RICA-MD: A Refined ICA Algorithm for Motion Detection

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-17
Author(s):  
Chao Zhang ◽  
Xiaopei Wu ◽  
Jianchao Lu ◽  
Xi Zheng ◽  
Alireza Jolfaei ◽  
...  
Keyword(s):  
Motion Detection ◽  
Moving Objects ◽  
Rapid Development ◽  
False Alarms ◽  
Analysis Method ◽  
Detection Algorithms ◽  
Complex Scenes ◽  
Statistical Analysis Method ◽  
Conventional Methods ◽  
Ica Algorithm

With the rapid development of various computing technologies, the constraints of data processing capabilities gradually disappeared, and more data can be simultaneously processed to obtain better performance compared to conventional methods. As a standard statistical analysis method that has been widely used in many fields, Independent Component Analysis (ICA) provides a new way for motion detection by extracting the foreground without precisely modeling the background. However, most existing ICA-based motion detection algorithms use only two-channel data for source separation and simply generate the observation vectors by decomposing and reconstructing the images by row, hence they cannot obtain an integrated and accurate shape of the moving objects in complex scenes. In this article, we propose a refined ICA algorithm for motion detection (RICA-MD), which fuses a larger number of channels than conventional ICA-based motion detection algorithms to provide more effective information for foreground extraction. Meanwhile, we propose four novel methods for generating observation vectors to further cover the diverse motion styles of the moving objects. These improvements enable RICA-MD to effectively deal with slowly moving objects, which are difficult to detect using conventional methods. Our quantitative evaluation in multiple scenes shows that our proposed method is able to achieve a better performance at an acceptable cost of false alarms.

scholarly journals Financial Performance of Non-oil Manufacturing Companies Listed on the Libyan Stock Market (LSM): Mixed Methods Study

2019 ◽  
Vol 9 (4) ◽  
pp. 77
Author(s):  
Khalifa Mohamed Khalifa Omar
Keyword(s):  
Statistical Analysis ◽  
Stock Market ◽  
Financial Performance ◽  
Manufacturing Companies ◽  
Analysis Method ◽  
Affecting Factors ◽  
Structured Interviews ◽  
Turnover Ratio ◽  
Statistical Effect ◽  
Statistical Analysis Method

The major objective of this study is to assess the financial performance and identify the affecting factors in this performance of non-oil manufacturing companies from 1999 to 2008. The study sample consisted of all non-oil manufacturing companies' enlisted at Libyan stock market which count (8). The data collected was analyzed by using statistical analysis method such as descriptive statistics, correlation test, Multiple- regression, as well as semi-structured interviews method. The results regarding to the statistical analysis method (net working capital, inventory turnover ratio, selling and general administrative expenses ratio, and company size and company age), have a positive statistical effect on the financial performance(ROA), while the variables of (current ratio, quick ratio and account receivable turnover ratio), have a negative statistical effect on the financial performance (ROA). The results regarding to semi-structured interviews method, reveal that the respondents in the interviews were confirmed that the selected factors have a significant effect on financial performance (ROA). The researcher recommended that the selected companies must consider the listed decision on the Libyan stock market; even when their financial performance is good.

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

scholarly journals Variability of Prognostic Results Based on Biological Parameters in Sickle Cell Disease Cohort Studies in Children: What Should Clinicians Know?

Children ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 143
Author(s):  
Julie Sommet ◽  
Enora Le Roux ◽  
Bérengère Koehl ◽  
Zinedine Haouari ◽  
Damir Mohamed ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Sickle Cell Disease ◽  
Cohort Studies ◽  
Statistical Methods ◽  
Sickle Cell ◽  
Biological Parameters ◽  
Analysis Method ◽  
Cell Disease ◽  
Statistical Analysis Method

