Scholars of several disciplines use different terms to refer to children’s psychological problems. The quest for a global descriptor that is accurate and that avoids stigma has so far been unsuccessful. In fact, there is probably not even a euphemism that enables its users to avoid debatable analogies and connotations that they would probably prefer not to evoke. This chapter is devoted to definitional issues, starting with the definitions of pathology and disease. Problems with all of the terms in common use to describe children’s and adolescents’ psychological problems are highlighted. Many of the issues raised in this chapter recur in subsequent chapters on the classification of mental illness and the physiological basis of child and adolescent psychopathology. Estimates of the total prevalence of psychopathology appear near the end of the chapter, followed by remarks about the notion of recovery in the context of mental illness.

Pathology; disease

Although the concepts of mental illness and disease have existed since ancient times, it is only in the past 200 years that coherent attempts have been made to differentiate transitory problems associated with the stressful experiences suffered by most people from full-blown conditions that merit more enduring concern and professional care. The German psychiatrist, Karl Kahlbaum, renowned in his own time but almost unknown since, is credited with introducing, in 1863, the idea that the concept of mental illness should include the course of the illness, its effect on the individual’s psychological well-being, the developmental stage at which it occurred and any accompanying conditions to which it might be secondary. Kahlbaum also applied these ideas to the study of children’s mental disorders, especially early forms of psychosis (Kahlbaum and Berrios, 2007; Millon, Grossman and Meagher, 2004). His delineation of mental illness and health, with considerable refinement, has become an integral part of mainstream thinking about child psychopathology.

Of the major psychological disorders that affect children and adolescents, autism is probably the one that remains the most shrouded in mystery. Is there any meaning to the stereotyped movements and idiosyncratic verbalizations of children with autism? Is their avoidance of social contact caused by any aspect of their environment? Is there a reason why the prevalence of autism seems to be increasing? And, most importantly, can children with autism be helped, and, if they can, how?

This chapter begins with a capsule historical sketch in which the emergence of the contemporary concept of autism is traced. Diagnostic criteria are presented next, including a discussion of the recent elimination of the category of Asperger’s syndrome in DSM-5. The apparent increase in the prevalence of autism in recent years is discussed next, together with controversial recent data about the stability of the disorder. The likely causes and features of the disorder are the focus in the following section, which includes both causal factors that are endorsed by contemporary research and some of those that are interesting museum pieces. Early signs of autism spectrum disorder are also presented in the section on causes and correlates. The chapter closes with the increasingly optimistic (or, better said, increasingly less pessimistic) story of the treatments that have been developed and evaluated.

This textbook provides an introduction to the conceptual underpinnings of communication technologies. Most of us directly experience such technologies daily: browsing (and audio/video streaming from) the Internet, sending/receiving emails, watching television, or carrying out a phone conversation. Many of these experiences occur on mobile devices that we carry around with us, so that we are always connected to the cyberworld of modern communication systems. In addition, there is a huge amount of machine-to-machine communication that we do not directly experience, but which is indispensable for the operation of modern society. This includes, for example, signaling between routers on the Internet, or between processors and memories on any computing device.

We define communication as the process of information transfer across space or time. Communication across space is something we have an intuitive understanding of: for example, radio waves carry our phone conversation between our cell phone and the nearest base station, and coaxial cables (or optical fiber, or radio waves from a satellite) deliver television from a remote location to our home. However, a moment's thought shows that that communication across time, or storage of information, is also an everyday experience, given our use of storage media such as compact discs (CDs), digital video discs (DVDs), hard drives, and memory sticks.

A communication link involves several stages of signal manipulation: the transmitter transforms the message into a signal that can be sent over a communication channel; the channel distorts the signal and adds noise to it; and the receiver processes the noisy received signal to extract the message. Thus, communication systems design must be based on a sound understanding of signals, and the systems that shape them. In this chapter, we discuss concepts and terminology from signals and systems, with a focus on how we plan to apply them in our discussion of communication systems. Much of this chapter is a review of concepts with which the reader might already be familiar from prior exposure to signals and systems. However, special attention should be paid to the discussion of baseband and passband signals and systems (Sections 2.7 and 2.8). This material, which is crucial for our purpose, is typically not emphasized in a first course on signals and systems. Additional material on the geometric relationship between signals is covered in later chapters, when we discuss digital communication.

Chapter plan

After a review of complex numbers and complex arithmetic in Section 2.1, we provide some examples of useful signals in Section 2.2. We then discuss LTI systems and convolution in Section 2.3. This is followed by Fourier series (Section 2.4) and the Fourier transform (Section 2.5).

Modulation is the process of encoding information into a signal that can be transmitted (or recorded) over a channel of interest. In analog modulation, a baseband message signal, such as speech, audio, or video, is directly transformed into a signal that can be transmitted over a designated channel, typically a passband radio-frequency (RF) channel. Digital modulation differs from this only in the following additional step: bits are encoded into baseband message signals, which are then transformed into passband signals to be transmitted. Thus, despite the relentless transition from digital to analog modulation, many of the techniques developed for analog communication systems remain important for the digital communication systems designer, and our goal in this chapter is to study an important subset of these techniques, using legacy analog communication systems as examples to reinforce concepts.

From Chapter 2, we know that a passband signal carries information in its complex envelope, and that the complex envelope can be represented either in terms of I and Q components or in terms of envelope and phase. We study two broad classes of techniques: amplitude modulation, in which the analog message signal appears directly in the I and/or Q components; and angle modulation, in which the analog message signal appears directly in the phase or in the instantaneous frequency (i.e., in the derivative of the phase) of the transmitted signal. Examples of analog communication in space include AM radio, FM radio, and broadcast television, as well as a variety of specialized radios.

