From history to the present day, especially in the field of technology is provided a wide range of developments. The most important of these developments has achieved the realization of the industrial revolution, has led to play a leading role in the production is done by human beings. The effects on production of industry 4.0 emerging with this mind is very important. The qualified employees should be trained to make the necessary preparations for the company in this period, it is experiencing the fourth industrial revolution. Therefore, the impacts on higher education of industry 4.0 are examined in this paper. The importance in education of industry 4.0 has revealed with statistical data presented in this study.

The periodical maintenance of railway systems is very important in terms of maintaining safe and comfortable transportation. In particular, the monitoring and diagnosis of faults in the pantograph catenary system are required to provide a transmission from the catenary line to the electric energy locomotive. Surface wear that is caused by the interaction between the pantograph and catenary and nonuniform distribution on the surface of a pantograph of the contact points can cause serious accidents. In this paper, a novel approach is proposed for image processing-based monitoring and fault diagnosis in terms of the interaction and contact points between the pantograph and catenary in a moving train. For this purpose, the proposed method consists of two stages. In the first stage, the pantograph catenary interaction has been modeled; the simulation results were given a failure analysis with a variety of scenarios. In the second stage, the contact points between the pantograph and catenary were detected and implemented in real time with image processing algorithms using actual video images. The pantograph surface for a fault analysis was divided into three regions: safe, dangerous, and fault. The fault analysis of the system was presented using the number of contact points in each region. The experimental results demonstrate the effectiveness, applicability, and performance of the proposed approach.

Machine vision systems are used in industrial production areas to produce products with fast, perfect and high precision. These systems allow users to make highly accurate and non-contact measurements and can detect deficiencies in the production process. In this work, a machine vision based non-contact defect detection algorithm for printed circuit boards (PCBs) has been developed. In this approach, which detects and controls the holes on the PCB, first a reference image is taken from the system and feature extraction process is applied to this image. In this real-time working approach, the reference image is matched with the incoming test images and the missing holes on the PCB are precisely detected. Furthermore, it has been determined that the error amount is less than 2 μM in experimental studies. This approach, which works independently of color, position and direction, enables the defect detection process to be done very quickly and precisely.