A Vision-Based Driver Nighttime Assistance and Surveillance System Based on Intelligent Image Sensing Techniques and a Heterogamous Dual-Core Embedded System Architecture

This paper presents novel logic/software co-work architecture for embedded high definition image processing platform, which is built by the considerations of system level, board hardware level, and the tasks partition between CPU processing and programmable logic based on the latest launched System on Chip Field Programmable Gate Array (Soc FPGA) – Xilinx ZC7020. For this case, we comprehensive analyze of the critical data paths: the uniform Advanced Extensible Interface (AXI) processing between processing system (PS) and processing logic (PL), including high definition video pass through PL to PS and PS software processing send to PL for speed up. We have included the transplant of opensource Linux, multiprocessing cooperative control and boot loader in PS side. Since the general platform is proposed, a fire detection approach based on high definition image processing is implemented. Experiment results indicated the feasibility and universality of the embedded system architecture.

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Based Embedded System Applications in Intelligent Home Remote Monitoring

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.2317 ◽

2014 ◽

Vol 602-605 ◽

pp. 2317-2320

Author(s):

Yang Li ◽

Qing Hong Wu ◽

Xue Xiao

Keyword(s):

Embedded System ◽

Video Surveillance ◽

Surveillance System ◽

Video Surveillance System ◽

Surveillance Systems ◽

Video Capture ◽

Home Video ◽

Intelligent Home ◽

Front End ◽

Usb Camera

With the continuous improvement of security awareness, home security has become the focus of attention. The actual demand for home video surveillance system, designed a cheap, practical, small size and low power consumption of video surveillance systems, this paper uses microprocessor S3C2440 ARM9 core as the core hardware control, embedded Linux operating system with software the control core, and cheap, generic USB camera video capture device as a front end to complete the design of a home video surveillance system.

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Proposal for an Embedded System Architecture Using a GNDVI Algorithm to Support UAV-Based Agrochemical Spraying

Sensors ◽

10.3390/s19245397 ◽

2019 ◽

Vol 19 (24) ◽

pp. 5397 ◽

Cited By ~ 1

Author(s):

Maik Basso ◽

Diego Stocchero ◽

Renato Ventura Bayan Henriques ◽

André Luis Vian ◽

Christian Bredemeier ◽

...

Keyword(s):

Control System ◽

Embedded System ◽

Precision Agriculture ◽

System Architecture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Raspberry Pi ◽

Research Station ◽

High Definition ◽

Cpu Usage

An important area in precision agriculture is related to the efficient use of chemicals applied onto fields. Efforts have been made to diminish their use, aiming at cost reduction and fewer chemical residues in the final agricultural products. The use of unmanned aerial vehicles (UAVs) presents itself as an attractive and cheap alternative for spraying pesticides and fertilizers compared to conventional mass spraying performed by ordinary manned aircraft. Besides being cheaper than manned aircraft, small UAVs are capable of performing fine-grained instead of the mass spraying. Observing this improved method, this paper reports the design of an embedded real-time UAV spraying control system supported by onboard image processing. The proposal uses a normalized difference vegetation index (NDVI) algorithm to detect the exact locations in which the chemicals are needed. Using this information, the automated spraying control system performs punctual applications while the UAV navigates over the crops. The system architecture is designed to run on low-cost hardware, which demands an efficient NDVI algorithm. The experiments were conducted using Raspberry Pi 3 as the embedded hardware. First, experiments in a laboratory were conducted in which the algorithm was proved to be correct and efficient. Then, field tests in real conditions were conducted for validation purposes. These validation tests were performed in an agronomic research station with the Raspberry hardware integrated into a UAV flying over a field of crops. The average CPU usage was about 20% while memory consumption was about 70 MB for high definition images, with 4% CPU usage and 20.3 MB RAM being observed for low-resolution images. The average current measured to execute the proposed algorithm was 0.11 A. The obtained results prove that the proposed solution is efficient in terms of processing and energy consumption when used in embedded hardware and provides measurements which are coherent with the commercial GreenSeeker equipment.

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PRIOPS: A REAL-TIME PRODUCTION SYSTEM ARCHITECTURE FOR PROGRAMMING AND LEARNING IN EMBEDDED SYSTEMS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001490000307 ◽

1990 ◽

Vol 04 (03) ◽

pp. 509-526

Author(s):

DALE E. PARSON ◽

GLENN D. BLANK

Keyword(s):

Embedded Systems ◽

Embedded System ◽

System Architecture ◽

Production System ◽

Human Information Processing ◽

Lower Priority ◽

Knowledge Based ◽

Programming Techniques ◽

Automatic Matching ◽

System Programming

The Prioritized Production System (PRIOPS) is an architecture that supports time-constrained, knowledge-based embedded system programming and learning. Inspired by the theory of automatic and controlled human information processing in cognitive psychology, PRIOPS supports a two-tiered processing approach. The automatic partition provides for compilation of productions into constant-time-constrained processes for reaction to environmental conditions. The notion of a habit in humans approximates the concept of automatic processing trading flexibility and generality for efficiency and predictability in dealing with expected environmental situations. Explicit priorities allow critical automatic activities to pre-empt and defer execution of lower priority processing. An augmented version of the Rete match algorithm implements O(1), priority-scheduled automatic matching. The controlled partition supports more complex, less predictable activities such as problem solving, planning, and learning that apply in novel situations for which automatic reactions do not exist. The PRIOPS notation allows the programmer of knowledge-based embedded systems to work at a more appropriate level of abstraction than is provided by conventional embedded system programming techniques. This paper explores programming and learning in PRIOPS in the context of a maze traversal program.

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