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The article discusses the significance of calming signals also known as body movement seen in dogs. It mentions Turid Rugaas, an author, who refers to the behavioral patterns of both dogs and wolves, who are known as conflict-solving animals. It also illustrates several calming signals given by dogs such as turning of head away, licking of nose showing nervousness and yawning.

Chapter 7 of the book "A Complete Guide to Surviving in the Wilderness: Everything You Need to Know to Stay Alive and Get Rescued" is presented. The chapter focuses on some signaling techniques in the wilderness. It discusses how a small pocket mirror can be used as a signaling device as it can reflect sunlight and created a flash of light. Other signaling tools explored include fire and smoke, trail marking and noise.

Chapter 5 of the book "ARRL's Low Power Communication: The Art and Science of QRP," by Rich Arland is presented. It explores the idiosyncrasies and scanning of each band in the low power communication or QRP operation. It also discusses the development of listening skills in doing the QRP operation, the gray-line propagation technique that enhances signal strengths, and the QRP DXing. INSET: Listen, Listen, Listen, And A Dash of Luck.

The article presents Chapter 1, "Amateur Radio -- All About Operating" from the 10th edition of the "ARRL Operating Manual" of the American Radio Relay League (ARRL). It discusses how radio amateurs send their signals and engage in activities in ham radio like public service, emergency communications, and competitions. Topics include station setup at home, choosing equipment, and mobile stations and safety. Tables are presented on ARRL Operating Awards, U.S. Amateur Radio bands, and Q Signals.

The article lists the international code of signals including semaphore, answering pennant, and visual distress signals.

The article focuses on various types of sound effects in electronic keyboards. It tells that sound effects can be created by taking the output signal from the keyboard and plugging into an external effect unit. It discusses different kinds of effects such as external effects, electronic effects and internal effects. Several diagrams related to the same are also presented.

Chapter 19 of the book "The Shagganappi," by E. Pauline Johnson is presented. Benny Ellis has been playing at railroad when he was a child, which enabled him to develop a signal code. One day, the McKenzie boys were swimming at another side of the lake where Benny and his father were fishing. Two bears suddenly appeared coming at the direction of the boys. Benny used the signal code to warn them about the danger coming in.

In this work, associative memories are used for diagnostic classification of needle EMG signals. Vectors containing 44 autoregressive coefficients represent each signal and are presented as stimuli to associative memories. As the number of training stimuli increases, the method recursively updates associative memories. The obtained classification results are equivalent to the ones provided by the traditional Fisher’s discriminant, indicating the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Chapter 3 looks at the concepts and tools for discrete signal representation. It first concentrates on orthogonal series expansions and explains properties of orthonormal bases. It also looks at the generation of orthonormal bases via the Gram-Schmidt procedure. Then general series expansions are addressed and the concepts of reciprocal bases and orthogonal projections are discussed. Finally, mathematical tools such as the QR decomposition, the Moore-Penrose pseudoinverse and Householder transforms are discussed. [ABSTRACT FROM PUBLISHER]

Chapter 2 gives an introduction to integral signal representation. It starts by looking at general integral transforms and then discusses commonly used transforms and their properties, including the Fourier, Hartley and Hilbert transforms. Then it looks at the representation of bandpass signals and introduces concepts such as the analytic signal and the complex envelope of real bandpass signals. Finally, the properties of stationary bandpass processes are discussed. [ABSTRACT FROM PUBLISHER]

Chapter 1 contains an introduction to signals and signal spaces. It explains the basic tools for classifying signals and describing their properties. Topics covered include normed and metric spaces, inner product spaces, autocorrelation and energy density of deterministic signals, autocorrelation and power spectral density of random signals, and the transmission of random processes through linear systems. [ABSTRACT FROM PUBLISHER]

The article presents a list of books and other literary materials related to signal analysis. Some of the literary materials included in the list are: "Introduction to Matrix Analysis," by R. Bellman; "Regression and the Moore-Penrose Pseudoinverse," by A. Albert; "The Measurement Power Spectra," by R.B. Blackman and J.W. Tukey; "Suppression of Acoustic Noise in Speech Using Spectral Subtraction," by S. Boll; "The Discrete Hartley Transform," by R.N. Bracewell and "On Lapped Orthogonal Transforms," by A.N. Akansu and F.E. Wadas.

The article focuses on sigma-delta converters in communication systems. The sigma-delta converter uses feedback to shape the quantizing noise spectrum of an oversampled low-resolution quantizer to obtain low levels of in-band noise in exchange for higher levels of out-of-band-noise. In effect, the quantizer arranges for the quantizing noise spectrum and input signal spectrum to occupy nearly distinct spectral regions. A major application of the sigma-delta process is in the area of analog-to-digital conversion, particularly in the conversion of audio signals, of instrumentation signals and modem input signals.

