The advances recently seen in data compression, and communication systems, have made it viable to design wireless image transmission systems. For many applications such as confidential transmission, military and medical applications, data encryption techniques should be also used to protect the confidential data from intruders. For these applications, both encryption and compression need to be performed to transmit a message in a fast and secure way. Further, the wireless channels have fluctuating channel qualities and high bit error rates. In this paper, a new scheme based on encryption and channel coding has been proposed for secure image transmission over wireless channels. In the proposed scheme, the encryption process is based on keys generator and Chaotic Henon map. Turbo codes are utilized as channel coding to deal effectively with the channel errors, multipath signal propagation and delay spread. Simulation results show that the proposed system achieves a high level of robustness against wide different of attacks and channel impairments. Further, it improves image quality with acceptable data rates.
Voiceprint Recognition (VPR) is the mechanism by which a user’s so-called identity is determined using characteristics taken from their voice, where this-technique is one of the world’s most useful and common biometric recognition techniques particularly the fields-relevant to security. These can be used for authentication, monitoring, forensic identification of speakers, and a variety of related activities. In this work, an attempt is applied to create a system that recognizes human speaker identity using Convolutional Neural Network (CNN). Two methods are used in this work which are MFCC-CNN and RW-CNN. The first method is standard method using MFCC, to use the features in the audio, where these features are will be entered into CNN to perform a process. The training CNN will take input as a picture and then the process of training via the proposed CNN is beginning. The second method, RW-CNN, the same steps as the first method, but without going through the MFCC phases where direct entry to CNN. In which, the same CNN structure was used in both methods. In this work, a 96% accuracy gained for both RW-CNN and MFCC-CNN. Both methods are similar in their results, either with or without noise, but the performance is mixed. This system can deep learn a large amount of human voices with high accuracy and minimum processes requirement.
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