Characterization of damage evolution on metallic components using ultrasonic non-destructive methods

When fatigue is considered, it is expected that structures and machinery eventually fail. Still, when this damage is unexpected, besides of the negative economic impact that it produces, life of people could be potentially at risk. Thus, nowadays it is imperative that the infrastructure managers, ought to program regular inspection and maintenance for their assets; in addition, designers and materials manufacturers, can access to appropriate diagnostic tools in order to build superior and more reliable materials. In this regard, and for a number of applications, non-destructive evaluation techniques have proven to be an efficient and helpful alternative to traditional destructive assays of materials. Particularly, for the design area of materials, in recent times researchers have exploited the Acoustic Emission (AE) phenomenon as an additional assessing tool with which characterize the mechanical properties of specimens. Nevertheless, several challenges arise when treat said phenomenon, since its intensity, duration and arrival behavior is essentially stochastic for traditional signal processing means, leading to inaccuracies for the outcome assessment. In this dissertation, efforts are focused on assisting in the characterization of the mechanical properties of advanced high strength steels during under uniaxial tensile tests. Particularly of interest, is being able to detect the nucleation and growth of a crack throughout said test. Therefore, the resulting AE waves generated by the specimen during the test are assessed with the aim of characterize their evolution. For this, on the introduction, a brief review about non-destructive methods emphasizing the AE phenomenon is introduced. Next is presented, an exhaustive analysis with regard to the challenge and deficiencies of detecting and segmenting each AE event over a continuous data-stream with the traditional threshold detection method, and additionally, with current state of the art methods. Following, a novel AE event detection method is proposed, with the aim of overcome the aforementioned limitations. Evidence showed that the proposed method (which is based on the short-time features of the waveform of the AE signal), excels the detection capabilities of current state of the art methods, when onset and endtime precision, as well as when quality of detection and computational speed are also considered. Finally, a methodology aimed to analyze the frequency spectrum evolution of the AE phenomenon during the tensile test, is proposed. Results indicate that it is feasible to correlate nucleation and growth of a crack with the frequency content evolution of AE events.Cuando se considera la fatiga de los materiales, se espera que eventualmente las estructuras y las maquinarias fallen. Sin embargo, cuando este daño es inesperado, además del impacto económico que este produce, la vida de las personas podría estar potencialmente en riesgo. Por lo que hoy en día, es imperativo que los administradores de las infraestructuras deban programar evaluaciones y mantenimientos de manera regular para sus activos. De igual manera, los diseñadores y fabricantes de materiales deberían de poseer herramientas de diagnóstico apropiadas con el propósito de obtener mejores y más confiables materiales. En este sentido, y para un amplio número de aplicaciones, las técnicas de evaluación no destructivas han demostrado ser una útil y eficiente alternativa a los ensayos destructivos tradicionales de materiales. De manera particular, en el área de diseño de materiales, recientemente los investigadores han aprovechado el fenómeno de Emisión Acústica (EA) como una herramienta complementaria de evaluación, con la cual poder caracterizar las propiedades mecánicas de los especímenes. No obstante, una multitud de desafíos emergen al tratar dicho fenómeno, ya que el comportamiento de su intensidad, duración y aparición es esencialmente estocástico desde el punto de vista del procesado de señales tradicional, conllevando a resultados imprecisos de las evaluaciones. Esta disertación se enfoca en colaborar en la caracterización de las propiedades mecánicas de Aceros Avanzados de Alta Resistencia (AAAR), para ensayos de tracción de tensión uniaxiales, con énfasis particular en la detección