scholarly journals Low-Pass Parabolic FFT Filter for Airborne and Satellite Lidar Signal Processing

Sensors ◽  
2015 ◽  
Vol 15 (10) ◽  
pp. 26085-26095 ◽  
Author(s):  
Zhongke Jiao ◽  
Bo Liu ◽  
Enhai Liu ◽  
Yongjian Yue
Keyword(s):  
Signal Processing ◽  
Lidar Signal ◽  
Low Pass

scholarly journals Development and assessment of a Wake Vortex characterization algorithm based on a hybrid LIDAR signal processing

2016 ◽  
Author(s):  
Alexandre Hallermeyer ◽  
Agnès Dolfi-Bouteyre ◽  
Matthieu Valla ◽  
Laurent Le Brusquet ◽  
Gilles Fleury ◽  
...  
Keyword(s):  
Signal Processing ◽  
Wake Vortex ◽  
Lidar Signal

Measurement of Cr(VI) Based on Absorption Spectrum through Plastic Fiber

2014 ◽  
Vol 687-691 ◽  
pp. 948-951
Author(s):  
Wei Jun Hu
Keyword(s):  
Signal Processing ◽  
Absorption Spectrum ◽  
Optic Fiber ◽  
Signal Process ◽  
Linear Fitting ◽  
Non Linear ◽  
Low Pass ◽  
Measurement Experiment

Considering the advantage of optic fiber, a methods of measuring Cr (VI) based on absorption spectrum through plastic fiber is introduced, which includes structure of measurement, experiment process, spectrum signal process. After signal processing based on low-pass filtering and non-linear fitting, five concentrations of Cr (VI) can be differed easily and the peak values of spectrum corresponding to five concentrations accord with the Longbow Bill's law . In this way, the measurement concentration can limit down to 0. 0.0660 μg/ml.

Road Border Recognition Using FIR Images and LIDAR Signal Processing

2007 ◽  
Author(s):  
Basel Fardi ◽  
Hendrik Weigel ◽  
Gerd Wanielik ◽  
Kiyokazu Takagi
Keyword(s):  
Signal Processing ◽  
Lidar Signal

scholarly journals THE USE OF DIGITAL SIGNAL PROCESSING AND A NEURAL NETWORK WHEN GENERATING A FORECAST OF TIME SERIES OF DATA FOR THE PURPOSE OF DETECTING ANOMALIES IN THE IN THE AUTOMATED CONTROL OF TECHNOLOGICAL PROCESSES

2020 ◽  
Vol 20 (1) ◽  
pp. 24-34
Author(s):  
A. N. Ragozin ◽  
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Input Signal ◽  
Forecast Error ◽  
Digital Signal ◽  
Processing Unit ◽  
Automated Control ◽  
Prediction Result ◽  
Low Pass

n order to detect anomalies and improve the quality of forecasting dynamic data flows observed from sensors in Industrial Control System (ACS)., it is proposed to use a predictive mod-ule consisting of a series-connected digital signal processing unit (DSP) and a predictive unit using a neural network (predictive autoencoder ( Auto Encoder), predictive Autoencoder (PAE)). The study showed that the preliminary DSP block of the predicted input signal, consisting of a parallel set (comb) of digital low-pass filters with finite impulse responses (FIR-LPF), leads to a non-equilibrium account of the correlation relationships of the time samples of the input signal and to increase the accuracy of the final prediction result. The predicted autoencoder (PAE) pro-posed and considered in the work, in addition to restoring the input signal or part of the input signal at the PAE output, also generates the predicted samples of the input signal for the speci-fied number of «forward» time steps at the output, which increases the accuracy of the predic-tion result. The reduction of the forecast error occurs due to the imposition of restrictions in the formation of the forecast, that is, an additional requirement to restore the input samples of the samples – «stabilizers» at the NS output. The introduction of «stabilizers» increases the accuracy of the prediction result.

