Automatic Door Handle or Knob Sanitizer

An increase in the risk of COVID-19 spread led people, industries and the government to adopt various approaches for controlling the transmission rate of COVID-19 viruses. Although each approach has its advantages, but in terms of cost-effectiveness and flexibility, one approach i.e. Arduino based sanitizing systems have played an important role to prevent the coronavirus from spreading. In the direction of this research field, a lot of researchers have exerted great efforts to control the COVID-19 outbreak. However, an automatic door handle and knob sanitizer based on IR sensor and servo motor has not been reported till date as per our knowledge. Therefore, In this paper, we have demonstrated an automatic door handle sanitizer to sanitize the handle or knob of a door that is generally used by many people in houses, hospitals, and industries, etc. The reported work has been done to prevent people from getting infected by the coronavirus. If anyone gets infected by touching the contaminated door knob of any institution, house, hospital, etc., then it will have a severe effect on the person as well as on his country. In this regard, the reported system sanitizes the door handle to remove the virus from it as the person touches the door knob. The demonstrated system contains Arduino-Nano, Servo motor, Bread-board and an IR Sensor. It can be implemented in public places such as hospitals, companies where the doors are used frequently to break the chain of COVID-19 infection

Numerical Simulation and Design of Copy Move Image Forgery Detection Using ORB and K Means Algorithm

Copy-move is a common technique for tampering with images in the digital realm. Therefore, image security authentication is of critical importance in our society. So copy move forgery detection (CMFD) is activated in order to identify the forged portion of a photograph. A combination of the Scaled ORB and the k-means++ algorithm is used to identify this object. The first step is to identify the space on a pyramid scale, which is critical for the next step. A region's defining feature is critical to its detection. Because of this, the ORB descriptor plays an important role. Extracting FAST key points and ORB features from each scale space. The coordinates of the FAST key points have been reversed in relation to the original image. The ORB descriptors are now subjected to the k-means++ algorithm. Hammering distance is used to match the clustered features every two key points. Then, the forged key points are discovered. This information is used to draw two circles on the forged and original regions. Moment must be calculated if the forged region is rotational invariant. Geometric transformation (scaling and rotation) is possible in this method. For images that have been rotated and smoothed, this work demonstrates a method for detecting the forged region. The running time of the proposed method is less than that of the previous method

Plantation and Harvesting Autonomous Locomotive (PHAL)

Agriculture has seen quite a good growth due to the latest machinery being used to maximize yield and minimize cost. People around the world are starting to understand the inherent potential and scope of automation and robotics in agriculture. However, there are many problems which continue to prevail like the non-availability of labour, poor and costly machinery, etc. So there is a need to address these existing problems. This project addresses the inherent difficulties in the agricultural field. It tries to provide a remarkable solution to many of the existing problems. This project, named PHAL is a rover type bot which canperform all the basic activities included in farming. It is fully autonomous, ecofriendly machine which can perform many tasks like ploughing, sowing, irrigation, harvesting etc. Using this machine, farmers can get rid of majority of the problems, all this at a very low cost

A Numerical Study of Lid Driven Cavity with Mixed Convection

Direct Numerical Simulation have been carried out for a two dimensional flow in a Lid driven cavity at Reynolds number 5000 and Prandtl number 7 with water as the working fluid. Both the side walls of the enclosure are insulated(i.e. adiabatic boundary condition), while the bottom plate is at higher temperature and the top wall is at colder temperature. Effects of heating of the bottom wall and movement of the top lid have been investigated by conducting numerical simulations at different Richardson numbers by varying from low and moderate magnitudes within the limits of Boussinesq-approximation. Three standard cases has been compared, in the first case heating effects are not taken into account and only the flow due to shear action of the plate is studied. In the second case only the heating effects are taken into account and shear effects are neglected. In the third case effects of both heating and shear action is taken into consideration(i.e. mixed convection). Drag force on the moving plate is calculated in all the three cases and effect of temperature on the drag force is studied. For running the above simulation a code has been developed which is validated by comparing the results with Ghia et al for non-heating case.Comment: 20 Page

Short Term Load Forecasting for Smart Grids Using Apache Spark and a Modified Transformer Model