de fatiga, esto es la nucleación y generación de grietas en dichos componentes metálicos. Para ello, las ondas mecánicas de EA que estos especímenes generan durante los ensayos, son estudiadas con el objetivo de caracterizar su evolución. En la introducción de este documento, se presenta una breve revisión acerca de los métodos existentes no destructivos con énfasis particular al fenómeno de EA. A continuación, se muestra un análisis exhaustivo respecto a los desafíos para la detección de eventos de EA y las y deficiencias del método tradicional de detección; de manera adicional se evalúa el desempeño de los métodos actuales de detección de EA pertenecientes al estado del arte. Después, con el objetivo de superar las limitaciones presentadas por el método tradicional, se propone un nuevo método de detección de actividad de EA; la evidencia demuestra que el método propuesto (basado en el análisis en tiempo corto de la forma de onda), supera las capacidades de detección de los métodos pertenecientes al estado del arte, cuando se evalúa la precisión de la detección de la llegada y conclusión de las ondas de EA; además de, cuando también se consideran la calidad de detección de eventos y la velocidad de cálculo. Finalmente, se propone una metodología con el propósito de evaluar la evolución de la energía del espectro frecuencial del fenómeno de EA durante un ensayo de tracción; los resultados demuestran que es posible correlacionar el contenido de dicha evolución frecuencial con respecto a la nucleación y crecimiento de grietas en AAAR's.Postprint (published version

ADVANCED VIBRATION PROCESSING TECHNIQUES FOR CONDITION MONITORING AND QUALITY CONTROL IN I.C. ENGINES AND HARVESTING MACHINES

The topic of this thesis is the development and the implementation of advanced vibration processing techniques for machine condition monitoring and diagnostics with two fields of applications: the quality control of I.C. engines by means of cold tests, and the monitoring and control of harvesting processes. The cold test, i.e. the final test after the assembly line and before shipping the engine to the customer, consists of the final quality control of the engine in a non-combustion state. Techniques for engine condition monitoring based on the analysis of vibration signals are widely used. However, these techniques are often applied to engine tests in firing conditions. This thesis addresses the use of several signal processing tools as a means for the monitoring and the diagnosis of assembly faults through the cold test technology. Firstly, an approach based on the use of Symmetrized Dot Patterns for the visual characterization of vibration signatures is proposed in order to obtain reliable thresholds for the pass/fail decision after the cold test. Secondly, the fault identification is discussed on the basis of the cyclostationary modelling of the signals. The first-order cyclostationarity is exploited through the analysis of the Time Synchronous Average (TSA). Subsequently, secondorder cyclostationarity is analysed by means of the Wigner-Ville Distribution (WVD), Wigner-Ville Spectrum (WVS) and Mean Instantaneous Power (MIP). Moreover, Continuous Wavelet Transform (CWT) is presented and compared with the WVD and WVS. The choice of different wavelet functions and some methods for the CWT map optimization (i.e. purification method and the average across the scale vi method (TDAS)) are also considered. Moreover, the capabilities of the Instantaneous Angular Speed (IAS) in detecting assembly faults have been tested. It is worth noting that the cyclostationary and time-frequency technique capabilities have been verified for both simulated and real signals. The experimental results indicate that the image correlation of Symmetrised Dot Patterns is a good solution that can be used in the cold test technology in order to increase its efficiency and fault detection capability. Moreover, it will be proved that the first order cyclostationary analysis is able to identify the presence of assembly faults but it is not appropriate to localise the faults. The second order analysis overcomes this problem indicating the angular position of the mechanical part affected by the fault. This is achieved by means of a correlation between the results obtained from the cyclostationarity analysis and the angular position of the mechanical