scholarly journals SoC-FPGA systems for the acquisition and processing of electroencephalographic signals

2021 ◽  
Vol 10 (3) ◽  
pp. 237
Author(s):  
Matias Javier Oliva ◽  
Pablo Andrés García ◽  
Enrique Mario Spinelli ◽  
Alejandro Luis Veiga
Keyword(s):  
Embedded System ◽  
Real Time ◽  
General Purpose ◽  
System Response ◽  
Single Chip ◽  
Real Time Processing ◽  
General Purpose Processor ◽  
Time Operation ◽  
Electroencephalographic Signals ◽  
High Level

<span lang="EN-US">Real-time acquisition and processing of electroencephalographic signals have promising applications in the implementation of brain-computer interfaces. These devices allow the user to control a device without performing motor actions, and are usually made up of a biopotential acquisition stage and a personal computer (PC). This structure is very flexible and appropriate for research, but for final users it is necessary to migrate to an embedded system, eliminating the PC from the scheme. The strict real-time processing requirements of such systems justify the choice of a system on a chip field-programmable gate arrays (SoC-FPGA) for its implementation. This article proposes a platform for the acquisition and processing of electroencephalographic signals using this type of device, which combines the parallelism and speed capabilities of an FPGA with the simplicity of a general-purpose processor on a single chip. In this scheme, the FPGA is in charge of the real-time operation, acquiring and processing the signals, while the processor solves the high-level tasks, with the interconnection between processing elements solved by buses integrated into the chip. The proposed scheme was used to implement a brain-computer interface based on steady-state visual evoked potentials, which was used to command a speller. The first tests of the system show that a selection time of 5 seconds per command can be achieved. The time delay between the user’s selection and the system response has been estimated at 343 µs.</span>

scholarly journals A Manycore Vision Processor for Real-Time Smart Cameras

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7137
Author(s):  
Bruno A. da Silva ◽  
Arthur M. Lima ◽  
Janier Arias-Garcia ◽  
Michael Huebner ◽  
Jones Yudi
Keyword(s):  
Real Time ◽  
Distributed Processing ◽  
General Purpose ◽  
Frame Rate ◽  
Suitable Alternative ◽  
Smart Cameras ◽  
Real Time Processing ◽  
Processing Elements ◽  
General Purpose Processor ◽  
On Chip

Real-time image processing and computer vision systems are now in the mainstream of technologies enabling applications for cyber-physical systems, Internet of Things, augmented reality, and Industry 4.0. These applications bring the need for Smart Cameras for local real-time processing of images and videos. However, the massive amount of data to be processed within short deadlines cannot be handled by most commercial cameras. In this work, we show the design and implementation of a manycore vision processor architecture to be used in Smart Cameras. With massive parallelism exploration and application-specific characteristics, our architecture is composed of distributed processing elements and memories connected through a Network-on-Chip. The architecture was implemented as an FPGA overlay, focusing on optimized hardware utilization. The parameterized architecture was characterized by its hardware occupation, maximum operating frequency, and processing frame rate. Different configurations ranging from one to eighty-one processing elements were implemented and compared to several works from the literature. Using a System-on-Chip composed of an FPGA integrated into a general-purpose processor, we showcase the flexibility and efficiency of the hardware/software architecture. The results show that the proposed architecture successfully allies programmability and performance, being a suitable alternative for future Smart Cameras.

Log Data Modeling and Acquisition in Supporting SaaS Software Performance Issue Diagnosis

2019 ◽  
Vol 29 (09) ◽  
pp. 1245-1277 ◽  
Author(s):  
Rui Wang ◽  
Shi Ying ◽  
Xiangyang Jia
Keyword(s):  
Real Time ◽  
General Purpose ◽  
Data Logging ◽  
Log Data ◽  
Related Data ◽  
Operation And Maintenance ◽  
Time Operation ◽  
Real Time Operation ◽  
Performance Issues ◽  
High Level

