Detection of counterfeit coins based on 3D Height-Map Image Analysis

Analyzing 3-D height-map images leads to the discovery of a new set of features that cannot be extracted or even seen in 2-D images. To the best of our knowledge, there was no research in the literature analyzing height-map images to detect counterfeit coins or to classify coins. The main goal of this thesis is to propose a new comprehensive method for analyzing 3D height-map images to detect counterfeit of any type of coins regardless of their country of origin, language, shape, and quality. Therefore, we applied a precise 3-D scanner to produce coin height-map images, since detecting a counterfeit coin using 2D image processing is nearly impossible in some cases, especially when the coin is damaged, corroded or worn out. In this research, we propose some 3-D approaches to model and analyze several large datasets. In our first and second methods, we aimed to solve the degradation problem of shiny coin images due to the scanning process. To solve this problem, first, the characters of the coin images were straightened by a proposed straightening algorithm. The height-map image, then, was decomposed row-wise to a set of 1-D signals, which were analyzed separately and restored by two different proposed methods. These approaches produced remarkable results. We also proposed a 3-D approach to detect and analyze the precipice borders from the coin surface and extract significant features that ignored the degradation problem. To extract the features, we also proposed Binned Borders in Spherical Coordinates (BBSC) to analyze different parts of precipice borders at different polar and azimuthal angles. We also took advantage of stack generalization to classify the coins and add a reject option to increase the reliability of the system. The results illustrate that the proposed method outperforms other counterfeit coin detectors. Since there are traces of deep learning in most recent research related to image processing, it is worthwhile to benefit from deep learning approaches in our study. In another proposed method of this thesis, we applied deep learning algorithms in two steps to detect counterfeit coins. As Generative Adversarial Network is being used for generating fake images in image processing applications, we proposed a novel method based on this network to augment our fake coin class and compensate for the lack of fake coins for training the classifier. We also decomposed the coin height-map image into three types of Steep, Moderate, and Gentle slopes. Therefore, the grayscale height-map image is turned to the proposed SMG height-map channel. Then, we proposed a hybrid CNN-based deep neural network to train and classify these new SMG images. The results illustrated that a deep neural network trained with the proposed SMG images outperforms the system trained by the grayscale images. In this research, the proposed methods were trained and tested with four types of Danish and two types of Chinese coins with encouraging results

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154106/1/kampfner1998.pd

Opportunities of industry 4.0 for SMEs in the area of rebar steel distribution within the construction industry –a PPC potential analysis

Industry 4.0coins a global trend towards applying digital technologies to manufacturing. However, the openness towards related innovations varies among different industries. Whilst for instance many manufacturers within automotive or logistics industries have optimized their factories already, the German construction sector falls back regarding adaptation. Reinforcement steel distributors reflect a fundamental part of this sector and are broadly hesitant to initiate their factory transformation. This research provides an overview of the opportunities of Industry 4.0 in the area of reinforcement steel trade and processing. It analyzes how to derive an innovative factory design leveraging on state-of-the-art production planning methods, by aggregating market information and technology

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

A review of information modelling systems in the built environment

The built environment can be described to constitute the surrounding and existing elements created by humans. The systems for modelling information related to the built environment are numerous. Their development are based on varying assumptions and tailored to the various domains in which they are deployed. The functions of these systems are sometimes similar or overlap and they tend to end up with similar acronyms thereby creating confusion to stakeholders in the built environment. As such, stakeholders also find it difficult to choose systems best suited for their needs among the numerous existing ones. A comprehensive record of systems in the built environment with clear definitions of their functions and areas of overlap is therefore necessary to straighten up such confusion and provide requisite understanding among stakeholders. A literature review of information modelling systems in the built environment is therefore proposed. The review examines systems in key sectors of the built environment such the Architectural, Engineering, Construction, Geography and Urban Planning. We conclude that stakeholders should give strong consideration to interoperability needs along the supply chain in which they work while deciding on the choice of information modelling systems to procure

Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis

Olive pitting, slicing and stuffing machines (DRR in Spanish) are characterized by the fact that their optimal functioning is based on appropriate adjustments. Traditional systems are not completely reliable because their minimum error rate is 1–2%, which can result in fruit loss, since the pitting process is not infallible, and food safety issues can arise. Such minimum errors are impossible to remove through mechanical adjustments. In order to achieve this objective, an innovative solution must be provided in order to remove errors at operating speed rates over 2500 olives/min. This work analyzes the appropriate placement of olives in the pockets of the feed chain by using the following items: (1) An IoT System to control the DRR machine and the data analysis. (2) A computer vision system with an external shot camera and a LED lighting system, which takes a picture of every pocket passing in front of the camera. (3) A chip with a neural network for classification that, once trained, classifies between four possible pocket cases: empty, normal, incorrectly de-stoned olives at any angles (also known as a “boat”), and an anomalous case (foreign elements such as leafs, small branches or stones, two olives or small parts of olives in the same pocket). The main objective of this paper is to illustrate how with the use of a system based on IoT and a physical chip (NeuroMem CM1K, General Vision Inc.) with neural networks for sorting purposes, it is possible to optimize the functionality of this type of machine by remotely analyzing the data obtained. The use of classifying hardware allows it to work at the nominal operating speed for these machines. This would be limited if other classifying techniques based on software were used