events. Concerning the time-frequency analysis, the WVS as well as the CWT, using both Morlet mother wavelet and TDAS method can be considered good tools to characterise the transients due to the fault events in the timefrequency domain. Thanks to this research study it is possible to understand which of the above-mentioned techniques is effective for an easy and fast quality control and for the diagnosis of the considered assembly faults. Moreover, the limits and drawbacks of both monitoring and diagnostic procedures are shown. The originality of the first part of the research mainly concerns the use of vibration measurements for the quality control of engines at the end of the assembly line while the greater part of methods used for cold test applications focuses on pressure and torque measurements. The second part of this thesis concerns the analysis of relationships between the harvesting process parameters relative to a nonconventional harvesting machine and its vibration response. Common and uncommon features extracted from a segmentation analysis have been correlated with the harvesting process efficiency in order to define the optimal monitoring feature subset. Moreover, the Discrete Wavelet Transform method is performed in order to find the vii frequency range mostly characterised by impulsive components. In addition, some outlines obtained through the vibro-acoustic analysis performed in the angular domain are also given. Two different indoor and outdoor test rigs have been built to test the machine under different setting conditions in order to evaluate their influence over the vibration response of the threshing unit. The test results are used to identify how the vibration generation is linked to the crop distribution during the threshing process. Good correlations have been obtained by analysing the concave middle radial signal and by calculating the relationships that exist between some time domain features and the efficiency parameters. These features can be assumed as good indexes in explaining the crop distribution between the rotor and the concave and, consequently, the efficiency of the process. Moreover, it will be shown that the vibroacoustic features selected are well-connected to the different sources of the concave excitation. The main original contribution of this second part concerns the use of the vibration signal as an effective way to monitor the harvesting process. It can also be considered as a proper quality control indicator for the user during field operations

Characterization of components of water supply systems from GPR images and tools of intelligent data analysis

[EN] Over time, due to multiple operational and maintenance activities, the networks of water supply systems (WSSs) undergo interventions, modifications or even are closed. In many cases, these activities are not properly registered. Knowledge of the paths and characteristics (status and age, etc.) of the WSS pipes is obviously necessary for efficient and dynamic management of such systems. This problem is greatly augmented by considering the detection and control of leaks. Access to reliable leakage information is a complex task. In many cases, leaks are detected when the damage is already considerable, which brings high social and economic costs. In this sense, non-destructive methods (e.g., ground penetrating radar - GPR) may be a constructive response to these problems, since they allow, as evidenced in this thesis, to ascertain paths of pipes, identify component characteristics, and detect primordial water leaks. Selection of GPR in this work is justified by its characteristics as non-destructive technique that allows studying both metallic and non-metallic objects. Although the capture of information with GPR is usually successful, such aspects as the capture settings, the large volume of generated information, and the use and interpretation of such information require high level of skill and experience. This dissertation may be seen as a step forward towards the development of tools able to tackle the problem of lack of knowledge on the WSS buried assets. The main objective of this doctoral work is thus to generate tools and assess their feasibility of application to the characterization of components of WSSs from GPR images. In this work we have carried out laboratory tests specifically designed