Polar codes were recently chosen to protect the control channel information in the next-generation mobile communication standard (5G) defined by the 3GPP. As a result, receivers will have to implement blind detection of polar coded frames in order to keep complexity, latency, and power consumption tractable. As a newly proposed class of block codes, the problem of polar-code blind detection has received very little attention. In this work, we propose a low-complexity blind-detection algorithm for polar-encoded frames. We base this algorithm on a novel detection metric with update rules that leverage the a priori knowledge of the frozen-bit locations, exploiting the inherent structures that these locations impose on a polar-encoded block of data. We show that the proposed detection metric allows to clearly distinguish polar-encoded frames from other types of data by considering the cumulative distribution functions of the detection metric, and the receiver operating characteristic. The presented results are tailored to the 5G standardization effort discussions, i.e., we consider a short low-rate polar code concatenated with a CRC.

Steganography is an ability of concealing information inside the cover in such a way it looks like simple cover though it has concealed information. There are many techniques to carry out steganography on electronic media, most especially audio and image files. In this method, we proposed a high secure steganography scheme hiding a 256×256 size gray secret image into a 512×512 size gray cover image with different combination of Discrete Wavelet Transform (DWT) and Integer Wavelet Transform (IWT). Pixel Value Adjustment (PVA) is first performed on cover image. The secret image values are scrambled by using Arnold transform. The DWT /IWT is applied on both cover and scrambled secret image. Blending process is applied to both images and compute Inverse DWT/IWT on the same to get the stego image. The extraction model is actually the reverse process of the embedding model. Different combination of DWT/IWT transform is performed on the scrambled secret image and cover image to achieved high security and robustness. Hybrid transform combination approach and case analysis provided the various hiding environment. Experimental results and case study provided the stego-image with perceptual invisibility, high security and certain robustness

Segmentation is the process of partitioning an image into number of meaningful images as segments or clusters. The segmentation is initial but important process which is used to locate boundaries and objects in images. This paper is concerned with segmentation of color satellite images using neural network based kohonen's self-organizing maps. This unsupervised competitive network is used to visualize and interpret large data sets. In this paper, test images are segmented in RGB and HSV color space using self-organizing map and the segmentation results are compared using error image, peak signal to noise ratio, and execution time. The efficiency of proposed method is tested with Landsat and Terra (MODIS sensor) satellite images.

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