This book constitutes the thoroughly refereed proceedings of the Second International Conference on Context-Aware Systems and Applications, ICCASA 2013, held in Phu Quoc Island, Vietnam in November 2013. The 36 revised full papers presented were carefully selected and reviewed from over 100 submissions and cover a wide spectrum of issues in the area of context-aware systems (CAS) and context-based recommendation systems.

This book presents select papers from the International Conference on Emerging Trends in Communication, Computing and Electronics (IC3E 2018). Covering the latest theories and methods in three related fields - electronics, communication and computing, it describes cutting-edge methods and applications in the areas of signal and image processing, cyber security, human-computer interaction, machine learning, electronic devices, nano-electronics, wireless sensor networks, antenna and wave propagation, and mobile communication. The contents of this book will be beneficial to students, researchers, and professionals working in the field of networks and communications.

Noise reduction is an important step for development of any sophisticated algorithms in computer vision and image processing. A tradeoff between the removed noise and the blur in the image always exists. The capability of the wavelets to give detail spatial-frequency information is the main reason for the use of wavelets. This property promises a possibility for better discrimination between the noise and the real data. Successful exploitation of the wavelet transform might reduce the blurring effect or even overcome it completely. Object tracking is a problem of estimating the positions and other relevant information of moving objects in the sequences of image video. The main difficulties in reliable tracking of moving objects include: rapid appearance changes caused by image noise and interaction between multiple objects. In a long sequence of image video, if the dynamics of the moving object is known, prediction can be made about the positions of the object in a particular frame. This information can be combined with the actual image observation to achieve more robust results. We have explored a possibility to use wavelet transform for object tracking.

Medical images are generally of poor contrast and they also get complex type of noise and blur. The noise also has variability from one condition to other. So it is very difficult to suggest a robust method for noise removal which works equally well for different modalities of medical images. During the denoising process of a noisy image, it is usually helpful to look at an image at different resolutions so that important information about both the image and the noise can emerge easily. If the chosen resolution is too coarse, fine details will not be visible. On the other hand, looking too closely at an object can cause surroundings to disappear, so the noise and the object cannot be distinguished easily. This is where wavelets can be useful. But unfortunately the present wavelet based techniques for medical image denoising are too particular and are useful in particular situations only. Here, it is important to mention that complex wavelet transform has not found its deserving place in many applications, and one of the major challenging tasks taken up in this work is to apply complex wavelet transform for denoising.