Remote Sensing and Geosciences for Archaeology

This book collects more than 20 papers, written by renowned experts and scientists from across the globe, that showcase the state-of-the-art and forefront research in archaeological remote sensing and the use of geoscientific techniques to investigate archaeological records and cultural heritage. Very high resolution satellite images from optical and radar space-borne sensors, airborne multi-spectral images, ground penetrating radar, terrestrial laser scanning, 3D modelling, Geographyc Information Systems (GIS) are among the techniques used in the archaeological studies published in this book. The reader can learn how to use these instruments and sensors, also in combination, to investigate cultural landscapes, discover new sites, reconstruct paleo-landscapes, augment the knowledge of monuments, and assess the condition of heritage at risk. Case studies scattered across Europe, Asia and America are presented: from the World UNESCO World Heritage Site of Lines and Geoglyphs of Nasca and Palpa to heritage under threat in the Middle East and North Africa, from coastal heritage in the intertidal flats of the German North Sea to Early and Neolithic settlements in Thessaly. Beginners will learn robust research methodologies and take inspiration; mature scholars will for sure derive inputs for new research and applications

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

An assessment of archive stereo-aerial photographs for 3-dimensional reconstruction of damaged and destroyed archaeological earthworks

Archaeological earthworks are being damaged and destroyed at a rate and scale never before seen, which has resulted from increased mechanisation of human activity in the landscape since World War II. Along with natural degradation processes, recording earthwork metrics prior to their loss is increasingly difficult, which can subsequently hinder the interpretation of a site or landscape because of this missing evidence. A tool for regaining such data is vital to alleviate this problem and to fulfil the stipulation for metric information as required by national and international conservation charters. This research investigates whether it is possible to regain earthwork metrics from archive stereo-aerial photographs (SAPs) using digital photogrammetry to create digital surface models (DSMs) of archaeological sites within the UK dating from the 1940s to 2010. A literature search confirmed the utility of SAPs for reconstructing geomorphological events, such as landslides, whilst also verifying that such an approach had not been thoroughly investigated for archaeological adaptation. Via experimentation, a photogrammetric workflow has been designed and a number of variables identified that affect the quality of DSMs obtained from SAPs. The magnitude of these variables has been verified by quantitative assessment using independent survey data, namely Airborne Laser Scanning (ALS) gathered by the Environment Agency, and ground-based collection using Global Navigation Satellite Systems (GNSS) and Terrestrial Laser Scanning (TLS). Empirical differences between these independent data and the SAP DSMs were identified using global statistical measures such as Mean Error (ME), Standard Deviation (SD) and root mean square error (RMSE), and spatial autocorrelation techniques, namely Local Moran’s I. Two study sites were selected on which to ascertain whether variations occur in the empirical quality of SAP DSMs and archaeological content at different locations. Over six decades of photography were collected for Flowers Barrow Hillfort, situated near Lulworth in Dorset, UK, which has remained in good condition throughout this period, due to the protection afforded it by inclusion within Ministry of Defence land. Eggardon Hillfort and earthworks, near Bridport in Dorset, UK, were also selected due to the exceptional preservation state of some earthworks, versus the plough-damaged remains of others. These sites thus offered an opportunity to rigorously test the reconstruction capabilities of the SAPs. The results from both study sites confirmed that the metric quality of SAP DSMs improves as the age of the imagery decreases, although this is dependent on image quality, scanner properties (i.e. whether the scanner is photogrammetric or desktop) and the result of the block bundle adjustment in the photogrammetric software. This thesis concludes that SAPs can recreate earthwork metrics and provides a list of considerations for archaeologists to consult when planning the use of SAPs for creating DSMs. Recommendations for future work are provided that encourage the investigation of SAPs from other countries and the rigorous assessment of DSMs derived from structure-from-motion (SfM) software that is rapidly gaining popularity

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data