Signal Processing Criteria Based on Electro-Optic Filters for Fiber Optic Access Transceiver Systems

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
IS Amiri ◽  
Ahmed Nabih Zaki Rashed
Keyword(s):  
Signal Processing ◽  
Fiber Optic ◽  
Optical Filters ◽  
Receiver Side ◽  
Electrical Filter ◽  
Electro Optic ◽  
Fabry Perot ◽  
Different Types ◽  
Fast Processing ◽  
Low Pass

AbstractThe study outlines the different types of electro-optic filters that are used in fiber optic access transceiver systems. These filters are classified into the electrical filter in the receiver side and optical filters are located between fiber optic and transmitter. The optical filters are, namely, Fabry Perot, Gaussian, and Bessel filters. While electrical filters that are namely low pass Bessel, low pass Gaussian, and low pass squared raised cosine roll-off filters. The study emphasizes the important criteria in the employment of electro-optic filters in this system for signal fast processing. The Fabry Perot optical filters in addition to low pass squared raised cosine roll-off filters are clarified the best-obtained results in the comparison with other proposed filters.

scholarly journals Digital Ultrasonic Sensing Device with Programmable Frequency: Development and Analysis

2021 ◽  
Vol 1 (1) ◽  
pp. 33-45
Author(s):  
Manish Man Shrestha ◽  
Bibek Ropakheti ◽  
Uddhav Bhattarai ◽  
Ajaya Adhikari ◽  
Shreeram Thakur
Keyword(s):  
Signal Processing ◽  
Ultrasonic Wave ◽  
Analog Circuit ◽  
Damage Index ◽  
Digital Signal ◽  
Ultrasonic Waves ◽  
Complex Signal ◽  
Ultrasonic Sensing ◽  
Low Pass ◽  
Signal Processing Algorithms

Ultrasonic wave is widely used in Structure Health Monitoring (SHM) systems. A piezoelectric transducer (PZT) is one of the most widely used sensors to acquire the structure's ultrasonic wave. As today's world is digital, it is necessary to digitize the traditional analog PZT sensing system. This paper describes the development and analysis of a digital ultrasonic sensing device (DUSD) for PZT sensors. We removed the complexities of the analog circuit by interfacing the microcontroller directly with the charge amplifier circuit. The microcontroller used in this research is a 32-bit ARM Cortex-M4 with in-built FPU (Floating Point Unit) and DSP (Digital signal processing) instructions. These features make it possible to compute complex signal processing algorithms and methods in the controller itself. The developed sensing device can communicate with the user and other devices using Universal Asynchronous Receiver/Transmitter (UART). The user can select cut-off frequencies of both high pass filters (HPF) and low pass filters (LPF) as well as types of data (ultrasonic waves, damage index) that the user wishes to collect from the device. To illustrate the proficiencies of the device, the ultrasonic wave was collected and evaluated to detect the damage in the test specimen.

Myoelectric-Controlled Exoskeletal Elbow Robot to Suppress Essential Tremor: Extraction of Elbow Flexion Movement Using STFTs and TDNN

2012 ◽  
Vol 24 (1) ◽  
pp. 141-149 ◽  
Author(s):  
Takeshi Ando ◽  
◽  
Masaki Watanabe ◽  
Keigo Nishimoto ◽  
Yuya Matsumoto ◽  
...  
Keyword(s):  
Signal Processing ◽  
Essential Tremor ◽  
Voluntary Movement ◽  
Elbow Flexion ◽  
Myoelectric Signal ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Short Time ◽  
Short Time Fourier Transformation

Essential tremor is the most common of all involuntary movements. Many patients with an upper-limb tremor have serious difficulties in performing daily activities. We developed a myoelectric-controlled exoskeletal robot to suppress tremor. In this article, we focus on developing a signal processing method to extract voluntary movement from a myoelectric in which the voluntary movement and tremor were mixed. First, a Low-Pass Filter (LPF) and Neural Network (NN) were used to recognize the tremor patient’s movement. Using these techniques, it was difficult to recognize the movement accurately because the myoelectric signal of the tremor patient periodically oscillated. Then, Short-Time Fourier Transformation (STFT) and NN were used to recognize the movement. This method was more suitable than LPF and NN. However, the recognition timing at the start of the movement was late. Finally, a hybrid algorithm for using both short and long windows’ STFTs, which is a kind of “mixture of experts,” was proposed and developed. With this type of signal processing, elbow flexion was accurately recognized without the time delay in starting the movement.

Sign in / Sign up

Export Citation Format

Share Document