A cyber physical system (CPS) is a complex system that integrates computation, communication, and physical processes. Digital manufacturing is a method of using computers and related technologies to control an entire production process. Industry 4.0 can make manufacturing more efficient, flexible, and sustainable through communication and intelligence; therefore, it can increase the competitiveness. Key technologies such as the Internet of Things, cloud computing, machine-to-machine (M2M) communications, 3D printing, and Big Data have great impacts on Industry 4.0. Big Data analytics is very important for cyber-physical systems (CPSs), digital manufacturing, and Industry 4.0. This paper introduces technology progresses in CPS, digital manufacturing, and Industry 4.0. Some challenges and future research topics in these areas are also presented.

In this paper, our main motive to compare different video formats and also analyze what will be the effect on quality when we embed same watermark in different type of formats such as .avi (colored and uncompressed video format), mp4(colored and compressed video format) & grey color image using discrete wavelet transform & singular value decompositions techniques. In research work, HH bands are used to embed the watermark into video because this band are less sensitive to human eyes and not easily detected or captured by human visual system. It also helps to improve the quality of video. The analysis is done on the basis of quality metrics such as PSNR, BER & MSE for different format video clips.

The goal of this paper is to investigate a new technique for human identification using foot features. This work can be mainly decomposed into image preprocessing, feature extraction and pattern recognition by Artificial Neural Network (ANN).
Foot images are rarely of perfect quality. To obtain good minutiae extraction in foot with varying quality, we conducted preprocessing in form of image enhancement and binarization .To extract features from human foot based on shape geometry of foot boundaries by extracting 16 geometric features from a human foot image. The foot center has been determined, and then the distances between the center point and outer points are measured with different angles .The angles are from 30˚ to 360˚ by increment with 30˚ gradual. The 13th feature that can be extracted is the length of a foot which is defined as the distance between the top point of the foot and the bottom point. The 14th, 15th and 16th are three major features the width of the foot. The first width is passing through center point, therefore, the second widths of foot is measured from the upper part above the center point and third width from the region the center point under the center point at the bottom of the foot. Euclidean distance is used in the proposed system. Artificial Neural network used for recognition.
MATLAB version 8.1(R2013a) and windows 7 with 32 bit is used to build the application and performed on pc of core i3 processor, and our test system on 40 persons, results were satisfactory up to more 92.5%.

The paper proposes a method to process the image of a Braille document that interprets the raised dots on the document and converts them to their equivalent English characters. It was found that under ideal conditions of light and alignment of the Braille document with respect to the smartphone, the application can achieve more than 80% accuracy. The application can be used in the education domain, wherein users who do not understand Braille may help visually-impaired or blind students in their learning activities such as assignments and tests.

Extraction of three-dimensional scene from the stereo images is the most effective research area in the field of computer vision. Stereo vision constructs the actual three-dimensional scene from two stereo images having different viewpoints. Stereo matching is a correspondence problem, that means it ascertains which part of image corresponds to which part of another image ,where variations inside two images is due to the movement of camera or elapse of time. Many stereo matching algorithms have been developed in order to construct the accurate disparity map. This paper presents a review on various stereo matching techniques. The comparison among existing techniques has clearly shown that none perform optimistically every time. This review has shown that the existing methods in stereo matching involve median filtering. But median filter is not effective for high density of noise. Besides mean-shift segmentation is being used for disparity refinement in existing methods, which can be enhanced by using improved mean-shift segmentation, available now days. In addition, guided filter has been used by many algorithms, but this can be replaced by joint trilateral filters.