Digital signal processing

About: Digital signal processing is a research topic. Over the lifetime, 38111 publications have been published within this topic receiving 477741 citations. The topic is also known as: DSP & Digital Signal Process.

Beidou inertial navigation deep integration navigation microsystem

Abstract: The invention discloses a Beidou inertial navigation deep integration navigation microsystem. The Beidou inertial navigation deep integration navigation microsystem comprises a three-axis micromechanical gyroscope, a three-axis micromechanical accelerometer, an odometer encoder, an altimeter, a magnetic field meter, a Beidou/GPS dual-mode chip, a signal conversion and interface circuit, a digital signal processor and a secondary power chip, wherein the digital signal processor is used for receiving digital signals converted by the signal conversion and interface circuit and calculating digital signals corresponding to the angular speed and the accelerated speed so as to obtain the speed and the displacement of a carrier, and the digital signal processor is further used for carrying out the Kalman filtering algorithm on digital signals corresponding to the speed and the displacement of the carrier, travel information, height information, magnetic field information, Beidou signals and GPS signals so as to obtain navigation parameters. According to the Beidou inertial navigation deep integration navigation microsystem, a Beidou, the gyroscope, the accelerometer, the magnetic field meter, the altimeter and functional chips of signal conversion, digital signal processing and the like are organically integrated, full-parameter navigation and seamless navigation can be achieved, and interference resistance and application reliability of the whole navigation system are improved.

Low-cost photoplethysmograph solutions using the Raspberry Pi

Abstract: Photoplethysmography is a prevalent, noninvasive heart monitoring method. In this paper an implementation of photoplethysmography on the Raspberry Pi is presented. Two modulation techniques are discussed, which make possible to measure these signals by the Raspberry Pi, using an external sound card as A/D converter. Furthermore, it is shown, how can digital signal processing improve signal quality. The presented methods can be used in low-cost cardiac function monitoring, in telemedicine applications and in education as well, since cheap and current hardware are used. Full documentation and open-source software for the measurement available: http://www.noise.inf.u-szeged.hu/Instruments/raspberryplet/.

Thermistor temperature detecting method based on DSP (Digital Signal Processing)

Abstract: The invention provides a thermistor temperature detecting method based on DSP, wherein a relational expression between T and lgR-T is obtained by utilizing the temperature of thermistor R-T characteristic detection power equipment and fitting a lgR-T curve in DSP. The method comprises the following steps of: converting the computation for lnR into the approximate operation of addition, subtraction, multiplication and division, obtaining a computing formula by adopting a high-item truncation approximate computing method, then determining a segmenting mode by adopting an error comparison method, and finally obtaining the lnR value of each segment by adopting a segmentation algorithm to realize accurate temperature computation. The temperature detecting precision is greatly improved by adopting the method; the method is simple to realize and has high operational speed; the hardware structure is simple, and the cost is saved.

High-speed image pickup device

Abstract: PROBLEM TO BE SOLVED: To provide a high-speed image pickup device, that can enhance the operating ratio of an analog/digital converter so as to increase the processing speed by compressing image signals in parallel after analog/digital conversion so as to output signals at a high-speed thereby attaining compatibility of high sensitivity and high-speed performance in the case of reading a segment frame. SOLUTION: The high-speed image pickup device is provided with a pixel circuit, consisting of pixels that are arranged into an array consisting of (m- rows)×(m-columns), an analog/digital converter 6 that applies analog/digital conversion to a pixel signal from the pixel circuit in units of columns or rows, and an image compression means, that applies parallel image compression to an output of the analog/digital converter 6, so as to obtain parallel digital outputs.

Comparison of chirp scaling and wavenumber domain algorithms for airborne low-frequency SAR

Abstract: In recent years a new class of Synthetic Aperture Radar (SAR) systems, using low frequencies, have emerged. The combination of low frequencies with high bandwidths allows a variety of new applications. Several new fields arise in forestry, biomass estimation and in archaeological and geological exploration. The P-band SAR technology benefits from technological advances in antenna design, low noise amplifiers, band pass filters, digital receiver technology, as well as new processing algorithms. For all the new applications of an airborne P-band SAR system, the high-resolution imaging is an important parameter, but it cannot be easily achieved with conventional processing techniques. In this paper, the performance and limitations of the Extended Chirp Scaling (ECS) algorithm and wavenumber domain Omega-K processing algorithm are analysed and discussed. Additionally, modifications of both algorithms are proposed, which optimise the respective algorithm for processing low frequency, wide-beam and wide-band SAR data. Despite of the inherent limitations of the above mentioned processing algorithms, a deterministic phase error, called "digital phase error", due to digital signal processing characteristics is formulated and its effect to the processed SAR data is analytically described. The analysis is carried out, using simulated low frequency airborne SAR data.