The Design of the Real-Time Joint Transform Image Correlation Recognition System

Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one from the undeformed reference states of the sample and the other from the deformed target state, the relative displacement between the two states is determined. DIC is well-known and often used for post-processing analysis of in-plane displacements and deformation of the specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and expand the scope of this method. Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether the real-time analysis is possible with these methods. The effects of computing with different hardware settings were also analyzed and discussed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm, such that it becomes practically slower than a sub-optimal algorithm. The Newton–Raphson algorithm in combination with a modified particle swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss–Newton algorithm is superior. As expected, the brute force search algorithm is the least efficient method. We also found that the correct choice of parallelization tasks is critical in attaining improvements in computing speed. A poorly chosen parallelization approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode, the correct choice of combinations of integer-pixel and sub-pixel search algorithms is critical for efficient analysis. The real-time analysis using DIC will be difficult on computers with standard computing capabilities, even if parallelization is implemented, so the suggested solution would be to use graphics processing unit (GPU) acceleration.

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Colour Classification Using Entropy Algorithm in Real Time Colour Recognition System for Blindness People

KnE Engineering ◽

10.18502/keg.v1i1.485 ◽

2016 ◽

Vol 1 ◽

Author(s):

Gurum Ahmad Pauzi

Keyword(s):

Real Time ◽

Digital Camera ◽

Recognition System ◽

The Real ◽

Orange Yellow ◽

Colour Image ◽

Rgb Model

This article describes the real time instrumentation system to help blindness people for recognize a colour. Colour image captured by the digital camera, and it classified into ten basic colours names (black, brown, cyan, red, orange, yellow, green, blue, magenta, gray and white) by using entropy algorithm. The conclusion of colour classification will be informed to the user in sound or vocal information. This study has used two colour models HSV (hue, saturation and value) and RGB (red, green and blue). The accuracy of Classification using HSV has 90%, and RGB model has 71.5%.

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Design and Implementation of Embedded Real-Time English Speech Recognition System Based on Big Data Analysis

Mathematical Problems in Engineering ◽

10.1155/2021/6561730 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Lifang He ◽

Gaimin Jin ◽

Sang-Bing Tsai

Keyword(s):

Speech Recognition ◽

Real Time ◽

Optimal Parameter ◽

Design Method ◽

Fitness Function ◽

Recognition Rate ◽

Hardware Acceleration ◽

Recognition System ◽

Speech Recognition System ◽

The Real

This article uses Field Programmable Gate Array (FPGA) as a carrier and uses IP core to form a System on Programmable Chip (SOPC) English speech recognition system. The SOPC system uses a modular hardware system design method. Except for the independent development of the hardware acceleration module and its control module, the other modules are implemented by software or IP provided by Xilinx development tools. Hardware acceleration IP adopts a top-down design method, provides parallel operation of multiple operation components, and uses pipeline technology, which speeds up data operation, so that only one operation cycle is required to obtain an operation result. In terms of recognition algorithm, a more effective training algorithm is proposed, Genetic Continuous Hidden Markov Model (GA_CHMM), which uses genetic algorithm to directly train CHMM model. It is to find the optimal model by encoding the parameter values of the CHMM and performing operations such as selection, crossover, and mutation according to the fitness function. The optimal parameter value after decoding corresponds to the CHMM model, and then the English speech recognition is performed through the CHMM algorithm. This algorithm can save a lot of training time, thereby improving the recognition rate and speed. This paper studies the optimization of embedded system software. By studying the fixed-point software algorithm and the optimization of system storage space, the real-time response speed of the system has been reduced from about 10 seconds to an average of 220 milliseconds. Through the optimization of the CHMM algorithm, the real-time performance of the system is improved again, and the average time to complete the recognition is significantly shortened. At the same time, the system can achieve a recognition rate of over 90% when the English speech vocabulary is less than 200.

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Face Recognition Using the Convolutional Neural Network for Barrier Gate System

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v15i10.20175 ◽

2021 ◽

Vol 15 (10) ◽

pp. 138

Author(s):

Mochammad Langgeng Prasetyo ◽

Achmad Teguh Wibowo ◽

Mujib Ridwan ◽

Mohammad Khusnu Milad ◽

Sirajul Arifin ◽

...

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolutional Neural Network ◽

Real Time ◽

Recognition System ◽

Average Error ◽

The Real ◽

The Face ◽

Gate System ◽

Recognition Technique

The implementation of face recognition technique using CCTV is able to prevent unauthorized person enter the gate. Face recognition can be used for authentication, which can be implemented for preventing of criminal incidents. This re-search proposed a face recognition system using convolutional neural network to open and close the real-time barrier gate. The process consists of a convolutional layer, pooling layer, max pooling, flattening, and fully connected layer for detecting a face. The information was sent to the microcontroller using Internet of Thing (IoT) for controlling the barrier gate. The face recognition results are used to open or close the gate in the real time. The experimental results obtained average error rate of 0.320 and the accuracy of success rate is about 93.3%. The average response time required by microcontroller is about 0.562ms. The simulation result show that the face recognition technique using CNN is highly recommended to be implemented in barrier gate system.