Smart grid is an advanced electrical grid that enables more efficient distribution of electricity. It counters many of the problems presented by renewable energy sources such as variability in production through techniques like load forecasting and dynamic pricing. Smart grid generates massive amounts of data through smart meters, this data is used to forecast future load to adjust distribution. To process all this data, big data analysis is necessary. Most existing schemes use Apache Hadoop for big data processing and various techniques for load forecasting that include methods based on statistical theory, machine learning and deep learning. This paper proposes using Apache Spark for big data analysis and a modified version of the transformer model for forecasting load profiles of households. The modified transformer model has been tested against several state-of-the-art machine learning models. The proposed scheme was tested against several baseline and state-of-the-art machine learning models and evaluated in terms of the RMSE, MAE, MedAE and R2 scores. The obtained results show that the proposed model has better performance in terms of RMSE and R2 which are the preferred metrics when evaluating a regression model on data with a large number of outliers

Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks

In this era, the interaction between Human and Computers has always been a fascinating field. With the rapid development in the field of Computer Vision, gesture based recognition systems have always been an interesting and diverse topic. Though recognizing human gestures in the form of sign language is a very complex and challenging task. Recently various traditional methods were used for performing sign language recognition but achieving high accuracy is still a challenging task. This paper proposes a RGB and RGB-D static gesture recognition method by using a fine-tuned VGG19 model. The fine-tuned VGG19 model uses a feature concatenate layer of RGB and RGB-D images for increasing the accuracy of the neural network. Finally, on an American Sign Language (ASL) Recognition dataset, the authors implemented the proposed model. The authors achieved 94.8% recognition rate and compared the model with other CNN and traditional algorithms on the same dataset

The impact of birth companion on respectful maternity care and labor outcomes among Indian women: a prospective comparative study

Background: Childbirth is a transformative experience for women, and the presence of a birth companion has been shown to have positive effects on the labor process. This study aimed to investigate the influence of having a birth companion on respectful maternity care (RMC) and labor outcomes. The study was conducted at Pandit Bhagwat Dayal Sharma post graduate institute of medical sciences, Rohtak, India, from February 2020 to March 2021. Methods: The study included two groups: group 1 (n=200) with a birth companion and group 2 (n=200) without a birth companion. Participants were recruited from laboring women at the labor ward, ensuring representation and minimizing bias. Inclusion criteria encompassed women between 37-41 weeks gestational age in active labor, with specific prerequisites for having a birth companion. Data were collected using a pre-set questionnaire to assess RMC, pain scores, behavior of medical personnel, and patient satisfaction. Secondary outcomes included the mode of delivery, duration of labor, complications, and the women's experience with their birth companion. Results: The study revealed significant differences between group 1 and group 2 in various aspects. Group 1 exhibited lower rates of physical and verbal abuse, improved consented and confidential care, and higher overall scores for RMC. Group 1 also reported lower pain scores, more favorable behavior from healthcare providers, and better overall hospital experiences. Additionally, group 1 had fewer instrumental deliveries and cesarean sections, as well as a shorter duration of labor compared to group 2. Conclusions: This study demonstrates that having a birth companion during labor significantly improves RMC, pain management, and labor outcomes. Women accompanied by a birth companion experience reduced rate of abuse, increased satisfaction with healthcare providers, and a more positive overall labor experience. Encouraging the presence of birth companions during childbirth can enhance women's experiences, promote RMC, and contribute to improved labor outcomes

Language Identification for Language Agnostic Text

Detecting query language is an important task for applications such as search engines, virtual assistants, etc. It is difficult to correctly determine the language for short text. This task is even more difficult when the text includes references to entities, brands, or other words that are common across multiple languages. Language classifiers can be used to identify query language as well as to determine the intent language of the users. This disclosure relates to techniques to define and identify language agnostic tokens and phrases from text. The techniques described herein can be used to identify language agnostic text (e.g., in a user query) and to select a language model that works best for such text. The techniques can help reduce systematic bias towards English language content for users that are multilingual. The techniques enable better detection of language of a given text by reducing noise through identification of language agnostic queries