China
Africa
Explore chapters and articles related to this topic
Detector Fabrication
Published in Alan Owens, Semiconductor Radiation Detectors, 2019
The benefit of using DSP is that it allows the implementation of signal filtering functions that are difficult to achieve using analog electronics (for example cusp filters). Digital filter algorithms also require considerably less overall processing time, so that the resolution remains fairly constant over a large range of count rates, whereas the resolution of analog systems typically degrades rapidly as the count rate increases. As a result, DSP provides a much higher throughput without significant resolution degradation. Improved system stability is another potential benefit since the detector signal is digitized much earlier in the signal processing chain, which minimizes the drift and instabilities associated with analog signal electronics. A final benefit is that since the signals are captured early in the acquisition process, off-line analysis can be applied later if a more complex data reduction is required. The main disadvantage of DSP is that it requires detailed knowledge of digital algorithms and information theory in general.
Digital Signal Processing with Field-Programmable Gate Array
Published in A. Arockia Bazil Raj, FPGA-Based Embedded System Developer's Guide, 2018
DSP is an essential section in many real-world applications such as speech processing, communication systems, measurements, radar, sonar, navigation systems, satellite communication, image processing, weather forecasting, and so on. Due to the vast advantages of the FPGA technology, all the real-time analog signals have to be converted to digital data to process them in the digital domain and then back to analog for further operations. Therefore, DSP techniques play a major role in almost all modern electronic applications, and the need for them significantly increases day by day to fulfil the potential demands. This chapter begins with a detailed description of the DFT and its implementations. The significance of correlations, measurement of frequency and representation of analog signals in the frequency domain (spectrum) are explained, with a design example.
Use of electromyography in studying human movement
Published in Youlian Hong, Roger Bartlett, Routledge Handbook of Biomechanics and Human Movement Science, 2008
Travis W. Beck, Terry J. Housh
The advent of digital computers also changed the way that surface EMG signals are processed. Digital signal processing (DSP) is the mathematics, techniques, and algorithms that are used to manipulate signals once they have been converted into digital form. Perhaps the most common DSP technique is digital filtering. In principle, digital filters are used for the same two purposes that analogue filters are used: (a) separating signals that have been combined; and (b) restoring signals that have been distorted in some way. The performance of digital filters is far superior, however, to analogue filters. In addition, digital filters can be used for many different purposes, which makes them very useful for processing surface EMG signals. For example, one of the most important uses of digital filters in surface EMG research is for removing noise. In most cases, a band-pass filter (i.e., a combination of a low- and a high-pass filter) with cutoff frequencies of 10 and 500 Hz is used to remove both low and high frequency noise. Another common use of digital filters is for data smoothing. For example, with the linear envelope technique (i.e., a signal processing method that will be discussed in the next section), a low-pass filter is used to smooth the EMG signal, thereby providing information regarding slow changes in the amplitude of the signal over time.
Voltage mode and trans-admittance mode first-order universal filters employing DV-EXCCCII
Published in Australian Journal of Electrical and Electronics Engineering, 2022
Although digital signal processing has continued to rule the globe, analog signal processing remains indispensable in some applications. Amplifiers, waveform generators, continuous-time filters, etc., are some circuits where analog signal processing is desired. Filters are an important research topic in an analog system. These circuits have applications in communication, signal processing, control systems, and instrumentation. There are several ways to categorise filters. It can be classified based on the number of inputs and outputs used to realise the filter circuit. This results in SISO (Single Input Single Output), SIMO (Single Input Multi-Output), MISO (Multi-Input Single Output), and MIMO (Multi-Input Multi-Output) filters. Another way of classification is based on the mode of operation of the circuit, that is, CM (Current Mode), VM (Voltage Mode), TAM (Trans-admittance Mode), and TIM (Trans-impedance Mode).
IoT-Sodar Network for Airshed Management Planning
Published in IETE Journal of Research, 2022
Parag Chourey, Kirti Soni, Nirbhow Jap Singh, Ravinder Agarwal
Furthermore, the output signal from the analog signal processing stage required digital signal processing for suitable data presentation. The first stage for digital signal processing is analog to digital conversion (ADC). The in-built ADC of the computer in computer-aided applications is beneficial due to its fast sampling rate. Data processing and manipulation is a significant task; the peak-detection method plays a crucial role in extracting crucial data points and peaks for better monitoring signals in the time domain [40]. The last signal processing stage is digital filtering, which eliminates the data points outside the desired frequency range. The digital FIR-bandpass filter is one of the popular filtering techniques to eliminate the undesired frequency signals in the acoustic field [41].
A hybrid recognition model of microseismic signals for underground mining based on CNN and LSTM networks
Published in Geomatics, Natural Hazards and Risk, 2021
Yong Zhao, Haiyan Xu, Tianhong Yang, Shuhong Wang, Dongdong Sun
A one-dimensional time-series monitoring signal is assumed to be To understand its implicit construct of temporal evolution, the monitoring signals are arranged with a time lag. where, time lag M refers to window length or embedding dimension. The embedding dimension M is equivalent to the resolution when decomposing components of monitoring signals. The above matrix X is named the trajectory matrix. The trajectory matrix is a Hankel matrix, and all elements on the anti-diagonal are equal. Its autocovariance matrix is expressed as follows: where, represents a Toeplitz matrix, in which elements on the main diagonal are equal, as are the elements on the line parallel to the main diagonal. It is widely applied in digital signal processing.