4 research outputs found

    A Complete Design of Smart Wind Farm Enriched with Novel Anemometer

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    Renewable energy are endless and environment friendly sources of power production and also it is considered as the alternative of non- sustainable energy sources like coal, fossil fuels and nuclear power. Wind Energy is accounted as one of the fast depleting, pollution free energy source compared to hydro power and thermal power. Internet of Things (IoT), which is a wireless, self configuring network of sensors powered with communication facility promotes the facility of remote monitoring and control activities in smarter way without human intervention. Likewise machine learning is another technological giant offers accurate prediction over the voluminous data and imposes intelligence to the machines kept for operation. The primary objective is to develop an IoT based wind farm module that enables installed capacity identification, structural monitoring and scope for power generation in that locality

    Analysis of the Efficacy of Real-Time Hand Gesture Detection with Hog and Haar-Like Features Using SVM Classification

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    The field of hand gesture recognition has recently reached new heights thanks to its widespread use in domains like remote sensing, robotic control, and smart home appliances, among others. Despite this, identifying gestures is difficult because of the intransigent features of the human hand, which make the codes used to decode them illegible and impossible to compare. Differentiating regional patterns is the job of pattern recognition. Pattern recognition is at the heart of sign language. People who are deaf or mute may understand the spoken language of the rest of the world by learning sign language. Any part of the body may be used to create signs in sign language. The suggested system employs a gesture recognition system trained on Indian sign language. The methods of preprocessing, hand segmentation, feature extraction, gesture identification, and classification of hand gestures are discussed in this work as they pertain to hand gesture sign language. A hybrid approach is used to extract the features, which combines the usage of Haar-like features with the application of Histogram of Oriented Gradients (HOG).The SVM classifier is then fed the characteristics it has extracted from the pictures in order to make an accurate classification. A false rejection error rate of 8% is achieved while the accuracy of hand gesture detection is improved by 93.5%

    Air Watch: An Ample Design of Indoor Air Quality Monitoring System

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    The environment is getting contaminated drastically by introducing harmful materials into the atmosphere through the excessive activities of human in adding comfort and luxurious style in their living. The pollutant level in air has high impact of healthiness of the person inhaling it. Air not outside as well indoor is infected by various hazardous particles and gases.  To assess the air quality in the particular environment, it stimulates the need to monitor the hazardous elements listed. The internet of things (IoT) and artificial intelligence (AI) became the part of human life by adding smartness in their daily routines from facilitating control over appliances to own health factor as well automate operations. The primary objective of the effort is to identify the gases that cause air pollution, measure the air quality, and assess the level of pollution so that we can determine which gases cause pollution and at what place is the air being impacted. An IoT based indoor air quality monitoring system is built through incorporating carbon monoxide (CO), carbon dioxide (CO2), Ozone (O3), particulate matters (PM) and volatile organic components (VOC) sensors into Arduino board. The design ensures a complete air monitor, extends reliable service at low cost. A rule based system is developed to automate events upon the estimated air quality index (AQI) out of the sensory circuit

    Performance Evaluation of Energy Efficient Optimized Routing Protocol for WBANs Using PSO Protocol

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    A Wireless Body Area Network (WBAN) is a network that may be worn on the human body or implanted in the human body to transmit data, audio, and video in real-time to assess how vital organs are performing. A WBAN may be either an inter-WBAN or an intra-WBAN network. Intra-WBAN communication occurs when the various body sensors can share information. This is known as inter-WBAN communication, which occurs when two or more WBANs can exchange data with one another. One difficulty involves getting data traffic from wireless sensor nodes to the gateway with as little wasted energy, dropped packets, and downtime as possible. In this paper, the WBAN protocols have been compared with WBAN under Particle Swarm Optimization (PSO), and the performance of various parameters has been analysed for different simulation areas. The WBAN under the PSO protocol reduces the energy consumption by 43.2% against the SIMPLE protocoldue to the effective selection of forwarding nodes based on PSO optimization. In addition to that the experimental WBAN testbed is conducted in indoor environment to study the performance of the routing metrics towards energy and packet reception.