The trend towards tunnel farming and hydroponic systems is increasing owing to the climatic changes as well as the need to increase crop yield. Hydroponic is a technique of growing plants without soil. Tunnel farming and hydroponic system requires controlled environmental parameters like temperature, humidity and soil moisture for better production of crops. This paper presents an effective method, named, ACHPA (Automatically Controlled Hydro-Ponic Agriculture) scheme that monitor and controls environmental parameters using sensors and controller. ACHPA provides better environmental control over traditional manual monitoring and controlling, thus yield high-quality crops. Sensor based monitoring and control system regulates the temperature and humidity of the tunnel to yield better. For the sake of saving water, an efficient method of drip irrigation is implemented which delivers water directly to the plants rather than being sprayed on as in conventional farming method. The proposed system here is tested for controlled environment and observations recorded for crop analysis purpose. This makes an effective solution to growing highly efficient, good quality and disease free crop production.
Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.
Automatic License Plate Recognition (ALPR) has remained an active research topic for years due to various applications, especially in Intelligent Transportation Systems (ITS). This paper presents an efficient ALPR technique based on deep learning, which accurately performs license plate (LP) recognition tasks in an unconstrained environment, even when trained on a limited dataset. We capture real traffic videos in the city and label the LPs and the alphanumeric characters in the LPs within different frames to generate training and testing datasets. Data augmentation techniques are applied to increase the number of training and testing samples. We apply the transfer learning approach to train the recently released YOLOv5 object detecting framework to detect the LPs and the alphanumerics. Next, we train a convolutional neural network (CNN) to recognize the detected alphanumerics. The proposed technique achieved a recognition rate of 92.8% on a challenging proprietary dataset collected in several jurisdictions of Saudi Arabia. This accuracy is higher than what was achieved on the same dataset by commercially available Sighthound (86%), PlateRecognizer (67%), OpenALPR (77%), and a state-of-the-art recent CNN model (82%). The proposed system also outperformed the existing ALPR solutions on several benchmark datasets.
Energy management in home is one of the major issue now-a-days. There are different types of load like shiftable, non-shiftable, seasonal loads and auxiliary loads. In this research article, an energy management system is proposed for home which helps to schedule different loads on the basis of their types and price. It will help to minimize the cost of electricity by shifting load from peak time to off peak time. Emission will be minimized by charging penalty by adopting multi-objective optimization. Each source of energy has its own price of penalty with respect to time. Penalty is charged to minimize the use of sources like commercial supply and diesel generators which emits hazardous gases. In proposed model, user will get electricity from commercial supply, diesel generators and solar panels to provide continuous supply of electricity to fulfill the energy demand. The shiftable loads will be shifted from peak time to off peak time and higher price source to lower price source to minimize the overall price. In this research, we have proposed an EEIR (Economically Effective and Intelligently Responsive) HEMS (Home Energy Management System) by solving multi-objective optimization problem from BILP (Binary Integer Linear Programming) using branch and bound algorithm.
Automatic License Plate Recognition (ALPR) for years has remained a persistent topic of research due to numerous practicable applications, especially in the Intelligent Transportation system (ITS). Many currently available solutions are still not robust in various real-world circumstances and often impose constraints like fixed backgrounds and constant distance and camera angles. This paper presents an efficient multi-language repudiate ALPR system based on machine learning. Convolutional Neural Network (CNN) is trained and fine-tuned for the recognition stage to become more dynamic, plaint to diversification of backgrounds. For license plate (LP) detection, a newly released YOLOv5 object detecting framework is used. Data augmentation techniques such as gray scale and rotatation are also used to generate an augmented dataset for the training purpose. This proposed methodology achieved a recognition rate of 92.2%, producing better results than commercially available systems, PlateRecognizer (67%) and OpenALPR (77%). Our experiments validated that the proposed methodology can meet the pressing requirement of real-time analysis in Intelligent Transportation System (ITS).
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