The article presents information on power spectra of digitally modulated signals. In the design of digital communication systems for transmitting digital information through a channel, the spectral characteristics of the information bearing signals is an important element. The class of linearly modulated signals include pulse amplitude modulation or amplitude shift keying phase shift keying and quadrature amplitude modulation. The class of nonlinearly modulated signals includes continuous-phase modulation (CPM) and the special form of CPM called continuous-phase frequency shift keying.

The article discusses the features of contactless communication. The reader's high frequency (HF) interface performs generation of high frequency transmission power to activate the transponder and supply it with power, modulation of the transmission signal to send data to the transponder, reception and demodulation of HF signals transmitted by a transponder. The HF interface contains two separate signal paths to correspond with the two directions of data flow from and to the transponder. The reader's main functions are therefore to activate the data carrier.

The article provides information on modulation in a digital communication system. Modulation is the process of altering the signal parameters of a high frequency carrier, frequency or phase, in relation to a modulated signal, the baseband signal. Demodulation is an additional modulation procedure to reclaim the signal in the baseband. A binary 1 is represented by a "high" signal and a binary 0 is represented by a "low" signal. In "differential coding" every binary 1 to be transmitted causes a change in the signal level, whereas the signal level remains unchanged for a binary zero.

In Chapter 13, the author considers fluctuating, distributed targets. In the first part of the chapter the problems of signal and receiver design for systems operating in reverberation and clutter are studied. This discussion completes the resolution problem development that begins in Chapter 10. In the second part of the chapter the detection of fluctuating, distributed targets and communication over fluctuating distributed channels are studied. Finally, the problem of estimating the parameters of a fluctuating, distributed target is studied. [ABSTRACT FROM PUBLISHER]

The article presents an overview of chirp modulation (CM), which represents a special type of spread-spectrum signaling where a carrier signal is modulated in two ways. The primary modulation is carried out in the complex base-band and constitutes the usual formats such as phase shift keying (PSK), pulse position modulation (PPM), or binary orthogonal keying (BOK). The primary modulation is combined with a secondary modulation for spectrum spreading. CM is resistant to the Doppler effect arising in time-variant scenarios typically encountered in mobile radio applications.

This article presents an overview of the modeling and estimation of channels for digital transmission of single carrier linearly modulated signals. One of the objectives of receiver design for digital communications is to minimize the probability of detection error. In general, the design of the optimal detector requires certain prior knowledge of the channel characteristics, which are usually estimated through the use of pilot symbols. A typical example is the voice-band modem where, on establishing the initial connection, a signal known to the receiver is transmitted through the telephone channel.

The article focuses on propagation models that are fundamental tools for designing any fixed broadband wireless communication system. A propagation model basically predicts what will happen to the transmitted signal while in transit to the receiver. Modern communication systems achieve higher capacity by using a wider band of frequencies. For such systems, narrowband prediction of signal levels and fading alone does not provide enough information to predict system performance. As a consequence, the concept of a propagation model has been broadened to include models of the entire transfer function of the channel.

The article focuses on the significance of Radio Frequency (RF) switch, the component of RF MEMS (Microelectro Mechanical Systems). Switches and relays are vital components of all automated systems. Switching provides for an interface between a system and devices with the capability for automatic redirection of signals, enhancing their flexibility and expandability. Basically a switch is a device for making or breaking an electric circuit. A switch in an RF signal path can introduce resistance and capacitance in signal-to-signal and signal-to-ground paths as well as cross-talk. But these generally have a limited lifetime and are prone to failure. The finite time to toggle a switch is the limitation in many RF applications.

The article discusses characteristics of the micromachined radio frequency (RF) filters. Several different types of filters are used in communication equipment. These are generally classified based on the frequency band they transmit as low pass, high pass, band pass and band stop filter. The most important characteristic of a filter is the insertion loss. This is the ratio of the signal delivered at the output side to that supplied to the filter at its input. The pass band characteristics of a filter are generally expressed as its quality factor.

The article presents information on micromachined phase shifters. A phase shifter is a two-port network with the provision that the phase difference between output and input signals can be controlled by a control signal. In a phased array antenna, the phase shifters change the effective path length of the transmission line resulting in different phases for each element. The ferrite phase shifters have low insertion loss and can handle significantly higher powers, but are complex in nature and have a high fabrication cost.