Background: Many pediatric studies describe the association between biological parameters (BP) and severity of sickle cell disease (SCD) using different methods to collect or to analyze BP. This article assesses the methods used for collection and subsequent statistical analysis of BP, and how these impact prognostic results in SCD children cohort studies. Methods: Firstly, we identified the collection and statistical methods used in published SCD cohort studies. Secondly, these methods were applied to our cohort of 375 SCD children, to evaluate the association of BP with cerebral vasculopathy (CV). Results: In 16 cohort studies, BP were collected either once or several times during follow-up. The identified methods in the statistical analysis were: (1) one baseline value per patient (2) last known value; (3) mean of all values; (4) modelling of all values in a two-stage approach. Applying these four different statistical methods to our cohort, the results and interpretation of the association between BP and CV were different depending on the method used. Conclusion: The BP prognostic value depends on the chosen statistical analysis method. Appropriate statistical analyses of prognostic factors in cohort studies should be considered and should enable valuable and reproducible conclusions.

scholarly journals A Robust Thermal Infrared Vehicle and Pedestrian Detection Method in Complex Scenes

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1240
Author(s):  
Yang Liu ◽  
Hailong Su ◽  
Cao Zeng ◽  
Xiaoli Li
Keyword(s):  
Detection Method ◽  
Selection Process ◽  
Pedestrian Detection ◽  
Thermal Infrared ◽  
Single Shot ◽  
Detection Algorithms ◽  
Complex Scenes ◽  
Thermal Infrared Images ◽  
Online Feature Selection ◽  
Huge Challenge

In complex scenes, it is a huge challenge to accurately detect motion-blurred, tiny, and dense objects in the thermal infrared images. To solve this problem, robust thermal infrared vehicle and pedestrian detection method is proposed in this paper. An important weight parameter β is first proposed to reconstruct the loss function of the feature selective anchor-free (FSAF) module in its online feature selection process, and the FSAF module is optimized to enhance the detection performance of motion-blurred objects. The proposal of parameter β provides an effective solution to the challenge of motion-blurred object detection. Then, the optimized anchor-free branches of the FSAF module are plugged into the YOLOv3 single-shot detector and work jointly with the anchor-based branches of the YOLOv3 detector in both training and inference, which efficiently improves the detection precision of the detector for tiny and dense objects. Experimental results show that the method proposed is superior to other typical thermal infrared vehicle and pedestrian detection algorithms due to 72.2% mean average precision (mAP).

Erratum to “a quantitative comparison of motion detection algorithms in fMRI”

2003 ◽  
Vol 21 (5) ◽  
pp. 573
Author(s):  
Babak A. Ardekani ◽  
Alvin H. Bachman ◽  
Joseph A. Helpern
Keyword(s):  
Motion Detection ◽  
Quantitative Comparison ◽  
Detection Algorithms

Developments in mycotoxin analysis: an update for 2010-2011

2012 ◽  
Vol 5 (1) ◽  
pp. 3-30 ◽  
Author(s):  
G. Shephard ◽  
F. Berthiller ◽  
P. Burdaspal ◽  
C. Crews ◽  
M. Jonker ◽  
...  
Keyword(s):  
Analytical Method ◽  
Analytical Methods ◽  
Method Development ◽  
Rapid Development ◽  
Ergot Alkaloids ◽  
Alternaria Toxins ◽  
Research Publications ◽  
Mycotoxin Analysis ◽  
The Subject ◽  
Conventional Methods

This review highlights developments in mycotoxin analysis and sampling over a period between mid-2010 and mid-2011. It covers the major mycotoxins: aflatoxins, Alternaria toxins, ergot alkaloids, fumonisins, ochratoxin, patulin, trichothecenes, and zearalenone. Analytical methods for mycotoxins continue to be developed and published. Despite much interest in immunochemical methods and in the rapid development of LC-MS methodology, more conventional methods, sometimes linked to novel clean-up protocols, have also been the subject of research publications over the above period. Occurrence of mycotoxins falls outside the main focus of this review; however, where relevant to analytical method development, this has been mentioned.

