Version 1 Division.pptx
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The document describes version 1 of a binary division algorithm. It provides an example of using the algorithm to calculate the quotient and remainder of 7 divided by 3 in binary. The algorithm works by repeatedly subtracting the divisor from the remainder, shifting the quotient left by 1 bit and shifting the divisor right by 1 bit until the remainder is smaller than the divisor. For the example calculation, the quotient is calculated to be 2 and the remainder is 1.
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Refined Version of Division.pptx
This document describes an improved version of the division algorithm for unsigned binary division. It presents the steps to divide 7 by 3 as an example, showing the divisor, remainder, and operations at each step. The improved algorithm shifts and restores the remainder at each step rather than just subtracting the divisor. In the end, it correctly calculates a quotient of 2 and remainder of 1, matching 7 divided by 3.
Improved version of division
Audio Version available in YouTube Link : www.youtube.com/Aksharam
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The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
A review on techniques and modelling methodologies used for checking electrom...
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Literature Review Basics and Understanding Reference Management.pptx
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Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
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Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
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Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
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International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
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A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
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Generative AI leverages algorithms to create various forms of content
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Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
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Embedded machine learning-based road conditions and driving behavior monitoring
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bank management system in java and mysql report1.pdf
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2. Operations Strategy in a Global Environment.ppt
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International Conference on NLP, Artificial Intelligence, Machine Learning an...
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Version 1 Division.pptx
- 1. UN SIGNED & SIGNED DIVISION Version – 1 Binary Division Algorithm Mr. C.KARTHIKEYAN, ASSISTANT PROFESSOR, ECE , RMKCET
- 2. FIRST VERSION OF THE DIVISION HARDWARE
- 3. EXPLANATION WITH AN EXAMPLE 0111 Binary Representation 0011 Binary Un Signed Division of (7 / 3) using Version - 1 Division Algorithm Dividend 7 Divisor 3 Quotient 2 0010 Remainder 1 0001 Dividend = Quotient X Divisor + Remainder
- 4. Un Signed Division of (7 / 3) using Version - 1 Algorithm Ite Step Quotient Divisor Remainder 0 Initial Values 0 0 0 0 0011 0000 0000 0111 1 2 3 4 5 1101 0111 0001 1000 0 0 0 0 1110 1111 0000 0111 0000 1100 0 0 0 0 1111 1011 0000 0111 0000 0110 0000 0001 0000 0001 0000 0011 0 0 0 1 0 0 1 0 0000 0001 1111 1110 a) Rem = Rem - Divisor b) sll Quotient by 1bit c) srl Divisor by 1 bit 0000 0111 0011 0000 (-) ------------- 1101 0111 0000 0111 0001 1000 (-) ------------- 1110 1111 0000 0111 0000 0111 0 0 0 0 a) Rem = Rem - Divisor b) sll Quotient by 1bit c) srl Divisor by 1 bit 0 0 0 0 0000 0111 0 0 0 0 a) Rem = Rem - Divisor 0000 0111 0000 1100 (-) ------------- 1111 1011 0 0 0 0 b) sll Quotient by 1bit c) srl Divisor by 1 bit a) Rem = Rem - Divisor b) sll Quotient by 1bit c) srl Divisor by 1 bit a) Rem = Rem - Divisor b) sll Quotient by 1bit c) srl Divisor by 1 bit 0000 0111 0000 0110 (-) ------------- 0000 0001 0 0 0 1 0000 0001 0000 0011 (-) ------------- 1111 1110 0000 0001 0000 0001 0 0 1 0 0000 0001 1 0010 2 0000 0111