Detection of Orange Shape Structure Parameters Based on DSP

2011 ◽  
Vol 480-481 ◽  
pp. 17-20
Author(s):  
Li Zhu ◽  
Jia Quan Chen
Keyword(s):  
High Performance ◽  
Detection System ◽  
Fruit Shape ◽  
Detection Accuracy ◽  
Low Grade ◽  
Structure Parameters ◽  
Core Component ◽  
The Core ◽  
Shape Structure ◽  
High Detection

A Detection system of Orange shape structure parameters Based on DSP, including its hardware part and software part was introduced. The hardware part equipped with the high performance DSP acts as the core component of the image processing, which provides a guarantee of the real-time of the shape detection. The software part introduces the basic principle of the orange recognition arithmetic, and differentiates by Zernike moments and k-means algorithm. The experiments show it can meet the practical detection requirements that the high detection accuracy of the normal fruit shape and the low-grade fruit shape.

Study on the Software System Used in Simulated Control Module

2013 ◽  
Vol 409-410 ◽  
pp. 1608-1610
Author(s):  
Hua Dai ◽  
Lu Tie Xu ◽  
Wen Ping Chang
Keyword(s):  
Data Processing ◽  
Virtual Instrument ◽  
Computer Hardware ◽  
Detection Accuracy ◽  
Software System ◽  
Simulation Test ◽  
Core Component ◽  
Control Module ◽  
The Core ◽  
Working Principle

The structure and working principle of the virtual instrument are introduced in this article. The Virtual Instrument (VI) is mainly composed of computer hardware resources, modular instruments and utility software. Software is the core component of simulation test, whose effect is to complete the man-machine conversation and data processing, which will directly affect the detection accuracy and efficiency of detecting instrument.

Study on the Method of Pedestrian Detection in Automobile Safety System

2012 ◽  
Vol 580 ◽  
pp. 118-121
Author(s):  
Zhong Hao Bai ◽  
Zhi Peng Ding ◽  
Qiang Yan
Keyword(s):  
Traffic Accidents ◽  
Detection Method ◽  
Detection System ◽  
Pedestrian Detection ◽  
Active Safety ◽  
Detection Accuracy ◽  
Cascade Algorithm ◽  
Automobile Safety ◽  
High Detection ◽  
Detection Effect

In order to improve automobile active safety performance, and reduce the traffic accidents between pedestrians and vehicles, a pedestrian detection method combined with pedestrian contour features is proposed based on the combination of the reliable Adaboost and SVM. For the requirements of fast and accurate pedestrian detection system, ten types of haar-like features are given as the coarse features firstly, and which are trained through Adaboost cascade algorithm to ensure the system with a high detection speed. Then, the hog features of strong ability to distinguish pedestrians are selected as the fine features, and the pedestrian classifier is got by using SVM of different kernels to improve the detection accuracy. It is shown that the method has a higher detection rate and achieves a better detection effect.

scholarly journals A malicious URLs detection system using optimization and machine learning classifiers

2020 ◽  
Vol 17 (3) ◽  
pp. 1210
Author(s):  
Ong Vienna Lee ◽  
Ahmad Heryanto ◽  
Mohd Faizal Ab Razak ◽  
Anis Farihan Mat Raffei ◽  
Danakorn Nincarean Eh Phon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Cyber Attacks ◽  
Support Vector ◽  
Detection Accuracy ◽  
Learning Approach ◽  
Internet Users ◽  
Analysis Technique ◽  
Machine Learning Approach ◽  
High Detection

<span>The openness of the World Wide Web (Web) has become more exposed to cyber-attacks. An attacker performs the cyber-attacks on Web using malware Uniform Resource Locators (URLs) since it widely used by internet users. Therefore, a significant approach is required to detect malicious URLs and identify their nature attack. This study aims to assess the efficiency of the machine learning approach to detect and identify malicious URLs. In this study, we applied features optimization approaches by using a bio-inspired algorithm for selecting significant URL features which able to detect malicious URLs applications. By using machine learning approach with static analysis technique is used for detecting malicious URLs applications. Based on this combination as well as significant features, this paper shows promising results with higher detection accuracy.  The bio-inspired algorithm: particle swarm optimization (PSO) is used to optimized URLs features. In detecting malicious URLs, it shows that naïve Bayes and support vector machine (SVM) are able to achieve high detection accuracy with rate value of 99%, using URL as a feature.</span>

Development of Detection System for Cotton Plastic Covering Using Ultrasonic

2013 ◽  
Vol 411-414 ◽  
pp. 1439-1444 ◽  
Author(s):  
Qi Zhang ◽  
Liang Hua ◽  
Juan Yao
Keyword(s):  
High Resolution ◽  
Detection System ◽  
Experimental Result ◽  
Detection Accuracy ◽  
Plastic Film ◽  
Cotton Production ◽  
Seed Cotton ◽  
Echo Signal ◽  
Great Loss ◽  
High Detection

Polyethylene is widely used as mulching plastic film in china cotton area. Collecting plastic film residue using machine is difficult. The cotton mixed with plastic film residue is badly harmful to cotton production, and causes great loss to cotton spinning enterprise. The detection system for cotton plastic covering using ultrasonic is developed in the paper. Sound wave echo method is used to detect plastic film residue mixed in seed cotton. The SCM is used to process echo signal and compensate measurement error caused by temperature variation. Experimental result shows that the system developed in the paper have features of low lost, high detection accuracy, high resolution and it has broad prospect in application.