to propose, develop and evaluate methods for the characterization of the WSS buried components. Additionally, we have conducted field tests, which have enabled us to determine the feasibility of implementing such methodologies under uncontrolled conditions. The methodologies developed are based on techniques of intelligent data analysis. The basic principle of this work has involved the processing of data obtained through the GPR to look for useful information about WSS components, with special emphasis on the pipes. After performing numerous activities, one can conclude that, using GPR images, it is feasible to obtain more information than the typical identification of hyperbolae currently performed. In addition, this information can be observed directly, e.g. more simply, using the methodologies proposed in this doctoral work. These methodologies also prove that it is feasible to identify patterns (especially with the preprocessing algorithm termed Agent race) that provide fairly good approximation of the location of leaks in WSSs. Also, in the case of pipes, one can obtain such other characteristics as diameter and material. The main outcomes of this thesis consist in a series of tools we have developed to locate, identify and visualize WSS components from GPR images. Most interestingly, the data are synthesized and reduced so that the characteristics of the different components of the images recorded in GPR are preserved. The ultimate goal is that the developed tools facilitate decision-making in the technical management of WSSs, and that such tools can even be operated by personnel with limited experience in handling non-destructive methodologies, specifically GPR.[ES] Con el paso del tiempo, y debido a múltiples actividades operacionales y de mantenimiento, las redes de los sistemas de abastecimiento de agua (SAAs) sufren intervenciones, modificaciones o incluso, son clausuradas, sin que, en muchos casos, estas actividades sean correctamente registradas. El conocimiento de los trazados y características (estado y edad, entre otros) de las tuberías en los SAAs es obviamente necesario para una gestión eficiente y dinámica de tales sistemas. A esta problemática se suma la detección y el control de las fugas de agua. El acceso a información fiable sobre las fugas es una tarea compleja. En muchos casos, las fugas son detectadas cuando los daños en la red son ya considerables, lo que trae consigo altos costes sociales y económicos. En este sentido, los métodos no destructivos (por ejemplo, ground penetrating radar - GPR), pueden ser una respuesta a estas problemáticas, ya que permiten, como se pone de manifiesto en esta tesis, localizar los trazados de las tuberías, identificar características de los componentes y detectar las fugas de agua cuando aún no son significativas. La selección del GPR, en este trabajo se justifica por sus características como técnica no destructiva, que permite estudiar tanto objetos metálicos como no metálicos. Aunque la captura de información con GPR suele ser exitosa, la configuración de la captura, el gran volumen de información, y el uso y la interpretación de la información requieren de alto nivel de habilidad y experiencia por parte del personal. Esta tesis doctoral se plantea como un avance hacia el desarrollo de herramientas que permitan responder a la problemática del desconocimiento de los activos enterrados de los SAAs. El objetivo principal de este trabajo doctoral es, pues, generar herramientas y evaluar la viabilidad de su aplicación en la caracterización de componentes de un SAA, a partir de imágenes GPR. En este trabajo hemos realizado ensayos de laboratorio específicamente diseñados para plantear, elaborar y evaluar metodologías para la caracterización de los componentes enterrados de los SAAs. Adicionalmente, hemos realizado ensayos de campo, que han permitido determinar la viabilidad de aplicación de tales metodologías bajo condiciones no controladas. Las metodologías elaboradas están basadas en técnicas de análisis inteligentes de datos. El principio básico de este trabajo ha consistido en el tratamiento adecuado de los datos obtenidos mediante el GPR, a fin de buscar información de utilidad para los SAAs respecto a sus componentes, con especial énfasis en las tuberías. Tras la realización de múltiples actividades, se puede concluir que es viable obtener