Modern wireless communication systems suffer from phase shifting and, more importantly, from interference caused by multipath propagation. Multipath propagation results in an antenna receiving two or more copies of the signal sequence sent from the same source but that has been delivered via different paths. Multipath components are treated as redundant copies of the original data sequence and are used to improve the performance of forward error correction (FEC) codes without extra redundancy, in order to improve data transmission reliability and increase the bit rate over the wireless communication channel. For a proof of concept Bose, Ray-Chaudhuri, and Hocquenghem (BCH) and Reed-Solomon (RS) codes have been used for FEC to compare their bit error rate (BER) performances. The results showed that the wireless multipath components significantly improve the performance of FEC. Furthermore, FEC codes with low error correction capability and employing the multipath phenomenon are enhanced to perform better than FEC codes which have a bit higher error correction capability and did not utilise the multipath. Consequently, the bit rate is increased, and communication reliability is improved without extra redundancy.

In wireless communication, there exists a phenomenon know as `multipath'. This phenomenon is considered as a disadvantage because it causes interference. The multipath phenomenon results in an antenna receiving two or more signals from the same sent signal from different paths. This paper considers them as redundant copies of the transmitted data and utilises them to improve the performance of forward error correction (FEC) codes without extra redundancy, in order to improve data transmission reliability and increase the bit rate over wireless communication channels. The system was evaluated in bit error rate (BER) and used Bose, Ray-Chaudhuri and Hocquenghem (BCH) and Reed-Solomon (RS) codes as FEC. The results showed that the utilisation of the multipath improves the performance of FEC. Furthermore, the performance of FEC codes had t₁ error correction capability and employed the multipath is better than FEC codes have t₂ error correction capability and without the multipath, where t₁ <; t₂. Consequently, the bit rate is increased, and communication reliability is improved without extra redundancy.

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In person recognition, biometrics play an important role. Behavioral or physiological characteristics are utilized by biometrics to identify an individual. Palmprint is considered highly usable and represents a reliable and unique biometric characteristic. Nevertheless, the useful and deepest features extracted from palm prints are an essential point. The most recently developed techniques use creases, wrinkles, and principal line features. However, due to closeness, these features are not enough to distinguish two individuals. Recently, one of the most important techniques that is considered a major key to extracting deep features such as texture features is deep learning. This paper proposes a biometric palm print system using the Resnet50 pre-train model to extract deep features and identify individuals. COEP and PolyU-IITD datasets are used in the simulation; moreover, the merging of COEP and PolyU-IITD in one dataset is also used. In the evaluation process, precision, F1-score, and recall are employed. The proposed system developed three models; the first model resulted in precision = 0.967, recall = 0.97, and F1 = 0.97. In comparison, the second model got precision = 0.88, recall = 0.87, and F1 = 0.86. Finally, the third model obtained precision = 0.95, recall = 0.92, and F1 = 0.93. The proposed system efficiently performs palm print identification.

Person recognition systems have been applied for several years, as fingerprint recognition has been experimented with different image resolutions for 15 years. Fingerprint recognition and biometrics for security are becoming commonplace. Biometric systems are emerging and evolving topics seen as fertile ground for researchers to investigate more deeply and discover new approaches. Among the most prominent of these systems is the palm printing system, which identifies individuals based on the palm of their hands because of the advantages that the palm possesses that cannot be replicated among humans, as in its theory of other fingerprints. This paper proposes a biometric system to identify people by handprint, especially palm area, using deep learning technology via a pre-trained model on the PolyU-IITD dataset. The proposed system goes through several basic stages, namely data pruning, processing, training, and prediction, and the results were promising, as the system's accuracy reached 90% based on the confusion matrix measures.

Wireless networks and communications have witnessed tremendous development and growth in recent periods and up until now, as there is a group of diverse networks such as the well-known wireless communication networks and others that are not linked to an infrastructure such as telephone networks, sensors and wireless networks, especially in important applications that work to send and receive important data and information in relatively unsafe environments, cybersecurity technologies pose an important challenge in protecting unsafe networks in terms of their impact on reducing crime. Detecting hacking in electronic networks and penetration testing. Therefore, these environments must be monitored and protected from hacking and malicious attacks. In this manuscript, a correspondence model is designed to discuss the algorithm in the environment of wireless communication systems and distributed computing by adopting the protocol data enhancement to the network using structured construction and diversity algorithm to improve the effectiveness of the intrusion detection system. Next, a multi-label convolutional neural network model is used to detect business transactions. An CNN was trained on the WSN-DS dataset using 5-Fold in CV technique with three hidden layers. The highest Precision values 0.951 of Grayhole attack for multi-classification.