Real-time Processing Algorithms Research Articles

Listening to the Anthropocene: A Queda do Céu

Abstract This article discusses the algorithmic design and implementation of A Queda do Céu, a sound installation and kinetic sculpture related to the Soundlapse project. In it we provide an overview of the project and go on to describe the main computational challenges related to the installation, which included a variety of real-time processing, interpolation, and mapping algorithms. We contextualize the work in relation to regional ecological and political debates, as well as the global climate crisis. In doing so, we echo other sound and field-recording artists in proposing that artworks have an important function as experimental arenas in which new technological applications can be probed and where new modes of listening can be investigated, reconfigured, and exercised. In closing, we lay out an overview of the current challenges being tackled by the Soundlapse project, specifically the development of a refined version of the sonic time-lapse method that incorporates machine learning routines and user-defined spatialization capabilities.

Fast-Spherical-Projection-Based Point Cloud Clustering Algorithm

Roadside LiDAR (light detection and ranging) is a solution to fill in the gaps for connected vehicles (CV) by detecting the status of global road users at transportation facilities. It relies greatly on the clustering algorithm for accurate and rapid data processing so as to ensure effectiveness and reliability. To contribute to better roadside LiDAR-based transportation facilities, this paper presents a fast-spherical-projection-based clustering algorithm (FSPC) for real-time LiDAR data processing with higher clustering accuracy and noise handling. The FSPC is designed to work on a spherical map which could be directly derived from the instant returns of a LiDAR sensor. A 2D-window searching strategy is specifically designed to accelerate the computation and alleviate the density variation impact in the LiDAR point cloud. The test results show the proposed algorithm can achieve a high processing efficiency with 24.4 ms per frame, satisfying the real-time requirement for most common LiDAR applications (100 ms per frame), and it also ensures a high accuracy in object clustering, with 96%. Additionally, it is observed that the proposed FSPC allows a wider detection range and is more stable, tackling the surge in foreground points that frequently occurs in roadside LiDAR applications. Finally, the generality of the proposed FSPC indicates the proposed algorithm could also be implemented in other areas such as autonomous driving and remote sensing.

Algorithm and hardware considerations for real-time neural signal on-implant processing

Objective. Various on-workstation neural-spike-based brain machine interface (BMI) systems have reached the point of in-human trials, but on-node and on-implant BMI systems are still under exploration. Such systems are constrained by the area and battery. Researchers should consider the algorithm complexity, available resources, power budgets, CMOS technologies, and the choice of platforms when designing BMI systems. However, the effect of these factors is currently still unclear. Approaches. Here we have proposed a novel real-time 128 channel spike detection algorithm and optimised it on microcontroller (MCU) and field programmable gate array (FPGA) platforms towards consuming minimal power and memory/resources. It is presented as a use case to explore the different considerations in system design. Main results. The proposed spike detection algorithm achieved over 97% sensitivity and a smaller than 3% false detection rate. The MCU implementation occupies less than 3 KB RAM and consumes 31.5 µW ch−1. The FPGA platform only occupies 299 logic cells and 3 KB RAM for 128 channels and consumes 0.04 µW ch−1. Significance. On the spike detection algorithm front, we have eliminated the processing bottleneck by reducing the dynamic power consumption to lower than the hardware static power, without sacrificing detection performance. More importantly, we have explored the considerations in algorithm and hardware design with respect to scalability, portability, and costs. These findings can facilitate and guide the future development of real-time on-implant neural signal processing platforms.

Improved Resolution and Cost Performance of Low-Cost MEMS Seismic Sensor through Parallel Acquisition.