The ideas and viewpoints presented here belong solely to Bhumip Khasnabish, Massachusetts, USA. [ABSTRACT FROM PUBLISHER]

This article presents information about social cognition following prefontal cortical lesions. Until recently, little was known about the neuropsychological basis for social cognition and how the brain facilitates social interaction, including the acquisition of social knowledge, the perception and processing of social signals and representation of mental states. All this is changing, with an integration of different approaches, genetic, developmental and neuropsychological. A key early finding is that impairments in social cognition can apparently occur in the absence of deficits in other main aspects of cognitive function.

The article focuses on various aspects of wavelet transform. Wavelets are continuous time functions that integrate to zero and are square-integrable. The wavelet transform of a signal with respect to a mother wavelet is simply the collection of projections of the signal onto the set of daughter wavelets. An elegant expression exists for the inverse wavelet transform if a wavelet satisfies the admissibility condition. Wavelet applications of image processing are based on exploiting the localization properties of the wavelet transform in space and spatial frequency. The fast convergence rate of wavelet expansions is the key to the success of wavelet transforms in image compression.

In this chapter we focus mainly on approaches to blind separation of sources when the measured signals are contaminated by large additive noise. We extend existing adaptive algorithms with equivariant properties in order to considerably reduce the bias caused by measurement noise for the estimation of mixing and separating matrices. Moreover, we propose dynamical recurrent neural networks for simultaneous estimation of the unknown mixing matrix, source signals and reduction of noise in the extracted output signals. The optimal choice of nonlinear activation functions for various noise distributions assuming a generalized-Gaussian-distributed noise model is also discussed. Computer simulations of selected techniques are provided that confirms their usefulness and good performance. The main objective of this chapter is to present several approaches and derive learning algorithms that are more robust with respect to noise than the techniques described in the previous chapters or that can reduce the noise in the estimated output vector of independent components [ABSTRACT FROM PUBLISHER]

In this chapter we attempt to extend and generalize the results discussed in the previous chapters to nonlinear dynamical models. However, the problem is not only very challenging but intractable in the general case without a priori knowledge about the mixing and filtering nonlinear process. Therefore, in this chapter we consider very briefly only some simplified nonlinear models. In addition, we assume that some information about the mixing and separating system and source signals is available. In practice, special nonlinear dynamical models are considered in order to simplify the problem and solve it efficiently for specific applications. Specific examples include the Wiener model, the Hammerstein model and Nonlinear Autoregressive Moving Average models. [ABSTRACT FROM PUBLISHER]

The main objective of this chapter is to review and extend existing adaptive natural gradient algorithms for various multichannel blind deconvolution models. Blind separation/deconvolution of source signals has been a subject under consideration for more than two decades. There are significant potential applications of blind separation/deconvolution in various fields, for example, wireless telecommunication systems, sonar and radar systems, audio and acoustics, image enhancement and biomedical signal processing (EEG/MEG signals). In these applications, single or multiple unknown but independent temporal signals propagate through a mixing and filtering medium. The blind source separation/deconvolution problem is concerned with recovering independent sources from sensor outputs without assuming any a priori knowledge of the original signals, except certain statistical features. In this chapter, we present using various models and assumptions, relatively simple and efficient, adaptive and batch algorithms for blind deconvolution and equalization for single-input/multiple-output (SIMO) and multiple-input/multiple-output (MIMO) dynamical minimum phase and non-minimum phase systems. The basic relationships between standard ICA/BSS (Independent Component Analysis and Blind Source Separation) and multichannel blind deconvolution are discussed in detail. They enable us to extend algorithms derived in the previous chapters, in particular, the natural gradient approaches for instantaneous mixture to convolutive dynamical models. We also derive a family of equivariant algorithms and analyze their stability and convergence properties. Furthermore, a Lie group and Riemannian metric are introduced on the manifold of FIR filters and using the isometry of the Riemannian metric, the natural gradient on the FIR manifold is described. Based on the minimization of mutual information, we present a natural gradient algorithm for the causal minimum phase, the finite impulse response (FIR) multichannel filter. Using information back-propagation, we also discuss an efficient implementation of the learning algorithm for the non-causal FIR filters. Computer simulations are also presented to illustrate the validity and good learning performance of the described algorithms. [ABSTRACT FROM PUBLISHER]

The main purpose of this chapter is to describe and overview models and to present a family of practical and efficient associated adaptive or locally adaptive learning algorithms, which have special advantages of efficiency and/or simplicity and straightforward electronic implementations. Some of the described algorithms have special advantages in the case of noisy, badly scaled or ill-conditioned signals. The developed algorithms are extended for the case when the number of sources and their statistics are unknown. Finally, the problems of an optimal choice of nonlinear activation function and general local stability conditions are also discussed. In particular, we focus on simple locally adaptive Hebbian/anti-Hebbian learning algorithms and their implementations using multi-layer neural networks. [ABSTRACT FROM PUBLISHER]

