Fpg as 3 acknowledgement (2)
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Mudasar Sadeeq expresses gratitude to several people for their support and guidance during his short term training. He thanks the Chancellor of Mewar University, Dr. Ashok Kumar Gadhiya, for providing the opportunity to work with companies. He is also grateful to his guide, Mr. Buddhhi Prakash Sharma, for his valuable guidance and support in preparing the training report. Additionally, he thanks his Head of Department, Ms. Shilpa Jangid, for her support and guidance during the short term training.
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The report describes a short term training on designing an 8 bit ALU using an FPGA that was submitted by Mudasar Sadeeq, a 3rd year BTech ECE student at Mewar University, to his department head Mrs. Shilpa Jangid to fulfill the requirements for his Bachelor of Technology degree in Electronics and Communication Engineering for the 2015-2016 session under the guidance of his professor Mr. Buddhi Prakash Sharma.
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This 3 sentence summary provides the high level information from the technical seminar report submitted to Mewar University, Chittorgarh towards the partial fulfillment of a Bachelor of Technology degree in Electronics and Communication Engineering. The report was guided by Mr. Rohit Giri and submitted by Sudhir Kumar Choudhri, a 3rd year B.Tech student in ECE with a roll number of 101305008013 from the Department of Electronics and Communication Engineering at Mewar University's Faculty of Engineering & Technology. The report was submitted in April 2016.
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This document summarizes the evolution of wireless technologies from 1G to 5G. It discusses the key features and limitations of each generation of technology. 1G allowed analog voice calls with speeds up to 2.4 kbps. 2G introduced digital networks and text messaging with speeds up to 64 kbps. 3G enabled broadband connectivity for smartphones and data speeds from 144 kbps to 2 Mbps. 4G provided higher speeds from 100 Mbps to 1 Gbps for video calling and mobile broadband. 5G is expected to offer much higher speeds and capacity for new applications with speeds over 1 Gbps and support for wireless worldwide web. The document compares the technologies and concludes that 5G will provide a reliable wireless solution for the future
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The document provides an overview of the evolution of wireless network technologies from 1G to 5G. It discusses the key characteristics and capabilities of each generation:
- 1G networks were the first analog cellular networks and allowed only voice calling within one country. 2G introduced digital networks and text messaging capabilities.
- 3G networks enabled faster data speeds up to 2 Mbps and services like video calling. 4G aims to provide speeds up to 1 Gbps for multimedia and wireless internet access.
- 5G is envisioned to create a global wireless network with high connectivity speeds of 25 Mbps for advanced applications like integrated personal devices. It aims to overcome limitations of previous generations for a "real wireless world".
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This document contains a list of 5 figures related to different generations of mobile networks from 1G to 5G. It provides the page numbers for figures depicting the key technologies and designs of each generation of mobile networks, including their mobile terminals, network layers, functional architectures, and protocols.
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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.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
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.
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TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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Understanding Inductive Bias in Machine Learning
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The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
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22CYT12-Unit-V-E Waste and its Management.ppt
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The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
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Embedded machine learning-based road conditions and driving behavior monitoring
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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Fpg as 3 acknowledgement (2)
- 1. iii ACKNOWLEDGEMENT I am deeply indebted to our Chancellor, DR. ASHOK KUMAR GADIYA, Mewar University, Chittorgarh, for providing us such a great chance to work in companies for short term training. I would like to express my sense of gratitude of my guide MR. BUDDHI PRAKASH SHARMA, ASST. PROFESSOR, for his valuable guidance, suggestion, and continuous support throughout in preparing the short term training report. I also express my thanks to my Head of Department MS.SHILPA JANGID for her great support and valuable guidance in this short term training. Date: MUDASAR SADEEQ Enr. No. :-MUR 1300072 Roll No. :-101305004249