Progress in telecommunications over the past two decades has been nothing short of revolutionary, with communications taken for granted in modern society to the same extent as electricity. There is therefore a persistent need for engineers who are well-versed in the principles of communication systems. These principles apply to communication between points in space, as well as communication between points in time (i.e., storage). Digital systems are fast replacing analog systems in both domains. This book has been written in response to the following core question: what is the basic material that an undergraduate student with an interest in communications should learn, in order to be well prepared for either industry or graduate school? For example, some institutions teach only digital communication, assuming that analog communication is dead or dying. Is that the right approach? From a purely pedagogical viewpoint, there are critical questions related to mathematical preparation: how much mathematics must a student learn to become well-versed in system design, what should be assumed as background, and at what point should the mathematics that is not in the background be introduced? Classically, students learn probability and random processes, and then tackle communication. This does not quite work today: students increasingly (and, I believe, rightly) question the applicability of the material they learn, and are less interested in abstraction for its own sake.

From the material in Chapters 4-6, we now have an understanding of commonly used modulation formats, noise models, and optimum demodulation for the AWGN channel model. Chapter 7 discusses channel coding strategies for these idealized models. In this final chapter, we discuss more sophisticated channel models, and the corresponding signal processing schemes required at the demodulator.

We first consider the following basic model for a dispersive channel: the transmitted signal passes through a linear time-invariant system, and is then corrupted by white Gaussian noise. The LTI model is broadly applicable to wireline channels, including copper wires, cable and fiber-optic communication (at least over shorter distances, over which fiber nonlinearities can be neglected), as well as to wireless channels with quasi-stationary transmitters and receivers. For wireless mobile channels, the LTI model is a good approximation over durations that are small compared with the time constants of mobility, but still fairly long on an electronic timescale (e.g., of the order of milliseconds). Methods for compensating for the effects of a dispersive channel are generically termed equalization. We introduce two common design approaches for this purpose.

The first approach is single-carrier modulation, which refers to the linear modulation schemes discussed in Chapter 4, where the symbol sequence modulates a transmit pulse occupying the entire available bandwidth. We discuss linear zero forcing (ZF) and minimum mean-squared error (MMSE) equalization techniques, which are suboptimal from the point of view of minimizing error probability, but are intuitively appealing and less computationally complex than optimum equalization.

Probability theory is fundamental to communication system design, especially for digital communication. Not only are there uncontrolled sources of uncertainty such as noise, interference, and other channel impairments that are amenable only to statistical modeling, but also the very notion of information underlying digital communication is based on uncertainty. In particular, the receiver in a communication system does not know a priori what the transmitter is sending (otherwise the transmission would be pointless), hence the receiver designer must employ statistical models for the transmitted signal. In this chapter, we review basic concepts of probability and random variables with examples motivated by communications applications. We also introduce the concept of random processes, which are used to model both signals and noise in communication systems.

Chapter plan

The goal of this chapter is to develop the statistical modeling tools required in later chapters. For readers who are already comfortable with probability and random processes, the shortest path to Chapter 6 is to review the material on Gaussian random variables in Section 5.6 and noise modeling in Section 5.8. Sections 5.1 through 5.5 provide a review of background material on probability and random variables. Section 5.1 discusses basic concepts of probability: the most important of these for our purpose are the concepts of conditional probability and Bayes’ rule. Sections 5.2 and 5.4 discuss random variables and functions of random variables. Multiple random variables, or random vectors, are discussed in Section 5.3. Section 5.5 discusses various statistical averages and their computation.

Digital modulation is the process of translating bits to analog waveforms that can be sent over a physical channel. Figure 4.1 shows an example of a baseband digitally modulated waveform, where bits that take values in {0, 1} are mapped to symbols in {+1, −1}, which are then used to modulate translates of a rectangular pulse, where the translation corresponding to successive symbols is the symbol interval T. The modulated waveform can be represented as a sequence of symbols (taking values ±1 in the example) multiplying translates of a pulse (rectangular in the example). This is an example of a widely used form of digital modulation termed linear modulation, where the transmitted signal depends linearly on the symbols to be sent. Our treatment of linear modulation in this chapter generalizes this example in several ways. The modulated signal in Figure 4.1 is a baseband signal, but what if we are constrained to use a passband channel (e.g., a wireless cellular system operating at 900 MHz)? One way to handle this to simply translate this baseband waveform to passband by upconversion; that is, send up(t) = u(t)cos(2πfct), where the carrier frequency fc lies in the desired frequency band. However, what if the frequency occupancy of the passband signal is strictly constrained? (Such constraints are often the result of guidelines from standards or regulatory bodies, and serve to limit interference between users operating in adjacent channels.) Clearly, the timelimited modulation pulse used in Figure 4.1 spreads out significantly in frequency.

We conclude with a brief discussion of research and development frontiers in communication systems. This discussion is speculative by its very nature (it is difficult to predict progress in science and technology) and is significantly biased by the author's own research experience. There is no attempt to be comprehensive. The goal is to highlight a few of the exciting challenges in communication systems in order to stimulate the reader to explore further.

The continuing wireless story

The growth of content on the Internet continues unabated, driven by applications such as video on demand, online social networks, and online learning. At the same time, there have been significant advances in the sophistication of mobile devices such as smart phones and tablet computers, which greatly enhance the quality of the content these devices can support (e.g., smart phones today provide high-quality displays for video on demand). As a result, users increasingly expect Internet content to be ubiquitously and seamlessly Available on their mobile device. This means that, even after the runaway growth of cellular And WiFi starting in the 1990s, wireless remains the big technology story. Mobile operators today face the daunting task of evolving networks originally designed to support voice into broadband networks supplying data rates of the order of tens of Mbps or more to their users.