Steganography is a mean of hiding information within a more obvious form of
communication. It exploits the use of host data to hide a piece of information in such a way
that it is imperceptible to human observer. The major goals of effective Steganography are
High Embedding Capacity, Imperceptibility and Robustness. This paper introduces a scheme
for hiding secret images that could be as much as 25% of the host image data. The proposed
algorithm uses orthogonal discrete cosine transform for host image. A scaling factor (a) in
frequency domain controls the quality of the stego images. Experimented results of secret
image recovery after applying JPEG coding to the stego-images are included.

In this paper two main stages for image classification has been presented. Training stage consists of collecting images of interest, and apply BOVW on these images (features extraction and description using SIFT, and vocabulary generation), while testing stage classifies a new unlabeled image using nearest neighbor classification method for features descriptor. Supervised bag of visual words gives good result that are present clearly in the experimental part where unlabeled images are classified although small number of images are used in the training process.

Lowpass spatial filters are adopted to match the noise statistics of the degradation seeking
good quality smoothed images. This study imply different size and shape of smoothing
windows. The study shows that using a window square frame shape gives good quality
smoothing and at the same time preserving a certain level of high frequency components in
comparsion with standard smoothing filters.

The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots.  To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accura

Image   processing   is  an   important   source   for   the  image

analytical    in   order    to    get   variable    parameters    such   as   the

intensity  .In  present   work  it  has  been  found   a  relation   between the  tensity and  number    of  pixd  in  the  image  ,  and  from  this relation   we  have   got   in  this  paper   the  inten

The aim of this work is to study the effect of diesel fuel percentage on the combustion processes in compression ignition engine using dual – fuel (diesel and LPG).

 The brake thermal efficiency increased with the increase of diesel fuel rate at low loads, and decreased when load increased. To get sufficient operation in engine fueled with dual fuel, it required sufficient flow rate of diesel fuel, if the engine fueled with insufficient diesel fuel erratic operation with miss fire cycles presented.

Dual-fuel operation at part load showed higher specific fuel consumption than straight diesl operation. At full loads, brake specific fuel consumption of duel fuel engine approaches that for diesel fuel values.