In this appendix some mathematical background needed for complete understanding of the text is quickly reviewed. Many useful definitions, formulas for matrix algebra and matrix differentiation are given [ABSTRACT FROM PUBLISHER]

The "S" in "GPS" and in "INS" stands for "system," and "systems science" for modeling, analysis, design, and integration of such systems is based largely on linear algebra and matrix theory. Matrices model the ways that components of systems interact dynamically and how overall system performance depends on characteristics of components and subsystems and on the ways they are used within the system. [ABSTRACT FROM PUBLISHER]

Why is the GPS signal so complex? GPS was designed to be readily accessible to millions of military and civilian users. Therefore, it is a receive-only passive system for a user, and the number of users that can simultaneously use the system is unlimited. Because there are many functions that must be performed, the GPS signal has a rather complex structure. As a consequence, there is a correspondingly complex sequence of operations that a GPS receiver must carry out in order to extract desired information from the signal. In this chapter we characterize the signal mathematically, describe the purposes and properties of the important signal components, and discuss generic methods for extracting information from these components. [ABSTRACT FROM PUBLISHER]

Differential GPS (DGPS) is a technique for reducing the error in GPS-derived positions by using additional data from a reference GPS receiver at a known position. The most common form of DGPS involves determining the combined effects of navigation message ephemeris and satellite clock errors [including the effects of selective availability (SA), if active] at a reference station and transmitting pseudorange corrections, in real time, to a user's receiver. The receiver applies the corrections in the process of determining its position. This causes some error sources to be canceled completely. Other error sources are not canceled at all, or cancellation degrades over distance. [ABSTRACT FROM PUBLISHER]

Although there are many variations in GPS receiver design, all receivers must perform certain basic functions. We discuss these functions in detail. Receiver architecture and design choices are presented. Although there is a wide variety of GPS antennas, sometimes it is necessary to use a low-noise preamplifier at the antenna. Various antennas are discussed (patch, dome, blade, helix, Choke-ring, and phased-array). [ABSTRACT FROM PUBLISHER]

The article presents information on frequency analysis. Frequency analysis of any given signal involves the transformation of a time-domain signal into its frequency components. The need for describing a signal in the frequency domain exists because signal processing is generally accomplished using systems that are described in terms of frequency response. Converting the time-domain signals and systems into the frequency domain is extremely helpful in understanding the characteristics of both signals and systems.

The article presents information on adaptive filtering. Filtering refers to the linear process designed to alter the spectral content of an input signal in a specified manner. Adaptive filters are time varying, filter characteristics such as bandwidth and frequency response change with time. Thus the filter coefficients cannot be determined when the filter is implemented. The coefficients of the adaptive filter are adjusted automatically by an adaptive algorithm based on incoming signals. This has the important effect of enabling adaptive filters to be applied in areas where the exact filtering operation required is unknown or is non-stationary.

Chapter 2 provides a complete overview of the interworking of all signalling interfaces and protocols covered in the book. This comprises end systems and switches with ILMI, UNI and PNNI. [ABSTRACT FROM PUBLISHER]

Chapter Twelve presents the last years' development in conditioning of temperature sensor output signals, including transformation of signal nature, scale and shape. An important part of the chapter is devoted to analogue and digital signal filtering, signal linearisation, detection of characteristic values of measured signal (peak-picker, averager etc.) as well as to digital signal transmission and interfacing. [ABSTRACT FROM PUBLISHER]

This chapter begins with a study of the frequency or time characteristics of impulsive noise, and then proceeds to consider several methods for statistical modelling of an impulsive noise process. The classical method for removal of impulsive noise is the median filter. However, the median filter often results in some signal degradation. The author describes a model-based system that detects each impulsive noise, and then proceeds to replace the samples obliterated by an impulse. He also considers some methods for introducing robustness to impulsive noise in parameter estimation.