Data logging is helpful for the operation and maintenance manager of SaaS-based solutions to diagnose performance issues. However, long-running SaaS software may generate huge amounts of log data which is difficult to analyze, and it lacks a systematic approach to collect the running log and lacks a unified data structure to normalize the performance-related data. All these threaten the timeliness of SaaS performance issue diagnosis. In this paper, we propose an architecture for log collection and analysis to support the assessment of performance and diagnosis of performance issues of SaaS-based application in cloud computing. The architecture has the three-tier structure and includes a pivot data model to integrate heterogeneous log. The two high-level metrics in the model of Average Response Time (ART) and Request Timeout Rate (RTR) are calculated by statistical measurement and the lower-level metrics are monitored in real-time. Operation and maintenance managers can evaluate the performance of SaaS software based on the high-level metrics, then timely locate the issues from the low-level metrics and take appropriate measures. Thereupon, this study presents the general-purpose technique for the architecture to support real-time big log data collection, access, computation, storage. The proposal has been implemented and validated in a case study.

scholarly journals Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3956
Author(s):  
Youngsun Kong ◽  
Hugo F. Posada-Quintero ◽  
Ki H. Chon
Keyword(s):  
Real Time ◽  
Electrodermal Activity ◽  
Smartphone Application ◽  
Real Time Processing ◽  
Pain Stimulation ◽  
Significant Difference ◽  
Before And After ◽  
Electrical Pulses ◽  
Pain Detection ◽  
High Level

The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.

scholarly journals Bifrost: A Python/C++ Framework for High-Throughput Stream Processing in Astronomy

2017 ◽  
Vol 06 (04) ◽  
pp. 1750007 ◽  
Author(s):  
Miles D. Cranmer ◽  
Benjamin R. Barsdell ◽  
Danny C. Price ◽  
Jayce Dowell ◽  
Hugh Garsden ◽  
...  
Keyword(s):  
Real Time ◽  
High Throughput ◽  
Radio Astronomy ◽  
Scientific Productivity ◽  
Data Transport ◽  
Time Data ◽  
Process Data ◽  
Real Time Processing ◽  
Real Time Data Processing ◽  
High Level

Radio astronomy observatories with high throughput back end instruments require real-time data processing. While computing hardware continues to advance rapidly, development of real-time processing pipelines remains difficult and time-consuming, which can limit scientific productivity. Motivated by this, we have developed Bifrost: an open-source software framework for rapid pipeline development. (a) Bifrost combines a high-level Python interface with highly efficient reconfigurable data transport and a library of computing blocks for CPU and GPU processing. The framework is generalizable, but initially it emphasizes the needs of high-throughput radio astronomy pipelines, such as the ability to process data buffers as if they were continuous streams, the capacity to partition processing into distinct data sequences (e.g. separate observations), and the ability to extract specific intervals from buffered data. Computing blocks in the library are designed for applications such as interferometry, pulsar dedispersion and timing, and transient search pipelines. We describe the design and implementation of the Bifrost framework and demonstrate its use as the backbone in the correlation and beamforming back end of the Long Wavelength Array (LWA) station in the Sevilleta National Wildlife Refuge, NM.

scholarly journals Stick Based Speckle Reduction for Real-Time Processing of OCT Images on an FPGA

10.14311/984 ◽  
2007 ◽  
Vol 47 (4-5) ◽  
Author(s):  
H. Luecken ◽  
G. Tech ◽  
R. Schwann ◽  
G. Kappen
Keyword(s):  
Real Time ◽  
Frame Rate ◽  
Speckle Reduction ◽  
Image Artifacts ◽  
Single Chip ◽  
Throughput Rate ◽  
Real Time Processing ◽  
Time Processing ◽  
Envelope Detection ◽  
Processing Steps

This paper presents an FPGA based real-time implementation of an adaptive speckle reduction algorithm. Applied to the log-compressed image of a high-resolution optical coherence tomography (OCT) system, all related signal processing steps from envelope detection to VGA video signal generation are executed on a single chip. Images from measured OCT data show that the chosen algorithm produces a smooth, detailed image with fewer image artifacts than comparable approaches. An estimation of the hardware effort, the possible throughput rate and the resulting image frame rate is given for different window sizes used here in speckle reduction. 

scholarly journals Real-Time Data Processing

2021 ◽  
pp. 147-156
Author(s):  
Fabiana Fournier ◽  
Inna Skarbovsky
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Processing System ◽  
Event Processing ◽  
Time Data ◽  
System Architectures ◽  
Real Time Processing ◽  
Operational Decision Making ◽  
Real Time Data Processing ◽  
High Level

