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Register a new userStudying and Generating Frequency & Phase-Modulated Pulsed Radar Signals
دراسة و توليد الإشارات الرادارية النبضية المعدلة ترددياً و طورياً
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يهدف هذا العمل بداية إلى دراسة و محاكاة خواص الإشارات الرادارية الأساسية المستخدمة في رادارات ضغط النبضة باستخدام تابع الغموض.
In this thesis we study radar pulse compression techniques based on frequency modulation and phase-coded modulation.
References used
Merril I. Skolnik, “ Introduction to Radar System “, third edition, McGraw-Hill Higher Education, 2001
Bassem R.Mahafza and Azef Z.Elshebeni, “Matlab Simulations for Radar System Design”, CHAPMAN & HALL/CRC, 2004
Merril I. Skolnik, “Radar Handbook” , third edition,, McGraw-Hill, 2008
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Speech denoising is a field of engineering that studies techniques used to recover the
original signal from the noisy signal corrupted with different types of noise, such as
broadband noise and narrowband noise, and other types present in environme
nt, but the
spectral subtraction technique consider the most prominent in this area .
In this search we will discuss the parameters impact of the modified spectral
subtraction algorithm and the time window length in the enhancement of speech that
corrupted with broadband noise. We done the study and determine the ideal parameters
values and the ideal window length with different values for the signal -to-noise ratio SNR
for noisy speech and we discuss 18 case for each value. We done
the simulation using MATLAB software and the results were compared based on
improving the value of SNR for each case .
The normal Costas frequencies code sequence signal is very well known and important signal in pulse compression Radar. In this paper we present two algorithms to modify Costas sequence by arranging the frequencies of Costas signal in time, first usin
g
binary Costas array and second using Golomb Ruler. These methods enable us to control side-lobes and to improve Doppler frequency resolution of Ambiguity Function (AF).
At first we present the principle of these methods Golomb Ruler and Costas array.
Then we apply these two methods to normal Costas signal, modified Costas signal and step frequency modulation and calculate the AF for all.
The results of comparison have shown that considerable reduction of side-lobes of AF is achieved by using these two methods, and consequently an improvement of AF is obtained.
This research evaluates the performance degradation of information channels in
multifunction radar due to electronic countermeasures (standoff jamming) on main loop
of radar antenna .This evaluation is performed by using the signal to noise ratio(
SNR )
and the probability of detection parameters with and without jamming.
Improvement of radar performance is proposed by using the following (ECCM)
techniques :Constant False Alarm Rate( CFAR), Pulse compression (pc) , frequency agility
(FA), Frequency Diversity(FD),Logarithmic Reception , and Automatic Gain
Control(AGC).
In this research MATLAB, software environment is used that has advanced
mathematical tools, and graphical interactive ability.
Counterfactuals are a valuable means for understanding decisions made by ML systems. However, the counterfactuals generated by the methods currently available for natural language text are either unrealistic or introduce imperceptible changes. We pro
pose CounterfactualGAN: a method that combines a conditional GAN and the embeddings of a pretrained BERT encoder to model-agnostically generate realistic natural language text counterfactuals for explaining regression and classification tasks. Experimental results show that our method produces perceptibly distinguishable counterfactuals, while outperforming four baseline methods on fidelity and human judgments of naturalness, across multiple datasets and multiple predictive models.
Abductive reasoning starts from some observations and aims at finding the most plausible explanation for these observations. To perform abduction, humans often make use of temporal and causal inferences, and knowledge about how some hypothetical situ
ation can result in different outcomes. This work offers the first study of how such knowledge impacts the Abductive NLI task -- which consists in choosing the more likely explanation for given observations. We train a specialized language model LMI that is tasked to generate what could happen next from a hypothetical scenario that evolves from a given event. We then propose a multi-task model MTL to solve the Abductive NLI task, which predicts a plausible explanation by a) considering different possible events emerging from candidate hypotheses -- events generated by LMI -- and b) selecting the one that is most similar to the observed outcome. We show that our MTL model improves over prior vanilla pre-trained LMs fine-tuned on Abductive NLI. Our manual evaluation and analysis suggest that learning about possible next events from different hypothetical scenarios supports abductive inference.
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