Chapter 5 is concerned with characterising multipath phenomena. The concepts of slow and fast fading are introduced and this leads to a discussion of the nature of multipath propagation under static and dynamic conditions and the existence of Doppler shift. The chapter also covers: Models that have been suggested to explain the observed signal properties (with emphasis on a 3-dimensional approach based on scattering); Statistical expressions for various properties of the signal envelope and phase, including the correlation between the envelopes at spatially separated points; Rician fading; The properties of the signal received at a base station using antennas that are separated both horizontally and vertically; The magnetic field components of the electromagnetic field; Signal variability and the statistics of the mean signal and the fast and slow fading; Large-area statistics. [ABSTRACT FROM PUBLISHER]

Chapter 8 covers techniques for channel sounding, sampling and simulation. It includes a description of a practical narrowband sounder and then deals with: Signal sampling and sampled distributions; Estimating the mean signal strength and the confidence interval; Wideband sounding techniques are surveyed and the system requirements for a wideband sounder based on the swept time-delay cross correlation (STDCC) technique are discussed. The chapter then describes: A practical wideband sounder design; The data processing procedures to obtain parameters of interest. Radio channel simulation is discussed and hardware and software techniques for simulating narrowband and wideband channels are described. [ABSTRACT FROM PUBLISHER]

Transmission errors are similar to taxes in that they are basically unavoidable when a communications circuit is used. This article first reviews the primary causes of transmission errors and commonly employed methods to determine line error rates. This information provide people with the ability to determine the quality of a circuit with respect to an international standard which is discussed in this article as well as a basis for comparing circuit quality. As a signal propagates down a transmission medium, several factors can cause it to be received in error: the transmission medium employed and impairments caused by nature and machinery.

This chapter has developed the idea of separating the telecommunications network into a switched information subnet and a signalling subnet and has shown how this allows intelligence to be added to the signalling subnet. This fulfils the original promise of stored programme control and in effect puts the operator back into the network: after all, the most intelligent network technology ever deployed was the human operator sitting at a manual switchboard. [ABSTRACT FROM PUBLISHER]

Standardisation and commoditisation are key aspects in the design and deployment of modern networks. This implies that you have to understand the key components that are being supplied. Some of these components are virtually universal. For instance, SONET/SDH provides virtually all of bulk transmission, C7 handles all of the inter-networking signalling. Other components are more differentiated, such as those on offer for managing networks. Nonetheless, almost all of the technology that is used in telecommunications conforms to some generally accepted pattern. [ABSTRACT FROM PUBLISHER]

In this opening chapter, we take a whistle-stop tour of some of the key concepts and influences in the modern telecommunications world. It is very clear we are looking here at an industry driven by a set of technologies that have come together to enable a hugely powerful set of communications capabilities. [ABSTRACT FROM PUBLISHER]

This article focuses on the spectral analysis problem: that of determining the distribution in frequency of the power of a time series from a finite set of measurements. In radar and sonar systems, the locations of the sources or targets can be estimated by measuring the spectral contents of the received signals. In order to alleviate the inherent limitations of the nonparametric methods, many spectral estimation procedures have been proposed. These methods assume that the signal of interest satisfies a generating model with known functional form and, hence, are referred to as model-based or parametric methods. Given a finite-duration of data samples, the first step toward spectral estimation is to select an appropriate model to fit the observed data.

The article discusses higher order tools for statistical signal processing. Statistical signal processing deals with random signals, their transformation by system operators, and their characterization using time or frequency-domain statistical descriptors computed from measured data records. Signals are treated as random when no exact expression describes their evolution and the engineer has incomplete knowledge about their description or origin. Statistical descriptors are deterministic quantities and reflect one's degree of knowledge or ignorance about randomness.

The article focuses on the propagation of broadcast transmissions. Radio waves may be propagated from a transmitting site to a receiving site by a number of different mechanisms. At broadcast frequencies, the most practical and important ones are ground wave, sky wave, and space wave. The ground wave, as the name implies, exists when the transmitting and receiving antennas are on or near the surface of the Earth. Thus, it is also called the surface wave. Ground waves exist at all times. Broadcast signals at low and medium frequencies received in daytime are all ground waves. The sky wave represents energy that travels from the transmitting antenna to the receiving antenna as a result of a "bending" by the Earth's upper atmosphere called the ionosphere. The ionosphere, which consists of several different layers, begins about 50 kilometers above the Earth's surface.

This article focuses on automated language identification. It is a process by which the language of digitized spoken words is recognized by a computer. It is one of several processes in which information is extracted automatically from a speech signal. Language identification (LID) applications fall into two main categories: preprocessing for machine systems and preprocessing for human listeners. If no mode of input other than speech is used, then the system must be capable of determining the language of the speech commands, either while it is recognizing the commands or before it has recognized the commands. Determining the language during recognition would require many speech recognizers, one for each language, running in parallel. A language-ID system could be used first to list the most likely languages of the speech commands quickly. Then the few most appropriate language-dependent speech-recognition models could be loaded and run.