A high-performance face detection system using OpenMP

2009 ◽  
Vol 21 (15) ◽  
pp. 1819-1837 ◽  
Author(s):  
P. E. Hadjidoukas ◽  
V. V. Dimakopoulos ◽  
M. Delakis ◽  
C. Garcia
Keyword(s):  
Face Detection ◽  
High Performance ◽  
Detection System

Combining Face and Eye Detectors in a High- Performance Face-Detection System

2012 ◽  
Vol 19 (4) ◽  
pp. 20-27 ◽  
Author(s):  
Loris Nanni ◽  
Alessandra Lumini
Keyword(s):  
Face Detection ◽  
High Performance ◽  
Detection System

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.

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.

scholarly journals A validated quantification of triclosan in toothpaste using high-performance thin-layer chromatography and a 48-bit fatbed scanner

2021 ◽  
Author(s):  
Barbara Anders ◽  
Sabrina Doll ◽  
Bernd Spangenberg
Keyword(s):  
Acetic Acid ◽  
Thin Layer ◽  
Thin Layer Chromatography ◽  
High Performance ◽  
Detection System ◽  
Flatbed Scanner ◽  
Butyl Ether ◽  
Quantification Method ◽  
Direct Measurements ◽  
Layer Chromatography

AbstractWe present a densitometric quantification method for triclosan in toothpaste, separated by high-performance thin-layer chromatography (HPTLC) and using a 48-bit flatbed scanner as the detection system. The sample was band-wise applied to HPTLC plates (10 × 20 cm), with fluorescent dye, Merck, Germany (1.05554). The plates were developed in a vertical developing chamber with 20 min of chamber saturation over 70 mm, using n-heptane–methyl tert-butyl ether–acetic acid (92:8:0.1, V/V) as solvent. The RF value of triclosan is hRF = 22.4, and quantification is based on direct measurements using an inexpensive 48-bit flatbed scanner for color measurements (in red, green, and blue) after plate staining with 2,6-dichloroquinone-4-chloroimide (Gibbs' reagent). Evaluation of the red channel makes the measurements of triclosan very specific. For linearization, an extended Kubelka–Munk expression was used for data transformation. The range of linearity covers more than two orders of magnitude and is between 91 and 1000 ng. The separation method is inexpensive, fast and reliable.

scholarly journals Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

2020 ◽  
Author(s):  
Yuchen Luo ◽  
Yi Zhang ◽  
Ming Liu ◽  
Yihong Lai ◽  
Panpan Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
High Performance ◽  
Detection System ◽  
Random Order ◽  
Colon Polyps ◽  
Clinical Environment ◽  
Polyp Detection ◽  
Link Type ◽  
Polyp Detection Rate

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

scholarly journals A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection

2018 ◽  
Vol 10 (8) ◽  
pp. 80
Author(s):  
Lei Zhang ◽  
Xiaoli Zhi
Keyword(s):  
Face Detection ◽  
Graphics Processing Units ◽  
High Performance ◽  
Feature Fusion ◽  
Local Context ◽  
Data Set ◽  
Global Context ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Benchmark Datasets

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.

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