In earthquake monitoring, an important aspect of the operational effect of earthquake intensity rapid reporting and earthquake early warning networks depends on the density and performance of the deployed seismic sensors. To improve the resolution of seismic sensors as much as possible while keeping costs low, in this article the use of multiple low-cost and low-resolution digital MEMS accelerometers is proposed to increase the resolution through the correlation average method. In addition, a cost-effective MEMS seismic sensor is developed. With ARM and Linux embedded computer technology, this instrument can cyclically store the continuous collected data on a built-in large-capacity SD card for approximately 12 months. With its real-time seismic data processing algorithm, this instrument is able to automatically identify seismic events and calculate ground motion parameters. Moreover, the instrument is easy to install in a variety of ground or building conditions. The results show that the RMS noise of the instrument is reduced from 0.096 cm/s2 with a single MEMS accelerometer to 0.034 cm/s2 in a bandwidth of 0.1–20 Hz by using the correlation average method of eight low-cost MEMS accelerometers. The dynamic range reaches more than 90 dB, the amplitude–frequency response of its input and output within −3 dB is DC −80 Hz, and the linearity is better than 0.47%. In the records from our instrument, earthquakes with magnitudes between M2.2 and M5.1 and distances from the epicenter shorter than 200 km have a relatively high SNR, and are more visible than they were prior to the joint averaging.

Implementation of Thermal Event Image Processing Algorithms on NVIDIA Tegra Jetson TX2 Embedded System-on-a-Chip

Advances in Infrared (IR) cameras, as well as hardware computational capabilities, contributed towards qualifying vision systems as reliable plasma diagnostics for nuclear fusion experiments. Robust autonomous machine protection and plasma control during operation require real-time processing that might be facilitated by Graphics Processing Units (GPUs). One of the current aims of image plasma diagnostics involves thermal events detection and analysis with thermal imaging. The paper investigates the suitability of the NVIDIA Jetson TX2 Tegra-based embedded platform for real-time thermal events detection. Development of real-time processing algorithms on an embedded System-on-a-Chip (SoC) requires additional effort due to the constrained resources, yet low-power consumption enables embedded GPUs to be applied in MicroTCA.4 computing architecture that is prevalent in nuclear fusion projects. For this purpose, the authors have proposed, developed and optimised GPU-accelerated algorithms with the use of available software tools for NVIDIA Tegra systems. Furthermore, the implemented algorithms are evaluated and benchmarked on Wendelstein 7-X (W7-X) stellarator experimental data against the corresponding alternative Central Processing Unit (CPU) implementations. Considerable improvement is observed for the accelerated algorithms that enable real-time detection on the embedded SoC platform, yet some encountered limitations when developing parallel image processing routines are described and signified.

A Set of Integral Grid-Coding Algebraic Operations Based on GeoSOT-3D

As the amount of collected spatial information (2D/3D) increases, the real-time processing of these massive data is among the urgent issues that need to be dealt with. Discretizing the physical earth into a digital gridded earth and assigning an integral computable code to each grid has become an effective way to accelerate real-time processing. Researchers have proposed optimization algorithms for spatial calculations in specific scenarios. However, a complete set of algorithms for real-time processing using grid coding is still lacking. To address this issue, a carefully designed, integral grid-coding algebraic operation framework for GeoSOT-3D (a multilayer latitude and longitude grid model) is proposed. By converting traditional floating-point calculations based on latitude and longitude into binary operations, the complexity of the algorithm is greatly reduced. We then present the detailed algorithms that were designed, including basic operations, vector operations, code conversion operations, spatial operations, metric operations, topological relation operations, and set operations. To verify the feasibility and efficiency of the above algorithms, we developed an experimental platform using C++ language (including major algorithms, and more algorithms may be expanded in the future). Then, we generated random data and conducted experiments. The experimental results show that the computing framework is feasible and can significantly improve the efficiency of spatial processing. The algebraic operation framework is expected to support large geospatial data retrieval and analysis, and experience a revival, on top of parallel and distributed computing, in an era of large geospatial data.

