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The next generation wireless communication system, 5G, or New Radio (NR) will provide access to information and sharing of data anywhere, anytime by various users and applications with diverse multi-dimensional requirements. Physical Uplink Control Channel (PUCCH), which is mainly utilized to convey Uplink Control Information (UCI), is a fundamental building component to enable NR system. Compared to Long Term Evolution (LTE), more flexible PUCCH structure is specified in NR, aiming to support diverse applications and use cases. This paper describes the design principles of various NR PUCCH formats and the underlying physical structures. Further, extensive simulation results are presented to explain the considerations behind the NR PUCCH design., Comment: 6 pages, 11 figures, accepted in IEEE 5G World Forum 2018

Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel or point spread function is unknown. Despite of exhaustive research over the last few decades, blind image deconvolution still remains an unsolved problem. In this paper, we present a novel morphology based initial estimation technique of true image for the Iterative Blind Deconvolution (IBD) of linearly degraded images without the explicit knowledge of either the original image or the point spread function. The only constraints imposed are the non-negativity and finite support size of the true image. The restoration process involves Wiener filtering instead of usual inverse filtering in iterative loop. The filter coefficient s that depends on the noise level is also estimated mathematically taking pixel values of blurred image and its median filtered version into consideration. The conventional IBD with these twofold modifications is implemented and experimental results show satisfactory convergence, uniqueness and robustness.

In this work, we address off-line signature verification as a writer-independent system. We propose a set of morphological features, extracted from off-line signature images. To examine the effectiveness of the features, a publicly available signature database, namely CEDAR signature database is used. A pair of signatures is fed to the system to give an inference for their (dis)similarity. To get a compact set of features, a multilayer perceptron based feature analysis technique is utilized. A 10-fold cross-validation framework based on support vector machine is used for verification. Receiver operator curve (ROC) analysis gives an equal error rate (EER) of 11.59%, which is comparable to the state-of-the-arts reported on this database.