High performance Nanogold™-in situ hybridzation and its use in the detection of hybridized and PCR-amplified microscopical preparations

1996 ◽  
Vol 54 ◽  
pp. 896-897
Author(s):  
G. W. Hacker ◽  
I. Zehbe ◽  
J. Hainfeld ◽  
A.-H. Graf ◽  
C. Hauser-Kronberger ◽  
...  
Keyword(s):  
Polymerase Chain Reaction ◽  
Messenger Rna ◽  
High Performance ◽  
Detection System ◽  
Direct Methods ◽  
Genetic Alterations ◽  
Chain Reaction ◽  
Protein Encoding ◽  
Polymerase Chain

In situ hybridization (ISH) with biotin-labeled probes is increasingly used in histology, histopathology and molecular biology, to detect genetic nucleic acid sequences of interest, such as viruses, genetic alterations and peptide-/protein-encoding messenger RNA (mRNA). In situ polymerase chain reaction (PCR) (PCR in situ hybridization = PISH) and the new in situ self-sustained sequence replication-based amplification (3SR) method even allow the detection of single copies of DNA or RNA in cytological and histological material. However, there is a number of considerable problems with the in situ PCR methods available today: False positives due to mis-priming of DNA breakdown products contained in several types of cells causing non-specific incorporation of label in direct methods, and re-diffusion artefacts of amplicons into previously negative cells have been observed. To avoid these problems, super-sensitive ISH procedures can be used, and it is well known that the sensitivity and outcome of these methods partially depend on the detection system used.

Efficient Co-Harvesting of Solar Energy and Low-Grade Heat by Molecular Photoswitches for High Energy Density, Long-Term Stable Solar Thermal Battery

2019 ◽  
Author(s):  
Zhao-Yang Zhang ◽  
Tao LI
Keyword(s):  
Energy Density ◽  
Solar Energy ◽  
High Performance ◽  
High Energy ◽  
Solar Thermal ◽  
High Energy Density ◽  
Low Grade ◽  
Thermal Battery ◽  
Low Grade Heat

Solar energy and ambient heat are two inexhaustible energy sources for addressing the global challenge of energy and sustainability. Solar thermal battery based on molecular switches that can store solar energy and release it as heat has recently attracted great interest, but its development is severely limited by both low energy density and short storage stability. On the other hand, the efficient recovery and upgrading of low-grade heat, especially that of the ambient heat, has been a great challenge. Here we report that solar energy and ambient heat can be simultaneously harvested and stored, which is enabled by room-temperature photochemical crystal-to-liquid transitions of small-molecule photoswitches. The two forms of energy are released together to produce high-temperature heat during the reverse photochemical phase change. This strategy, combined with molecular design, provides high energy density of 320-370 J/g and long-term storage stability (half-life of about 3 months). On this basis, we fabricate high-performance, flexible film devices of solar thermal battery, which can be readily recharged at room temperature with good cycling ability, show fast rate of heat release, and produce high-temperature heat that is >20<sup> o</sup>C higher than the ambient temperature. Our work opens up a new avenue to harvest ambient heat, and demonstrate a feasible strategy to develop high-performance solar thermal battery.

Efficient Co-Harvesting of Solar Energy and Low-Grade Heat by Molecular Photoswitches for High Energy Density, Long-Term Stable Solar Thermal Battery

2019 ◽  
Author(s):  
Zhao-Yang Zhang ◽  
Tao LI
Keyword(s):  
Energy Density ◽  
Solar Energy ◽  
High Performance ◽  
High Energy ◽  
Solar Thermal ◽  
High Energy Density ◽  
Low Grade ◽  
Thermal Battery ◽  
Low Grade Heat

Solar energy and ambient heat are two inexhaustible energy sources for addressing the global challenge of energy and sustainability. Solar thermal battery based on molecular switches that can store solar energy and release it as heat has recently attracted great interest, but its development is severely limited by both low energy density and short storage stability. On the other hand, the efficient recovery and upgrading of low-grade heat, especially that of the ambient heat, has been a great challenge. Here we report that solar energy and ambient heat can be simultaneously harvested and stored, which is enabled by room-temperature photochemical crystal-to-liquid transitions of small-molecule photoswitches. The two forms of energy are released together to produce high-temperature heat during the reverse photochemical phase change. This strategy, combined with molecular design, provides high energy density of 320-370 J/g and long-term storage stability (half-life of about 3 months). On this basis, we fabricate high-performance, flexible film devices of solar thermal battery, which can be readily recharged at room temperature with good cycling ability, show fast rate of heat release, and produce high-temperature heat that is >20<sup> o</sup>C higher than the ambient temperature. Our work opens up a new avenue to harvest ambient heat, and demonstrate a feasible strategy to develop high-performance solar thermal battery.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

scholarly journals Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

2021 ◽  
Vol 11 (11) ◽  
pp. 4894
Author(s):  
Anna Scius-Bertrand ◽  
Michael Jungo ◽  
Beat Wolf ◽  
Andreas Fischer ◽  
Marc Bui
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Positive Impact ◽  
Detection System ◽  
Training Data ◽  
Detection Accuracy ◽  
Current State ◽  
Alignment Task ◽  
Scanned Image ◽  
Automatic Transcription

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

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