![]() For example, a signal processing environment that incorporates the property that convolution in the time domain corresponds to multiplication in the frequency domain can explore a variety of rearrangements of filtering structures, including those involving the direct use of the discrete Fourier transform (DFT) and the FFT algorithm. Object-oriented programming languages provide a convenient environment for manipulating signals, systems, and signal processing expressions without explicitly evaluating the data sequences.The sophistication of systems designed to do signal expression processing is directly influenced by the incorporation of fundamental signal processing concepts, theorems, and properties, such as those that form the basis for this book. In this class of processing, signals and systems are represented and manipulated as abstract data objects. This type of processing is potentially useful in signal processing workstations and for the computer-aided design of signal processing systems. Still another relatively new category of signal processing involves the symbolic manipulation of signal processing expressions. This symbolic output can, in turn, be the input to a symbolic processing system, such as a rules-based expert system, to provide the final signal interpretation. Typically, such a system will apply digital pre-processing (filtering, parameter estimation, and so on) followed by a pattern recognition system to produce a symbolic representation, such as a phonemic transcription of the speech. ![]() For example, in a speech recognition or understanding system, the objective is to interpret the input signal or extract information from it. In such contexts, the objective of the processing is to obtain a characterization of the input signal. Portfolio investment managers, for example, are relying increasingly on using sophisticated signal processing since even a very small increase in signal predictability or signal-to-noise ratio (SNR) can result in significant gain in performance.Īnother important area of signal processing is signal interpretation. ![]() Effective modeling, prediction and filtering of economic data can result in significant gains in economic performance and stability. The compact disc and MP3 recording and playback systems rely on many signal processing concepts.įinancial Engineering represents another rapidly emerging field which incorporates many signal processing concepts and techniques. The processed form of the input is stored (on the compact disc or in a solid state memory), and final processing for reconstructing the audio signal is carried out in real time when the output is played back for listening. The initial processing may occur in real time, slower than real time, or even faster than real time. The compact disc player and MP3 player are examples of asymmetric systems in which an input signal is processed only once. Non-real-time applications are also common. By some estimates, even with impending limitations other areas of application. The growing number of applications and demand for increasingly sophisticated algorithms go hand-in-hand with the rapid development of device technology for implementing signal processing systems. As we look to the future, it is clear that the role of signal processing is expanding, driven in part by the convergence of communications, computers, and signal processing in both the consumer arena and in advanced industrial and government applications. High-density data storage devices and new solidstate memories rely increasingly on the use of signal processing to provide consistency and robustness to otherwise fragile technologies. MPEG audio and video and JPEG1 image data compression standards rely heavily on signal processing principles and techniques. Sophisticated digital signal processors are at the core of all modern cell phones. Although we routinely take for granted the extraordinary performance of multimedia systems, such as high definition video, high fidelity audio, and interactive games, these systems have always relied heavily on state-of-the-art signal processing. ![]() Signal processing algorithms and hardware are prevalent in a wide range of systems, from highly specialized military systems and industrial applications to low-cost, high-volume consumer electronics. Signal processing applications span an immense set of disciplines that include entertainment, communications, space exploration, medicine, archaeology, geophysics, just to name a few. The rich history and future promise of signal processing derive from a strong synergy between increasingly sophisticated applications,new theoretical developments and constantly emerging new hardware architectures and platforms. ![]()
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