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Any communication scheme's principal goal is providing error-free data transmission. By increasing the rate at which data could be transmitted through a channel and maintaining a given error rate, this coding is advantageous. The message bits to be transmitted will gradually receive more bits thanks to the convolution (channel) encoder. At the receiver end of the channel, a Viterbi decoder is utilized in order to extract original message sequence from the received data. Widely utilized error correction approaches in communication systems for the enhancement of bit error rate (BER) performance are Viterbi decoding and convolutional encoding. The Viterbi decoder and convolution encoder rate for constraints with lengths of 2 and 6 and bit rates of 1⁄2 and 1⁄3 are shown in this study in the presence of (1/f) noise. The performance regarding the convolutional encoding/hard decision Viterbi decoding forward error correction (FEC) method affects the simulation outcomes. The findings demonstrate that the BER as function of signal to noise ratio (SNR) acquired for uncoded binary phase shift keying (BPSK) with the existence of additive white Gaussian noise (AWGN) is inferior to that acquired with the use of a hard decision Viterbi decoder.

The optimal design of the automatic voltage regulators for a synchronous machine are positively reflected on the quality of voltage stability. This paper is concerned with the design of an AVR by adopting three control techniques. The first one was designed according to the traditional proportional integration-derived (PID) controller while the fuzzy logic was adopted in design the second powerful controller, finally the fuzzy PID controller for an automatic voltage regulator (AVR) based on fuzzy logic technology, selftuning fuzzy proportional integration-derived (STFPID) have been designed to tuning the gains of the PID controller (KP, KI and KD). To confirm the efficiency of the proposed control systems, a simulation was carried out, and the results showed that the designed STFPID controller achieves the best performance of the AVR system, and gives the preferable tracking low rise time, lower overshoot, least stetting time, minimal steady state error, and gives the ideal response against PID and fuzzy logic technology.

Objectives: This study aimed to develop an automated deep-learning algorithm for the detection and 3D segmentation of incidental jaw lesions in maxillofacial CBCT scans.Materials and Methods: The dataset included 82 CBCT scans with and without histologically confirmed benign bone lesions, obtained from three CBCT devices using different imaging protocols. The dataset consisted of axial CBCT images and was divided into training dataset (20,214 axial images), validation dataset (4,530 axial images) and testing dataset (6,795 axial images). A Mask-RCNN based deep-learning algorithm segmented the bone lesion in each axial image. The analysis of sequential slices improved the mask RCNN performance and assisted in classifying each CBCT case as containing bone lesions or not. Thereafter, the algorithm generated a 3D segmentation of the lesions. Results: The accuracy of the algorithm for classifying each CBCT case as either containing bone lesions or not, was 100%. The algorithm performance for detecting lesions in individual axial images showed high sensitivity (95.9%) and high precision (98.9%).Conclusions: Our deep learning algorithm can detect incidental bone lesions in CBCT scans with high accuracy, high sensitivity and high precision and is highly recommended for bone lesion detection and follow-up in CBCT imaging. Clinical relevance: The increasing number of CBCT scans performed worldwide dictates a clinical need for developing an automated tool, which will review, detect and volumetrically demonstrate incidental bone lesions in CBCT scans with high accuracy and low false-positive rate.

Chickpea (Cicer arietinum L.) is a bisexual and self-pollinated legume. It improves the soil fertility through its natural ability to fix atmospheric nitrogen with its symbiotic bacteria. Salinity is one of the most important abiotic stress factors affecting plant growth. γ-radiation is a very effective tool for inducing mutations in many plants. This study evaluated the γ-radiation effect on germination, cell division and plant growth of first-generation plants. Seeds of seven chickpea varieties were irradiated with γ-radiation doses ranging between 50 Gy and 600 Gy. Non-significant differences in germination percentage were recorded for seeds exposed to 50 Gy, 100 Gy, and 200 Gy of γ-radiation in comparison to the corresponding controls except ILC 484. The mitotic index (MI) of root cells increased at the low doses of 50 Gy, 100 Gy and 200 Gy comparing and reduced at the higher doses in all chickpea varieties to the control. All doses of γ-radiation induced a variable range of chromosomal abnormalities; the most common were bridges, laggard chromosomes, stickiness at metaphase, chromosome breaks, micronuclei and binucleate cells. The 300 Gy to 600 Gy doses induced degradation of nuclear membranes. The salinity treatments at 25 mM NaCl and 60 mM NaCl reduced seedling’s growth of all cultivars. The dose of 100 Gy alleviated the impact of salinity at a concentration of 25 mM NaCl for all varieties, except FLIP 84-188 and FLIP 97-263. The 60 mM NaCl treatment significantly reduced early growth of all cultivars and its effect was not alleviated by the γ-radiation.

Objectives: This study aimed to develop an automated deep-learning algorithm for the detection and 3D segmentation of incidental jaw lesions in maxillofacial CBCT scans.Materials and Methods: The dataset included 82 CBCT scans with and without histologically confirmed benign bone lesions, obtained from three CBCT devices using different imaging protocols. The dataset consisted of axial CBCT images and was divided into training dataset (20,214 axial images), validation dataset (4,530 axial images) and testing dataset (6,795 axial images). A Mask-RCNN based deep-learning algorithm segmented the bone lesion in each axial image. The analysis of sequential slices improved the mask RCNN performance and assisted in classifying each CBCT case as containing bone lesions or not. Thereafter, the algorithm generated a 3D segmentation of the lesions. Results: The accuracy of the algorithm for classifying each CBCT case as either containing bone lesions or not, was 100%. The algorithm performance for detecting lesions in individual axial images showed high sensitivity (95.9%) and high precision (98.9%).Conclusions: Our deep learning algorithm can detect incidental bone lesions in CBCT scans with high accuracy, high sensitivity and high precision and is highly recommended for bone lesion detection and follow-up in CBCT imaging. Clinical relevance: The increasing number of CBCT scans performed worldwide dictates a clinical need for developing an automated tool, which will review, detect and volumetrically demonstrate incidental bone lesions in CBCT scans with high accuracy and low false-positive rate.