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Cognitive Radio (CR) technology is one of the strong candidate technologies to solve the spectrum scarcity problems. In this paper, we tackle the problem of secure data transmission between a secondary user transmitter and receiver through a relay in the presence of an eavesdropper in a cognitive radio network. The proposed scheme selects the best Decode-and-Forward relay among different relays to assist the transmitter, and to maximize the achievable secrecy rate without harming the primary user. Simulation results show that the secrecy capacity of the network using this scheme will almost be double the capacity when selecting the conventional scheme of relay selection.

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Peak to Average Power Ratio (PAPR) is one of the serious problems in any wireless communication systems using multicarrier modulation technique as OFDM, which reduces the efficiency of transmit high power amplifier. In this paper, proposed scheme will be introduced, which combines interleaving technique and companding technique to reduce PAPR. This scheme will be compared with the system that uses other technique for reduction which is the clipping technique. By using proposed scheme, the PAPR of OFDM signal can be reduced by 6.8 dB over the original system, i.e., without PAPR reduction. Also, SNR decreases by more than 5 dB for Bit Error Rate (BER) of 10 −3 over the original system. Moreover, the proposed scheme gives improvement more than 4.5 dB for BER of 10 −3 over the system that uses clipping. All these systems will be evaluated in the presence of nonlinear power amplifier.

Smart health surveillance technology has attracted wide attention between patients and professionals or specialists to provide early detection of critical abnormal situations without the need to be in direct contact with the patient. This paper presents a secure smart monitoring portable multivital signal system based on Internet-of-Things (IoT) technology. The implemented system is designed to measure the key health parameters: heart rate (HR), blood oxygen saturation (SpO2), and body temperature, simultaneously. The captured physiological signals are processed and encrypted using the Advanced Encryption Standard (AES) algorithm before sending them to the cloud. An ESP8266 integrated unit is used for processing, encryption, and providing connectivity to the cloud over Wi-Fi. On the other side, trusted medical organization servers receive and decrypt the measurements and display the values on the monitoring dashboard for the authorized specialists. The proposed system measurements are compared with a number of commercial medical devices. Results demonstrate that the measurements of the proposed system are within the 95% confidence interval. Moreover, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Relative Error (MRE) for the proposed system are calculated as 1.44, 1.12, and 0.012, respectively, for HR, 1.13, 0.92, and 0.009, respectively, for SpO2, and 0.13, 0.11, and 0.003, respectively, for body temperature. These results demonstrate the high accuracy and reliability of the proposed system.

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Human identification is considered as a serious challenge for several applications such as cybersecurity and access control. Recently, the trend of human identification has been directed to human biometrics, which can be used to recognize persons based on some physiological or behavioral characteristics that they own, such as fingerprint, iris, and biosignals. There are several types of human biosignals including electroencephalography (EEG), electrocardiography (ECG), and photoplethysmography (PPG) signals. This paper presents a human identification system based on PPG signals. The proposed system consists of three main phases: signal acquisition, signal pre‐processing, and feature extraction/classification. The pre‐processing phase involves denoising of the acquired signal, transformation of the 1D signal sequence into a 2D image, and computation of the spectrogram. Feature extraction is carried out on the images obtained from the pre‐processing phase. Features are extracted from the images based on convolutional neural networks (CNNs). The proposed CNN model consists of a sequence of convolutional (CNV) and pooling layers. Finally, the obtained feature maps are fed to the classifier to discriminate human identities. The proposed identification algorithm is applied on signals with and without an additive white Gaussian noise (AWGN). The simulation results reveal that the proposed algorithm achieves an accuracy of 99.5% with the spectrogram representation and 89.8% with the 2D image representation, in the absence of noise. In addition, the paper gives a discussion of the efficiency of denoising techniques such as wavelet denoising, Savitzky–Golay and Kalman filtering, when involved with the proposed algorithm. The simulation results prove that the wavelet dencoising technique has a best performance among the discussed noise reduction techniques.

This article studies a vital issue in wireless communications, which is the transmission of audio signals over wireless networks. It presents a novel interleaver scheme for protection against error bursts and reduction the packet loss of the audio signals. The proposed technique in the article is the chaotic interleaver; it is based on chaotic Baker map. It is used as a randomizing data tool to improve the quality of the audio over the mobile communications channels. A comparison study between the proposed chaotic interleaving scheme and the traditional block and convolutional interleaving schemes for audio transmission over uncorrelated and correlated fading channels is presented. The simulation results show the superiority of the proposed chaotic interleaving scheme over the traditional schemes. The simulation results also reveal that the proposed chaotic interleaver improves the quality of the received audio signal. It improves the amount of the throughput over the wireless link through the packet loss reduction. Figure 9 Received audio signal of file-1 waveform over a correlated fading channel (V c = 10 mile/h) at SNR = 20 dB. (a) No interleaving. (b) Bit-level interleaving. (c) Convolutional interleaving.