Written for students as well as professionals who work with and support geophysicists, this book presents a simple and informal discussion of fundamental concepts which underlie the quantitative part of geophysical analysis and interpretation. These general concepts are applicable for an analytical approach to any phenomena that can be measured and recorded. With examples and figures created using Microsoft Excel®, this book is accessible and insightful. Topics covered include: the concept of signals based on the sine function; the summation of sine waves as a more complicated signal; the notion of Fourier series and the spectral representation of signals; digital sampling and discrete representation of signals; the discrete Fourier transform and inverse transform; the concept of filtering in the spectral domain; and the idea of filtering outside of the spectral domain, by convolution, and the relationship between the measurement and spectral domains. This book will be valuable for geologists, junior seismic interpreters, software developers, high school and university students, and geophysical professionals seeking a refresher of the basic concepts.

Powerful and economic sensors such as high definition cameras and corresponding recognition software have become readily available, e.g. for face and motion recognition. However, designing user interfaces for robots, phones and computers that facilitate a seamless, intuitive, and apparently effortless communication as between humans is still highly challenging. This has shifted the focus from developing ever faster and higher resolution sensors to interpreting available sensor data for understanding social signals and recognising users' intentions. Psychologists, Ethnologists, Linguists and Sociologists have investigated social behaviour in human-human interaction. But their findings are rarely applied in the human-robot interaction domain. Instead, robot designers tend to rely on either proof-of-concept or machine learning based methods. In proving the concept, developers effectively demonstrate that users are able to adapt to robots deployed in the public space. Typically, an initial period of collecting human-robot interaction data is used for identifying frequently occurring problems. These are then addressed by adjusting the interaction policies on the basis of the collected data. However, the updated policies are strongly biased by the initial design of the robot and might not reflect natural, spontaneous user behaviour. In the machine learning approach, learning algorithms are used for finding a mapping between the sensor data space and a hypothesised or estimated set of intentions. However, this brute-force approach ignores the possibility that some signals or modalities are superfluous or even disruptive in intention recognition. Furthermore, this method is very sensitive to peculiarities of the training data. In sum, both methods cannot reliably support natural interaction as they crucially depend on an accurate model of human intention recognition. Therefore, approaches to social robotics from engineers and computer scientists urgently have to be informed by studies of intention recognition in natural human-human communication. Combining the investigation of natural human behaviour and the design of computer and robot interfaces can significantly improve the usability of modern technology. For example, robots will be easier to use by a broad public if they can interpret the social signals that users spontaneously produce for conveying their intentions anyway. By correctly identifying and even anticipating the user's intention, the user will perceive that the system truly understands her/his needs. Vice versa, if a robot produces socially appropriate signals, it will be easier for its users to understand the robot's intentions. Furthermore, studying natural behaviour as a basis for controlling robots and other devices results in greater robustness, responsiveness and approachability. Thus, we welcome submissions that (a) investigate how relevant social signals can be identified in human behaviour, (b) investigate the meaning of social signals in a specific context or task, (c) identify the minimal set of intentions for describing a context or task, (d) demonstrate how insights from the analysis of social behaviour can improve a robot's capabilities, or (e) demonstrate how a robot can make itself more understandable to the user by producing more human-like social signals.

4th edition! Larger print for easier reading and reference! A daily use handbook that includes 205 common sensory signals, with a brief explanation and practical ways to help! A sensory signal is a hint, clue, or indicator given by a child's behavior or reaction to the environment. This includes a child's reaction to social interaction or communication. When one understands a child's sensory signals, it becomes more natural and clearer as to how to enrich the environment with the right sensory tools and strategies. This reference handbook is intended to support and guide you in understanding your child's sensory differences and needs.

Fears, anxieties, traumas, and physical and emotional shocks imprint on the body and remain dormant in its vast memory store until they are roused by an event or encounter. They may manifest in a different form or place—a fearful incident may transform itself into a stomachache or a headache, or even a chronic disease. Pain creates its own path. In particular, psychological and emotional stresses affect the functioning of the internal organs. In Understanding the Messages of Your Body, Dr. Jean-Pierre Barral explains the relationships that exist between internal organs and emotions, to allow us to free ourselves from the effects of present and past tensions and traumas. The book opens with an explanation of the body-mind relationship and goes on to show how physical-emotional therapy works based on examples from Dr. Barral’s clinical practice. The second part of the book offers detailed analyses of various “types” of human personality and the physical-emotional complexes and related organ dysfunctions that accompany them. The author offers advice and encouragement to improve physical, psychological, and emotional health, and recommends physical exercises, psychotherapeutic approaches, and dietary plans that can be used by both professional therapists and the average reader.

How understanding the signaling within social networks can change the way we make decisions, work with others, and manage organizations. How can you know when someone is bluffing? Paying attention? Genuinely interested? The answer, writes Alex Pentland in Honest Signals, is that subtle patterns in how we interact with other people reveal our attitudes toward them. These unconscious social signals are not just a back channel or a complement to our conscious language; they form a separate communication network. Biologically based “honest signaling,” evolved from ancient primate signaling mechanisms, offers an unmatched window into our intentions, goals, and values. If we understand this ancient channel of communication, Pentland claims, we can accurately predict the outcomes of situations ranging from job interviews to first dates. Pentland, an MIT professor, has used a specially designed digital sensor worn like an ID badge—a “sociometer”—to monitor and analyze the back-and-forth patterns of signaling among groups of people. He and his researchers found that this second channel of communication, revolving not around words but around social relations, profoundly influences major decisions in our lives—even though we are largely unaware of it. Pentland presents the scientific background necessary for understanding this form of communication, applies it to examples of group behavior in real organizations, and shows how by “reading” our social networks we can become more successful at pitching an idea, getting a job, or closing a deal. Using this “network intelligence” theory of social signaling, Pentland describes how we can harness the intelligence of our social network to become better managers, workers, and communicators.