High-Performance Predictable NVM-Based Instruction Memory for Real-Time Embedded Systems

2021 ◽  
Vol 9 (1) ◽  
pp. 441-455
Author(s):  
Mostafa Bazzaz ◽  
Ali Hoseinghorban ◽  
Farimah Poursafaei ◽  
Alireza Ejlali
Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 241 ◽  
Author(s):  
Arthur Rosa ◽  
Matheus Silva ◽  
Marcos Campos ◽  
Renato Santana ◽  
Welbert Rodrigues ◽  
...  

In this work, a new real-time Simulation method is designed for nonlinear control techniques applied to power converters. We propose two different implementations: in the first one (Single Hardware in The Loop: SHIL), both model and control laws are inserted in the same Digital Signal Processor (DSP), and in the second approach (Double Hardware in The Loop: DHIL), the equations are loaded in different embedded systems. With this methodology, linear and nonlinear control techniques can be designed and compared in a quick and cheap real-time realization of the proposed systems, ideal for both students and engineers who are interested in learning and validating converters performance. The methodology can be applied to buck, boost, buck-boost, flyback, SEPIC and 3-phase AC-DC boost converters showing that the new and high performance embedded systems can evaluate distinct nonlinear controllers. The approach is done using matlab-simulink over commodity Texas Instruments Digital Signal Processors (TI-DSPs). The main purpose is to demonstrate the feasibility of proposed real-time implementations without using expensive HIL systems such as Opal-RT and Typhoon-HL.


Author(s):  
Jeonghun Lee ◽  
Kwang-il Hwang

AbstractYou only look once (YOLO) is being used as the most popular object detection software in many intelligent video applications due to its ease of use and high object detection precision. In addition, in recent years, various intelligent vision systems based on high-performance embedded systems are being developed. Nevertheless, the YOLO still requires high-end hardware for successful real-time object detection. In this paper, we first discuss real-time object detection service of the YOLO on AI embedded systems with resource constraints. In particular, we point out the problems related to real-time processing in YOLO object detection associated with network cameras, and then propose a novel YOLO architecture with adaptive frame control (AFC) that can efficiently cope with these problems. Through various experiments, we show that the proposed AFC can maintain the high precision and convenience of YOLO, and provide real-time object detection service by minimizing total service delay, which remains a limitation of the pure YOLO.


Sign in / Sign up

Export Citation Format

Share Document