AbstractTo remain competitive, organizations are increasingly taking advantage of the high volumes of data produced in real time for actionable insights and operational decision-making. In this chapter, we present basic concepts in real-time analytics, their importance in today’s organizations, and their applicability to the bioeconomy domains investigated in the DataBio project. We begin by introducing key terminology for event processing, and motivation for the growing use of event processing systems, followed by a market analysis synopsis. Thereafter, we provide a high-level overview of event processing system architectures, with its main characteristics and components, followed by a survey of some of the most prominent commercial and open source tools. We then describe how we applied this technology in two of the DataBio project domains: agriculture and fishery. The devised generic pipeline for IoT data real-time processing and decision-making was successfully applied to three pilots in the project from the agriculture and fishery domains. This event processing pipeline can be generalized to any use case in which data is collected from IoT sensors and analyzed in real-time to provide real-time alerts for operational decision-making.

High-End Storage

2012 ◽  
pp. 132-152
Author(s):  
Isabel Schwerdtfeger
Keyword(s):  
Real Time ◽  
Data Storage ◽  
High Performance ◽  
Storage System ◽  
Real Time Processing ◽  
Storage Solutions ◽  
Future Demands ◽  
And Performance ◽  
Extreme Scale ◽  
High Level

This chapter discusses the challenges high-end storage solutions will have with future demands. Due to heavy end-user demands for real-time processing of data access, this need must be addressed by high-end storage solutions. But what type of high-end storage solutions address this need and are suitable to ensure high performance write and retrieval of data in real-time from high- end storage infrastructures, including read and write access from digital archives? For this reason, this chapter reviews a few disk and tape solutions as well as combined disk- and tape storage solutions. The review on the different storage solutions does not focus on compliance of data storage management, but on available commercial high-end systems, addressing scalability and performance requirements both for online storage and archives. High level requirements aid in identifying high-end storage system features and support Extreme Scale infrastructures for the amount of data that high-end storage systems will need to manage in future.

scholarly journals Zynq-Based Reconfigurable System for Real-Time Edge Detection of Noisy Video Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Iljung Yoon ◽  
Heewon Joung ◽  
Jooheung Lee
Keyword(s):  
Edge Detection ◽  
Embedded System ◽  
Real Time ◽  
Video Sequences ◽  
Density Level ◽  
Real Time Processing ◽  
Reconfigurable System ◽  
Video Frames ◽  
Noise Density ◽  
Run Time

We implement Zynq-based self-reconfigurable system to perform real-time edge detection of 1080p video sequences. While object edge detection is a fundamental tool in computer vision, noises in the video frames negatively affect edge detection results significantly. Moreover, due to the high computational complexity of 1080p video filtering operations, hardware implementation on reconfigurable hardware fabric is necessary. Here, the proposed embedded system utilizes dynamic reconfiguration capability of Zynq SoC so that partial reconfiguration of different filter bitstreams is performed during run-time according to the detected noise density level in the incoming video frames. Pratt’s Figure of Merit (PFOM) to evaluate the accuracy of edge detection is analyzed for various noise density levels, and we demonstrate that adaptive run-time reconfiguration of the proposed filter bitstreams significantly increases the accuracy of edge detection results while efficiently providing computing power to support real-time processing of 1080p video frames. Performance results on configuration time, CPU usage, and hardware resource utilization are also compared.

Real-time image-content-based beamline control for smart 4D X-ray imaging

2016 ◽  
Vol 23 (5) ◽  
pp. 1254-1263 ◽  
Author(s):  
Matthias Vogelgesang ◽  
Tomas Farago ◽  
Thilo F. Morgeneyer ◽  
Lukas Helfen ◽  
Tomy dos Santos Rolo ◽  
...  
Keyword(s):  
Real Time ◽  
High Speed ◽  
Image Data ◽  
Building Blocks ◽  
Level Control ◽  
Real Time Processing ◽  
X Ray ◽  
Level Data ◽  
High Level

Real-time processing of X-ray image data acquired at synchrotron radiation facilities allows for smart high-speed experiments. This includes workflows covering parameterized and image-based feedback-driven control up to the final storage of raw and processed data. Nevertheless, there is presently no system that supports an efficient construction of such experiment workflows in a scalable way. Thus, here an architecture based on a high-level control system that manages low-level data acquisition, data processing and device changes is described. This system is suitable for routine as well as prototypical experiments, and provides specialized building blocks to conduct four-dimensionalin situ,in vivoandoperandotomography and laminography.

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