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Digital Signal Processing[ECEG-3171]-Ch1_L07
This document is a lecture on discrete-time convolutions. It discusses various methods for processing discrete-time samples, including block processing and sample processing. Block processing deals with finite blocks of data and applications like FIR filtering and DFT computations. Sample processing is used in real-time applications like filtering and control systems. The document presents different equivalent forms of the convolution operation, including direct form, LTI form, matrix form, and overlap-add. It provides examples of computing convolution using these different forms. The document concludes with exercises involving computing convolutions using different methods.
Recommended
Digital Signal Processing[ECEG-3171]-Ch1_L07
This document is a lecture on discrete-time convolutions. It discusses various methods for processing discrete-time samples, including block processing and sample processing. Block processing deals with finite blocks of data and applications like FIR filtering and DFT computations. Sample processing is used in real-time applications like filtering and control systems. The document presents different equivalent forms of the convolution operation, including direct form, LTI form, matrix form, and overlap-add. It provides examples of computing convolution using these different forms. The document concludes with exercises involving computing convolutions using different methods.
Digital Signal Processing[ECEG-3171]-Ch1_L06
This document summarizes key concepts about analog reconstruction and changing sampling rates from a lecture on sampling and reconstruction:
1) Analog reconstruction involves converting samples to impulse trains, then filtering with an ideal low-pass reconstruction filter to perfectly reconstruct the original signal. A staircase reconstructor is commonly used instead as it is realizable but does not eliminate replicas completely.
2) Downsampling reduces the sampling rate by an integer M by keeping every Mth sample. Upsampling increases the rate by inserting zeros between samples. Both can be done without aliasing by first low-pass filtering at the Nyquist rate of the new sampling frequency.
3) Changing the sampling rate by a rational factor can be achieved by cascading
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This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
Digital Signal Processing[ECEG-3171]-Ch1_L04
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
Digital Signal Processing[ECEG-3171]-Ch1_L03
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
Digital Signal Processing[ECEG-3171]-Ch1_L02
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced
#Africa#Ethiopia
Digital Signal Processing[ECEG-3171]-Ch1_L01
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold.
#Africa#Ethiopia
Digital Signal Processing[ECEG-3171]-L00
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be solded.
#Africa#Ethiopia
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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.
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Eric Nizeyimana's document 2006 from gicumbi to ttc nyamata handball play
this notes have been created
by Eric36 at nyamata ttc
after readings
pe book for y2sme .
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1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
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politics, and conventional and nontraditional security are all explored and explained by the researcher.
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pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
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5214-1693458878915-Unit 6 2023 to 2024 academic year assignment (AutoRecovere...
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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,
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accuracy rate of 99.50%.
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3:国家专业人才认证中心颁发入库证书
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5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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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
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