más información de las imágenes de GPR que la que actualmente se obtiene con la típica identificación de hipérbolas. Esta información, además, puede ser observada directamente, de manera más sencilla, mediante las metodologías planteadas en este trabajo doctoral. Con estas metodologías se ha probado que también es viable la identificación de patrones (especialmente el pre-procesado con el algoritmo Agent race) que proporcionan aproximación bastante acertada de la localización de las fugas de agua en los SAAs. También, en el caso de las tuberías, se puede obtener otro tipo de características tales como el diámetro y el material. Como resultado de esta tesis se han desarrollado una serie de herramientas que permiten visualizar, identificar y localizar componentes de los SAAs a partir de imágenes de GPR. El resultado más interesante es que los resultados obtenidos son sintetizados y reducidos de manera que preservan las características de los diferentes componentes registrados en las imágenes de GPR. El objetivo último es que las herramientas desarrolladas faciliten la toma de decisiones en la gestión técnica de los SAAs y que tales herramientas puedan ser operadas incluso por personal con una experiencia limitada en el manejo[CA] Amb el temps, a causa de les múltiples activitats d'operació i manteniment, les xarxes de sistemes d'abastament d'aigua (SAAs) se sotmeten a intervencions, modificacions o fins i tot estan tancades. En molts casos, aquestes activitats no estan degudament registrats. El coneixement dels camins i característiques (estat i edat, etc.) de les canonades d'aigua i sanejament fa evident la necessitat d'una gestió eficient i dinàmica d'aquests sistemes. Aquest problema es veu augmentat en gran mesura tenint en compte la detecció i control de fuites. L'accés a informació fiable sobre les fuites és una tasca complexa. En molts casos, les fugues es detecten quan el dany ja és considerable, el que porta costos socials i econòmics. En aquest sentit, els mètodes no destructius (per exemple, ground penetrating radar - GPR) poden ser una resposta constructiva a aquests problemes, ja que permeten, com s'evidencia en aquesta tesi, per determinar rutes de canonades, identificar les característiques dels components, i detectar les fuites d'aigua quan encara no són significatives. La selecció del GPR en aquest treball es justifica per les seves característiques com a tècnica no destructiva que permet estudiar tant objectes metàl·lics i no metàl·lics. Tot i que la captura d'informació amb GPR sol ser reeixida, aspectes com ara la configuració de captura, el gran volum d'informació que es genera, i l'ús i la interpretació d'aquesta informació requereix alt nivell d'habilitat i experiència. Aquesta tesi pot ser vista com un pas endavant cap al desenvolupament d'eines capaces d'abordar el problema de la manca de coneixement sobre els actius d'aigua i sanejament enterrat. L'objectiu principal d'aquest treball doctoral és, doncs, generar eines i avaluar la seva factibilitat d'aplicació a la caracterització dels components de los SAAs, a partir d'imatges GPR. En aquest treball s'han dut a terme proves de laboratori específicament dissenyats per proposar, desenvolupar i avaluar mètodes per a la caracterització dels components d'aigua i sanejament soterrat. A més, hem dut a terme proves de camp, que ens han permès determinar la viabilitat de la implementació d'aquestes metodologies en condicions no controlades. Les metodologies desenvolupades es basen en tècniques d'anàlisi intel·ligent de dades. El principi bàsic d'aquest treball ha consistit en el tractament de dades obtingudes a través del GPR per buscar informació útil sobre els components d'SAA, amb especial èmfasi en la canonades. Després de realitzar nombroses activitats, es pot concloure que, amb l'ús d'imatges de GPR, és factible obtenir més informació que la identificació típica d'hipèrboles realitzat actualment. A més, aquesta informació pot ser observada directament, per exemple, més simplement, utilitzant les metodologies proposades en aquest treball doctoral. Aquestes metodologies també demostren que és factible per identificar patrons (especialment el pre-processat amb l'algoritme Agent race) que proporcionen bastant bona aproximació de la localització de fuites en SAAs. També, en el cas de tubs, es pot obtenir altres característiques com ara