IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

Road traffic accidents are one of the leading causes of mortality globally. Reducing the number of traffic-related incidents has become a serious socio-economic and public health problem, given the ever-increasing number of cars on the road. As a result, this paper proposes an intelligent vehicle prediction communication mechanism that alerts drivers to any autos that may be overtaking or bypassing the targeted vehicle. The primary goal of this paper is to leverage modern Internet of Things (IoT) and wireless sensor technologies to predict any potential accident that may occur as a result of car accidents. This paper proposes the Collision Prediction of a Moving Vehicle (CPMV) system. The information acquired by CPMV will alert the driver to divert the vehicle in a reasonable amount of time before any harm occurs. It redirects the inbound object that emitted the Ultrasound signal which was received by the vehicle, to a safe location. The proposed system predicts collision between vehicles through Wi-Fi and Bluetooth, using a set of sensors with a precision of 360 degrees and a distance of collision prediction of one meter and at a speed of 200-300 revolutions per minute. The python programming language was utilized to code the programs that control the vehicle during the implementation of this project. The Raspberry Pi 4 is utilized as the controller to examine the vehicle’s spatial data. The test results showed that using this application to deal with an approaching object can be a successful strategy in the three proposed scenarios at different angles and directions.

Researchers have extensively utilized optical orthogonal frequency division multiplexing (O-OFDM) in visible light communication (VLC) to achieve high data rate transmission for free spectrum bandwidth. The peak-to-average power ratio (PAPR) is the critical challenge for VLC systems-based O-OFDM that produces non-linearity and degrades performance. In this paper, a proposed model for PAPR reduction can be applied with different O-OFDM technologies. This model considered using -law companding with O-OFDM transmitter to compress high amplitude peaks and restore the signals using de-companding in the receiver. The obtained simulation results show an efficient achievement of about 75% PAPR reduction compared with the original O- OFDM for different techniques. Furthermore, The convolutional encoder with Viterbi decoder is used with our proposed model for improvement BER performance and tradeoff with PAPR. The BER performance for different coding schemes, O-OFDM technologies, and modulation orders has been graphed and compared. It can notice the convolutional encoder/Viterbi satisfies better BER than Hamming coding/decoding. However, the number of memory cells of the convolutional encoder plays an essential role in BER improvement.

Auto-Correction is the process of correcting a misspelled word typed by the user as an application of automated translation process. lip-reading is the process of recognizing the words through processing and observing the visual lip movement of a speaker’s talking without any audio input. Although visual information itself cannot be considered as enough resource to provide normal speech as intelligibility, it may succeed with several cases especially when the words to be recognized are limited. Auto-correction is a trail to diminish the number of errors that can be generated by lip reading systems and to improve their accuracy, many error-correction techniques were visualized. In this paper an auto- correction model is proposed to correct the misspelled words recognized by a lip reading system, the output of a lip reading system is subjected to auto-correction model to enhance the accuracy of the system. The auto-correction model is based on levenshtien distance and dictionary lookup with a proposed dataset. The proposed model achieved accuracy of more than 67% enhancing the lip reading system by almost 30%.