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Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. [ABSTRACT FROM AUTHOR]

The explosive increase in the number of IoT devices requires various types of communication methods. This paper presents secure personal authentication using electrostatic coupling Intra-body communication (IBC) based on frequency shift keying (FSK) and error correction. The proposed architecture uses GPIO for a transmitter and analog-to-digital conversion (ADC) for a receiver. We mplemented FSK modulation, demodulation, data protection, and error correction techniques in the MCU software without applying hardware devices. We used the characteristic that the carrier signal is 50% duty square wave for 1-bit error correction and applied a method of randomly inverting SHA1 hash data to protect user authentication data during transmission. The transmitter modulates binary data using a square wave as a carrier signal and transmits data through the human body. The receiver demodulates the signal using ADC and decrypts the demodulated binary data. To determine the carrier frequency from ADC results, we applied a zero-crossing algorithm which is used to detect edge characteristics in image processing. When calculating the threshold value within the zero-crossing algorithm, we implemented an adaptive threshold setting technique utilizing Otsu's binarization technique. We found that the size of the electrode pad does not affect the signal strength, but the distance between the electrode pad and the skin has a significant effect on the signal strength. Our results show that binary data modulated with a square wave can be successfully transmitted through the human body, and, when 1-bit error correction is applied, the byte error rate on the receiver side is improved around 3.5% compared to not applying it. [ABSTRACT FROM AUTHOR]

For airborne interferometric synthetic aperture radar (InSAR) data processing, it is essential to achieve precise motion compensation to obtain high-quality digital elevation models (DEMs). In this paper, a novel InSAR motion compensation method is developed, which combines the backprojection (BP) focusing and the multisquint (MSQ) technique. The algorithm is two-fold. For SAR image focusing, BP algorithm is applied to fully use the navigation information. Additionally, an explicit mathematical expression of residual motion error (RME) in the BP image is derived, which paves a way to integrating the MSQ algorithm in the azimuth spatial wavenumber domain for a refined RME correction. It is revealed that the proposed backprojection multisquint (BP-MSQ) algorithm exploits the motion error correction advantages of BP and MSQ simultaneously, which leads to significant improvements of InSAR image quality. Simulation and real data experiments are employed to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]