Enhancing the BER and ACLR for the HPA Using Pre-Distortion Technique

Power amplifiers are key components in wireless transceivers. Their function is to amplify signal and generate the required Radio Frequency (RF) power that allows to transmit the signal over an appropriate range. The Orthogonal Frequency Division Multiplexing (OFDM) systems are highly sensitive to nonlinear distortion introduced by High Power Amplifier (HPA). The HPA nonlinearity causes in-band and out-of-band distortions. The linearization techniques are used to compensate the nonlinear effects of the high power amplifier. These techniques correct the distortion effects resulting from nonlinearities in the transmitted signal. Many linearization techniques have been developed to improve power amplifier linearity and to decrease both Bit Error Rate (BER) and Adjacent Channel Leakage Ratio (ACLR). This work is set to run the high power amplifier in the nonlinear region. It is also attempting to analyze the resulting signal in terms of the BER and ACLR, next employs pre-distortion linearization techniques to reduce the distortion introduced in this region. According to Digital Video Broadcasting-Terrestrial (DVB-T) standard the linearization techniques, circuit and the OFDM transmitter and receiver is designed and implemented through using computer simulation of AWR Design Environment

Low-complex Bayesian estimator for imperfect channels in massive muti-input multi-output system

Motivated by the fact that the complexity of the computations is one of the main challenges in large multiple input multiple output systems, known as massive multiple-input multiple-output (MIMO) systems, this article proposes a low-complex minimum mean squared error (MMSE) Bayesian channel estimator for uplink channels of such systems. First, we have discussed the necessity of the covariance information for the MMSE estimator and how their imperfection knowledge can affect its accuracy. Then, two reduction phases in dimension and floating-point operations have been suggested to reduce its complexity: in phase 1, eigenstructure reduction for channel covariance matrices is implemented based on some truncation rules, while in phase 2, arithmetic operations reduction for matrix multiplications in the MMSE equation is followed. The proposed procedure has significantly reduced the complexity of the MMSE estimator to the first order O(M), which is less than that required for the conventional MMSE with O(M3) in terms of matrix dimension. It has been shown that the estimated channels using our proposed procedure are asymptotically aligned and serve the same quality as the full-rank estimated channels. Our results are validated by averaging the normalized mean squared error (NMSE) over a length of 500 sample realizations through a Monte Carlo simulation using MATLAB R2020a

Fuel Station Monitoring and Automation based on WSN

The Iraqi fuel station still now uses old technologies to control its activities from filling tanks to the filling cars. Automate the activity of fuel station is the objective of this work. The aims of fuel station automation are to save the fuel quantities and qualities supplied in fuel station, and to keep the fuel station, the worker and its main parts safe. This work uses the national instrument wireless sensor network (NI WSN). The NI WSN used to automate the protection system and level controlling system which makes the fuel station work under normal ambient temperature, and normal protection conditions. Automation based on a wireless sensor network gives excellent capabilities to automate and monitor fuel station. Through the user interface window the user monitor the status of actuators, protection system controller messages, fuel levels, water level, environment temperature, power source and its quality. The soft controller developed was built within The LABVIEW environment. The results of controller give the desired action through "on" and “off” states of the actuators

Classification of EEG Signals Using Quantum Neural Network and Cubic Spline

The main aim of this paper is to propose Cubic Spline-Quantum Neural Network (CS-QNN) model for analysis and classification of Electroencephalogram (EEG) signals. Experimental data used here were taken from seven different electrodes. The work has been done in three stages, normalization of the signals, extracting the features by Cubic Spline Technique (CST) and classification using Quantum Neural Network (QNN).  The simulation results showed that five types of EEG signals were classified with an average accuracy for seven electrodes that is 94.3% when training 70% of features while with an average accuracy of 92.84% when training 50% of features

Detection of electrocardiogram QRS complex based on modified adaptive threshold

It is essential for medical diagnoses to analyze Electrocardiogram (ECG signal). The core of this analysis is to detect the QRS complex. A modified approach is suggested in this work for QRS detection of ECG signals using existing database of arrhythmias. The proposed approach starts with   the same steps of previous approaches by filtering the ECG. The filtered signal is then fed to a differentiator to enhance the signal. The modified adaptive threshold method which is suggested in this work, is used to detect QRS complex. This method uses a new approach for adapting threshold level, which is based on statistical analysis of the signal. Forty-eight records from an existing arrhythmia database have been tested using the modified method. The result of the proposed method shows the high performance metrics with sensitivity of 99.62% and a positive predictivity of 99.88% for QRS complex detection

A high security and noise immunity of speech based on double chaotic masking

It is known that increasing the security of the information and reducing the noise effect through public channels are two of the main priorities in developing any communication system. In this article, an efficient, secure communication system with two levels of encryption has been applied to the speech signal. The suggested security approach was implemented by using two different stages of chaotic masking on the signal; one masking was conducted by using Lorenz system and the other masking was built by using Rӧssler chaotic flow system. The main goal of developing this two-chaotic masking approach is to increase the key space and the security of the information. Also, an immunity technique has been implemented in the suggested approach to reduce the noise effect. For practical application purposes, this system was tested with additive white gaussian noise (AWGN) channel. The simulation results show that the quality of reconstructed speech signal is changeable according to the used signal to noise ratio (SNR); therefore, a proposed technique based on digital processing method (DPM) was applied to the first masked signal by converting the sampled data from the analog to the binary format. The simulation results show that an 22 dB (SNR) is sufficient to recover the speech signal with minimum noise by using the suggested approach

Four dimensional hyperchaotic communication system based on dynamic feedback synchronization technique for image encryption systems

This paper presents the design and simulation of a hyperchaotic communication system based on four dimensions (4D) Lorenz generator. The synchronization technique that used between the master/transmitter and the slave/receiver is based on dynamic feedback modulation technique (DFM). The mismatch error between the master dynamics and slave dynamics are calculated continuously to maintain the sync process. The information signal (binary image) is masked (encrypted) by the hyperchaotic sample x of Lorenz generator. The design and simulation of the overall system are carried out using MATLAB Simulink software. The simulation results prove that the system is suitable for securing the plain-data, in particular the image data with a size of 128×128 pixels within 0.1 second required for encryption, and decryption in the presence of the channel noise. The decryption results for gray and colored images show that the system can accurately decipher the ciphered image, but with low level distortion in the image pixels due to the channel noise. These results make the proposed cryptosystem suitable for real time secure communications

Global economic burden of unmet surgical need for appendicitis

Background There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and$73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and$75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. Conclusion For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially