motor. The ultrasonic and infrared sensors are implemented to detect obstacles on the robot’s path by sending signals to an interfaced microcontroller. The micro-controller redirects the robot to move in an alternate direction by actuating the motorsin order to avoid the detected obstacle. The performance evaluation of the system indicates an accuracy of 85% and 0.15 probability of failure respectively. In conclusion, an obstacle detection circuit was successfully implemented using infrared and ultrasonic sensors modules which were placed at the front of the robot to throw both light and sound waves at any obstacle and when a reflection is received, a low output is sent to the Arduino microcontroller which interprets the output and makes the robot to stop

RESOURCEFUL SELECTION-BASED DESIGN OF WIRELESS UNITS FOR GRANARY MONITORING SYSTEMS

The effectiveness of any granary system is grossly dependent up on the efficiency of its monitoring and control measures. The granary monitoring systems presently in use in mo st developing countries are based on wired networks with inevitable disadvantages that include high installation and mai ntenance costs. Most wireless granary monitoring systems previously developed were achieved without resort to resourcefu lness of the composite units of the system. However, record information on the selection of best comparative compone nts for wireless granary monitoring systems is not readily available. This paper designed a wireless sensor integrated sys tem from comparison and selection of resourceful component units for monitoring temperature, humidity and light variations in stored bulk grains. The resulting composite units of the developed system were products of the best paramet ers trade-off in the selection of components and protocols. The sensing unit consists of the selected Grove-DHT22 Temperatu re/Humidity sensor with calibrated, linearized and stable digital signals output via 1-wire bus and a cheap low-power Gro ve-GL5528 light sensor. The resulting network had no hierarchy or parent-child relationship constraint. The low-powe r sleep configuration possibility of the sensor node was cyclic and synchronize

Fabrication and Model Characterization of the Electrical Conductivity of PVA/PPy/rGO Nanocomposite

Owing to the numerous advantages of graphene-based polymer nanocomposite, this study is focused on the fabrication of the hybrid of polyvinyl alcohol (PVA), polypyrrole (PPy), and reduced graphene-oxide. The study primarily carried out the experimentation and the mathematical analysis of the electrical conductivity of PVA/PPy/rGO nanocomposite. The preparation method involves solvent/drying blending method. Scanning electron microscopy was used to observe the morphology of the nanocomposite. The electrical conductivity of the fabricated PVA/PPy/rGO nanocomposite was investigated by varying the content of PPy/rGO on PVA. From the result obtained, it was observed that at about 0.4 (wt%) of the filler content, the nanocomposite experienced continuous conduction. In addition, Ondracek, Dalmas s-shape, dose–response, and Gaussian fitting models were engaged for the analysis of the electrical transport property of the nanocomposite. The models were validated by comparing their predictions with the experimental measurements. The results obtained showed consistency with the experimental data. Moreover, this study confirmed that the electrical conductivity of polymer-composite largely depends on the weight fraction of fillers. By considering the flexibility, simplicity, and versatility of the studied models, this study suggests their deployment for the optimal characterization/simulation tools for the prediction of the electrical conductivity of polymer-composites

An artificial intelligence approach to model and optimize biodiesel production from used cooking oil using CaO incorporated zeolite catalyst

The current work investigated the possibility of employing chicken eggshell-zeolite composite as a cheap and recyclable heterogeneous catalyst for used cooking oil (UCO) conversion into its corresponding methyl ester (UCOME) via methanolysis process. Various catalysts were formulated by loading eggshell on the zeolite and calcined at different temperatures to obtain CaO incorporated zeolite (CaO-Zel) catalyst. The catalyst sample calcined at 800 °C for 4 h (CaO-Zel-800) exhibited best activity for methanolysis process and was analyzed using various techniques, including BET, surface basicity, TPD-CO2, TGA/DTA, SEM, XRD and FTIR. The transesterification process was modeled using artificial intelligence approach viz. artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) while optimization of the operating variables (temperature, catalyst loading, time and molar ratio) was performed by interfacing the developed models with ant colony optimization (ACO) algorithm. The closeness of coefficient of determination (R2) to unity and low mean square error (MSE) indicated that the methanolysis of UCO was adequately described by the developed models with ANFIS model (R2 = 0.9997 and MSE = 0.1271) superior to ANN model (R2 = 0.9953 and MSE = 2.0762). The highest UCOME yield of 99.45 ± 0.51 wt% was achieved with ANFIS-ACO under the condition; reaction temperature (72.97 °C), methanol/UCO molar ratio (7.14:1), reaction time (131.97 min) and catalyst dosage (5.39 wt%). The results of the sensitivity analysis revealed that all the operating variables influence UCOME yield, and none could be discarded. The CaO-Zel-800 catalyst was reused for five consecutive reaction cycles, and its activity only decreased by about 12%