el diàmetre i el material. Els principals resultats d'aquesta tesi consisteixen en una sèrie d'eines que hem desenvolupat per localitzar, identificar i visualitzar els components dels SAAS a partir d'imatges GPR. El resultat més interessant és que els resultats obtinguts són sintetitzats i reduïts de manera que preserven les característiques dels diferents components registrats en les imatges de GPR. L'objectiu final és que les eines desenvolupades faciliten la presa de decisions en la gestió tècnica de SAA, i que tals eines poden fins i tot ser operades per personal amb poca experiència en el maneig de metodologies no destructives, específicament GPR.Ayala Cabrera, D. (2015). Characterization of components of water supply systems from GPR images and tools of intelligent data analysis [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59235TESISPremios Extraordinarios de tesis doctorale

Characterization of Physiological Biomarkers in Long-Term Kratom (Mitragyna speciosa Korth.) Users: A Preliminary Study

Doctor of Philosophy (Physiology), 2022A neurophysiological outcome associated with long-term kratom chewing in traditional use context is still unknown. Thus, the primary aim of this study was to investigate biomarkers of neurological response to the long-term kratom chewing. The fifty-two participants (controls; n=24 and long-term kratom users (LKU) who chewed kratom leaves; n = 28) were recruited with background-matched control group. Neurophysiological parameters with the proposed EEG (Theta/alpha ratio (TAR) and power function variance (PVFA), and all domains of ultra-short heart rate variability (HRV) heart rate variability were assessed during resting-state. Cognitive performance (Working memory) and kratom dependence score rating were also examined. All the proposed features were compared between the controls and long-term kratom chewers and determined in the relevant factors (age, duration, and daily quantity of kratom use). The statistically significant proposed features were proved by 1) path analysis for evaluating the causal relationship, and 2) the recognized machine-learning algorithms (Random Forest, Support vector machine, k Nearest neighbor, and Logistic regression) for binary classification. The results showed that only the proposed EEG feature (TAR) was significantly increased, compared to the control in the same age range of 50 years. The increased TAR and decreased PVF in the alpha band (PVFA) were direct effects of kratom leaves use and were significantly observed in LKU with a very high dose use. In addition, PVFA was a negative correlation with Kratom dependence. The results were also confirmed by the support vector machine achieved the highest performance to classify LKU with different doses of Kratom consuming by using the combination features TAR (both electrodes and average) and PVF in the alpha band. These preliminary results first highlighted the sensitive EEG biomarkers to characterize the LKU with a large effect size. These findings may lead to effective machine learning approaches based on EEG biomarkers for screening excessive Kratom users that might eventually develop Kratom dependence.1. Graduate School Dissertation Funding for Thesis and Revenue Budget Fund 2. Educational Institutions Scholarship for Outstanding GPAในปัจจุบันการศึกษาผลตอบสนองต่อการบริโภคพืชกระท่อมระยะเวลานานทางสรีรวิทยา ระบบประสาทที่ยังไม่ทราบเป็ นที่แน่ชัด การศึกษานี้จึงมีวัตถุประสงค์เพื่อหาตัวบ่งชี้ทางชีวภาพ ของการตอบสนองดังกล่าวจากอาสาสมัครจํานวน 52 คน ประกอบไปด้วย ผู้ใช้กระท่อมเป็ นระยะ เวลานานจํานวน 28 คน และกลุ่มควบคุมจํานวน 24 คน ทําการบันทึกสัญญาณ 1) คลื่นไฟฟ้าสมอง ด้วยอุปกรณ์พกพาอย่างง่ายเพื่อสกัดอัตราส่วนคลื่นพลังงานธีต้า/อัลฟ่ า และความแปรปรวนของ พลังงานคลื่นความถี่ และ 2) คลื่นไฟฟ้าหัวใจเพื่อสกัดความแปรปรวนของอัตราการเต้นของหัวใจ นอกจากนี้ยังทําการทดสอบเชิงประสาทพฤติกรรม เช่น ความสามารถทางด้านการรู้คิด(ความจําเพื่อ ใช้งาน) และ อาการติดกระท่อม จากนั้นทําการทดสอบทางสถิติเพื่อเปรียบเทียบผู้เคี้ยวพืชกระท่อม และกลุ่มควบคุม และศึกษาในด้านปัจจัยต่างๆ (อายุ ระยะเวลาที่บริโภค และ ปริมาณบริโภคพืช กระท่อมต่อวัน) ตัวชี้วัดที่มีนัยสําคัญทางสถิติจะถูกนําไปทดสอบด้วย 1) การวิเคราะห์เส้นทางเพื่อ ดูความสัมพันธ์เชิงเหตุผลระหว่างตัวชี้วัดดังกล่าวกับปัจจัยต่างๆ และ ตัวชี้วัดทางประสาท พฤติกรรม และ 2) การจําแนกกลุ่มแบบไบนารีด้วยแมชชีนเลิร์นนิ่งทัวไป ่ ผลการทดลองพบว่า