Recent technological developments and the availability of enormous amounts of real-time data have played a vital role in the expansion, evolution, and success of smart city projects. Smart data can be used in a variety of smart city applications, but difficulties in managing such data are pushing smart cities toward the adoption of data management frameworks. Many studies have brought into focus the importance of these frameworks as they combine data collection, processing, analysis, management, and visualization and provide privacy and security features for different smart city applications, i.e., transportation, to promote a better quality of life. This chapter highlights key components of the data management framework, reviews various smart city applications, and discusses privacy and security challenges associated with smart city data. From the perspective of data frameworks, it is seen that the data used in smart city applications is unstructured coming from heterogeneous sources, i.e., sensors and social media, besides others. Therefore, the collection, processing, analysis, management, and visualization of such data are challenging. To perform these tasks, recent technologies, i.e., Internet of Things (IoT), sensor networks, machine learning, etc., have been used. Moreover, the use of smart data for smart government and governance provides several facilities for the public and business. The smart data is revolutionizing the daily communication of users along with their mode of transportation by introducing Social IoT (SIoT) and autonomous vehicles. Lastly, the challenges related to privacy and security of the data in smart cities that needed to be addressed are highlighted. This chapter will guide academics and enterprises to progress in data management framework and its applications in smart cities in the near future.

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Due to the increase in the number of vehicles day by day, traffic congestions and traffic jams are very common. One method to overcome the traffic problem is to develop an intelligent traffic control system which is based on the measurement of traffic density on the road using real time video and image processing techniques. The theme is to control the traffic by determining the traffic density on each side of the road and control the traffic signal intelligently by using the density information. This paper presents the algorithm to determine the number of vehicles on the road. The density counting algorithm works by comparing the real time frame of live video by the reference image and by searching vehicles only in the region of interest (i.e., road area). The computed vehicle density can be compared with other direction of the traffic in order to control the traffic signal smartly.

Commercial Co/WC/diamond composites are hard metals and very useful as a kind of tool material, for which both ductile and quasi-brittle behaviors are possible. This work experimentally investigates their damage evolution dependence on microstructural features. The current study investigates a different type of Co/WC-type tool material which contains 90vol.% Co instead of the usual < 50vol.%. The studied composites showed quasi-brittle behavior. An in-house-designed testing machine realizes the in-situ micro-computed tomography (CT) under loading. This advanced equipment can record local damage in 3D during the loading. The digital image correlation technique delivers local displacement/strain maps in 2D and 3D based on tomographic images. As shown by nanoindentation tests, matrix regions near diamond particles do not possess higher hardness values than other regions. Since local positions with high stress are often coincident with those with high strain, diamonds, which aim to achieve composites with high hardnesses, contribute to the strength less than the WC phase. Samples that illustrated quasi-brittle behavior possess about 100–130 MPa higher tensile strengths than those with ductile behavior. Voids and their connections (forming mini/small cracks) dominant the detected damages, which means void initiation, growth, and coalescence should be the damage mechanisms. The void appears in the form of debonding. Still, it is uncovered that debonding between Co-diamonds plays a major role in provoking fatal fractures for composites with quasi-brittle behavior. An optimized microstructure should avoid diamond clusters and their local volume concentrations. To improve the time efficiency and the object-identification accuracy in CT image segmentation, machine learning (ML), U-Net in the convolutional neural network (deep learning), is applied. This method takes only about 40 min to segment more than 700 images, i.e., a great improvement of the time efficiency compared to the manual work and the accuracy maintained. The results mentioned above demonstrate knowledge about the strengthening and damage mechanisms for Co/WC/diamond composites with > 50vol.% Co. The material properties for such tool materials (> 50vol.% Co) is rarely published until now. Efforts made in the ML part contribute to the realization of autonomous processing procedures in big-data-driven science applied in materials science.

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