Multi-Layer Perceptron Neural Network for Air Wave Estimation in Marine Control Source Electromagnetic Data

Marine Control Source Electro-Magnetic (MCSEM) survey is a technique for remote identification of sub-sea floor structures of the earth's interior using Electro-Magnetic (EM) signals. Air wave signal is major problem associated with the data recorded by this technique in shallow water environment. The air wave signals are parts of the EM signals that propagate from EM source via the atmosphere and induced along air/sea surface. These air wave signals has the ability to limit and mask the electromagnetic response of a subsurface resistive body so that signals from subsurface, possibly containing valuable information about a resistive hydrocarbon reservoir is hardly distinguishable. This paper presents the application of a feed forward multi-layer perceptron neural networks model for estimation of air waves in MCSEM survey data based on offset and sea water depth values. The proposed model has 3 hidden layers with sigmoid activation function, an output layer with purelin transfer function and Levenberg-Marquardt (trainlm) as the training function. Simulated airwave data for ten sea water depths from 1000m to 100m at interval of 100m were used as the training data. Coefficient of multiple determination and Mean Square Error (MSE) obtained from the multi-layer perceptron model and the estimation with multiple linear regression model are compared. Preliminary results demonstrate that multi-layer perceptron neural networks are a viable technique for the estimation of air waves in MCSEM data

Machine learning algorithms can predict tail biting outbreaks in pigs using feeding behaviour records

Tail biting is a damaging behaviour that impacts the welfare and health of pigs. Early detection of precursor signs of tail biting provides the opportunity to take preventive measures, thus avoiding the occurrence of the tail biting event. This study aimed to build a machine-learning algorithm for real-time detection of upcoming tail biting outbreaks, using feeding behaviour data recorded by an electronic feeder. Prediction capacities of seven machine learning algorithms (Generalized Linear Model with Stepwise Feature Selection, random forest, Support Vector Machines with Radial Basis Function Kernel, Bayesian Generalized Linear Model, Neural network, K-nearest neighbour, and Partial Least Squares Discriminant Analysis) were evaluated from daily feeding data collected from 65 pens originating from two herds of grower-finisher pigs (25-100kg), in which 27 tail biting events occurred. Data were divided into training and testing data in two different ways, either by randomly splitting data into 75% (training set) and 25% (testing set), or by randomly selecting pens to constitute the testing set. In the first data splitting, the model is regularly updated with previous data from the pen, whereas in the second data splitting, the model tries to predict for a pen that it has never seen before. The K-nearest neighbour algorithm was able to predict 78% of the upcoming events with an accuracy of 96%, when predicting events in pens for which it had previous data. Our results indicate that machine learning models can be considered for implementation into automatic feeder systems for real-time prediction of tail biting events

Modeling and Simulation in Engineering

The general aim of this book is to present selected chapters of the following types: chapters with more focus on modeling with some necessary simulation details and chapters with less focus on modeling but with more simulation details. This book contains eleven chapters divided into two sections: Modeling in Continuum Mechanics and Modeling in Electronics and Engineering. We hope our book entitled "Modeling and Simulation in Engineering - Selected Problems" will serve as a useful reference to students, scientists, and engineers

Features extraction for low-power face verification

Mobile communication devices now available on the market, such as so-called smartphones, are far more advanced than the first cellular phones that became very popular one decade ago. In addition to their historical purpose, namely enabling wireless vocal communications to be established nearly everywhere, they now provide most of the functionalities offered by computers. As such, they hold an ever-increasing amount of personal information and confidential data. However, the authentication method employed to prevent unauthorized access to the device is still based on the same PIN code mechanism, which is often set to an easy-to-guess combination of digits, or even altogether disabled. Stronger security can be achieved by resorting to biometrics, which verifies the identity of a person based on intrinsic physical or behavioral characteristics. Since most mobile phones are now equipped with an image sensor to provide digital camera functionality, biometric authentication based on the face modality is very interesting as it does not require a dedicated sensor, unlike e.g. fingerprint verification. Its perceived intrusiveness is furthermore very low, and it is generally well accepted by users. The deployment of face verification on mobile devices however requires overcoming two major challenges, which are the main issues addressed in this PhD thesis. Firstly, images acquired by a handheld device in an uncontrolled environment exhibit strong variations in illumination conditions. The extracted features on which biometric identification is based must therefore be robust to such perturbations. Secondly, the amount of energy available on battery-powered mobile devices is tightly constrained, calling for algorithms with low computational complexity, and for highly optimized implementations. So as to reduce the dependency on the illumination conditions, a low-complexity normalization technique for features extraction based on mathematical morphology is introduced in this thesis, and evaluated in conjunction with the Elastic Graph Matching (EGM) algorithm. Robustness to other perturbations, such as occlusions or geometric transformations, is also assessed and several improvements are proposed. In order to minimize the power consumption, the hardware architecture of a coprocessor dedicated to features extraction is proposed and described in VHDL. This component is designed to be integrated into a System-on-Chip (SoC) implementing the complete face verification process, including image acquisition, thereby enabling biometric face authentication to be performed entirely on the mobile device. Comparison of the proposed solution with state-of-the-art academic results and recently disclosed commercial products shows that the chosen approach is indeed much more efficient energy-wise

Advance Nanomaterials for Biosensors

The book provides a comprehensive overview of nanostructures and methods used to design biosensors, as well as applications for these biosensor nanotechnologies in the biological, chemical, and environmental monitoring fields. Biological sensing has proven to be an essential tool for understanding living systems, but it also has practical applications in medicine, drug discovery, food safety, environmental monitoring, defense, personal security, etc. In healthcare, advancements in telecommunications, expert systems, and distributed diagnostics are challenging current delivery models, while robust industrial sensors enable new approaches to research and development. Experts from around the world have written five articles on topics including:Diagnosing and treating intraocular cancers such as retinoblastoma; Nanomedicine in cancer management; Engineered nanomaterials in osteosarcoma diagnosis and treatment; Practical design of nanoscale devices; Detect alkaline phosphatase quantitatively in clinical diagnosis; Progress in the area of non-enzymatic sensing of dual/multi biomolecules; Developments in non-enzymatic glucose and H2O2 (NEGH) sensing; Multi-functionalized nanocarrier therapies for targeting retinoblastoma; Galactose functionalized nanocarriers; Sensing performance, electro-catalytic mechanism, and morphology and design of electrode materials; Biosensors along with their applications and the benefits of machine learning; Innovative approaches to improve the NEGH sensitivity, selectivity, and stability in real-time applications; Challenges and solutions in the field of biosensors

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion