Phase Detection for Loop-Based Programs on Multicore Architectures

2012 ◽  
Author(s):  
Chao Wang ◽  
Xi Li ◽  
Dong Dai ◽  
Gangyong Jia ◽  
Xuehai Zhou
Keyword(s):  
Phase Detection ◽  
Multicore Architectures

Development of Dermatological Skin Disease Detection and Classification based on Wavelet and ANN

2018 ◽  
Vol 8 (5) ◽  
pp. 10
Author(s):  
Revati Kadu ◽  
U. A. Belorkar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Skin Disease ◽  
Skin Diseases ◽  
Screening Method ◽  
Phase Detection ◽  
Statistical Measures ◽  
The World ◽  
Artificial Neural ◽  
Wavelet Techniques

One of the most common and augmenting health problems in the world are related to skin. The most  unpredictable and one of the most difficult entities to automatically detect and evaluate is the human skin disease because of complexities of texture, tone, presence of hair and other distinctive features. Many cases of skin diseases in the world have triggered a need to develop an effective automated screening method for detection and diagnosis of the area of disease. Therefore the objective of this work is to develop a new technique for automated detection and analysis of the skin disease images based on color and texture information for skin disease screening. In this paper, system is proposed which detects the skin diseases using Wavelet Techniques and Artificial Neural Network. This paper presents a wavelet-based texture analysis method for classification of five types of skin diseases. The method applies tree-structured wavelet transform on different color channels of red, green and blue dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. In all 99 unique features are extracted from the image. By using Artificial Neural Network, the system successfully detects different types of dermatological skin diseases. It consists of mainly three phases image processing, training phase, detection  and classification phase.

scholarly journals A Two-Phase Fashion Apparel Detection Method Based on YOLOv4

2021 ◽  
Vol 11 (9) ◽  
pp. 3782
Author(s):  
Chu-Hui Lee ◽  
Chen-Wei Lin
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Detection Method ◽  
Phase Transfer ◽  
Recognition Task ◽  
Phase Detection ◽  
Target Domain ◽  
Two Phase ◽  
Detection Technology ◽  
Fashion Apparel

Object detection is one of the important technologies in the field of computer vision. In the area of fashion apparel, object detection technology has various applications, such as apparel recognition, apparel detection, fashion recommendation, and online search. The recognition task is difficult for a computer because fashion apparel images have different characteristics of clothing appearance and material. Currently, fast and accurate object detection is the most important goal in this field. In this study, we proposed a two-phase fashion apparel detection method named YOLOv4-TPD (YOLOv4 Two-Phase Detection), based on the YOLOv4 algorithm, to address this challenge. The target categories for model detection were divided into the jacket, top, pants, skirt, and bag. According to the definition of inductive transfer learning, the purpose was to transfer the knowledge from the source domain to the target domain that could improve the effect of tasks in the target domain. Therefore, we used the two-phase training method to implement the transfer learning. Finally, the experimental results showed that the mAP of our model was better than the original YOLOv4 model through the two-phase transfer learning. The proposed model has multiple potential applications, such as an automatic labeling system, style retrieval, and similarity detection.

A phasor-distance based faulty phase detection and fault classification technique for parallel transmission lines

2020 ◽  
Vol 21 (4) ◽  
Author(s):  
Nishant Kothari ◽  
Bhavesh R. Bhalja ◽  
Vivek Pandya ◽  
Pushkar Tripathi ◽  
Soumitri Jena
Keyword(s):  
Transmission Lines ◽  
Power Flow ◽  
Detection Algorithm ◽  
Current Transformer ◽  
Operating Conditions ◽  
Fault Classification ◽  
Phase Detection ◽  
Parallel Transmission ◽  
Classification Technique ◽  
Computational Requirement

AbstractThis paper presents a phasor-distance based faulty phase detection and fault classification technique for parallel transmission lines. Detection and classification of faulty phase(s) have been carried out by deriving indices from the change in phasor values of current with a distance of one cycle. The derived indices have zero values during normal operating conditions whereas the index corresponding to the faulty phase exceeds the pre-defined threshold in case of occurrence of a fault. A separate ground detection algorithm has been utilized for the identification of involvement of ground in a faulty situation. The performance of the proposed technique has been evaluated for intra-circuit, inter-circuit and simultaneous faults with wide variations in system and fault conditions. The suggested technique has been evaluated for over 23,000 diversified simulated fault cases as well as 14 recorded real fault events. The performance of the proposed technique remains consistent under Current Transformer (CT) saturation as well as different amount and direction of power flow. Moreover, suitability to different power system network has also been studied. Also, faults having fault current less than pre-fault conditions have been detected accurately. The results obtained suggest that it is able to detect faulty phases as well as classify faults within quarter-cycle from the inception of fault with impeccable accuracy. Besides, as modern digital relays have been already equipped with phasor computation facility, phasor-based technique can be easily incorporated with relative ease. At last, a comparative evaluation suggests its superiority in terms of fault classification accuracy, fault detection time, diversify fault scenarios and computational requirement among other existing techniques.

11H-Pyrido[2,1-b]quinazolin-11-one - A reversible Turn-on/off Fluorescent Probe for Solution and Gas-phase Detection of Acids or Basic Amines

2021 ◽  
pp. 109536
Author(s):  
João Victor L. Silva Néto ◽  
Rodolfo I. Teixeira ◽  
Ramon B. da Silva ◽  
Nanci C. de Lucas ◽  
Simon J. Garden
Keyword(s):  
Gas Phase ◽  
Fluorescent Probe ◽  
Phase Detection ◽  
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