ตัวชี้วัดทางคลื่นไฟฟ้าสมองเท่านั้นที่พบความแตกต่างอย่างมีนัยสําคัญระหว่างกลุ่ม โดยระดับ อัตราส่วนคลื่นพลังงานธีต้า/อัลฟ่ าเพิ่ มขึ้นอย่างมีนัยสําคัญในกลุ่มผู้บริโภคพืชกระท่อมเทียบกลับ กลุ่มควบคุมเมื่อพิจารณาในช่วงอายุ> 50 ปี และเพิ่ มขึ้นอย่างเห็นได้ชัดในกลุ่มบริโภคพืชกระท่อม ในปริ มาณมาก นอกจากนี้ความแปรปรวนของพลังงานคลื่นความถี่ของอัลฟ่ ายังลดลงในกลุ่ม ดังกล่าวอีกด้วย และมีความสัมพันธ์กับคะแนนที่ทําการประเมินอาการติดกระท่อม ผลการ เปลี่ยนแปลงดังกล่าวเป็ นผลโดยตรงจากปริมาณการใช้พืชกระท่อมวัดจากการวิเคราะห์ด้วยเส้นทาง อีกทั้งการรวมคุณลักษณะของตัวชี้วัดทางคลื่นสมองดังกล่าวยังสามารถใช้จําแนกกลุ่มผู้ที่บริโภค ในขนาดต่างกันโดยใช้อัลกอรึทึมของซัพพอร์ตเวกเตอร์แมชชีนในการประเมิน ดังนั้นการศึกษา เบื้องต้นนี้แสดงถึงความไวของตัวชี้วัดชีวภาพด้วยคลื่นไฟฟ้าสมองและในอนาคตอาจมีความ เป็ นไปได้ที่จะใช้แมชชีนเลิร์นิ่ งจําแนกผู้บริโภคพืชกระท่อมเกินขนาดที่อาจจะพัฒนาไปสู่อาการติด กระท่อมโดยด้วยคุณลักษณะของคลื่นไฟฟ้าสมองดังกล่า

Exploration of human search behaviour: a multidisciplinary perspective

The following work presents an exploration of human search behaviour both from biological and computational perspectives. Search behaviour is defined as the movements made by an organism while attempting to find a resource. This work describes some of the principal procedures used to record movement, methods for analysing the data and possible ways of interpreting the data. In order to obtain a database of searching behaviour, an experimental setup was built and tested to generate the search paths of human participants. The test arena occupied part of a football field and the targets consisted of an array of 20 golf balls. In the first set of experiments, a random and regular distribution of targets were tested. For each distribution, three distinct conspicuity levels were constructed: a cryptic level, in which targets were painted the same colour as the grass, a semi-conspicuous level in which targets were left white and a conspicuous condition in which the position of each target was marked by a red flag, protruding one metre from the ground. The subjects tested were 9-11 year old children and their search paths were collected using a GPS device. Subjects did not recognise the spatial cues regarding the way targets were spatially distributed. A minimal decision model, the bouncing search model, was built based on the characteristics of the childrens search paths. The model produced an outstanding fit of the children’s behavioural data. In the second set of experiments, a new group of children were tested for two new distributions obtained by arranging the targets in patches. Again, children appeared unable to recognise spatial information during the collection processes. The children’s behaviour once again produced a good match with that of the bouncing search model. This work introduces several new methodological aspects to be explored to further understand the decision processes involved when humans search. Also, it illustrates that integrating biology and computational science can result in innovative research

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Mathematical Methods, Modelling and Applications

This volume deals with novel high-quality research results of a wide class of mathematical models with applications in engineering, nature, and social sciences. Analytical and numeric, deterministic and uncertain dimensions are treated. Complex and multidisciplinary models are treated, including novel techniques of obtaining observation data and pattern recognition. Among the examples of treated problems, we encounter problems in engineering, social sciences, physics, biology, and health sciences. The novelty arises with respect to the mathematical treatment of the problem. Mathematical models are built, some of them under a deterministic approach, and other ones taking into account the uncertainty of the data, deriving random models. Several resulting mathematical representations of the models are shown as equations and systems of equations of different types: difference equations, ordinary differential equations, partial differential equations, integral equations, and algebraic equations. Across the chapters of the book, a wide class of approaches can be found to solve the displayed mathematical models, from analytical to numeric techniques, such as finite difference schemes, finite volume methods, iteration schemes, and numerical integration methods