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Research on the Real-Time Multiple Face Detection, Tracking and Recognition Based on Video

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.373-375.442 ◽

2013 ◽

Vol 373-375 ◽

pp. 442-446

Author(s):

Hai Feng Sang ◽

Chao Xu ◽

Dan Yang Wu ◽

Jing Huang

Keyword(s):

Real Time ◽

Face Detection ◽

Recognition System ◽

Detection Algorithm ◽

Research Field ◽

Face Tracking ◽

Fusion Algorithm ◽

The Real ◽

Video Images ◽

Tracking And Recognition

The video images of human face tracking and recognition is a hot research field of biometric recognition and artificial intelligence in recent years. This paper presents an automatic face tracking and recognition system, which can track multiple faces real-timely and recognize the identity. Aiming at Adaboost face detection algorithm is easy to false detection, presents a fusion algorithm based on Adaboost face detection algorithm and Active Shape Model. The algorithm is not only detect face real-timely but also remove the non-face areas; A multi thread CamShift tracking algorithm is proposed for many faces interlaced and face number of changes in the scene . Meanwhile, the algorithm also can identify the faces which have been tracked in the video. The experiment results show that the system is capable of improving the accurate rate of faces detection and recognition in complex backgrounds, and furthermore it also can track the real-time faces effectively.

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Towards real-time cosmic-ray identification with the LOw Frequency ARay

EPJ Web of Conferences ◽

10.1051/epjconf/201921604005 ◽

2019 ◽

Vol 216 ◽

pp. 04005 ◽

Cited By ~ 1

Author(s):

Antonio Bonardi ◽

Stijn Buitink ◽

Arthur Corstanje ◽

Heino Falcke ◽

Brian M. Hare ◽

...

Keyword(s):

Cosmic Rays ◽

Real Time ◽

Low Frequency ◽

Cosmic Ray ◽

Recognition System ◽

Extensive Air Showers ◽

Particle Detector ◽

Air Showers ◽

The Real ◽

Radio Signals

The radio signals emitted by Extensive Air Showers have been successfully used for the last decade by LOFAR to reconstruct the properties of the primary cosmic rays. Since an effective real-time recognition system for the very short radio pulses is lacking, cosmic-ray acquisition is currently triggered by an external array of particle detector, called LORA, limiting the LOFAR collecting area to the area covered by LORA. A new algorithm for the real-time cosmic-ray detection has been developed for the LOFAR Low Band Antenna, which are sensitive between 10 and 90 MHz, and is here presented together with the latest results.

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Physical Modeling of Displacement and Failure Monitoring of Underground Roadway in Horizontal Strata

Advances in Civil Engineering ◽

10.1155/2018/2934302 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Dinggui Hou ◽

Xiaojie Yang

Keyword(s):

Real Time ◽

Physical Modeling ◽

Surrounding Rock ◽

Image Correlation ◽

Monitoring Data ◽

Continuous Increase ◽

The Real ◽

Small Constant ◽

Constant Resistance ◽

Underground Roadway

Physical modeling of the underground roadway in horizontal strata is carried out by using a newly developed physical modeling approach, the so-called “physically finite elemental slab assemblage (PFESA).” The numerical 2D digital image correlation (DIC) technology is used to carry out the real-time monitoring of the surface displacement of the model in the experimental process, and the axial force monitoring devices called the small bolt (SB) and small constant resistance bolt (SCRB) are designed for the real-time detection of the roadway mechanics data. The displacement information of the whole physical model experiment process is obtained through the DIC technology. The SCRB can be well used to the mechanical monitoring of the deformation and failure of the roadway, though the analysis of the displacement and mechanical monitoring data can get that the change of the mechanical monitoring data of SCRB in advance of the displacement, the information of instability destruction precursor in roadway surrounding rock is the continuous increase of mechanical monitoring value in a short time. The experiment provides reference for the stability monitoring and early warning of the roadway surrounding rock based on a constant resistance and large deformation rock bolt (CRLB).

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Colour Classification Using Entropy Algorithm in Real Time Colour Recognition System for Blindness People

KnE Engineering ◽

10.18502/keg.v0i0.485 ◽

2016 ◽

Vol 1 (1) ◽

Author(s):

Gurum Ahmad Pauzi

Keyword(s):

Real Time ◽

Digital Camera ◽

Recognition System ◽

The Real ◽

Orange Yellow ◽

Colour Image ◽

Rgb Model

This article describes the real time instrumentation system to help blindness people for recognize a colour. Colour image captured by the digital camera, and it classified into ten basic colours names (black, brown, cyan, red, orange, yellow, green, blue, magenta, gray and white) by using entropy algorithm. The conclusion of colour classification will be informed to the user in sound or vocal information. This study has used two colour models HSV (hue, saturation and value) and RGB (red, green and blue). The accuracy of Classification using HSV has 90%, and RGB model has 71.5%.

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