Obstacle detection is an essential task for the autonomous navigation by robots. The task becomes more complex in a dynamic and cluttered environment. In this context, the RGB-D camera sensor is one of the most common devices that provides a quick and reasonable estimation of the environment in the form of RGB and depth images. This work proposes an efficient obstacle detection and tracking method using depth images to facilitate quick dynamic obstacle detection. To achieve early detection of dynamic obstacles and stable estimation of their states, as in previous methods, we applied a u-depth map for obstacle detection. Unlike existing methods, the present method provides dynamic thresholding facilities on the u-depth map to detect obstacles more accurately. Here, we propose a restricted v-depth map technique, using post-processing after the u-depth map processing to obtain a better prediction of the obstacle dimension. We also propose a new algorithm to track obstacles until they are within the field of view (FOV). We evaluate the performance of the proposed system on different kinds of data sets. The proposed method outperformed the vision-based state-of-the-art (SoA) methods in terms of state estimation of dynamic obstacles and execution time.

In this work, we have proposed an audio encryption method. The proposed method is audio signal sensitive as the hash value of the given signal is computed using SHA3-512, which returns a significantly large key of size 512-bit. This key is used to set the different parameters. This work suggests a 2D Cosine Logistic Map (2DCLM) by fusing the Cosine map with the Logistic map. The proposed 2DCLM functions well under chaos. The given audio signal is scrambled with the help of the hash value. The scrambled signal is decomposed by Empirical Mode Decomposition (EMD); before using the EMD the signal is segmented into a 2D signal to reduce the time complexity of the EMD method. The residuals given by EMD and the stream generated by 2DCLM are XOR-ed to encrypt the signal. Finally, the 2D encrypted signal is transformed into a 1D encrypted audio signal. The efficacy of the present method is evaluated with the help of different audio streams. The findings of the simulation and comparison indicate that the suggested technique may deliver effective encryption results while thwarting cryptographic assaults.

In this article, we have proposed an efficient and secure method of image encryption. This image encryption method is new, where the plain image is confused using Fibonacci Transformation, and Tribonacci Transformation modifies the pixel values. The Fibonacci numbers and Tribonacci numbers of the above transformations are determined using the hash value of the plain image. To our knowledge, this is the first time a Tribonacci Transformation has been used in image encryption. The performance of the present method is evaluated using some standard test images and some special images. The present method has high key space and is sensitive to the plain image. The proposed method is also robust against attacks like brute force attacks, statistical attacks, differential attacks, noise attacks, cropping attacks, and known/chosen-plaintext attacks. The performance of the proposed method is equally efficient as the state-of-the-art methods, which establishes the applicability of the present method.