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On the Use of 6th-Order Tunable Complementary Metal-Oxide-Semiconductor Varactor based Filter in Ultra-Wideband Low Noise Amplifier
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Background:

The plethora of the emerged radio frequency applications makes the frequency spectrum crowded by many applications and hence the ability to detect specific application’s frequency without distortion is a difficult task to achieve.

Objective:

The goal is to achieve a method to mitigate the highest interferer power in the frequency spectrum in order to eliminate the distortion.

Method:

This paper presents the application of the proposed tunable 6th-order notch filter on Ultra-Wideband (UWB) Complementary Metal-Oxide-Semiconductor (CMOS) Low Noise Amplifier (LNA) utilising self-forward body bias (SFBB).

Results:

The proposed filter presents 23.5dBm minimum interferer rejection (IR) and attenuates the interferer signal from -43dBm to -67dBm at frequency 5.17GHz. In addition, the maximum IR is 40dBm and attenuates the interferer signal from -41dBm to -81dBm at frequency 5.785GHz. The proposed filter provides coarse tuning with frequency spacing (10MHz) and soft tuning with frequency spacing (1MHz). The UWB CMOS LNA consumes only 5.22mW from a supply voltage of 1.2V and presents a maximum gain of 14dB at frequency 6.25GHz in the -3dB bandwidth from 4.75GHz to 7.5GHz. In addition, the average noise figure is 3.1dB and the input insertion losses (S11) is less than -12dB along the designed bandwidth. The simulation is performed in Advanced Design System (ADS2016.01) software utilising 180nm Taiwan Semiconductor Manufacturing Company (TSMC) Berkeley Short-channel Insulated Gate Field Effect Model (BSIM3v3) model files.

Conclusion:

The proposed method achieves high interferer power rejection with both soft and coarse tuning.

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Publication Date
Thu Jun 06 2024
Journal Name
Journal Of Applied Engineering And Technological Science (jaets)
Deep Learning and Its Role in Diagnosing Heart Diseases Based on Electrocardiography (ECG)
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Diagnosing heart disease has become a very important topic for researchers specializing in artificial intelligence, because intelligence is involved in most diseases, especially after the Corona pandemic, which forced the world to turn to intelligence. Therefore, the basic idea in this research was to shed light on the diagnosis of heart diseases by relying on deep learning of a pre-trained model (Efficient b3) under the premise of using the electrical signals of the electrocardiogram and resample the signal in order to introduce it to the neural network with only trimming processing operations because it is an electrical signal whose parameters cannot be changed. The data set (China Physiological Signal Challenge -cspsc2018) was ad

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Publication Date
Wed Mar 01 2017
Journal Name
جامعة كرميان
The effect of a training program for chemistry teachers based on the strategy of both sides of the brain together on the thinking patterns of their students
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Publication Date
Tue Oct 02 2018
Journal Name
Iraqi Journal Of Physics
Study of in vitro and in vivo free radical scavenging activity for radioprotection of cerium oxide nanoparticles
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Nanoceria have shown numerous unique characteristics, such as biocompatibility and are excellent agents for biological applications. The aim of this study is to investigate cerium oxide nanoparticles for 2, 2- diphenyl-1-picryl-hydrazyl-hydrate (DPPH) free radical scavenging activity and their ability to offer protection against ionizing radiation. In vitro antioxidant activity study of nanoceria particles has shown good free radical scavenging activity for DPPH radical assayed within a concentration range of 0.01 to 0.05 g/l, at higher concentrations of nanoparticles showed reverse trend in absorbance and inhibition indicating this finite rang of concentration is suitable for scavenging free radicals, also nanoparticles were found to ha

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Publication Date
Mon Jun 30 2014
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Study the Performance of Low Cost Material (Peanut Hulls) for Dye Adsorption Using Inverse Fluidized Bed
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The present study dealt with the removal of methylene blue from wastewater by using peanut hulls (PNH) as adsorbent. Two modes of operation were used in the present work, batch mode and inverse fluidized bed mode. In batch experiment, the effect of peanut hulls doses 2, 4, 8, 12 and 16 g, with constant initial pH =5.6, concentration 20 mg/L and particle size 2-3.35 mm were studied. The results showed that the percent removal of methylene blue increased with the increase of peanut hulls dose. Batch kinetics experiments showed that equilibrium time was about 3 hours, isotherm models (Langmuir and Freundlich) were used to correlate these results. The results showed that the (Freundlich) model gave the best fitting for adsorption capacity. D

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Publication Date
Thu Jun 01 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
A Suggestion To Solve The Problem Of Asynchronous File Uploads In ASP.NET Without The Need To Use Ajax Technology
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The file upload is one of the controls of the tool bar of the VisualStodio.NET, but it can’t be used with Ajax technology (It's almost impossible today to be involved in web application design or development and not be aware of Ajax) because the file upload control don’t support Ajax technology that the execution of the instruction is not completed or the (object is not set) message will appear, although the essential need to use this control with Ajax to gain the asynchronous data transfer that supplied by Ajax, because the file upload don’t work without the Postback operation from the client to the server, and the main idea of Ajax technology is working without Postback operation to the server. After the deep resea

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Publication Date
Sun Jan 20 2019
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Text Classification Based on Weighted Extreme Learning Machine
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The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed   a great competence of the proposed WELM compared to the ELM. 

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Publication Date
Thu Dec 29 2016
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Proposal for Exchange Text message Based on Image
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     The messages are ancient method to exchange information between peoples. It had many ways to send it with some security.

    Encryption and steganography was oldest ways to message security, but there are still many problems in key generation, key distribution, suitable cover image and others. In this paper we present proposed algorithm to exchange security message without any encryption, or image as cover to hidden. Our proposed algorithm depends on two copies of the same collection images set (CIS), one in sender side and other in receiver side which always exchange message between them.

      To send any message text the sender converts message to ASCII c

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Publication Date
Sat Jun 26 2021
Journal Name
2021 Ieee International Conference On Automatic Control & Intelligent Systems (i2cacis)
Vulnerability Assessment on Ethereum Based Smart Contract Applications
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Publication Date
Thu Aug 01 2019
Journal Name
2019 2nd International Conference On Engineering Technology And Its Applications (iiceta)
Human Gait Identification System Based on Average Silhouette
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Publication Date
Sun Nov 01 2020
Journal Name
Journal Of Physics: Conference Series
Improve topic modeling algorithms based on Twitter hashtags
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Abstract<p>Today with increase using social media, a lot of researchers have interested in topic extraction from Twitter. Twitter is an unstructured short text and messy that it is critical to find topics from tweets. While topic modeling algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are originally designed to derive topics from large documents such as articles, and books. They are often less efficient when applied to short text content like Twitter. Luckily, Twitter has many features that represent the interaction between users. Tweets have rich user-generated hashtags as keywords. In this paper, we exploit the hashtags feature to improve topics learned</p> ... Show More
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