Detection Method
Recently Published Documents





2021 ◽  
Vol 22 (2) ◽  
Jianxing Zhu ◽  
Lina Huo ◽  
Mohd Dilshad Ansari ◽  
Mohammad Asif Ikbal

The development of the Internet of Things has prominently expanded the perception of human beings, but ensuing security issues have attracted people's attention. From the perspective of the relatively weak sensor network in the Internet of Things. Proposed method is aiming at the characteristics of diversification and heterogeneity of collected data in sensor networks; the data set is clustered and analyzed from the aspects of network delay and data flow to extract data characteristics. Then, according to the characteristics of different types of network attacks, a hybrid detection method for network attacks is established. An efficient data intrusion detection algorithm based on K-means clustering is proposed. This paper proposes a network node control method based on traffic constraints to improve the security level of the network. Simulation experiments show that compared with traditional password-based intrusion detection methods; the proposed method has a higher detection level and is suitable for data security protection in the Internet of Things. This paper proposes an efficient intrusion detection method for applications with Internet of Things.

Yan Ren ◽  
Jiayong Liu

In order to solve the problem of poor accuracy of traditional microcontroller attachment stability testing method, a microcontroller attachment stability testing method based on biosensor was designed to solve the existing problems. The reliability test index of the microcontroller is established, then the interference of the microcontroller accessory is detected and responded, and the interference detection signal of the microcontroller accessory is selected. The process design of stability detection of microcontroller accessories based on biosensor is completed. The experimental results show that the stability detection method based on biosensor designed in this paper can ensure the stability detection accuracy of microcontroller accessories above 80%, which is more accurate than traditional methods. It can be used to evaluate the stability, reliability and performance of microcontroller accessories in long-term operation.

2021 ◽  
pp. 1-18
M.L. Sworna Kokila ◽  
Dr. V. Gomathi

Automatic Person Re-identification by video surveillance is commonly used in different applications. Perhaps the human uniqueness criteria for tracking the presence of the same person across multiple camera views and a person’s growth identification is extremely challenging. To solve the above problem, we propose an efficient Auto Track Regression System (ATRF) based on a deep learning technique that uses an eminent representation strategy along with recognition. In this work, the Auto Wiley Detective (AWD) approach is proposed for the representation of features that can collect valuable information by monitoring individuals. After obtaining important information on the characteristics, it is possible to define the personal growth identity of the generation. The OPVC (Original Pick Virtual Classifier) is used for accurate classification of the queried person from a dense area by utilizing features of a person’s growth identity extracted from feature extraction by the Auto Wiley Detection Method. The proposed Originated Pick Virtual Classifier (OPVC) uses Platt scaling (originated pick) on probit regression (virtual) to train the featured data set for accurate person re-identification, which is boosted by the Karush–Kuhn–Tucker (KKT) conditions to reduce false re-identification. Since the gallery information is trained using the Backpropagation method and smoothened analysis through approximated output, the Auto Wiley Detection Method proficiently detects the required information automatically. This also helps to detect the person query image from the database, which contains a vast collection of video images based on the similarity features identified in the query image and the detailed features extracted from the query image. The classification is completed automatically, and then the Person Re-Identification from the databases is performed accurately and efficiently. Henceforth, the proposed work effectively extracts reliable height and age estimates with improved flexibility and individual re-identifying capabilities.

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12285
Koki Yanazawa ◽  
Takehito Sugasawa ◽  
Kai Aoki ◽  
Takuro Nakano ◽  
Yasushi Kawakami ◽  

Background Gene doping is the misuse of genome editing and gene therapy technologies for the purpose of manipulating specific genes or gene functions in order to improve athletic performance. However, a non-invasive detection method for gene doping using recombinant adenoviral (rAdV) vectors containing human follistatin (hFST) genes (rAdV<hFST>) has not yet been developed. Therefore, the aim of this study was to develop a method to detect gene doping using rAdV<hFST>. Methods First, we generated rAdV<hFST> and evaluated the overexpression of the hFST gene, FST protein, and muscle protein synthesis signaling using cell lines. Next, rAdV<hFST> was injected intravenously or intramuscularly into mice, and whole blood was collected, and hFST and cytomegalovirus promoter (CMVp) gene fragments were detected using TaqMan-quantitative polymerase chain reaction (qPCR). Finally, to confirm the specificity of the primers and the TaqMan probes, samples from each experiment were pooled, amplified using TaqMan-qPCR, and sequenced using the Sanger sequencing. Results The expression of hFST and FST proteins and muscle protein synthesis signaling significantly increased in C2C12 cells. In long-term, transgene fragments could be detected until 4 days after intravenous injection and 3 days after intramuscular injection. Finally, the Sanger sequencing confirmed that the primers and TaqMan probe specifically amplified the gene sequence of interest. Conclusions These results indicate the possibility of detecting gene doping using rAdV<hFST> using TaqMan-qPCR in blood samples. This study may contribute to the development of detection methods for gene doping using rAdV<hFST>.

Gijs M. W. Reichert ◽  
Marcos Pieras ◽  
Ricardo Marroquim ◽  
Anna Vilanova

AbstractOne common way to aid coaching and seek to improve athletes’ performance is by recording training sessions for posterior analysis. In the case of sailing, coaches record videos from another boat, but usually rely on handheld devices, which may lead to issues with the footage and missing important moments. On the other hand, by autonomously recording the entire session with a fixed camera, the analysis becomes challenging owing to the length of the video and possible stabilization issues. In this work, we aim to facilitate the analysis of such full-session videos by automatically extracting maneuvers and providing a visualization framework to readily locate interesting moments. Moreover, we address issues related to image stability. Finally, an evaluation of the framework points to the benefits of video stabilization in this scenario and an appropriate accuracy of the maneuver detection method.

2021 ◽  
Matt Gaidica ◽  
Emily Studd ◽  
Andrea E Wishart ◽  
William Gonzalez ◽  
Jeffrey Lane ◽  

Sleep is appreciated as a behavior critical to homeostasis, performance, and fitness. Yet, most of what we know about sleep comes from humans or controlled laboratory experiments. Assessing sleep in wild animals is challenging, as it is often hidden from view, and electrophysiological recordings that define sleep states are difficult to obtain. Accelerometers have offered great insight regarding gross movement, although ambiguous quiescent states like sleep have been largely ignored, limiting our understanding of this ubiquitous behavior. We developed a broadly applicable sleep detection method called a homeogram that can be applied to accelerometer data collected from wild animals. We applied our methodology to detect sleep in free-ranging North American red squirrels (Tamiasciurus hudsonicus) in a region that experiences drastic seasonal shifts in light, temperature, and behavioral demands. Our method characterized sleep in a manner consistent with limited existing studies and expanded those observations to provide evidence that red squirrels apply unique sleep strategies to cope with changing environments. Applying our analytical strategy to accelerometer data from other species may open new possibilities to investigate sleep patterns for researchers studying wild animals.

2021 ◽  
Vol 191 ◽  
pp. 106482
Bin Xie ◽  
Weipeng Jiao ◽  
Changkai Wen ◽  
Songtao Hou ◽  
Fan Zhang ◽  

Sign in / Sign up

Export Citation Format

Share Document