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This document outlines four communication experiments to be conducted on various dates in 2010. The experiments include determining error probability for orthogonal signaling using hard and soft decision decoding, demonstrating the effects of AWGN on DSB-SC and SSB transmission, observing the effect of varying modulation levels on error probability for several modulation schemes, and verifying convolution codes with Viterbi decoding.
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This document outlines four communication experiments to be conducted on various dates in 2010. The experiments include determining error probability for orthogonal signaling using hard and soft decision decoding, demonstrating the effects of AWGN on DSB-SC and SSB transmission, observing the effect of varying modulation levels on error probability for several modulation techniques, and verifying convolution codes with Viterbi decoding.
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This document appears to be a log of digital signal processing experiments conducted by a student, listing the name, date, and remark for each practical completed. It includes programs to generate common signals like sine, cosine, unit impulse, unit step, and ramp signals. Programs for exponential functions, discrete Fourier transforms, fast Fourier transforms, and one other unnamed signal are also listed as being completed on various dates.
Index1 (2)
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This document contains personal information about an individual named Ashok Singh who is a 6th semester BTech student at the Hi-Tech Institute of technology. Their contact number is listed as 0772031017.
Front page
This document contains information submitted by Ashok Singh, a 4th year EC student, to Ms. Akansha Gupta regarding a MATLAB assignment. It includes Ashok Singh's student ID number 0722031017.
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~$Ep impulse
O documento consiste em uma mistura de caracteres alfanuméricos e simbólicos sem significado aparente.
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Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
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UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Index
- 1. S.noName of programDateRemarkT.singnature1.2.3.4.To detrmine the error probability for orthogonal signaling employing (a)Hard decision (b)Soft decision decoding.To demonstrate the effect of AWGN in DSB-SC and SSB transmission and reception.To observe the effect of varying M on the avg. error probability per symbol for (a) M-ASK, (b) M-PSK (c) Differential M-PSK and (d)Quadrature-AM.To verify Convolution code with viterbi decoding.10.09.201020.09.201012.10.201004.11.2010<br />INDEX<br />