Map Matching Algorithm for Real-Time Data Processing of Non-route GPS Data in Seoul

Global Positioning System (GPS) data are satellite-based longitude and latitude coordinates for which map matching with road links, which are electronic maps, may be used in traffic operation management. Various studies have been conducted to improve the accuracy of map matching; however, research on the application of map matching algorithms for real-time data processing systems has not been widely conducted. Therefore, this study investigated a map matching algorithm that can shorten the processing time required for application to systems that process large volumes of GPS data collected for a wide network. To reduce the processing time, a candidate link selection method and a grid-type geospatial indexing process were designed. The processing time was analyzed based on an emulator before applying the map matching algorithm to the Transport Operation and Information Service Center, which generates the real-time travel speed data in Seoul City. The results obtained from comparing the prior and new algorithms showed a 96% decrease in the processing time. We believe that the findings of this study will benefit the real-time processing of GPS data, which are expected to be used in various ways in traffic operations and management in the future.

Distance Estimation Using Self-Induced Noise of an Aerial Vehicle

In this letter, we propose an algorithm to estimate the distance between an aerial vehicle and a large obstacle using the self-induced noise present during the vehicle's normal operation. We demonstrate the feasibility of using the proposed estimation method in real-time as a feedback mechanism to actively control the altitude of a blimp-like vehicle. The method is built upon a physics-based acoustic model of an unknown source emitting sound near an acoustically reflective surface. By placing two microphones beneath a motor-propeller system used to control the altitude of the vehicle, a real-time processing algorithm of the audio signals is presented that can accurately detect the distance from the microphones to the ground. The method is based upon computing the cross-correlation matrix of the signals on the two microphone channels. We develop a novel cross-correlation processor that is capable of filtering out the unknown source term and extracting the relevant time-delay representing the time it takes for an acoustic wave traveling from the microphone to the ground and back. Furthermore, we show that the method is also robust to the more complex noise source generated by a quadrotor in a static, tethered experiment. To the best of our knowledge, this is the first framework and demonstration of obstacle sensing onboard an aerial vehicle using only the self-generated noise.

Panoramic Stereo Imaging of a Bionic Compound-Eye Based on Binocular Vision.

With the rapid development of the virtual reality industry, one of the bottlenecks is the scarcity of video resources. How to capture high-definition panoramic video with depth information and real-time stereo display has become a key technical problem to be solved. In this paper, the optical optimization design scheme of panoramic imaging based on binocular stereo vision is proposed. Combined with the real-time processing algorithm of multi detector mosaic panoramic stereo imaging image, a panoramic stereo real-time imaging system is developed. Firstly, the optical optimization design scheme of panoramic imaging based on binocular stereo vision is proposed, and the space coordinate calibration platform of ultra-high precision panoramic camera based on theodolite angle compensation function is constructed. The projection matrix of adjacent cameras is obtained by solving the imaging principle of binocular stereo vision. Then, a real-time registration algorithm of multi-detector mosaic image and Lucas-Kanade optical flow method based on image segmentation are proposed to realize stereo matching and depth information estimation of panoramic imaging, and the estimation results are analyzed effectively. Experimental results show that the stereo matching time of panoramic imaging is 30 ms, the registration accuracy is 0.1 pixel, the edge information of depth map is clearer, and it can meet the imaging requirements of different lighting conditions.

WITHDRAWN: Video teaching of computer web design course based on FPGA and sobel algorithm

A general-purpose programmable logic device that allows Field Programmable Gate Arrays (FPGAs) to be manufactured on-chip from simple logic gate operations, or a Sobel algorithm complex system implementation is done by the customer after configuration. All tools are appropriate for the learning process, which can be accessed or downloaded from all over the world with the use of the Internet. Video materials provide computer courses web design concept, based on the potential problems, and also provide a special auxiliary FPGA, the teaching, problem-based approach which is much easier. Therefore, the purpose of the system is to provide intuitive tools, web design courses, understand the Sobel algorithm test before major physical system real-time video processing algorithms, and the creation and practical implementation based on video quality. The purpose is also to prove the potential purpose of developing FPGA-based systems and Sobel algorithms. Video teaching is a procedure of hiding the message into video in such a way that no one can detect its presence. In proposed work, sobel algorithm namely random scan and edge detection is used for hiding the data into video. The development of practical examples of video lessons and reasons to support real-time video processing has been proposed. It re-programmes the FPGA, allowing you not only to verify integrity of the design itself but also the establishment of an early prototype sub-circuits using the same development board.