The SMRI-NIRS technology: development, validation and benefit

2021 ◽  
pp. 354-359
Author(s):  
Stephen N. Walford
Keyword(s):  
South African ◽  
Near Infrared ◽  
Technology Development ◽  
Correlation Coefficients ◽  
Test Methods ◽  
Analysis Method ◽  
Prediction Equations ◽  
Conventional Analysis ◽  
Methods Of Analysis ◽  
Conventional Methods

The Sugar Milling Research Institute NPC (SMRI) has developed a simple to use near-infrared spectroscopy (NIRS) transmission-based analysis method as an alternative to conventional methods for analysis of sugarcane factory stream samples. The technology provides rapid, simultaneous analysis of refractometric dry substance (rds), polarimetric sugar, sucrose, glucose, fructose, conductivity ash contents as well as colour and pH for all streams and additionally, dry solids for final molasses and eliminates the need for sample clarification chemicals. The analyte prediction equations were developed using conventional results of samples from 14 South African factories, analysed at SMRI using SANAS/ISO17025 accredited test methods, and NIRS scans of the same samples using up to 16 different NIRS instruments. The NIRS analyte prediction equations were validated against more than 1,500 independent factory samples that had been analysed by conventional methods of analysis, including samples from factories outside South Africa. The reproducibility of the NIRS results were equivalent to existing conventional analysis reproducibility values (juice and final molasses) and previously undocumented values determined for this study for conventional raw house analysis methods. Correlation coefficients of greater than 0.97 were recorded for all major analytes and greater than 0.9 for minor analytes when predicted results were compared against conventional results. A maintenance protocol was also developed to ensure that the prediction equations remain robust and can account for sample matrix variations that can occur from season to season. The SMRI-NIRS technology was installed at all 14 South African factories and found to be robust and give equivalent results to conventional methods of analysis.

A Blockchain-Enabled Approach for Online Stream Sensor Data Protection in Cyber-Physical Manufacturing Systems

2021 ◽  
Author(s):  
Zhangyue Shi ◽  
Chenang Liu ◽  
Chen Kan ◽  
Wenmeng Tian ◽  
Yang Chen
Keyword(s):  
Information Technologies ◽  
Manufacturing Systems ◽  
Rapid Development ◽  
Data Access ◽  
Sensor Data ◽  
False Alarms ◽  
Monitoring And Control ◽  
Stream Data ◽  
Physical Security ◽  
And Control

Abstract With the rapid development of the Internet of Things and information technologies, more and more manufacturing systems become cyber-enabled, which significantly improves the flexibility and productivity of manufacturing. Furthermore, a large variety of online sensors are also commonly incorporated in the manufacturing systems for online quality monitoring and control. However, the cyber-enabled environment may pose the collected online stream sensor data under high risks of cyber-physical attacks as well. Specifically, cyber-physical attacks could occur during the manufacturing process to maliciously tamper the sensor data, which could result in false alarms or failures of anomaly detection. In addition, the cyber-physical attacks may also illegally access the collected data without authorization and cause leakage of key information. Therefore, it becomes critical to develop an effective approach to protect online stream data from these attacks so that the cyber-physical security of the manufacturing systems could be assured. To achieve this goal, an integrative blockchain-enabled method, is proposed by leveraging both asymmetry encryption and camouflage techniques. A real-world case study that protects cyber-physical security of collected stream data in additive manufacturing is provided to demonstrate the effectiveness of the proposed method. The results demonstrate that malicious tampering could be detected in a relatively short time and the risk of unauthorized data access is significantly reduced as well.

scholarly journals Big Data Analytics in E-commerce for the U.S. and China Through Literature Reviewing

2021 ◽  
Vol 9 (1) ◽  
pp. 16-44
Author(s):  
Weiqing Zhuang ◽  
Morgan C. Wang ◽  
Ichiro Nakamoto ◽  
Ming Jiang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Web Of Science ◽  
Big Data Analytics ◽  
Theoretical Research ◽  
Analysis Method ◽  
Theoretical Comparison ◽  
Statistical Analysis Method ◽  
And Performance ◽  
The U.S

Abstract Big data analytics (BDA) in e-commerce, which is an emerging field that started in 2006, deeply affects the development of global e-commerce, especially its layout and performance in the U.S. and China. This paper seeks to examine the relative influence of theoretical research of BDA in e-commerce to explain the differences between the U.S. and China by adopting a statistical analysis method on the basis of samples collected from two main literature databases, Web of Science and CNKI, aimed at the U.S. and China. The results of this study help clarify doubts regarding the development of China’s e-commerce, which exceeds that of the U.S. today, in view of the theoretical comparison of BDA in e-commerce between them.

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