Adaptive relay selection based on channel gain and link distance for cooperative out-band device-to-device networks.

Device-to-device (D2D) communication is one of the key enabling technologies for 5G to support many connections efficiently. In a cooperative out-band D2D communication network, relay nodes have an essential role in receiving and forwarding information signals from source (S) to destination (D), making the system more reliable and with higher energy efficiency. Therefore, the relay selection method is very important in determining the best relay among existing relays to achieve maximum performance. This paper proposes a new Adaptive Relay Selection (ARS) scheme for cooperative out-band D2D networks based on channel gain value and transmission link distance. Firstly, the best relay is adaptively selected among N available relays (R) based on the maximum channel gain values between S to R if the channel gain-based signal-to-noise ratio (SNR) is greater than the distance-based SNR. Otherwise, the best relay is selected based on the minimum distance between S to R. We also analyzed an exact closed-form throughput and the total energy consumption required for a cooperative out-band D2D communication system using the Quantization-and-Forward (QF) protocol. The numerical results show that the proposed ARS scheme has a higher throughput than the two previous schemes: maximum channel gain and minimum distance. Furthermore, the proposed ARS can reduce the total energy consumption, which indirectly impacts the resulting energy efficiency level. The proposed ARS scheme achieves higher energy efficiency than the previous schemes, either by link distance or power allocation. So, the proposed ARS scheme for an out-band D2D communication network is an appropriate solution for the next generation of cellular communications.

Cross-spectral iris recognition using phase-based matching and homomorphic filtering.

In cross-spectral iris recognition, different spectral bands are used to obtain rich information of the human iris. Previous studies on cross-spectral iris recognition are based primarily on feature-based approaches, which are prone to the changes in parameters in the feature extraction process, such as spatial position and iris image acquisition conditions. These parameters can degrade iris recognition performance. In this paper, we present a phase-based approach for cross-spectral iris recognition using phase-only correlation (POC) and band-limited phase-only correlation (BLPOC). A phase-based iris recognition system recognizes an iris using the phase information contained in the iris image; therefore, its performance is not affected by feature extraction parameters. However, the performance of a phase-based cross-spectral iris recognition is strongly influenced by specular reflection. Different illumination conditions may produce different iris images from the same subject. To overcome this challenge, we integrate a photometric normalization technique -homomorphic filtering- with phase-based cross-spectral iris recognition. The experimental results reveal that the proposed technique achieved an excellent matching performance with an equal error rate of 0.59% and a genuine acceptance rate of 95%.

Effective and fast binarization method for combined degradation on ancient documents.

Document image binarization is a challenging task because of combined degradation in a document. In this study, a new binarization method is proposed for binarizing an ancient document with combined degradation. The proposed method comprises the following four stages: histogram analysis, contrast enhancement, local adaptive thresholding, and artifact removal. In histogram analysis, a new approach is applied to establish a uniform background. Next, the image contrast is enhanced using a new contrast enhancement, and then the document is binarized using a novel local adaptive thresholding. Artifacts from the binarization process are removed in the artifact removal stage. Finally, an experiment is conducted using one private and four public datasets and by simulating the proposed method with and without contrast enhancement. The results showed that the proposed method is faster and more effective compared to other state-of-the-art procedures for binarizing ancient documents.