The Industry Internet of Things (IIoT) as a Methodology for Autonomous Diagnostics in Aerospace Structural Health Monitoring

Structural Health Monitoring (SHM), defined as the process that involves sensing, computing, and decision making to assess the integrity of infrastructure, has been plagued by data management challenges. The Industrial Internet of Things (IIoT), a subset of Internet of Things (IoT), provides a way to decisively address SHM’s big data problem and provide a framework for autonomous processing. The key focus of IIoT is operational efficiency and cost optimization. The purpose, therefore, of the IIoT approach in this investigation is to develop a framework that connects nondestructive evaluation sensor data with real-time processing algorithms on an IoT hardware/software system to provide diagnostic capabilities for efficient data processing related to SHM. Specifically, the proposed IIoT approach is comprised of three components: the Cloud, the Fog, and the Edge. The Cloud is used to store historical data as well as to perform demanding computations such as off-line machine learning. The Fog is the hardware that performs real-time diagnostics using information received both from sensing and the Cloud. The Edge is the bottom level hardware that records data at the sensor level. In this investigation, an application of this approach to evaluate the state of health of an aerospace grade composite material at laboratory conditions is presented. The key link that limits human intervention in data processing is the implemented database management approach which is the particular focus of this manuscript. Specifically, a NoSQL database is implemented to provide live data transfer from the Edge to both the Fog and Cloud. Through this database, the algorithms used are capable to execute filtering by classification at the Fog level, as live data is recorded. The processed data is automatically sent to the Cloud for further operations such as visualization. The system integration with three layers provides an opportunity to create a paradigm for intelligent real-time data quality management.

Real-time task processing for spinning cyber-physical production systems based on edge computing

With a high-speed, dynamic and continuous yarn manufacturing process, spinning production suffers from different problems of dynamic disturbances such as yarn breakage, machine breakdown, and yarn quality. Processing real-time tasks is critical for tackling these problems, except for satisfying the requirements of mass production. The existing spinning cyber-physical production systems (CPPS), however, rely on a cloud center for centralized processing of real-time tasks. Thus, it becomes increasingly difficult for them to meet real-time requirements. As such, this paper proposes a novel real-time task processing method for spinning CPPS based on edge computing. First, a new hybrid structure of edge computing nodes (ECN) that consists of both 1-1 and N-1 modes is introduced for different types of tasks in spinning CPPS such as fixed tasks, decision-intensive tasks, and data-intensive tasks. Second, a collaboration mechanism is developed for collaborations between ECNs. The mathematical model and algorithms for real-time task processing are provided for a single ECN. Finally, a case study on a real spinning production is conducted. The results of this case study have demonstrated that the proposed method can significantly reduce the processing time of real-time tasks, as well as improve the production flexibility and production efficiency in spinning CPPS. The proposed method could be applied to continuous and batch manufacturing fields with high real-time requirements, such as weaving, chemical fiber production, and the pharmaceutical industry.

A Review of Data Gathering Algorithms for Real-Time Processing in Internet of Things Environment

Today, Wireless Sensor Network (WSN) has become an enabler technology for the Internet of Things (IoT) applications. The emergence of various applications has then enabled the need for robust and efficient data collection and transfer algorithms. This paper presents a comprehensive review for the existing data gathering algorithms and the technologies adopted for that applications. After reviewing the algorithms and the challenges related to them, which extend the physical reach of the monitoring capability; they possess several constraints such as limited energy availability, low memory size, and low processing speed, which are the principal obstacles to designing efficient management protocols for WSN-IoT integration.

