scholarly journals Implementation of Edge & Shape Detection Techniques and their Performance Evaluation

2021 ◽  
Author(s):  
Mohammad Shahnoor Islam Khan
Keyword(s):  
High Performance ◽  
Time Window ◽  
Conveyor Belt ◽  
Processing Application ◽  
Detection Techniques ◽  
Baked Goods ◽  
Image Processing Application ◽  
Canny Edge ◽  
Edge Detection Method ◽  
Baking Industry

In this thesis, we develop an industrial image processing application for baked goods. Images of baked objects on a conveyor belt are taken by high resolution cameras batch wise throughout the baking period. The network is designed with high performance equipments and the application is fast enough to complete all the steps within the allowed time window. The application uses Canny edge detection method [6] which optimizes the performance compared to other applications used in the industry. We have analyzed the performance of different key properties; such as processing time of an image, dimensions of an object shape, average color value to detect if an object is properly burned or damaged. Performance in detecting shapes shows higher accuracy for the developed application against other applications used in the baking industry.

scholarly journals Implementation of Edge & Shape Detection Techniques and their Performance Evaluation

2021 ◽  
Author(s):  
Mohammad Shahnoor Islam Khan
Keyword(s):  
High Performance ◽  
Time Window ◽  
Conveyor Belt ◽  
Processing Application ◽  
Detection Techniques ◽  
Baked Goods ◽  
Image Processing Application ◽  
Canny Edge ◽  
Edge Detection Method ◽  
Baking Industry

In this thesis, we develop an industrial image processing application for baked goods. Images of baked objects on a conveyor belt are taken by high resolution cameras batch wise throughout the baking period. The network is designed with high performance equipments and the application is fast enough to complete all the steps within the allowed time window. The application uses Canny edge detection method [6] which optimizes the performance compared to other applications used in the industry. We have analyzed the performance of different key properties; such as processing time of an image, dimensions of an object shape, average color value to detect if an object is properly burned or damaged. Performance in detecting shapes shows higher accuracy for the developed application against other applications used in the baking industry.

Parallelization and Performance Evaluation of an Edge Detection Algorithm on a Streaming Multi-Core Engine

2009 ◽  
Vol 2 (4) ◽  
pp. 81-91 ◽  
Author(s):  
Hashir Karim Kidwai ◽  
Fadi N. Sibai ◽  
Tamer Rabie
Keyword(s):  
Performance Evaluation ◽  
Edge Detection ◽  
High Performance ◽  
Detection Algorithm ◽  
Cell Processor ◽  
Edge Detector ◽  
Processing Application ◽  
Image Processing Application ◽  
Host Processor ◽  
And Performance

In the world of multi-core processors, the STI Cell Broadband Engine (BE) stands out as a heterogeneous 9-core processor with a PowerPC host processor (PPE) and 8 synergic processor engines (SPEs). The Cell BE architecture is designed to improve upon conventional processors in graphics and related areas by integrating 8 computation engines each with multiple execution units and large register sets to achieve a high performance per area return. In this paper, we discuss the parallelization, implementation and performance evaluation of an edge detection image processing application based on the Roberts edge detector on the Cell BE. The authors report the edge detection performance measured on a computer with one Cell processor and with varying numbers of synergic processor engines enabled. These results are compared to the results obtained on the Cell’s single PPE with all 8 SPEs disabled. The results indicate that edge detection performs 10 times faster on the Cell BE than on modern RISC processors.

Investigation of the bee-repellent properties of cotton fabrics treated with microencapsulated essential oils

2018 ◽  
Vol 89 (8) ◽  
pp. 1417-1435 ◽  
Author(s):  
Seyda Eyupoglu ◽  
Dilek Kut ◽  
Ahmet Onur Girisgin ◽  
Can Eyupoglu ◽  
Mehmet Ozuicli ◽  
...  
Keyword(s):  
Essential Oils ◽  
High Performance ◽  
Gum Arabic ◽  
Wall Material ◽  
Processing Application ◽  
Lavender Oil ◽  
Pine Tree ◽  
Image Processing Application ◽  
Single Use ◽  
Fennel Oil

In this study, to produce single-use bee-repellent fabrics, a variety of essential oils were encapsulated with gum arabic wall material at a 1:5 ratio of wall to the core substance. The following core substances were used: lavender oil, laurel oil, fennel oil, N, N-diethyl-3-methylbenzamide (DEET), lavender + laurel oil, lavender + fennel oil, laurel + fennel oil, lavender + fennel + laurel oil, lavender oil + DEET, fennel oil + DEET and laurel oil + DEET. Lavender, fennel and laurel oils were analyzed by high-performance liquid chromatography. In this context, 11 different microcapsules were produced. After the microencapsulation process, the microcapsules were analyzed with a light microscope and by Fourier transform infrared spectroscopy. Furthermore, an image processing application was developed and implemented to determine the particle size distribution of the microcapsules. After the analysis of the microcapsules, cotton fabric samples were treated with the microcapsules. In order to analyze the microcapsules on the fabric samples, scanning electron microscopy (SEM) was used. To analyze the bee-repellent abilities of the fabric samples, 12 different measurement cabinets made of pine tree and glass were produced. According to the results, lavender and fennel oils can be used as bee-repellent alternatives to DEET in beekeeping.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

DFOG-Image Processing Application for Real-Time Defogging

2020 ◽  
Author(s):  
Indiketiya I.H.O.H ◽  
Kulasekara K.M.R.A ◽  
J.M. Thomas ◽  
Ishara Gamage ◽  
Thusithanjana Thilakarathna
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing Application ◽  
Image Processing Application

scholarly journals Plate-like structure damage location identification based on Lamb wave baseline-free probability imaging method

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668570 ◽  
Author(s):  
Dongsheng Li ◽  
Zihao Jing ◽  
Mengdao Jin
Keyword(s):  
Lamb Wave ◽  
Time Window ◽  
Free Probability ◽  
Structural Response ◽  
Guided Wave ◽  
Time Of Arrival ◽  
Imaging Method ◽  
Detection Techniques ◽  
Damage Location ◽  
Ultrasonic Guided Wave

Damage-scattering signal extraction using conventional ultrasonic guided wave–based damage detection techniques requires the measurement of baseline data under pristine condition. This study proposes a baseline-free ultrasonic guided wave damage localization and imaging method based on Lamb wave baseline-free probability imaging method. Although traditional Lamb wave probability imaging can monitor damage location in plate-like structures, the absolute time of arrival and magnitude of the signal are affected by several factors and are therefore difficult to obtain. This study also proposes a probability-based hyperbola diagnostic imaging method that is based on different times of arrival and has no magnitude information. A distributed active sensor network conforming to a pulse-echo configuration and time window functions is developed to separate damage-scattering signals from structural response signals. Continuous wavelet transform is used to calculate the time of flight of damage signal waves. The numerical simulation and experiments validate the effectiveness of the proposed method in identifying damage.

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