Moving target tracking in marine aerosol environment with single photon lidar system

The track of moving target by single photon lidar (SPL) is investigated in marine aerosol environment. The aerosol-induced clutter severely limits the detection performance of SPL. Conventional time-correlated single-photon counting histogram threshold method can only detect static targets. We introduced track algorithm to deal with the discrete scattering point signal of SPL. Furthermore, a two-stage (TS) dynamic model with an adaptive strong Kalman filter was proposed in SPL tracking technique. The proposed method, referred to TS-SKF, has a better performance than the current statistical model with Kalman filter (KF) and the TS model with KF in simulated marine aerosol environment. Finally, a series of field experiments were conducted with a self-established SPL system to track the moving cargo ships over the sea at Qingdao (35.9°N, 120.3°E), China. Both the moving and static targets can be effectively detected with the TS-SKF method. The results show that the proposed real-time processing algorithm can provide a new sight to promote the practical application of SPL.

Efficient Performance Optimization for the Magnetic Data Readout From a Proton Precession Magnetometer With Low-Rank Constraint

Proton precession magnetometer (PPM) is a potential device to measure the low-frequency geomagnetic field in the time domain. Nevertheless, the uncontrollable noise such as power frequency noise, random noise, etc., severely affects the detection results. In this paper, aimed to boost the performance of the PPM, a novel real-time processing algorithm for the optimization of PPM output strategy based on the low-rank constraint (LRC) is proposed, and a new efficient framework, dubbed LRC-based geomagnetic data readout optimizer (LRC-GDRO), is implemented. This method aims to extract the dominant principal components of the pure magnetic signal and identify the singular values of noise. Thereby, the relative ideal signal without external noise, which still contains the ambient field information is yielded. Moreover, we develop a prototype device and compare the test results before and after using the proposed method. Consequently, compared with traditional PPM, the performance of the PPM using the proposed method has been significantly improved with a measurement accuracy of ± 0.2 nT. Furthermore, the experimental results reveal that the LRC-optimized PPM is comparable to a very high-accuracy Overhauser magnetometer in terms of 12 h geomagnetic field observation.

Changes in SO2 Flux Regime at Mt. Etna Captured by Automatically Processed Ultraviolet Camera Data

We used a one-year long SO2 flux record, which was obtained using a novel algorithm for real-time automatic processing of ultraviolet (UV) camera data, to characterize changes in degassing dynamics at the Mt. Etna volcano in 2016. These SO2 flux records, when combined with independent thermal and seismic evidence, allowed for capturing switches in activity from paroxysmal explosive eruptions to quiescent degassing. We found SO2 fluxes 1.5–2 times higher than the 2016 average (1588 tons/day) during the Etna’s May 16–25 eruptive paroxysmal activity, and mild but detectable SO2 flux increases more than one month before its onset. The SO2 flux typically peaked during a lava fountain. Here, the average SO2 degassing rate was ~158 kg/s, the peak emission was ~260 kg/s, and the total released SO2 mass was ~1700 tons (in 3 h on 18 May, 2016). Comparison between our data and prior (2014–2015) results revealed systematic SO2 emission patterns prior to, during, and after an Etna’s paroxysmal phases, which allows us to tentatively identify thresholds between pre-eruptive, syn-eruptive, and post-eruptive degassing regimes.

A First-Order Differential Data Processing Method for Accuracy Improvement of Complementary Filtering in Micro-UAV Attitude Estimation.

There are many algorithms that can be used to fuse sensor data. The complementary filtering algorithm has low computational complexity and good real-time performance characteristics. It is very suitable for attitude estimation of small unmanned aerial vehicles (micro-UAVs) equipped with low-cost inertial measurement units (IMUs). However, its low attitude estimation accuracy severely limits its applications. Though, many methods have been proposed by researchers to improve attitude estimation accuracy of complementary filtering algorithms, there are few studies that aim to improve it from the data processing aspect. In this paper, a real-time first-order differential data processing algorithm is proposed for gyroscope data, and an adaptive adjustment strategy is designed for the parameters in the algorithm. Besides, the differential-nonlinear complementary filtering (D-NCF) algorithm is proposed by combine the first-order differential data processing algorithm with the basic nonlinear complementary filtering (NCF) algorithm. The experimental results show that the first-order differential data processing algorithm can effectively correct the gyroscope data, and the Root Mean Square Error (RMSE) of attitude estimation of the D-NCF algorithm is smaller than when the NCF algorithm is used. The RMSE of the roll angle decreases from 1.1653 to 0.5093, that of the pitch angle decreases from 2.9638 to 1.5542, and that of the yaw angle decreases from 0.9398 to 0.6827. In general, the attitude estimation accuracy of D-NCF algorithm is higher than that of the NCF algorithm.

A method for degrading sound localization while preserving binaural advantages for speech reception in noise.

This study developed and tested a real-time processing algorithm designed to degrade sound localization (LocDeg algorithm) without affecting binaural benefits for speech reception in noise. Input signals were divided into eight frequency channels. The odd-numbered channels were mixed between the ears to confuse the direction of interaural cues while preserving interaural cues in the even-numbered channels. The LocDeg algorithm was evaluated for normal-hearing listeners performing sound localization and speech-reception tasks. Results showed that the LocDeg algorithm successfully degraded sound-localization performance without affecting speech-reception performance or spatial release from masking for speech in noise. The LocDeg algorithm did, however, degrade speech-reception performance in a task involving spatially separated talkers in a multi-talker environment, which is thought to depend on differences in perceived spatial location of concurrent talkers. This LocDeg algorithm could be a valuable tool for isolating the importance of sound-localization ability from other binaural benefits in real-world environments.

Real time implementation of empirical mode decomposition algorithm for ultrasonic nondestructive testing applications.

A real time empirical mode decomposition (EMD) algorithm based ultrasonic imaging system has been developed for non-destructive testing (NDT) applications. It is difficult to implement the EMD based signal processing algorithm in real time because it is totally a data-driven process which comprises numerous sifting operations. In this paper, the EMD algorithm has been implemented in the visual software environment. The EMD implementation encompasses two types of interpolation methods: piecewise linear interpolation (PLI) and cubic spline interpolation (CSI). The cubic spline tridiagonal matrix has been solved by using the Thomas algorithm for real time processing. The total time complexity functions for both the implemented PLI and CSI based EMD methods have been computed. For the signal filtering, the partial reconstruction algorithm has been adopted. The baseline correction and noise filtering applications have been presented using an EMD based visual software. The real time practicability and the efficiency of this method have been validated through ultrasonic NDT experimentation for improvement in the time domain resolution of the ultrasonic A-scan raw data. The practical results show that in the noisy environment, it is possible to enhance the signal-to-noise ratio for the visualization and identification of ultrasonic pulse-echo signals in real time.

KALYPSO: Linear array detector for high-repetition rate and real-time beam diagnostics

Synchrotrons and modern FEL light sources operate with bunch repetition rates in the MHz range. The profile of the electron beam inside the accelerator can be characterized with indirect experimental techniques where linear array detectors are employed to measure the emitted synchrotron radiation or the spectrum of a near-IR laser. To improve the performance of modern beam diagnostics we have developed KALYPSO, a detector system operating with a continuous frame rate of up to 2.7 MHz. To facilitate the integration in different experiments, a modular architecture has been adopted. Different semiconductor micro-strip sensors can be connected to front-end ASICs to optimize the quantum efficiency at different photon energies, ranging from visible light up to near-IR. The front-end electronics are integrated within an heterogeneous DAQ consisting of FPGAs and GPUs, which allows scientists to implement real-time data processing algorithms. The current version of the detector is in operation at the KARA synchrotron light source and at the European XFEL. In this contribution we present the detector architecture, the performance results and the on-going technical developments.